Sistemasi: Jurnal Sistem Informasi is a scientific journal in the field of computer science and engineering. General Focus and Scope of Sistemasi: Jurnal Sistem Informasi, are Information Systems, Information Technology, Data Science, Artificial Intelligence, Software Engineering and Computer Engineering. Sistemasi: Jurnal Sistem Informasi is abstracted and indexed in Directory of Open Access Journal (DOAJ) since 2016 and Science and Technology Index (SINTA) since 2018. Sistemasi: Jurnal Sistem Informasi are published 12 times a year, in January, February, March, April, May, June, July, August, September, October, November and December. Sistemasi: Jurnal Sistem Informasi (p-ISSN: 2302-8149 e-ISSN: 2540–9719) published by Universitas Islam Indragiri, Tembilahan Riau Indonesia.
Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer
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Analysis of Factors Influencing the Adoption of the PELNI Mobile Application for Ship Ticket Purchases in Sorong using the Unified Theory of Acceptance and Use of Technology 2
This study aims to analyze the factors influencing the adoption of the PELNI Mobile application for ship ticket purchases using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) approach. The research employed a quantitative method with data collected through questionnaires distributed to users of the PELNI mobile application. The collected data were analyzed using Structural Equation Modeling–Partial Least Squares (SEM-PLS). The results indicate that Facilitating Conditions and Habit significantly influence the actual usage behavior of the PELNI mobile application. In contrast, Performance Expectancy, Effort Expectancy, Social Influence, Hedonic Motivation, and Price Value do not have a significant effect on usage intention. Furthermore, Behavioral Intention was also found to have no significant influence on actual usage behavior. These findings suggest that the adoption of the PELNI mobile application in the context of maritime transportation is more strongly influenced by the availability of supporting facilities and users’ habitual behavior than by perceived usefulness, ease of use, or social influence. This study is expected to contribute theoretically to the development of the UTAUT2 model and provide practical implications for the management of digital-based maritime transportation services.
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Digital Inclusion Evaluation: A User Experience Study of Praja Mobile among Elderly and Adult Users
This study aims to evaluate the user experience of the Praja Mobile application among two age groups, namely adults and elderly users, as an effort to assess the level of digital inclusion. The research employed a quantitative descriptive approach using the User Experience Questionnaire (UEQ) instrument, which measures six main aspects: attractiveness, perspicuity, efficiency, dependability, stimulation, and novelty. The study involved 40 respondents, consisting of 20 adult users and 20 elderly users. Based on the analysis results, the elderly group achieved scores of 2.00 for attractiveness, 1.88 for efficiency, 1.76 for dependability, 1.75 for stimulation, 1.82 for perspicuity, and 0.78 for novelty. Meanwhile, the adult group obtained scores of 2.30 for attractiveness, 2.30 for efficiency, 2.05 for dependability, 2.07 for stimulation, 2.03 for perspicuity, and 0.72 for novelty. The most notable difference was observed in the perspicuity aspect. This finding indicates that elderly users still experience difficulties in understanding the application’s navigation and usage flow compared to adult users. The implications of this study emphasize the importance of designing interfaces that are more elderly-friendly, particularly in improving information clarity, navigation structure, and ease of interaction. Therefore, the findings of this study contribute practical recommendations for application developers to support greater digital inclusion, enabling applications to be more accessible across different age groups.
Comparison of Machine Learning Algorithms for Credit Score-based Banking Customer Churn Prediction
A high customer churn rate represents a significant challenge for the banking industry, leading to substantial financial losses and higher acquisition costs for new customers. Proactively identifying customers who are likely to churn is essential for implementing effective retention strategies. This study aims to address this issue by implementing and comprehensively comparing three different machine learning classification algorithms: Logistic Regression, Random Forest, and XGBoost. The study utilized a secondary dataset consisting of bank customer profiles from 10,000 customers with various characteristics, including credit scores, account balances, and transaction activities. The research methodology followed the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework. The models were evaluated using several metrics, including Accuracy, Precision, Recall, F1-Score, and ROC-AUC. The findings indicate that the ensemble models significantly outperformed the linear model (Logistic Regression), which achieved an F1-Score of only 0.286. Random Forest emerged as the best-performing model in this study, achieving the highest Accuracy (0.864), F1-Score (0.590), and ROC-AUC (0.852). In comparison, XGBoost demonstrated competitive performance with an F1-Score of 0.579 and a ROC-AUC of 0.832. The study concludes that Random Forest provides the most optimal overall performance, offering the strongest capability for identifying at-risk customers within the dataset.
Optimization of Support Vector Machine for the Classification of Nutritional Status in Children
Toddlerhood is a critical developmental period that requires precise nutritional monitoring. However, automated classification systems are often challenged by imbalanced data, which makes minority classes difficult to detect accurately. This study aims to optimize a Support Vector Machine (SVM) using a polynomial kernel to improve detection sensitivity for critical classes. By excluding BMI features to avoid redundancy, the proposed model achieved an accuracy of 98%. The main novelty of this research lies in its achievement of an F1 Macro Score of 0.86, confirming that the model provides balanced and reliable classification performance across all nutritional status categories. These results demonstrate the model’s superiority in identifying the minority classes of Severe Malnutrition and Undernutrition more effectively than previous studies. Therefore, the model is highly recommended as an objective decision support system for the early detection of stunting.
