Sistemasi: Jurnal Sistem Informasi is a scientific journal in the field of computer science and computer engineering. General Focus and Scope of Sistemasi: Jurnal Sistem Informasi, are Information Systems, Information Technology, Data Science, Artificial Intelligence and Software Engineering. Sistemasi: Jurnal Sistem Informasi has been indexed in the Directory of Open Access Journals (DOAJ) since 2016 and the Science and Technology Index (SINTA) since 2018. Sistemasi: Jurnal Sistem Informasi is published monthly, with 12 issues per year (January through December). Sistemasi: Jurnal Sistem Informasi (p-ISSN: 2302-8149 e-ISSN: 2540–9719) published by Universitas Islam Indragiri, Tembilahan Riau Indonesia.
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Sentiment Analysis of User Perceptions Toward the Gemini Application using the Naïve Bayes Algorithm
Sentiment analysis, a pivotal technique in Natural Language Processing (NLP), facilitates the classification of textual data into positive or negative sentiment categories. This study applies a Naïve Bayes classification model to analyse 1,000 user reviews of the Gemini application, an AI-powered assistant developed by Google AI, collected from the Google Play Store. The research adopts the Knowledge Discovery in Database (KDD) methodology, encompassing data selection, preprocessing, transformation, data mining, and evaluation stages. Reviews were pre-processed using text normalization techniques and transformed into numerical vectors using Term Frequency–Inverse Document Frequency (TF-IDF). The sentiment classifier achieved a high accuracy of 94.57%, with a precision of 95.7% and an F1-score of 96.7%, indicating effective classification performance. The results revealed a predominance of positive sentiment, with frequently occurring terms such as “assist,” “good,” and “application.” Conversely, negative sentiments were often associated with issues regarding language support and system limitations. These findings provide actionable insights into user satisfaction and areas for improvement in the Gemini application. While the Naïve Bayes algorithm proved efficient and interpretable, limitations include difficulty handling nuanced linguistic features such as sarcasm or ambiguity. Future research may enhance sentiment classification performance by incorporating larger datasets and advanced models such as BERT or deep neural networks. Overall, this study demonstrates the practical utility of machine learning-based sentiment analysis in guiding user-centric application development.
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Design, Development, and User Acceptance Evaluation of the “Trigosolver” Trigonometry Educational Game using the Unity Engine and the Agile Method
Mathematics, particularly trigonometry, is often perceived by high school students as abstract and difficult to understand due to the complex spatial visualization it requires. Conventional teacher-centered instruction, which primarily emphasizes memorizing formulas, frequently fails to foster a strong conceptual understanding, resulting in low student motivation. This study aims to design, develop, and evaluate an interactive educational game called TrigoSolver as an alternative learning medium for trigonometry. The application was developed using the Unity Engine with the C# programming language and the Agile (Scrum) software development methodology, which supports iterative and adaptive development. The game incorporates a Pythagorean Triples algorithm based on Euclid's formula to dynamically generate trigonometry problems through Procedural Content Generation (PCG) while avoiding irrational numbers. It also includes an automatic fraction-answer validation feature to improve the learning experience. The system was evaluated through Black Box Testing to verify functional correctness and a User Acceptance Test (UAT) using a Likert-scale questionnaire developed based on the ISO/IEC 25010 software quality standard and the Technology Acceptance Model (TAM). The Black Box Testing results confirmed that all game functionalities operated as intended without errors. The UAT, involving seven subject-matter and media experts, yielded an overall feasibility score of 82.57% (classified as "Feasible") with excellent instrument reliability (Cronbach's Alpha = 0.922). These findings indicate that TrigoSolver is a feasible alternative learning medium for helping students visualize abstract trigonometric concepts, including sine, cosine, and tangent. Nevertheless, further improvements to the user interface and navigation are recommended to enhance the overall user experience.
Analysis Of Whatsapp Business Features On The Effectiveness Of Marketing Communications For Craft MSMEs In Jambi City
This study analyzes the influence of WhatsApp Business features (Broadcast Message, Quick Reply, Catalog, and Labeling) on marketing communication effectiveness for creative SMEs in Jambi City. Using a mixed-methods approach, quantitative data were collected through structured questionnaires from creative SME respondents, while qualitative data were gathered through in-depth interviews, case studies, and focus group discussions. Multiple linear regression analysis revealed that WhatsApp Business features simultaneously significantly influence marketing communication effectiveness (R²=0.596, p=0.003), explaining 59.6% of the variance. However, partial analysis showed only the Catalog feature had significant positive influence (β=0.903, p=0.034), while Broadcast Message and Labeling showed positive but insignificant effects, and Quick Reply demonstrated negative influence. The novelty lies in focusing specifically on creative SMEs in Jambi with cultural heritage products and comprehensively analyzing four WhatsApp Business features simultaneously. This research contributes to digital marketing literature by demonstrating how digital tools can preserve local cultural values while enhancing business competitiveness. The findings imply that creative SMEs should prioritize optimizing Catalog features with high-quality visual content that showcases cultural values, supported by government digital literacy programs tailored to heritage-based businesses.
Prediction of Air Pollution in the Sultanate of Oman using Machine Learning Approaches
Air pollution has become a major environmental and public-health concern worldwide, and understanding its behaviour is essential for effective monitoring and management. This study investigates air-quality patterns across four regions in the Sultanate of Oman—Al Khuwair, Salalah, Al Khoud, and Bediya—using a combination of statistical modelling and machine-learning techniques. Hourly data for 2023, including pollutant concentrations and key meteorological variables, were obtained from the Environment Authority of Oman, cleaned, and pre-processed to construct region-specific datasets. Air Quality Index (AQI) values were calculated for each pollutant and classified into three categories (Good, Moderate, and Unhealthy). Kernel Support Vector Machine (KSVM) and Gaussian Process Regression and models were trained using a 70/30 temporal split to classify AQI levels. Results showed that KSVM achieved the highest accuracy in Salalah (96.97%), Al Khoud (94.33%), and Bediya (93.37%), while Gaussian Process Regression performed best in Al Khuwair (70.32%). In conclusion, this research demonstrates that advanced kernel-based classifiers can effectively model non-linear environmental data, providing a scalable solution for regional environmental management.
