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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 920 Documents
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Sentiment Analysis of Public Satisfaction with the 'INFO BMKG' Application using Naive Bayes, SVM, and KNN Aditiya, Natasya; Setiaji, Pratomo; Supriyono, Supriyono
Sistemasi: Jurnal Sistem Informasi Vol 14, No 3 (2025): 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.v14i3.5223

Abstract

This study aims to analyze public sentiment regarding the Info BMKG application on the Google Play Store. With the increasing number of users of information-based applications, understanding how users perceive and evaluate such applications has become essential. This research employs three classification algorithms—Naive Bayes, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN)—to categorize user reviews into positive, neutral, or negative sentiments. The dataset was obtained through web scraping from the Google Play Store, consisting of usernames, dates, star ratings, and user comments. Data preprocessing was conducted to clean and prepare the data for analysis. Additionally, a web-based data mining application was developed to facilitate data processing and result visualization. The study aims to present the distribution of sentiment (positive, neutral, and negative) toward the Info BMKG app and help developers understand the factors that influence user satisfaction. Moreover, it contributes to the field of sentiment analysis and information technology, particularly in disaster-related information applications. Based on model evaluation, the Naive Bayes algorithm demonstrated the best performance with an accuracy of 79.84%, precision of 60%, recall of 58%, and the fastest runtime at 0.19 seconds. KNN achieved an accuracy of 74.35% with the lowest recall at 44%, while SVM had an accuracy of 72.26% but required the longest runtime at 611 seconds. AUC validation further confirmed the superiority of Naive Bayes, with the highest scores across all sentiment categories. Thus, Naive Bayes is shown to be the most optimal for sentiment analysis in this study, whereas KNN and SVM showed certain limitations, particularly in efficiency and classification accuracy.
Comparative Analysis of Oversampling and SMOTEENN Techniques in Machine Learning Algorithms for Breast Cancer Prediction Yulian, Tri; Susanto, Erliyan Redy
Sistemasi: Jurnal Sistem Informasi Vol 14, No 3 (2025): 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.v14i3.5146

Abstract

Breast cancer is the leading cause of cancer-related death among women, with one of the major challenges in developing predictive models being the class imbalance in medical datasets. This imbalance hinders the detection of minority classes (patients with cancer), which is critical for early diagnosis. This study aims to analyze the performance of Support Vector Machine (SVM) and Random Forest algorithms in predicting breast cancer using oversampling and SMOTEENN preprocessing techniques. The dataset used is the SEER Breast Cancer Dataset, which was balanced using both techniques. Model performance was evaluated using metrics such as accuracy, precision, recall, and F1-score. The results showed that SVM with oversampling achieved the highest accuracy of 98.97%, followed by SVM with SMOTEENN at 97.20%. Random Forest with oversampling reached an accuracy of 96.63%, while with SMOTEENN it achieved 95.90%. SVM proved more effective in identifying both classes with minimal error, particularly when combined with oversampling. These findings highlight that selecting the appropriate model and data preprocessing technique—such as oversampling or SMOTEENN—can significantly enhance predictive accuracy. This research contributes to the development of more accurate and reliable breast cancer prediction systems, supporting early diagnosis and clinical decision-making in medical applications.
User Acceptance Analysis of the BRImo Mbanking Application using the UTAUT and TPB Methods Winata, Rahmad Waviq; Fronita, Mona; Angraini, Angraini; Megawati, Megawati
Sistemasi: Jurnal Sistem Informasi Vol 14, No 4 (2025): 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.v14i4.5317

Abstract

The advancement of digital technology has revolutionized financial services through the development of mobile banking applications such as BRImo. This study aims to analyze user acceptance of Bank Rakyat Indonesia’s BRImo mobile banking application by integrating two theoretical approaches: the Unified Theory of Acceptance and Use of Technology (UTAUT) and the Theory of Planned Behavior (TPB). The core issue addressed is that despite BRImo being one of the most popular mobile banking applications in Indonesia, various user complaints still persist, potentially affecting its acceptance level. Identified problems include technical issues such as account blocking, login errors, and undelivered transaction notifications. This research employs a quantitative approach through the distribution of Likert-scale questionnaires to active university students in Pekanbaru who use the BRImo application. The results show that the model explains 20.0% of the variance in behavioral intention (R² = 0.200) and 24.3% of the variance in actual use behavior (R² = 0.243). Of the twelve proposed hypotheses, only three relationships were found to be significant: Attitude Toward Behavior on Behavioral Intention, Facilitating Conditions on Use Behavior, and Perceived Behavioral Control on Use Behavior. Conversely, variables such as Performance Expectancy, Effort Expectancy, Subjective Norms, Habit, Hedonic Motivation, and Behavioral Intention showed no significant effect. These findings indicate that internal factors such as user attitudes and behavioral control, along with external factors like the availability of support facilities, play an important role in driving actual usage behavior of the BRImo application. The study provides valuable insights, suggesting that users’ perceptions of accessibility and technical support have a stronger influence on actual usage than mere intention to use.
Data Analysis using Business Intelligence and Tableau for Visualizing Indonesia's Poverty Line Senduk, Fabianus Kevin; Waluyo, Retno; Isnaini, Khairunnisak Nur
Sistemasi: Jurnal Sistem Informasi Vol 14, No 3 (2025): 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.v14i3.4993