Charitas Fibriani (SCOPUS ID=57192643331)
Machine Learning Model Optimization and Interpretability Analysis for Classifying Student Stress Levels
This study aims to compare and analyze the performance of several algorithms in classifying student stress levels. The dataset used in this research is the Student Lifestyle Dataset obtained from the Kaggle repository, consisting of 2,000 records with eight student lifestyle features. The methods employed include the implementation of three classification algorithms: Logistic Regression, K-Nearest Neighbors, and Support Vector Machine (SVM), across four experimental scenarios. These scenarios include a baseline model, handling imbalanced data using the Synthetic Minority Oversampling Technique (SMOTE), feature selection using Recursive Feature Elimination (RFE), and hyperparameter tuning. The results were evaluated using accuracy, precision, recall, and F1-score metrics. Furthermore, interpretability analysis of the best-performing model was conducted using SHAP. The findings indicate that the integration of data balancing techniques, feature selection, and parameter optimization with the SVM algorithm significantly improved performance, achieving an accuracy of 0.98, precision of 0.96, recall of 0.98, F1-score of 0.97, and a computation time of 0.011 seconds. The interpretability analysis revealed that lifestyle factors such as study duration and sleep duration had the most dominant influence on stress levels. These results demonstrate that the integrated optimization strategy successfully supports fast and accurate detection of student stress levels.
Modified Cipher Feedback Mode based on a Dynamic Hénon Map Key for Image Data Security
The security of digital image transmission is vulnerable to statistical analysis attacks due to the high spatial correlation among pixels. In addition, the implementation of chaotic systems on digital devices often suffers from periodic degradation problems. To address these issues, this study proposes a modified Cipher Feedback (CFB) mode integrated with a dynamic key generator based on the Hénon Map. The main contribution of this research lies in the use of SHA-256 hashing for initializing the initial parameters, as well as the utilization of ciphertext values as feedback (perturbation) to update the Hénon Map parameters in each iteration. The proposed algorithm was evaluated using 25 test images in both grayscale and RGB formats. The experimental results achieved an average Shannon entropy value of 7.998, with differential sensitivity metrics reaching 99.606% for the Number of Pixels Change Rate (NPCR) and 33.464% for the Unified Average Changing Intensity (UACI). Meanwhile, the decryption process produced a Root Mean Square Error (RMSE) value of 0 and a Peak Signal-to-Noise Ratio (PSNR) of 100 dB. These findings indicate that the proposed architecture is capable of mitigating chaos degradation, resisting chosen-plaintext attacks, and reconstructing visual data without information degradation (lossless).
Sentiment Analysis of the MyTelkomsel App based on Support Vector Machines: A Kernel Performance Comparison
MyTelkomsel is a customer service application developed by one of the largest cellular operators, with more than 100 million users. Due to the high volume of application users, sentiment analysis is essential for examining user opinions to optimize service quality. However, sentiment classification often faces challenges caused by imbalanced sentiment class distributions, which can affect model performance. This study analyzes sentiment toward the MyTelkomsel application using the Support Vector Machine (SVM) algorithm, focusing on evaluating the performance of Linear, RBF, and Polynomial kernels. The dataset consisted of 1,000 user reviews randomly collected from the Google Play Store, with positive and negative labels assigned based on the Indonesia Sentiment Lexicon (InSet). The dataset was divided into training and testing sets using an 80:20 ratio. The model development process was carried out using RapidMiner. The optimal performance was achieved by the Linear kernel through the implementation of the Synthetic Minority Over-sampling Technique (SMOTE) and K-Fold Cross Validation, resulting in an accuracy of 100%, precision of 100%, recall of 100%, and F1-score of 100%. These results indicate that the data can be effectively separated using a linear boundary. SMOTE was applied to address class imbalance in the dataset, while K-Fold Cross Validation (k = 10) was used to ensure the absence of overfitting by testing the entire dataset divided into 10 folds. The findings of this study can serve as a foundation for optimizing application services, enabling improvement strategies to be implemented in accordance with feedback derived from user reviews.
Evaluation of the Impact of Labeling Quality and Class Imbalance on Sentiment Classification of the Palestine–Israel Conflict
This study aims to evaluate the performance of sentiment classification on social media data related to the Palestine–Israel conflict, with a particular emphasis on the role of labeling quality and data distribution. The proposed approach combines TF-IDF text representation with lexicon-based labeling using InSet, along with two classification algorithms: Support Vector Machine (SVM) and Random Forest. The dataset was collected from the social media platform X and consisted of 2,831 Indonesian-language tweets that had undergone preprocessing. The results indicate that the sentiment distribution was dominated by the negative class (39.35%), followed by neutral (38.43%) and positive (22.21%) classes, indicating the presence of class imbalance. The labeling validity evaluation produced a Cohen’s Kappa value of 0.0175, indicating a low level of agreement between automatic labeling and manual annotation. The SVM model achieved an accuracy of 0.69 and a weighted F1-score of 0.68. However, both models demonstrated poor performance on the positive class as the minority class. These findings suggest that the limitations in model performance are not solely caused by the classification algorithms themselves, but are also significantly influenced by labeling quality and data distribution characteristics. This study contributes by emphasizing the importance of comprehensive evaluation throughout the sentiment analysis pipeline, particularly when dealing with complex and uncontrolled data sources such as social media.