Spatial Variability Analysis of Earthquake Magnitude and Depth in Indonesia using Random Forest based on Historical Data
Indonesia lies at the convergence of three major tectonic plates, resulting in high seismic activity and complex spatial earthquake patterns. This study analyzes the spatial variability of earthquake magnitude and depth in Indonesia using a Random Forest Regressor based on historical earthquake data from the United States Geological Survey (USGS). A total of 24,981 earthquake records (1976–2026) were collected, and 21,886 valid observations were obtained after data cleaning. To reduce data heterogeneity caused by different magnitude measurement methods, the dataset was grouped into six magnitude types (Mb, Ms, Mw, Mwb, Mwc, and Mww), and separate Random Forest models were developed for each group using longitude and latitude as input features. The novelty of this study lies in the magnitude-type-specific modeling approach, which allows spatial patterns to be analyzed more consistently across different earthquake measurement scales. The models were evaluated using 10-fold cross-validation and standard regression metrics. Results show that spatial coordinates have limited ability to explain earthquake magnitude, as indicated by low R² values across all models. In contrast, depth prediction shows better performance, with the best result achieved by Mw (R² = 0.6801), indicating stronger spatial structure in earthquake depth distribution. Overall, the findings demonstrate that geographic location is more informative for modeling earthquake depth than magnitude. The proposed approach effectively captures spatial depth variability and provides a more structured analysis of earthquake characteristics across Indonesia.
Integrating FAST Methodology and Hybrid Recommendation for Government Asset Platforms
Optimizing Regional Owned Property (BMD) presents a critical challenge in modern government management, where numerous potential assets remain underutilized due to information asymmetry between the state and prospective tenants. Existing research indicates that current platforms predominantly focus on administrative recording rather than proactive commercialization, largely due to rigid regulations requiring strict appraisal-based pricing. This study proposes a novel conceptual integration to resolve this dichotomy by bridging strict public sector compliance with commercial-grade user experience. Instead of utilizing it merely as a development lifecycle tool, the FAST (Framework for the Application of Systems Technique) methodology is positioned as a regulatory guardrail to deterministically lock appraisal pricing and ensure strict adherence to Ministerial Regulations. Conversely, a Hybrid Recommendation System (merging Content-Based and Collaborative Filtering) is deployed explicitly at the tenant front-end to liberate and personalize the asset discovery process. These conflicting logic domains are seamlessly separated within a decoupled multi-tier architecture. Research findings reveal that the hybrid model successfully addresses the cold-start problem, achieving a top recommendation relevance score of 0.900 for newly listed assets. User Acceptance Testing (UAT) results demonstrate high efficacy, with a 94% acceptance rate from government administrators regarding regulatory logic validation and an 89% satisfaction score from public users regarding personalized recommendations. The explicit contribution of this study is a validated technological blueprint that harmonizes government legal compliance with commercial-grade discovery algorithms, effectively boosting Regional Original Revenue (PAD) optimization. In conclusion, this research confirms that encapsulating AI within a structured regulatory framework can transform rigid public sector assets into a responsive E-Business ecosystem. The explicit contribution of this study is a validated technological blueprint that harmonizes government legal compliance with commercial-grade discovery algorithms, boosting Regional Original Revenue (PAD) optimization, with future studies recommended to explore blockchain integration for auditability.
Indonesian Coffee Recommendation System Based on Aroma and Flavor Profiles Using the K-Means Clustering Algorithm
The diversity of Indonesian coffee sensory profiles presents both potential and a practical challenge: consumers struggle to find coffee that matches their taste preferences due to the absence of a system that recommends options based on measurable sensory attributes. This study develops a Nusantara coffee recommendation system using K-Means Clustering with 50 Indonesian coffee sensory records collected automatically via web scraping from Coffee Review, a professional coffee rating platform, covering five quantitative attributes: Aroma, Acidity, Body, Flavor, and Aftertaste. Following Min-Max normalization, the K-Means algorithm with k-means++ initialization produced three optimal clusters (K=3), determined through a combination of the Elbow Method, Silhouette Score (0.4889), Davies-Bouldin Index (0.831), and Calinski-Harabasz Score (45.41). The system recommends the top-3 coffees based on Euclidean distance between user preference input and the nearest cluster centroid. Black Box Testing across five scenarios confirmed all functions performed as expected (5/5). This study contributes a sensory-profile-based approach that eliminates the cold-start problem in coffee recommendation systems, with potential integration into local coffee applications and Indonesian coffee e-commerce platforms.
Analysis and Development of Transformer (DistilBERT)-LSTM and Reinforcement Learning Models for Adaptive Phishing Email Detection
Phishing detection faces two major challenges: performance degradation caused by domain shift and a heavy reliance on costly labeled data. This study proposes an adaptive phishing email detection model that integrates a hybrid DistilBERT–LSTM architecture with a Proximal Policy Optimization (PPO)-based Reinforcement Learning agent. The proposed methodology employs a multi-stage transfer learning framework using three datasets: Enron as the source domain, Phishing_Validation for supervised domain adaptation, and CEAS_08 to simulate an unlabeled data stream through pseudo-labeling. Experimental results demonstrate excellent performance on the source-domain dataset (Enron), achieving an F1-score of 0.9935. However, the model's performance declined on the Phishing_Validation dataset (F1-score = 0.9067), confirming the impact of domain shift. By incorporating the PPO agent, the proposed model autonomously recovered its performance on the CEAS_08 dataset, achieving an F1-score of 0.9516, an accuracy of 0.9468, and a ROC–AUC of 0.9915. The stability of the adaptation process was validated by the convergence of the Kullback–Leibler (KL) divergence to 0.000173, although a minor overconfidence of approximately 5% was observed between the model's confidence estimates and the ground-truth labels. These findings demonstrate the effectiveness of PPO in mitigating domain shift within an unsupervised adaptation environment. Future research should focus on improving model calibration and exploring multimodal feature integration to further strengthen cybersecurity defenses.