Abstract

Poverty is the condition of being unable to meet an adequate standard of living. The poverty line serves as a key indicator for measuring poverty, particularly in developing countries. In Indonesia, poverty line data provided by the Central Statistics Agency (Badan Pusat Statistik – BPS) is typically presented in static tables, lacking in-depth analysis or annual trend insights needed to understand poverty dynamics across 578 regions. This study aims to analyze poverty line data in Indonesia using a Business Intelligence (BI) approach and visualize it through Tableau Public. BI was chosen for its capability to process complex data into more accessible and actionable information for decision-making. The output of this study is an interactive visualization dashboard that illustrates the distribution patterns and trends of the poverty line in Indonesia over the period 2022–2024. The dashboard offers in-depth insights into regional poverty shifts, including the identification of high-poverty areas and analysis of poverty line growth rates. It also serves as a strategic data-driven decision support tool. This research can be further developed by exploring the underlying factors driving poverty line fluctuations, applying the method to other dimensions such as income inequality, and leveraging alternative data visualization tools for a more comprehensive analysis.
Lung Cancer Classification Using the Extreme Gradient Boosting (XGBoost) Algorithm and Mutual Information for Feature Selection Zizilia, Regitha; Chrisnanto, Yulison Herry; Abdillah, Gunawan
Sistemasi: Jurnal Sistem Informasi Vol 14, No 5 (2025): 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.v14i5.5345

Abstract

Lung cancer is one of the deadliest types of cancer worldwide and is often detected too late due to the absence of early symptoms. This study aims to evaluate the impact of feature selection using Mutual Information on the performance of lung cancer classification with the XGBoost algorithm. Mutual Information is employed to select relevant features, including those with linear and non-linear relationships with the target variable, while XGBoost is chosen for its ability to handle large datasets and reduce overfitting. The study was conducted on a dataset containing 30,000 data entries, with data split scenarios of 90:10, 80:20, 70:30, and 60:40. The results show that the testing accuracy before applying Mutual Information ranged from 93.42% to 93.83%, while K-Fold Cross-Validation accuracy ranged from 94.59% to 94.76%. After feature selection, testing accuracy remained stable for the 70:30 and 60:40 split scenarios, at 93.60% and 93.42% respectively. However, K-Fold Cross-Validation accuracy decreased to 89.26% and 90.88%. In contrast, for the 90:10 and 80:20 split scenarios, a decline in accuracy was observed — testing accuracy dropped to 88.63% and 88.85%, and K-Fold Cross-Validation accuracy fell to 88.87% and 90.24%. Feature selection using Mutual Information improves computational efficiency by reducing the number of features, and it can be effectively applied to simplify feature sets without significantly compromising model performance in certain data scenarios, depending on the characteristics of the dataset.
Deep Learning-based Identification of Personal Protective Equipment in Construction Area Fadhilla, Mutia; Sapitri, Sapitri; Wandri, Rizky
Sistemasi: Jurnal Sistem Informasi Vol 14, No 4 (2025): 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.v14i4.5271

Abstract

Work safety in the construction environment is highly dependent on the correct use of personal protective equipment (PPE). This study aims to develop an automatic PPE detection system using a YOLO-based deep learning model to improve supervision and compliance with PPE use in the field. Two variants of the YOLO model, namely YOLOv10 and YOLOv11, were tested and their performance was compared through a fine-tuning process using custom dataset consisting of 16,568 annotated images of construction workers wearing various types of PPE. The model was evaluated using precision, recall, and mAP50. The results showed that the YOLOv11s model performed the best with with an mAP50 of 0.718 and a precision score of 0.804, indicating good detection and classification ability. This model is able to detect various types of PPE effectively, so it can be used as a tool in real-time occupational safety monitoring. This study proves that the application of YOLO-based deep learning technology can be an effective solution to improve compliance with PPE use and reduce the risk of work accidents in the construction sector. The implications of this study open up opportunities for the development of more sophisticated and adaptive automatic monitoring systems in the future such as deploying the model on edge devices for real-time inference and expanding detection capabilities to include additional safety violations such as the absence of safety harnesses or proximity to hazardous zones.
Development of an AHP Model for Evaluating WiFi Quality based on Multicriteria Hartono, David; Fibriani (SCOPUS ID=57192643331), Charitas
Sistemasi: Jurnal Sistem Informasi Vol 14, No 4 (2025): 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.v14i4.5216