Enhanced Network Intrusion Detection and Classification based on Ensemble Learning Techniques: A Study on the NSL-KDD Dataset
This research explores an improved Network Intrusion Detection System (NIDS) on the NSL-KDD dataset using machine learning, deep learning and ensemble learning methods. Our approach involves essential steps such as data preparation, feature engineering with Random Forest, feature reduction, model building, hyperparameter tuning with GridSearchCV, and evaluation. We perform binary and multiclass classification tasks with Naïve Bayes, Logistic Regression, Random Forest, LightGBM, CNN, and LSTM approaches. The findings show ensemble techniques enhance classification accuracy. Random Forest and LightGBM models in binary classification, and CNN and LSTM models in multiclass classification achieved up to 99% and 97.99% and 97.80% accuracy, respectively. Additionally, the proposed stacked ensemble model, with XGBoost as the meta-learner, achieved a final test accuracy of 99.03%, and improved precision, recall, F1-score and ROC-AUC compared to the individual models. Tuning the hyperparameters also improved model stability and accuracy. This research is novel in combining feature selection, hyperparameter-tuned deep learning models and a stacking ensemble to enhance accuracy and stability in intrusion detection. The research also emphasizes the need for interpretability, real-time considerations and transfer learning in future NIDS research.
Design Thinking Model for Digitalizing Information Systems in Indonesia–Timor Leste Border Vocational Schools
SMK Suarna Wisata Tes is located along the border area between Indonesia and Timor-Leste. As part of Indonesia’s 3T region (Tertinggal, Terdepan, dan Terluar — disadvantaged, frontier, and outermost areas), the school represents the progress of Indonesian education in neighboring border communities, particularly for the people of the Oecusse District. One of the main limitations faced by SMK Suarna Wisata Tes is the absence of a centralized digital information system, particularly a website that can serve as an integrated platform for disseminating school information. Currently, the school relies on several social media platforms to distribute information, which requires additional time and effort for information management and communication with the public. This study developed a website for SMK Suarna Wisata Tes to facilitate the delivery and dissemination of school-related information. The digitalization of the school information system provides several benefits. Information related to teachers, students, and school profiles can be centrally stored and made accessible to the wider community. In addition, all school announcements and news can be managed in an integrated, efficient, and persistent manner. The website development implemented the Design Thinking method, which focuses on user-centered needs within the school environment. The implementation results demonstrated a positive impact on SMK Suarna Wisata Tes. Information dissemination became faster, more accurate, and more accessible to the broader community. Furthermore, data could be stored digitally and managed more efficiently. The findings of this study can serve as a reference model for the education sector in designing digital systems for schools located in border regions.
Analysis of Automated Web Testing Frameworks Based on Performance and Usability
Advancements in information technology in recent years have accelerated digital transformation across various business sectors. This is reflected in the increasing use of web-based applications that require systems to be responsive, stable, and reliable. However, software testing processes are still often carried out manually, making them less efficient due to the time required, high resource consumption, and the potential for human error, especially when applications undergo continuous feature updates. Therefore, a more effective testing approach is needed through automation testing. This study aims to analyze and compare several automation testing framework to determine the most suitable framework for web application testing. Test scenarios are developed using a Domain Specific Language approach based on Cucumber Gherkin to ensure better readability and structure. The evaluation is conducted based on two aspects main, namely Automated Testing Progress and Tools Usability. The performance aspect includes time complexity, covered test cases, execution start, and element inspection. Meanwhile, the usability aspect covers platform compatibility, supported browsers, scripting languages, and parallel execution. The case study is conducted on a web-based logistics application, muatmuat.com, focusing on the register, login, and reset password features. The data were analyzed using the Multiple Attribute Decision Making (MADM) method based on the Distance to the Ideal Alternative (DIA) approach. The results show that Selenium WebDriver has an Ri value of 0.25799, followed by Cypress (0.31747), as well as Playwright and WebDriverIO (0.33426), making it the most optimal framework.
Implementation of ResNet-50 in a Fresh Fruit Bunch (FFB) Ripeness Detection System for Oil Palm
The quality of Crude Palm Oil (CPO) is highly dependent on the accuracy of sorting the ripeness level of oil palm Fresh Fruit Bunches (FFB). Manual sorting processes currently used in factories are vulnerable to human error and subjectivity. This study aims to automate the objectivity of the sorting process using a deep learning model based on the ResNet-50 architecture with a transfer learning approach to classify FFB into three categories: Unripe, Ripe, and Overripe. The computational model was integrated into a web-based application using the Flask framework to support wireless operational use in factories. Experimental results showed a validation accuracy of 90.94% and an F1-score of 91%. Direct field validation using 42 primary data samples achieved a classification success rate of 83.33%. The implementation of a 75% confidence threshold proved effective in preventing prediction errors (zero misclassification), while the Cohen’s Kappa reliability test achieved a score of 0.769, indicating Substantial Agreement with expert evaluators. In conclusion, the ResNet-50-based system demonstrated reliable and objective performance and is considered ready for replication to maintain quality consistency in the palm oil processing industry.