Implementation of the Game Development Life Cycle for an Android-based Color Theory Educational Game
Students often experience difficulties in understanding color theory, particularly when it is taught through conventional theoretical instruction without interactive or visual learning support. Although they pay attention to the instructional content, approximately 65% of students still struggle to apply color theory concepts in graphic design. This study aims to develop an Android-based educational game to facilitate students' understanding of color theory through an interactive and visual learning experience. The game was developed using the Game Development Life Cycle (GDLC), which consists of six phases: initiation, pre-production, production, testing, beta, and release. Construct 2 was used as the game development platform. The game incorporates two-dimensional animations covering key topics, including color theory, color meanings, color temperature, color dimensions, and color schemes. The learning materials are presented through engaging visual storytelling to enhance comprehension. The game was evaluated through alpha and beta testing, while user responses were assessed using a Likert-scale questionnaire. The evaluation results indicate that the game is functional, engaging, enjoyable, and effective in delivering educational content. Therefore, the proposed educational game provides an alternative and interactive approach to teaching color theory to students.
Comparison of Machine Learning Algorithms for Sentiment Analysis of Trans Jogja on Social Media
The social media platform X has become an important channel for the public to express opinions and share experiences regarding public services, including Trans Jogja. User-generated content from this platform provides valuable insights into public perceptions of service quality. However, because these data consist of unstructured text, sentiment classification techniques based on machine learning are required to analyze them effectively. This study aims to compare the performance of several machine learning algorithms for sentiment classification, including Naïve Bayes, Support Vector Machine (SVM), Random Forest, Neural Network, Logistic Regression, and Decision Tree, in classifying user sentiment toward Trans Jogja on the X platform. Data were collected through a web crawling process using Tweet Harvest with keywords related to Trans Jogja, covering the period from January 1, 2025, to June 10, 2026, resulting in a dataset of 3,035 tweets. The preprocessing stage included data cleaning, case folding, tokenization, normalization, stopword removal, and stemming. Text representation was performed using the Term Frequency–Inverse Document Frequency (TF-IDF) method. The dataset was then divided into training and testing sets using five train–test split ratios: 90:10, 85:15, 80:20, 75:25, and 70:30. Model performance was evaluated using a confusion matrix and the corresponding accuracy, precision, recall, and F1-score metrics. The experimental results demonstrate that the Support Vector Machine (SVM) consistently outperformed the other algorithms across different data split ratios. At the 85:15 train–test split, the SVM achieved an accuracy of 91%, precision of 91%, recall of 91%, and an F1-score of 91%, indicating that it is the most effective algorithm for sentiment classification of Trans Jogja users on the X platform.
Performance Analysis of Client-Side Rendering in a Hybrid LMS using Core Web Vitals Under Diverse Network Conditions
The implementation of a Hybrid Learning Management System (Hybrid LMS), which integrates structured learning and self-directed learning within a single platform, presents challenges in website performance, particularly in the implementation of Client-Side Rendering (CSR). This study aims to analyze the performance of Client-Side Rendering in a Hybrid LMS website using Core Web Vitals under diverse network conditions. A quantitative descriptive approach was employed, with laboratory-based testing conducted using Google Lighthouse. Performance evaluations were carried out three times under each network condition—morning, afternoon, and evening—and the results were compared using the average values of each performance metric. The primary contribution of this study is the provision of empirical evidence regarding the performance stability of Client-Side Rendering in a Hybrid LMS under varying network conditions. The findings indicate that the Hybrid LMS website maintains relatively stable performance across different network environments. The best Largest Contentful Paint (LCP) value was 1.2 seconds, the best Cumulative Layout Shift (CLS) value was 0, and the best Total Blocking Time (TBT) was 500 ms. Furthermore, Lighthouse audits consistently reported Accessibility, Best Practices, and SEO scores of 89, 100, and 92, respectively, across all testing locations. Although certain network conditions resulted in increased Total Blocking Time (TBT) and Speed Index values, the overall results demonstrate that the Hybrid LMS website provides a sufficiently optimized user experience to effectively support digital learning activities.
Comparison of the Performance of Vector Space Model and Latent Semantic Indexing Algorithms in Book Search Information Retrieval
Selecting an appropriate method for an information retrieval system is a critical factor in achieving accurate and efficient search performance. This study aims to compare the performance of the Vector Space Model (VSM) and Latent Semantic Indexing (LSI) in the context of book information retrieval, with particular emphasis on semantic capability and computational efficiency. The dataset was constructed by merging two book datasets obtained from the Kaggle platform, which were originally sourced from Amazon's book catalog. After data normalization and duplicate removal, the final dataset consisted of 133,491 book records. The analysis focused on two primary attributes: book titles and book descriptions. The evaluation was conducted through two experimental scenarios: polysemy and synonymity testing, assessed using the Mean Absolute Percentage Error (MAPE) across 20 documents and five search queries, and retrieval speed testing, measured by response time on Google Colab using 20 dataset size variations. The experimental results indicate that LSI outperformed VSM in three of the five search queries, achieving the best MAPE score of 33.20%, whereas VSM recorded its lowest MAPE of 36.35% but deteriorated to 72.01% for queries with high semantic ambiguity. In contrast, VSM demonstrated superior computational efficiency in the retrieval speed evaluation, with response times ranging from 0.4583 ms to 7.2597 ms, substantially faster than LSI, which required between 2.5290 ms and 27.6777 ms. Both algorithms exhibited a linear increase in response time as the dataset size increased, with coefficients of determination of R² = 0.999 for VSM and R² = 0.997 for LSI. The findings reveal a significant trade-off between semantic accuracy and computational efficiency: LSI provides superior semantic understanding for information retrieval, whereas VSM offers substantially faster retrieval performance.
K-Means Application to Rice Production with DBI Evaluation
Rice production is a key indicator of regional food security, making it essential to analyze production patterns and identify differences in productivity across districts. This study aims to apply the K-Means clustering algorithm to classify rice production data in Bireuen Regency and evaluate the quality of the resulting clusters using the Davies–Bouldin Index (DBI). The study employed secondary data from 2020 to 2024 covering 17 districts, with four variables: cultivated area, harvested area, productivity, and total rice production. The K-Means algorithm was used to partition the data into three clusters representing high, medium, and low production levels based on similarities in their characteristics. The results indicate that the clustering process consistently classified the districts across the five-year observation period. Cluster quality evaluation using the Davies–Bouldin Index yielded values of 0.9147 in 2020, 0.8292 in 2021, 0.6119 in 2022, 0.8821 in 2023, and 0.8597 in 2024. The best clustering performance was achieved in 2022, as indicated by the lowest DBI value, reflecting a more compact and well-separated clustering structure. These findings provide valuable insights for supporting agricultural planning, improving rice production strategies, and informing policy decisions related to agricultural development in Bireuen Regency.