Abstract

Wi-Fi service quality not only supports academic and administrative activities, but also significantly impacts user productivity and satisfaction. The criteria examined in this study regarding Wi-Fi services include ease of access, network stability, connection speed, coverage, and security. This research is based on questionnaire data collected from 100 Wi-Fi users at the Faculty of Information Technology, Satya Wacana Christian University (FTI UKSW), which was used to determine the relative importance of each criterion. The main objective of this study is to determine the weight of each Wi-Fi service criterion using the Analytic Hierarchy Process (AHP) method. Based on the AHP analysis, the criterion with the highest priority is ease of access, with a weight of 0.417087. The weights for the other criteria are as follows: network stability at 0.259637, connection speed at 0.165161, coverage at 0.104853, and security at 0.053262, making security the lowest-priority criterion. From these findings, it can be concluded that ease of access to Wi-Fi has a significant influence on user satisfaction. This insight can serve as a recommendation for the university to improve its services—for instance, by adding more access points or implementing a single sign-on system for Wi-Fi access.
Optimizing Feature Selection in Sentiment Analysis of Bank Saqu: A Comparative Study of SVM and Random Forest using Information Gain and Chi-Square Subandono, Anelta Tirta Putri; Ariatmanto, Dhani
Sistemasi: Jurnal Sistem Informasi Vol 14, No 3 (2025): 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.v14i3.5106

Abstract

The selection of an optimal feature selection method is a crucial factor in improving the accuracy and efficiency of text classification models. Irrelevant features can degrade model performance, increase computational complexity, and lead to overfitting. Although various feature selection techniques have been employed in sentiment analysis, systematic studies comparing the effectiveness of Information Gain and Chi-Square in enhancing classification performance remain limited. This study aims to evaluate and optimize the impact of different feature selection methods on the performance of Support Vector Machine (SVM) and Random Forest (RF) in sentiment analysis. Experiments were conducted using eight testing schemes, including models without feature selection, with Information Gain, Chi-Square, and a combination of both. The results showed that SVM with Chi-Square achieved the highest accuracy at 93%, while Random Forest with Chi-Square achieved the best performance at 91%. These findings indicate that Chi-Square is more effective than Information Gain in improving accuracy, and that SVM outperforms Random Forest in text classification tasks. In conclusion, selecting the appropriate feature selection method significantly contributes to enhancing the accuracy of text classification models. This research can serve as a reference for optimizing feature selection techniques in the development of more accurate and efficient machine learning-based systems.
Evaluation of Traffic Sign Educational Game Based on Augmented Reality (AR) Using Marker Based Purwanto, Dimas Yudistira; Norhikmah, Norhikmah; Mu’arif, Zidan; Muharam, Fatta
Sistemasi: Jurnal Sistem Informasi Vol 14, No 5 (2025): 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.v14i5.4448

Abstract

This study aims to develop a 3D educational game themed around traffic signs to enhance children's understanding and discipline regarding traffic rules. The research methodology includes game development using Unity and evaluation through black-box testing and questionnaires. The results show that the educational game successfully increased student interest by 80% and improved their understanding of traffic sign material, while also providing an interactive and enjoyable learning experience.
Implementation of the K-Means Algorithm for Customer Churn Segmentation in Developing Bank Marketing Strategies Rahmadiana, Reva Nur; Lestarini, Dinda
Sistemasi: Jurnal Sistem Informasi Vol 14, No 4 (2025): 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.v14i4.5341

Abstract

Customer churn, or the loss of banking clients, represents a major challenge in the banking industry due to its potential to cause significant financial losses. This study aims to segment customers based on characteristics that influence their churn risk using the K-Means algorithm. The data used in this research is secondary data consisting of 9,763 customer records from a bank customer churn dataset obtained via the Kaggle platform. The data processing follows the CRISP-DM framework. Clustering was conducted using RapidMiner, and performance was evaluated using the Davies-Bouldin Index to determine the optimal number of clusters (K). The results indicate that the optimal number of clusters is K = 4. Centroid analysis revealed that balance and estimated salary are the primary variables contributing to cluster formation. Cluster 1 and Cluster 3 had the highest number of churned customers. Cluster 1 consisted of customers with high balances but low salaries, while Cluster 3 included customers with both high balances and high salaries. These findings suggest that a high balance does not necessarily guarantee customer loyalty, and that income level plays an important role in preventing churn. Based on the analysis, recommended strategies include providing financial education and loyalty programs for customers in Cluster 1, and offering exclusive services and personalized approaches for those in Cluster 3. This study demonstrates that the K-Means algorithm is effective in producing relevant customer segmentation, serving as a valuable foundation for developing more targeted and efficient marketing strategies.

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