Application of Natural Language Processing for Emotion Detection and Motivational Response Generation in Indonesian Text using the CRISP-DM Method
The rapid growth of social media has encouraged teenagers to express their emotions through text-based social media posts. However, existing systems still face limitations in understanding emotional meaning and providing appropriate responses. This study aims to develop a Natural Language Processing (NLP)-based system for detecting emotions in Indonesian-language text and generating contextual motivational responses. The research methodology employed the Cross Industry Standard Process for Data Mining (CRISP-DM), which consists of business understanding, data understanding, data preparation, modeling, evaluation, and deployment stages. Text processing was conducted through tokenizing, filtering, stemming using the Enhanced Confix Stripping algorithm, negation checking, and matching with an emotion lexicon based on Plutchik's Wheel of Emotions classification. The dataset consisted of 14,182 emotion lexicon entries used as reference data and 100 emotional expression sentences collected from 100 respondents as testing data. Evaluation using the Weighted F1-score produced a result of 87%. These findings indicate that the proposed system is capable of identifying emotions and generating relevant motivational responses. The integration of emotion detection and response generation within a single system enables outputs that are adaptive to users’ emotional conditions. However, the system still has limitations in detecting non-standard language and slang expressions, indicating that further lexicon enrichment and more adaptive methods are still required.
Comparative Analysis of TinyBERT, SVM, and Char-CNN Models for Phishing URL Detection
Phishing is one of the most prevalent cybersecurity threats that exploits malicious URLs to deceive users and steal sensitive information. This study proposes a URL-based phishing detection method using the lightweight Transformer model TinyBERT and compares its performance with three baseline models: SVM based on character n-grams, Random Forest based on lexical URL features, and Char-CNN. The dataset used in this study consists of 49,750 URLs with multi-class labels (benign, defacement, malware, and phishing), which were subsequently binarized into phishing (label 1) and non-phishing (label 0). The data were divided using a stratified split into training, validation, and testing sets with a ratio of 70%–15%–15%. To address class imbalance, the TinyBERT model was trained using a weighted loss approach based on class weights. The evaluation was conducted using a confusion matrix, accuracy, precision, recall, F1-score, as well as ROC and Precision–Recall curves. Experimental results demonstrate that TinyBERT achieved the best performance, with an accuracy of 0.9925, phishing recall of 0.9512, and an F1-score of 0.9387. In addition, the model produced the lowest number of false negatives (22) compared with the baseline models. These findings indicate that TinyBERT is more effective in minimizing phishing URLs that are incorrectly classified as benign, making it more suitable for implementing URL-based phishing detection in cybersecurity systems.
Analysis of MikroTik Firewall Effectiveness in Mitigating DDoS Attacks using the Network Development Life Cycle (NDLC) Approach
This study is motivated by the increasing network security threats accompanying the growth of digital-based services in government institutions. One of the most common threats is the Distributed Denial of Service (DDoS) attack, which can cause network performance degradation and service disruption. This research aims to analyze the effectiveness of implementing a MikroTik firewall in mitigating DDoS attacks on the network of the Diskominfo Kota Kediri using the Network Development Life Cycle (NDLC) method. The research stages consisted of analysis, design, simulation, implementation, monitoring, and management. The testing process was conducted by simulating SYN Flood attacks using the Hping3 application. The evaluated parameters included throughput, delay, packet loss, and CPU utilization on the router. The experimental results showed that under DDoS attack conditions without firewall protection, throughput increased to approximately 40 Mbps, delay reached 5–15 ms, packet loss ranged from approximately 20–30%, and CPU utilization increased to 99–100%. After the firewall was implemented, network performance improved significantly, with throughput decreasing to approximately 2 Mbps, delay stabilizing below 1 ms, packet loss reduced to 0%, and CPU utilization decreasing to approximately 3%. These findings indicate that the MikroTik firewall is effective in filtering abnormal traffic and reducing system load, thereby improving network stability and maintaining service quality based on Quality of Service (QoS) parameters.
Design and Development of an Android-based Rentak Bulian Dance Adventure Role-Playing Game using a Story-Driven Approach and Quest System
The low level of engagement among younger generations in learning local culture has created a need for interactive and structured cultural introduction media. This study designs and develops a prototype of an Android-based RPG adventure game featuring the Rentak Bulian Dance using a story-driven approach and quest system as a progressive structure for delivering cultural content in stages. The research employed the Research and Development (R&D) method using the ADDIE model (Analysis, Design, Development, Implementation, and Evaluation), integrated with the Game Development Life Cycle (GDLC) framework to ensure process traceability throughout the game development stages. The product implements several core components, including a story module, dialogue system, quest manager, quest log, and reward/feedback mechanisms, while formalizing missions through a quest tree structure consisting of main quests and side quests. The feasibility evaluation was conducted using the System Usability Scale (SUS) involving 30 respondents. The results showed an average SUS score of 76.0 (SD = 5.9; min = 65.0; max = 87.5), with 93.3% of respondents classified within the acceptable category (≥70). These findings indicate that the prototype is feasible as a medium for introducing the Rentak Bulian Dance, with recommendations for improvement focusing on the clarity of mission instructions and navigation consistency. The main contribution of this study lies in the integration model between narrative and quest systems for cultural content delivery, as well as empirical evidence of usability feasibility based on SUS evaluation for an Android-based prototype.