Classification of Online Game Player Engagement Levels using the Random Forest Algorithm
The rapid growth in the number of online game players has generated large volumes of behavioral data that can be leveraged to analyze player engagement levels. However, the primary challenges include class imbalance in engagement data and the limited interpretability of predictive models regarding the factors influencing their decisions. This study aims to develop a classification model for online game player engagement levels using the Random Forest algorithm, while addressing class imbalance through the Synthetic Minority Over-sampling Technique (SMOTE) and identifying the most influential features using Feature Importance analysis. The study utilized the Online Gaming Behavior Dataset from Kaggle, comprising 40,034 records. The Random Forest model was optimized using RandomizedSearchCV with five-fold cross-validation to determine the optimal hyperparameter configuration. The experimental results demonstrate that the proposed model achieved an overall accuracy of 92%, with recall values of 90%, 94%, and 89% for the Low, Medium, and High engagement classes, respectively. Feature Importance analysis using both impurity-based and permutation approaches consistently identified SessionsPerWeek (0.4375 and 0.4309) and AvgSessionDurationMinutes (0.3371 and 0.3540) as the two most influential features, jointly accounting for more than 77% of the model's predictive decisions, whereas demographic features contributed only marginally. The novelty of this study lies in the integration of Random Forest, SMOTE, and Feature Importance to develop a classification model that is not only highly accurate but also interpretable. These findings provide valuable insights for game developers in designing evidence-based player retention strategies, such as implementing daily login rewards to increase session frequency and time-limited events to encourage longer gameplay sessions.
Charitas Fibriani (SCOPUS ID=57192643331)
Provincial Clustering using GARCH-based Chili Price Volatility Features
Bird's eye chili is a strategic food commodity in Indonesia whose prices are highly susceptible to interregional fluctuations due to differences in distribution systems and supply chain conditions. These fluctuations often occur over short time horizons, making analyses based on monthly or annual data less capable of capturing short-term price spikes that have the greatest impact on consumers' purchasing power and price stabilization policies. Previous studies have generally clustered regions based on nominal or average prices, which do not adequately represent the dynamics of daily price movements. This study aims to cluster Indonesian provinces according to the volatility characteristics of bird's eye chili prices by proposing a clustering approach that utilizes GARCH(1,1)-based conditional volatility features to represent daily price dynamics, combined with the K-Means algorithm for cluster formation. Daily bird's eye chili price data from 34 provinces covering the period from April 2024 to April 2026 were obtained from the National Strategic Food Price Information Center (PIHPS). The price data were transformed into daily returns and modeled using GARCH(1,1) to estimate the average conditional volatility of each province, which was subsequently used as the clustering feature. The optimal number of clusters was determined using the Elbow Method and the Silhouette Score. The evaluation results identified five as the optimal number of clusters, achieving a silhouette score of 0.65 and classifying the 34 provinces into five volatility categories: very high, high, moderate, low, and very low. The findings reveal that provinces with higher price levels do not necessarily belong to the highest volatility cluster, indicating that a volatility-based approach provides additional insights into price dynamics beyond those captured by nominal price-based clustering. These results can support regional food price volatility monitoring and serve as a reference for developing data-driven decision support systems for food price management.
Design of an Artificial Intelligence-based Personalized Learning Governance Framework
The rapid advancement of Artificial Intelligence (AI) in education has created significant opportunities for personalized learning while simultaneously introducing governance challenges for learners with disabilities. Existing studies have examined AI, adaptive learning, accessibility, and inclusive education; however, these areas remain fragmented and lack an integrated governance-oriented framework. This study aims to develop a Personalized Learning Governance Framework (PLGF) to support inclusive digital literacy through a systematic literature review and bibliometric analysis. The research methodology consisted of Scopus database retrieval, PRISMA-based screening, Biblioshiny-assisted bibliometric analysis, literature synthesis, gap identification, and conceptual framework development. From 196 Scopus-indexed records published between 2023 and 2026, a total of 97 studies were selected for analysis. The findings reveal strong conceptual relationships among AI, personalized learning, inclusive education, accessibility, disability, and ethical technology; however, their systematic integration remains limited. The proposed Personalized Learning Governance Framework (PLGF) comprises five interconnected layers: Learner Disability Profile Input, Machine Learning Personalization Engine, Inclusive Accessibility Adaptation, Governance and Ethical Control Center, and Digital Literacy Outcome Evaluation. The framework provides a comprehensive governance model for supporting accountable, inclusive, and AI-driven personalized learning systems while promoting equitable digital literacy for learners with disabilities.
Crypto Price Analysis: An AI Perspective
Accurate prediction of Bitcoin market prices is challenging because of extreme volatility, nonlinear dynamics and rapid sentiment-driven fluctuations observed in the cryptocurrency market, especially Bitcoin. In this research, the authors investigate the use of machine learning and deep learning methods for forecasting Bitcoin prices using historical market data, technical indicators, and sentiment analysis from social media. A comprehensive dataset was used to develop and test three experimental setups from 2015 to 2024. The first configuration, an LSTM model was trained on historical OHLCV (Open, High, Low, Close, Volume) data only. In the second setup, the model was improved with the inclusion of technical indicators based on market behavior, including momentum indicators and trend indicators. The third configuration used social media sentiment features (extracted from over 2.5 million tweets related to Bitcoin, employing polarity and subjectivity scoring via TextBlob), added to the previous configuration. The results show that the LSTM model with technical indicators gave the best prediction performance with R2 of 0.9084 and Mean Absolute Error (MAE) of 0.0387, which is 8.5% better than the historical data-only model. By comparison, the accuracy of prediction did not significantly improve with the addition of Twitter sentiment features. The feature importance analysis also revealed that Relative Strength Index (RSI) and MACD histogram were the most significant features in predicting the price movements of Bitcoin. The study brings three significant findings: (1) a methodological framework based on LSTM neural networks for comparing the performance of various sets of features in predicting Bitcoin prices; (2) the discovery of the most relevant features in cryptocurrency markets; and (3) proof that technical indicators have a significant impact on the predictive accuracy of this model, while social media sentiment has a minor impact under the circumstances. The results offer valuable implications for researchers and practitioners building data-driven models for predicting cryptocurrencies.
