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INDONESIA
Sistemasi: Jurnal Sistem Informasi
Published by Universitas Islam Indragiri
ISSN : 23028149     EISSN : 25409719     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Sistemasi adalah nama terbitan jurnal ilmiah dalam bidang ilmu sains komputer program studi Sistem Informasi Universitas Islam Indragiri, Tembilahan Riau. Jurnal Sistemasi Terbit 3x setahun yaitu bulan Januari, Mei dan September,Focus dan Scope Umum dari Sistemasi yaitu Bidang Sistem Informasi, Teknologi Informasi,Computer Science,Rekayasa Perangkat Lunak,Teknik Informatika
Arjuna Subject : -
Articles 1,078 Documents
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Improving Bioethanol Sentiment Analysis Performance using SMOTE in Machine Learning Model Comparison Rajhu Ilham Pradana; Jasmir Jasmir; Gunardi Gunardi
Sistemasi: Jurnal Sistem Informasi Vol 15, No 5 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i5.6300

Abstract

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.
Development of an Android-based Barbershop Information System for Queue Management and Financial Reporting Nanang Kurniawan; Novi Setiani
Sistemasi: Jurnal Sistem Informasi Vol 15, No 5 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i5.6373

Abstract

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.
Sentiment Analysis of the MyTelkomsel App based on Support Vector Machines: A Kernel Performance Comparison Fitri Novianti Hidayah; Endah Setyowati
Sistemasi: Jurnal Sistem Informasi Vol 15, No 5 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i5.6289

Abstract

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.
Optimization of IndoBERT-Lite Fine-Tuning for Spam Detection in Digital Customer Services Farouq Mulya Al Simabua; Lathifah Alfat
Sistemasi: Jurnal Sistem Informasi Vol 15, No 5 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i5.6398

Abstract

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.
Implementation of ResNet-50 in a Fresh Fruit Bunch (FFB) Ripeness Detection System for Oil Palm M. Rafli Al Thoriq Mustafa; Muhammad Fikry; Said Fadlan Anshari
Sistemasi: Jurnal Sistem Informasi Vol 15, No 5 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i5.6331

Abstract

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.
GAN-CNN-based Android Ransomware Detection System using Network Traffic Analysis Mahmood S. Mahmood
Sistemasi: Jurnal Sistem Informasi Vol 15, No 5 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i5.6363

Abstract

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.
Comparison of Machine Learning Algorithms for Credit Score-based Banking Customer Churn Prediction Suryadillah Hendrawinata; Jasmir Jasmir; Gunardi Gunardi
Sistemasi: Jurnal Sistem Informasi Vol 15, No 5 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i5.6148

Abstract

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.
Analysis of User Satisfaction Levels in the Shopee PayLater System using the User Experience Questionnaire (UEQ) Aliyah Khofifah; Apriansyah Putra; Ari Wedhasmara; Mira Afrina
Sistemasi: Jurnal Sistem Informasi Vol 15, No 5 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i5.6461

Abstract

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.
Implementation of Zero-Shot Learning on Edge AI for Deterministic Interpretation of Electrical Metrics using Quantized Large Language Models Salman Al Majali; Ganjar M Faisal; Novi Rukhviayanti
Sistemasi: Jurnal Sistem Informasi Vol 15, No 5 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i5.6352

Abstract

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.
Hybrid CNN and Autoencoder Deep Learning Model for Network Malware Detection Mayra Anggraini; Rama Aria Megantara
Sistemasi: Jurnal Sistem Informasi Vol 15, No 5 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i5.6382

Abstract

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.

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