Implementation of Zero-Shot Learning on Edge AI for Deterministic Interpretation of Electrical Metrics using Quantized Large Language Models
Automatic interpretation of building electrical metric data is essential for assessing the reliability and suitability of electrical systems. The emergence of Large Language Models (LLMs) has created new opportunities to automate the inspection and deterministic interpretation of these metric values without requiring manual input. This study evaluates the performance of local computing (Edge AI) in interpreting and classifying electrical system status using a Zero-Shot Learning approach without the need for model retraining. The interpretation rules were based on the PUIL 2020 standard and included parameters such as voltage deviation, frequency, current load, imbalance, and power factor. The comparative evaluation involved two 8-bit quantized models: Llama 3.1 (8B) and Qwen 2.5 (7B), tested using 200 historical building electrical panel data samples (100 normal and 100 anomalous). The assessment covered LLM performance metrics (syntactic and semantic accuracy), anomaly detection classification, and hardware resource efficiency. The results show that Qwen 2.5 (7B) outperformed Llama 3.1 (8B) in mathematical reasoning tasks, achieving an accuracy of 91.50% and a precision of 95.60%, with minimal false positives. In addition, Qwen completed the analysis 42 minutes faster while using a peak RAM consumption of 8.9 GB. In contrast, Llama 3.1 demonstrated excessive sensitivity, resulting in an accuracy of 57.50%, a precision of 54.19%, and higher memory usage (11.9 GB). These findings indicate that the effectiveness of Zero-Shot Learning in LLMs for logical reasoning tasks depends more on the model’s training bias than on the number of parameters. Models specifically trained for programming and mathematical reasoning, such as Qwen 2.5, are more reliable, consistent, and efficient in interpreting electrical metrics compared to general conversational models.
GAN-CNN-based Android Ransomware Detection System using Network Traffic Analysis
Android ransomware poses a major threat to cybersecurity, resulting in financial losses, data thefts, and service disruptions for mobile users. In this paper, a network traffic-based ransomware detection framework is proposed, which combines the feature selection and data augmentation approaches with machine learning and deep learning algorithms. The proposed methodology consists of data preprocessing, data normalization, class balancing, and feature reduction based on the Random Forest importance and SHAP analysis to select the most informative features. Different classification models such as Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), TabNet, Deep Neural Network (DNN), and Convolutional Neural Network (CNN) are evaluated and compared. Generative Adversarial Networks (GANs) are used to generate synthetic ransomware samples for training, to cope with class imbalance, and to enhance detection capability. The results of the experiments proved that the GAN-improved CNN model's overall accuracy is 99.5%, recall is 99.8%, precision is 99.6%, F1 score is 99.6%, and AUC is 98.9%. The results further show that feature reduction resulted in reduced time in training and testing with high detection performance. This paper emphasizes the importance of the proposed feature selection, augmentation using GAN, and deep learning approach for detecting Android ransomware. The framework proposed, however, led to decreased feature space and increased computational efficiency, but additional testing on real Android devices is still needed to confirm the claims of lightweight deployment and low resource usage.
Feature Importance Analysis of SMV Gap and Manpower Variables on Garment Production Output based on Ensemble Learning
In the labor-intensive garment manufacturing industry, there is a conventional assumption that achieving production targets largely depends on increasing the number of workers (manpower), causing evaluations of operational time efficiency to often be overlooked. To examine this assumption, this study aims to identify the dominant factors affecting garment production output using a Machine Learning approach based on Ensemble Learning methods, namely Random Forest and Gradient Boosting. The dataset consisted of 700 observations collected at 20-minute intervals, including variables such as actual Standard Minute Value (SMV), SMV gap, actual manpower, and manpower gap. The evaluation results indicate that the Random Forest model outperformed Gradient Boosting, achieving a Mean Absolute Error (MAE) of 4.55, Root Mean Square Error (RMSE) of 6.85, Mean Absolute Percentage Error (MAPE) of 19.12%, and an R² value of 0.758. In comparison, Gradient Boosting obtained an MAE of 4.88, RMSE of 7.21, MAPE of 20.78%, and an R² value of 0.733. Based on the best-performing model, the feature importance analysis revealed that actual SMV was the most dominant factor (>0.70), followed by the SMV gap (>0.20). In contrast, manpower variables had a very limited influence (<0.05). Unlike previous studies that generally focused on optimizing the number of workers, the novelty of this research lies in the simultaneous use of SMV gap and manpower gap variables within a predictive model. This approach provides empirical evidence that job complexity and operational time efficiency are significantly more critical in determining garment production output than merely increasing the workforce.
Improving Identity Validation in a Flutter-based Attendance System
This study aims to improve identity validation in a Flutter-based attendance system that remains vulnerable to attendance manipulation, such as buddy punching and fake GPS usage. The primary issue in the previous system was that location-based validation mechanisms were unable to ensure that attendance activities were genuinely performed by authorized users. As a system implementation and software engineering study, this research applies a multi-feature similarity approach based on face embeddings, where appearance similarity serves as the primary component calculated using cosine similarity. Supporting features include geometry similarity, quality score, color similarity, and texture similarity. The system was developed using the FAST methodology, with implementation based on Flutter, Google ML Kit for face and landmark detection, and MobileFaceNet for face embedding extraction. Testing was conducted through direct implementation trials, API testing, and black-box testing involving 655 employees using a similarity threshold of 0.80. The results from 14 testing scenarios showed that all system outputs matched the expected outcomes, resulting in 100% scenario accuracy. Compared to the previous GPS-based attendance system, indications of attendance manipulation decreased from 70 cases (10.7%) to only 1 case (0.15%). In addition, the False Acceptance Rate decreased from 12.8% to 0.2%, with an average verification time of 1200 ms. These findings demonstrate that the multi-feature similarity approach based on face embeddings is capable of improving the validity and integrity of real-time attendance data on mobile devices.