IT Governance Priorities using COBIT 2019 Design Factors for the STIE AMA Academic Information System
The Academic Information System (SIAKAD) is a core service that supports higher education operations and requires effective information technology (IT) governance to ensure regulatory compliance, data quality, risk management, and service continuity. This study aims to identify IT governance priorities for the STIE AMA Salatiga Academic Information System using the COBIT 2019 Design Factors before conducting a capability assessment. A qualitative research approach was employed, utilizing a structured COBIT 2019 assessment based on observations, interviews, and Design Factors questionnaires. The analysis evaluated the ten COBIT 2019 Design Factors to determine the importance scores of the 40 governance and management objectives. The results indicate that service stability orientation, regulatory compliance, risk management, and dependence on cloud/SaaS services collectively define eight priority objectives: MEA03, EDM03, APO12, APO14, DSS05, DSS02, APO08, and DSS04. The findings further reveal that, within a cloud/SaaS environment, IT governance priorities primarily focus on regulatory compliance, risk management, data governance, service security, external service provider management, and service continuity. The study demonstrates that the COBIT 2019 Design Factors provide an effective mechanism for identifying relevant governance and management objectives prior to capability assessment, thereby establishing a structured basis for prioritizing IT governance evaluation and improvement in accordance with organizational characteristics.
Implementation of Agile Methods in the Development of a Mobile-based Hybrid Learning Management System Application
The rapid advancement of digital technology in higher education has increased the demand for learning systems that not only support academic activities but are also easily accessible through smartphones, the devices most frequently used by university students. Although Learning Management Systems (LMSs) have been widely adopted, most existing implementations remain web-based, limiting the optimization of mobile learning experiences. This study aims to develop a mobile-based Hybrid Learning Management System (HyLMS) that integrates synchronous and asynchronous learning within a single platform. The system was developed using the Agile Development methodology to facilitate iterative development and continuous adaptation to user requirements. The development process consisted of requirements gathering, system design, implementation, testing, and evaluation. The resulting application provides essential features, including user registration, authentication, course management, learning material access, assignment submission, discussion forums, notifications, and assessment. Black Box Testing confirmed that all system functionalities operated as expected. Furthermore, user experience was evaluated using the User Experience Questionnaire (UEQ) involving 35 respondents, with positive results across all evaluation dimensions. The highest score was achieved in the Stimulation dimension (1.52), indicating a high level of user engagement and motivation. These findings demonstrate that the proposed mobile-based HyLMS effectively supports hybrid learning by providing a more flexible, integrated, and user-centered learning environment for higher education institutions.
Feasibility Classification of Free Nutritious Meal Kitchen Partners Using C4.5 for Food Safety
The Free Nutritious Meal Program requires a rigorous selection process for kitchen partners because kitchen quality, sanitation, clean water availability, human resources, production capacity, and food distribution are directly associated with food safety assurance. This study aims to develop a C4.5-based classification model to replicate the operational feasibility assessment rules used for evaluating kitchen partners in the Free Nutritious Meal Program based on field survey data. The dataset comprised 200 kitchen partners, including micro, small, and medium enterprises (MSMEs) and catering providers, located in Asahan Regency, North Sumatra, Indonesia, and was collected through structured field observations. Each partner was evaluated using ten assessment criteria: legal compliance, location, facilities and infrastructure, sanitation, clean water availability, human resources, production capacity, food safety, distribution, and risk history. Individual criterion scores were converted into a weighted composite score, after which feasibility labels were assigned according to an operational decision rule based on a minimum threshold score of 80 and the presence of critical failure criteria, defined as mandatory indicators that automatically disqualify a partner regardless of whether the minimum score threshold is achieved. Consequently, the class labels were not derived from independent expert audits or official institutional decisions but were generated from policy-based operational rules and used as the ground truth for model training. The dataset was divided into training and testing sets using an 80:20 ratio, resulting in 160 training instances and 40 testing instances. The results showed that 48 partners were classified as Feasible, while 152 were classified as Not Feasible. The C4.5 model achieved an accuracy of 95.00%, with 90.00% precision, 90.00% recall, and a 90.00% F1-score. The most influential predictor was the number of critical failure criteria (92.97%), followed by the clean water score (7.03%). These findings demonstrate that the C4.5 algorithm can effectively extract and replicate operational feasibility assessment rules, providing a transparent, consistent, and interpretable decision-support tool for selecting kitchen partners while supporting food safety assurance.
UI/UX Design of a Web-based Automated Essay Scoring System using the Double Diamond Method
Manual essay assessment is often time-consuming and increases educators' workload, creating the need for an automated essay scoring system that can improve the efficiency of the assessment process. To address this need, the Nurul Fikri Integrated Institute of Technology (STT-NF) has developed the Essay Analytic Online (ESAO) platform. However, discussions with the platform developers and administrators revealed several UI/UX issues, including an outdated interface design, unclear feature presentation, login-related usability problems, and inconsistencies in interface elements that negatively affect system usability. This study aims to design the UI/UX of the ESAO web platform using the Double Diamond Design Framework to produce a more intuitive, consistent, user-friendly interface that better meets user needs. The design process followed the four phases of the Double Diamond Framework—Discover, Define, Develop, and Deliver—resulting in user flows, a design system, and high-fidelity prototypes developed in Figma. The proposed design was evaluated using the Single Ease Question (SEQ) method involving 20 participants, comprising students and lecturers from STT-NF, across five task scenarios. The evaluation yielded an overall mean SEQ score of 6.46 on a 7-point scale, with average scores of 6.48 for students and 6.30 for lecturers. All task scenarios achieved scores above the usability acceptance threshold of 5.0, indicating a high level of perceived ease of use. These findings demonstrate that the proposed UI/UX design effectively enhances the usability of the ESAO platform. Nevertheless, the evaluation was limited to ESAO users within STT-NF. Future studies are therefore recommended to involve a more diverse group of participants to obtain more comprehensive and generalizable usability evaluation results.