Evaluation of Shopee Live Feature Usability among Millennial Consumers using the System Usability Scale
Digital transformation has driven e-commerce toward real-time interaction through live commerce, increasing user engagement while also introducing greater usage complexity. This study aims to evaluate the usability of the Shopee Live feature among millennial consumers. A quantitative approach was employed using the System Usability Scale (SUS) instrument. Data were collected from 100 respondents through purposive sampling and analyzed descriptively along with reliability testing. The results indicate an average SUS score of 66.55, which falls within the Marginal-High category, suggesting that the Shopee Live feature is generally acceptable but has not yet reached an optimal level of usability. Although ease of use, functional integration, and learnability were evaluated positively, users still experienced challenges related to system complexity, consistency, and usage effort. In the context of live commerce, usability is determined not only by ease of use but also by the stability of interaction under real-time conditions. Cognitive load resulting from multitasking demands was identified as a key mechanism affecting usage efficiency. These findings highlight the importance of integrating real-time interaction and cognitive load considerations into usability evaluation, while also providing practical implications for the development of more adaptive live commerce features.
Hybrid CNN and Autoencoder Deep Learning Model for Network Malware Detection
Malware remains one of the primary threats to network security, continuously evolving with increasingly complex attack patterns that are difficult to detect using conventional methods. Data imbalance and high feature dimensionality are major challenges in improving the performance of malware detection models. This study aims to develop a deep learning-based malware detection model using a hybrid approach that combines Convolutional Neural Networks (CNN) and Autoencoders. The dataset used in this study was the improved version of the CICIDS2017 dataset, consisting of more than 2 million records and 91 features. The research stages included data collection, exploratory data analysis (EDA), data preprocessing, feature selection, and data balancing using SMOTE, followed by model design and evaluation. The Autoencoder was employed for dimensionality reduction, generating a compressed representation of 32 features, which was subsequently used as input for the CNN model in multi-class classification. The results demonstrate that the proposed model achieved high accuracy, along with strong precision, recall, and F1-score values across most classes. However, performance on minority classes still exhibited limitations due to data imbalance. Therefore, the hybrid CNN–Autoencoder approach proved effective in improving network malware detection performance.
Design and Development of a Hybrid NLP and Rule-based QA Assistant for Indonesian User Stories
This study aims to address challenges in Agile-based software testing, where manually creating test cases from user stories is often time-consuming and produces inconsistent quality. Although Natural Language Processing (NLP) techniques and rule-based systems have been proposed, each approach has limitations in handling linguistic ambiguity and variations in sentence structure, particularly in the Indonesian language context. This research proposes a hybrid Quality Assurance (QA) assistant that integrates an IndoBERT-based Named Entity Recognition (NER) model with a deterministic rule-based system. The NER model is used to extract functional elements, including actors, actions, objects, conditions, and expected outcomes, while the rule-based system maps these elements into structured test case templates. Qualitative evaluation conducted by QA practitioners showed that the hybrid approach achieved an average score of 4.67 on a 5-point Likert scale, outperforming both the NLP-only approach (3.87) and the rule-only approach (4.60). The proposed system was proven to improve testing efficiency by more than 99% while generating test cases that are more complete, readable, and traceable. These findings confirm that integrating the flexibility of NLP with the consistency of rule-based systems is highly effective for automating Quality Assurance processes in the Indonesian local context.
Design of a Sentiment Analysis System for Indonesian-Language International Conflict News using Naïve Bayes and SVM
The rapid growth of digital news content, particularly coverage related to international conflicts, has created an urgent need for sentiment classification systems capable of operating automatically and objectively. This study designs a sentiment analysis system for Indonesian-language international conflict news using two classification algorithms, namely Naïve Bayes and Support Vector Machine (SVM), while also comparing their effectiveness. A total of 339 news articles sourced from Reuters, BBC, CNN, and Al Jazeera were used as the dataset, consisting of 146 negative, 123 positive, and 70 neutral sentiment texts. The data processing stages included preprocessing (case folding, tokenizing, stopword removal, and stemming), TF-IDF feature weighting, and classification using an 80:20 train–test split scheme. The system was developed using Python and deployed through the Streamlit framework as a web-based interface. The experimental results indicate that SVM achieved an accuracy of 82.35%, with more balanced precision, recall, and F1-score values across all classes, outperforming Naïve Bayes, which achieved an accuracy of 77.94%. The main novelty of this study lies in the use of complete news articles from credible international journalistic sources as the object of analysis, unlike most previous studies that primarily relied on short-form social media data. The study concludes that SVM is more effective for sentiment classification of Indonesian-language international conflict news and recommends expanding the dataset and exploring semantic representation methods such as IndoBERT to improve performance in future research.
SAST Implementation for Evaluating LLM-Generated Code Quality using Prompt Engineering
The use of Large Language Models (LLMs) for generating programming code has become increasingly widespread; however, the quality of the generated output heavily depends on the instructions or prompts provided. This study aims to evaluate the influence of prompt engineering techniques on the quality of non-functional code generated by LLMs. The research employed a quantitative experimental approach involving five Python game development tasks using four prompt variations: zero-shot, few-shot, chain-of-thought, and role-based prompting. A total of 200 code snippets were analyzed using Static Application Security Testing (SAST) with the DeepSource tool to detect issues across seven categories: secrets, bug risk, anti-pattern, security, performance, style, and documentation. The results indicate that few-shot prompting produced the lowest total number of issues overall (1,328 out of 6,932 issues), demonstrating particular advantages in the anti-pattern and performance categories. However, this technique also recorded a higher number of critical issues (3 issues) compared to zero-shot and role-based prompting (1 issue each), indicating a trade-off between the overall volume of issues and the severity of certain issues. Role-based prompting generated the highest number of issues (2,516 issues), particularly in the style and documentation categories. This study recommends few-shot prompting as a foundational approach for AI-assisted software development and highlights the importance of integrating SAST into CI/CD pipelines to ensure code security and quality.
Optimization of IndoBERT-Lite Fine-Tuning for Spam Detection in Digital Customer Services
Automated text moderation systems on public service platforms are often exploited by manipulative spam messages from brokers offering illegal financial services. Previous text classification studies have frequently prioritized high accuracy metrics while overlooking the impact of data leakage caused by repetitive spam templates, a methodological flaw that can lead to severe model overfitting. This study aims to design and optimize a Natural Language Processing (NLP) model using the IndoBERT-Lite architecture to distinguish between organic user complaints and manipulative broker-generated comments. The proposed methodology focuses on extreme data deduplication, reducing 55,156 raw records into a balanced dataset containing 4,626 unique samples (57.1% organic and 42.9% spam). The training process was optimized using Gradient Accumulation and Early Stopping to ensure genuine model generalization capability. The evaluation results demonstrate that the optimized model successfully mitigated the initial overfitting problem, achieving both accuracy and F1-score values of 98% on unseen test data. These findings provide a reliable and data leakage–free automated moderation solution for internal digital customer service systems.
Design and Development of a File Sharing System with Integrity Assurance and Audit Trail using Permissioned Blockchain and IPFS: A Case Study of DISPUSIPDA West Java
Digital transformation in government institutions requires file-sharing systems capable of ensuring document integrity, secure access control, user activity transparency, and traceable audit trails. This study designs and implements a file-sharing system based on Hyperledger Besu and InterPlanetary File System (IPFS) for document management at the Dinas Perpustakaan dan Kearsipan Daerah Jawa Barat (DISPUSIPDA). The research employed the Design Science Research Methodology (DSRM), which includes problem identification, system design, implementation, demonstration, and evaluation stages. The proposed system integrates IPFS for off-chain document storage, blockchain technology for immutable metadata recording and audit trails, and smart contracts to permanently record access rights modifications. Documents are encrypted using AES-256 before storage, while integrity verification is performed through SHA-256 hash comparison. The testing results demonstrate that all core system features operated according to the specified requirements. Document manipulation attempts were successfully detected across all testing scenarios, and the revocation mechanism effectively restricted access for users whose permissions had been revoked. Blockchain transaction latency remained within an acceptable range, while IPFS upload time increased proportionally with file size. This study contributes to the development of a file-sharing system architecture that integrates encryption, Role-Based Access Control (RBAC), revocation mechanisms, IPFS, and permissioned blockchain technology within a unified framework to support document integrity and institutional auditability in government environments.
UI/UX Design for Web-Based Syshoe Information System Using Design Thinking Approach
This study aims to design the UI/UX of the Syshoe web-based information system to improve service efficiency, expand market reach through digital media, and facilitate online ordering by providing users with more comprehensive information. The UI/UX design of the Syshoe information system was developed using the Design Thinking method to ensure that the system aligns with user needs and delivers a more effective user experience. The design process resulted in both low-fidelity and high-fidelity prototypes tailored to user requirements, which were subsequently evaluated using the System Usability Scale (SUS). The findings indicate that the UI/UX design of the Syshoe information system achieved an acceptable usability category, with a final SUS score of 79.7 from user evaluations and 80.8 from administrator evaluations. These results demonstrate that the system is feasible, comfortable, and effective for both users and administrators. Therefore, this study is expected to serve as a reference for the development of similar systems, particularly for MSMEs seeking to adopt digital transformation initiatives.
Improving Bioethanol Sentiment Analysis Performance using SMOTE in Machine Learning Model Comparison
Sentiment analysis of public policies on social media is crucial for government evaluation; however, it is often challenged by highly imbalanced datasets. This study aims to address this issue through a case study on public sentiment toward bioethanol fuel policies on YouTube, where the cleaned dataset after preprocessing consisted of 2,409 comments dominated by negative sentiment (1,430 comments), followed by neutral sentiment (734 comments), and only a small number of positive sentiments (245 comments). The performance of classical Machine Learning (ML) models was severely degraded due to this imbalance, particularly in detecting the minority class. This study applied TF-IDF weighting for feature extraction, followed by the Synthetic Minority Oversampling Technique (SMOTE) to balance the training data (1,927 samples) before comparing the performance of three ML algorithms: Logistic Regression, Support Vector Machine (SVM), and LightGBM. The evaluation results on the testing dataset (482 samples) demonstrate that the implementation of SMOTE significantly improved the models’ ability to recognize the “Positive” class. The LightGBM model combined with SMOTE achieved the best performance, with an accuracy of 64.11%. In particular, the application of SMOTE successfully increased the minority-class F1-score from a baseline of 18.18% to 35.29%. These findings confirm that handling imbalanced data is a critical step in producing valid and reliable sentiment analysis results.