Analysis of Service Quality Improvement Strategies for the PTSP Online System using E-SERVQUAL and Importance–Performance Analysis (IPA)
The digital transformation of higher education has increased the demand for high-quality, efficient, and user-centered electronic services. Sunan Kudus State Islamic University has implemented the Online One-Stop Integrated Service (PTSP Online) system to support academic and administrative service delivery. However, a comprehensive evaluation of its service quality from the users' perspective has not yet been conducted. This study aims to evaluate the service quality of PTSP Online, identify the gaps between users' expectations and perceptions, and determine service improvement priorities as a basis for developing strategies to enhance digital service quality. A quantitative research approach was employed using the E-SERVQUAL and Importance–Performance Analysis (IPA) methods. Data were collected through a questionnaire survey involving 382 users who had previously accessed PTSP Online for academic services. The E-SERVQUAL analysis was conducted to measure the service quality gaps between users' perceptions and expectations across five dimensions: Efficiency, System Availability and Reliability, Responsiveness, Privacy Security Assurance, and Contact and Ease of Use. Subsequently, Importance–Performance Analysis (IPA) was applied to map service attributes according to their perceived importance and performance. The results indicate that the overall service quality of PTSP Online is satisfactory, with relatively small gap values across all dimensions. The Responsiveness dimension exhibited the highest positive gap (0.06), whereas Efficiency recorded the largest negative gap (−0.03). The IPA results identified service data and information accuracy (SAR4) as the highest priority for improvement, while nine service attributes were positioned in Quadrant II (Keep Up the Good Work). This study extends the application of the integrated E-SERVQUAL and IPA approach to the context of digital administrative services in Islamic higher education institutions, an area that remains underexplored in the existing literature. Furthermore, the findings provide empirical evidence to support PTSP Online administrators in formulating sustainable strategies for improving digital service quality.
EduCode: Development of a Mobile-based Coding Educational Game for Children
The rapid advancement of digital technology has significantly transformed the educational landscape. However, its utilization as an interactive learning medium for children remains limited. At the same time, the integration of Computational Thinking into Indonesia's Merdeka Curriculum highlights the importance of introducing coding at an early age. Nevertheless, many children continue to face difficulties in understanding fundamental programming logic due to the limited availability of engaging and effective learning media. This study aims to develop and evaluate EduCode, an educational game designed to provide an interactive and enjoyable coding learning experience for children. The game was developed using the Game Development Life Cycle (GDLC) methodology, which consists of six phases: initiation, pre-production, production, testing, beta, and release. EduCode was implemented using the Flutter framework and the Dart programming language. System functionality was evaluated through Black-Box Testing, while usability was assessed using a Likert-scale questionnaire administered to elementary school students as the end users. The usability evaluation yielded an average score of 89.80%, which falls into the "Very Good" category. These findings indicate that EduCode demonstrates a high level of usability in terms of ease of use, user comfort, and interface design. Therefore, EduCode is considered a highly suitable alternative learning medium for enhancing children's engagement and motivation in learning coding while improving their Computational Thinking skills at the elementary school level.
Enhancing Hate Speech and Offensive Language Detection using CatBoost with RoBERTa-based Contextual Embeddings
The widespread dissemination of hate speech and offensive content on social media platforms has become a critical societal issue, highlighting the need for reliable automated detection systems. This study proposes a hybrid approach that leverages frozen embeddings from the pre-trained language model cardiffnlp/twitter-roberta-base-offensive as a high-level semantic feature extractor, combined with the CatBoost gradient boosting algorithm as the final classifier. The proposed method was evaluated on the HASOC 2021 English dataset through six experimental scenarios and compared with a TF-IDF baseline using CatBoost's default hyperparameters. Experimental results demonstrate that the proposed approach achieved a Macro F1-score of 0.7924 for the binary classification task (Task 1A) and 0.6113 for the multiclass classification task (Task 1B), outperforming the TF-IDF baseline, which achieved scores of 0.7724 and 0.5798, respectively. The proposed system demonstrated a clear performance improvement and achieved results comparable to those of the top-ranked teams on the official HASOC 2021 leaderboard, while avoiding the computational cost associated with fine-tuning large pre-trained language models.
Comparative Performance Analysis of Convolutional Neural Network and EfficientNet-B0 Transfer Learning for Pneumonia Detection in Chest X-Ray Images
Pneumonia is an acute lung infection and remains one of the leading causes of mortality among children under five in Indonesia. Pneumonia is commonly diagnosed through the analysis of chest X-ray (CXR) images by radiologists; however, this process is still subject to human variability and the limited availability of medical specialists. This study aims to develop a Convolutional Neural Network (CNN)-based pneumonia detection system by comparing four model variants: CNN with Color Jitter (Model 1), CNN with Color Jitter and Layer Augmentation (Model 2), CNN with Batch Normalization and Color Jitter (Model 3), and EfficientNet-B0 transfer learning (Model 4). The experiments were conducted using the Chest X-Ray Images (Pneumonia) dataset from Kaggle, comprising 5,856 images, which were divided into 70% for training, 15% for validation, and 15% for testing. The experimental results demonstrate that the EfficientNet-B0 transfer learning model achieved the best performance, with an accuracy of 96.92%, precision of 97.67%, recall of 98.13%, and an F1-score of 97.90%. This model improved classification accuracy by 1.93% compared with the conventional CNN baseline (Model 1), which achieved an accuracy of 94.99%. Furthermore, the incorporation of Layer Augmentation in Model 2 effectively reduced the number of false negatives from 21 to 10 cases. These findings demonstrate the effectiveness of EfficientNet-B0 transfer learning for medical image classification and highlight its potential as a clinical decision support tool for pneumonia diagnosis in healthcare settings, rather than as a replacement for clinical diagnosis performed by qualified medical professionals.
Analysis of INLISLite Management based on the COBIT 2019 DSS01 Domain at the Salatiga City Library and Archives Office
The utilization of information technology in library services plays a crucial role in improving operational efficiency and the quality of public services. The Salatiga City Library and Archives Office has implemented INLISLite as its integrated library automation system to support the management of library collections, membership, and circulation services. However, the effectiveness of the system largely depends on the quality of its operational management. This study aims to analyze the operational management of the INLISLite system based on the COBIT 2019 framework, specifically the DSS01 (Managed Operations) management objective. A qualitative descriptive research approach was employed, with data collected through interviews, observations, and document analysis. The evaluation focused on the five DSS01 management practices: Perform Operational Procedures, Manage Outsourced IT Services, Monitor IT Infrastructure and Operations, Manage Environmental Conditions, and Manage Facilities. The findings indicate that the operational management capability of INLISLite is predominantly at Capability Level 1 (Performed Process) for DSS01.01, DSS01.02, and DSS01.04, while DSS01.03 and DSS01.05 have achieved Capability Level 2 (Managed Process). These results suggest that although most operational processes have been implemented, they have not yet been fully standardized or systematically documented. Therefore, improvements are required through the development of dedicated Standard Operating Procedures (SOPs), enhanced IT infrastructure monitoring, strengthened management of outsourced IT services, improved supporting facilities, and more robust backup and risk mitigation procedures to achieve the target Capability Level 3 (Defined Process) in accordance with the IT governance principles of COBIT 2019.
Redesign of the UI/UX of the Klik Indomaret Application using Design Thinking and System Usability Scale
The development of e-commerce encourages retail companies to provide digital services that offer a better user experience. However, observations and user review analysis indicate that the Klik Indomaret application still has several issues, including the absence of an order cancellation feature, a chat feature with stores, and integrated communication with delivery drivers, which affect the application's usability. This study aims to redesign the User Interface (UI) and User Experience (UX) of the Klik Indomaret application using the Design Thinking method and evaluate the redesigned interface using the System Usability Scale (SUS). Data were collected through observation, user review analysis on the Google Play Store and App Store, and questionnaires distributed to 60 participants. The redesign process consisted of the empathize, define, ideate, prototype, and testing stages, followed by a SUS evaluation involving 30 participants. The results showed that the average SUS score increased from 42.04 before the redesign to 77.58 after the redesign, representing an improvement of 35.54 points. These findings indicate that the redesign improved the application's usability and produced a design that better meets user needs. The findings are expected to serve as a reference for developing more user-centric retail e-commerce applications through features that enhance digital service quality and overall user experience.
Analysis of User Satisfaction Factors for the FOTOYU Application using EUCS Method
The increasing adoption of digital technologies, particularly those powered by Artificial Intelligence (AI), has accelerated the development of applications aimed at improving service quality and user experience, one of which is the FotoYu application. This study aims to evaluate user satisfaction with the FotoYu application using the End-User Computing Satisfaction (EUCS) model, which comprises five dimensions: Content, Accuracy, Format, Ease of Use, and Timeliness. A quantitative research approach was employed using a survey method, with questionnaires distributed to 145 FotoYu users selected through purposive sampling. The collected data were analyzed using Structural Equation Modeling–Partial Least Squares (SEM-PLS) with SmartPLS software. The results indicate that all measurement indicators satisfied the required validity and reliability criteria, confirming that the research instrument was appropriate for assessing user satisfaction. The structural model evaluation produced an adjusted R² value of 0.784, indicating that the five EUCS dimensions collectively explained 78.4% of the variance in user satisfaction, while the remaining 21.6% was attributable to factors outside the proposed research model. Furthermore, the effect size (f²) analysis revealed that Content had the strongest influence on user satisfaction, followed by Ease of Use, Format, Timeliness, and Accuracy. These findings demonstrate that the EUCS model is an effective approach for evaluating user satisfaction with the FotoYu application while providing valuable insights to support service quality improvement and enhance the overall user experience.
Analysis of User Acceptance of the SATUSEHAT Application using the Extended UTAUT 2 Model
The advancement of information technology in the healthcare sector has encouraged the Indonesian government to introduce the SATUSEHAT application as an integrated digital health platform. User acceptance of the SATUSEHAT application is influenced by various factors, including perceived usefulness, ease of use, trust, and privacy protection. This study aims to investigate the factors influencing user acceptance of the SATUSEHAT application using an Extended UTAUT2 model by incorporating Trust and Perceived Privacy as additional constructs. A quantitative research approach was employed by distributing an online questionnaire to SATUSEHAT users, and the collected data were analyzed using Structural Equation Modeling–Partial Least Squares (SEM-PLS) with SmartPLS software. The results indicate that Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Hedonic Motivation, Habit, Trust, and Perceived Privacy have positive and significant effects on Behavioral Intention. Furthermore, Facilitating Conditions, Habit, and Behavioral Intention were found to have positive and significant effects on Use Behavior. In contrast, Price Value was not found to have a significant influence on users' Behavioral Intention. These empirical findings confirm the explanatory capability of the Extended UTAUT2 model in understanding user acceptance of digital health technology. The results further suggest that the adoption of the SATUSEHAT platform is driven primarily by perceived usefulness, ease of use, social support, habitual use, trust, and confidence in data privacy protection, rather than by economic cost considerations.
Comparative Analysis of User Experience with the Agoda and Traveloka Applications Among University Students using the User Experience Questionnaire (UEQ) Method
Online Travel Agent (OTA) applications have become increasingly popular in supporting digital travel services; however, the quality of the user experience may vary across different applications. This study aims to compare the user experience of the Agoda and Traveloka applications among university students using the User Experience Questionnaire (UEQ) and to examine the influence of the six UEQ dimensions on user satisfaction using SmartPLS. A quantitative research approach was employed using purposive sampling among students at Universitas Sriwijaya. The primary dataset consisted of 100 evaluations from Agoda users and 100 evaluations from Traveloka users, following a pilot test involving 30 participants. The results of the UEQ benchmark comparison indicate that Traveloka outperformed Agoda in the dimensions of Attractiveness, Stimulation, and Novelty, whereas Agoda achieved higher scores in Perspicuity, Efficiency, and Dependability. A statistically significant difference between the two applications was identified only for the Stimulation dimension (p = 0.026). The SmartPLS analysis, conducted after indicator elimination, produced R² values of 0.207 for Agoda and 0.374 for Traveloka. For the Traveloka model, Stimulation was found to have a positive and significant effect on user satisfaction (t = 2.166, p = 0.030), whereas none of the structural paths in the Agoda model were statistically significant. These findings suggest that Traveloka provides a more engaging and motivating user experience than Agoda. Nevertheless, both applications require further improvement in their pragmatic quality dimensions, as indicated by the UEQ benchmark results.