Classification of Diabetes Mellitus using the K-Nearest Neighbor (KNN) Algorithm: A Case Study of Patient Data at Salatiga Regional Hospital
One of the most common metabolic diseases in Indonesia, including at RSUD Salatiga, is Diabetes Mellitus. Early diagnosis of this disease is crucial to prevent the development of more severe complications; however, this process is often challenging because the initial symptoms are difficult to recognize. This study implements the K-Nearest Neighbor (KNN) algorithm as a classification technique for predicting diabetes mellitus risk based on clinical data from patients at RSUD Salatiga. The dataset used in this research included variables such as gender, age, hypertension history, glucose level, HbA1c level, smoking status, liver disease history, and Body Mass Index (BMI). The research stages consisted of data collection, preprocessing (data cleaning, normalization, and variable encoding), feature selection, and optimization of the k parameter. Model evaluation was conducted using a confusion matrix with performance metrics including accuracy, precision, recall, and F1-score. The results indicate that the KNN algorithm achieved the highest accuracy of 92.08% when feature selection was applied with k = 6. This improvement demonstrates that both k-parameter optimization and feature selection significantly affect model performance. Therefore, the KNN algorithm can serve as an effective early prediction tool to assist medical personnel in identifying diabetes mellitus patients, enabling faster and more accurate interventions.
Development of an Android-based Barbershop Information System for Queue Management and Financial Reporting
The rapid advancement of information technology has encouraged various sectors, including the barbershop industry, to adopt digitalization in order to improve service quality and operational efficiency. However, customer queue management and financial reporting in many barbershops are still handled conventionally, making the processes less effective and more prone to errors. This study aims to design an Android-based mobile application prototype called Barber-Link to support digital queue management and financial reporting. The development method employed in this research was the Prototype method, while system modeling utilized Unified Modeling Language (UML) as a supporting tool. The result of this study is an Android-based prototype system equipped with key features such as customer queue management, transaction data recording, and a financial reporting dashboard. These features are designed to assist barbershop owners in managing customer queues while simplifying financial reporting processes, making operations more effective and efficient. Keywords: barbershop management, mobile application, prototype, UML.
Spatiotemporal Analysis of Deforestation and Comparative Accuracy Assessment of Flood Susceptibility Models based on Machine Learning, MCDA, and GFI using Multispectral Satellite Imagery
Flood disasters are often triggered by deforestation and land cover changes, highlighting the need for precise spatial flood susceptibility modeling. This study aims to analyze the spatiotemporal dynamics of deforestation (2021–2025) in Langsa and compare the accuracy of three flood hazard prediction models: Multi-Criteria Decision Analysis (MCDA), Geomorphic Flood Index (GFI), and Machine Learning using Random Forest. The study employed a quantitative experimental approach through the cloud-computing architecture of Google Earth Engine (GEE) and offline simulation using QGIS to process multispectral Landsat 9 satellite imagery along with landscape physical data. The results indicate a reduction in vegetation cover area of 1,606.12 hectares (-11.48%), which directly contributed to the expansion of built-up areas and open land. Comparative evaluation demonstrated that the Random Forest algorithm achieved the highest flood modeling accuracy, with an Overall Accuracy of 91.17% and a Kappa Coefficient of 0.87, outperforming MCDA, which was prone to over-prediction bias, and GFI, which exhibited algorithmic blind spots in localized pluvial flood areas. Risk exposure analysis further revealed that 38.5% of current built-up infrastructure areas are located within high flood susceptibility zones due to the loss of ecological infiltration areas. The outputs of this modeling process were successfully integrated into an interactive Decision Support System (DSS) based on a WebGIS dashboard to facilitate spatial disaster mitigation dissemination for policymakers.
Analysis of User Satisfaction Levels in the Shopee PayLater System using the User Experience Questionnaire (UEQ)
The rapid advancement of information technology in the digital era has triggered significant changes across various aspects of life, including the e-commerce sector. This study aims to analyze the level of user satisfaction with the Shopee PayLater system using the User Experience Questionnaire (UEQ) method. The research was motivated by the increasing use of PayLater services in e-commerce and the importance of evaluating user experience to improve system quality. This study employed a quantitative method using the User Experience Questionnaire (UEQ) approach, which consists of six dimensions: attractiveness, perspicuity, efficiency, dependability, stimulation, and novelty. Data were collected through questionnaires distributed to 100 students from the Faculty of Computer Science at Sriwijaya University and analyzed using the UEQ Data Analysis Tools. The results indicate that Shopee PayLater achieved positive user satisfaction across all UEQ dimensions. The highest scores were obtained in perspicuity (2.03), efficiency (1.89), dependability (1.89), and attractiveness (1.66), indicating that Shopee PayLater is easy to understand, efficient to use, and capable of providing user comfort. Meanwhile, the stimulation (1.65) and novelty (1.56) dimensions still require improvement through feature development and service innovation to create a more engaging user experience. In addition, the benchmark results show that all dimensions fall within the excellent category and are included in the top 10% of benchmark results, indicating a very high-quality user experience. Based on these findings, it can be concluded that Shopee PayLater provides an excellent user experience overall. However, the stimulation and novelty aspects still need enhancement through feature innovation and interface improvements to make the service more attractive and less monotonous for users.