Double Diamond Approach to Design Web-based Library Clearance Management System for Graduation Requirements
The library clearance process is one of the main administrative requirements that students must complete before participating in the graduation process. In many higher education institutions, the library clearance process is still manual or fragmented across multiple information systems, resulting in inefficient verification, lengthy delays, and limited transparency for students and librarians. This study aims to design LibrePass, a library clearance management information system that digitizes and streamlines the submission and verification process for graduation requirements, including library clearance, submission of hard and soft copy final report documents, and other institutional requirements. This study adopts the Double Diamond approach, comprising four stages: Discover, Define, Develop, and Deliver. In the Discover stage, user needs and current business processes are identified through interviews, observations, and document analysis. Next, in the Define stage, the results of interviews and observations are synthesized to determine user experience, pain points, and user needs. Then, in the Develop stage, user flows and low- and high-fidelity wireframes are designed. The final stage is Deliver, usability testing is carried out on the prototype using the UEQ instrument with UEQ Data Analysis Tools. The test results show that all UEQ dimensions, namely attractiveness, perpicuity, efficiency, dependability, stimulation, and novelty, show excellent values, with an average score of 2,15. These results indicate that the double diamond approach is successful in designing a prototype that is acceptable and has met user needs.
The Effect of Information Quality and Video Clipper Popularity on Users’ Trust in Information: An Elaboration Likelihood Model Approach
The rapid growth of short-form video-based social media has transformed the way users consume information, enabling faster and more immediate access to digital content. One emerging phenomenon is video clipper content, which consists of short clips extracted from longer videos and presented in a concise format that may influence users' understanding of and trust in the information presented. This study aims to examine the effects of information quality and video clipper popularity on users' trust in information using the Elaboration Likelihood Model (ELM) as the theoretical framework. A quantitative research approach was employed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS software. Data were collected through an online questionnaire from 150 respondents who had previously consumed video clipper content. The results indicate that information quality, representing the central route, does not have a significant effect on users' trust in information. In contrast, popularity, representing the peripheral route, has a positive and significant effect on users' trust in information. Collectively, the two variables explain users' trust in information with an R² value of 0.872, indicating substantial explanatory power. These findings suggest that, in the context of video clipper content consumption, users tend to base their trust more on popularity cues than on the quality of the information itself. This study highlights the importance for content creators and social media platforms to prioritize information credibility and quality in order to reduce the risk of user misunderstanding and the spread of misleading information.
Analysis of the Influence of ShopeeFood Services on Customer Loyalty of Pempek Checklist using the E-Service Quality Model
The rapid advancement of information and communication technology in the digital era has transformed consumer behavior, enabling people to fulfill their daily needs more quickly and conveniently through application-based platforms. One notable development is the widespread adoption of online food delivery services such as ShopeeFood, which offer convenience, speed, and a variety of features that support digital transactions. This trend has the potential to influence customer loyalty toward food service businesses, particularly through the quality of electronic services provided. This study aims to examine the effect of E-Service Quality on customer loyalty toward Pempek Checklist using the E-Service Quality model. A quantitative research approach was employed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS software. Data were collected through an online questionnaire from 100 customers who had previously purchased Pempek Checklist through ShopeeFood. The results indicate that the Efficiency, Fulfillment, and Privacy dimensions have positive and significant effects on customer loyalty, whereas System Availability does not have a significant effect. Collectively, the four dimensions explain customer loyalty with an R² value of 0.967, indicating very strong explanatory power. These findings suggest that, in the context of ShopeeFood services, customer loyalty is driven primarily by ease of use, reliable service fulfillment, and transaction security rather than by system availability. The study highlights the importance for both business owners and platform providers to continuously enhance electronic service quality in order to strengthen long-term customer loyalty.
User Experience Analysis of the Tomoro Coffe Application using the UEQ Method and SmartPLS
The rapid development of digital technology has encouraged food and beverage service providers to offer mobile applications that deliver an optimal User Experience (UX). This study aims to analyze the User Experience of the Tomoro Coffee application using the User Experience Questionnaire (UEQ) and examine the influence of the six UEQ dimensions on User Experience using SmartPLS 4. A quantitative approach was employed involving 97 respondents who had used the Tomoro Coffee application. Data were analyzed using the UEQ Data Analysis Tool and SmartPLS 4.The UEQ results indicate that Dependability (1.702) falls into the Excellent category, while Efficiency (1.854), Perspicuity (1.795), and Attractiveness (1.635) are categorized as Good. Meanwhile, Stimulation (0.955) and Novelty (0.399) are categorized as Below Average. The SmartPLS analysis shows that all UEQ dimensions have a positive and significant effect on User Experience, with Dependability having the strongest influence, followed by Efficiency, Attractiveness, Perspicuity, Stimulation, and Novelty. The novelty of this study lies in the integration of UEQ and SmartPLS 4, enabling the evaluation to not only measure UX levels through UEQ benchmarks but also empirically validate the contribution of each UEQ dimension to User Experience. The findings provide a more comprehensive evaluation framework and serve as an empirical basis for determining application development priorities based on the most influential UX dimensions.
User Satisfaction Analysis on the Strava Application in Palembang City using the Usability Method
This study aims to evaluate user satisfaction with the Strava application in Palembang through a usability approach. A quantitative research design was employed involving 100 Strava users as respondents. The study examined Learnability, Efficiency, Memorability, and Errors as usability dimensions influencing User Satisfaction. Data were collected through a structured questionnaire and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4 software. The results indicate that all measurement indicators satisfied the required validity and reliability criteria. Hypothesis testing revealed that, individually, only the Errors dimension had a significant effect on user satisfaction, whereas Learnability, Efficiency, and Memorability did not show significant effects. However, when considered simultaneously, all usability dimensions collectively contributed to user satisfaction. These findings suggest that system stability and a low error rate are the primary factors influencing user satisfaction with the Strava application.