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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 40 Documents
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Search results for , issue "Vol 14, No 6 (2025): Sistemasi: Jurnal Sistem Informasi" : 40 Documents clear
Comparison of Machine Learning Models for Predicting Lung Cancer Severity Lestari, Ninik; Susanto, Erliyan Redy
Sistemasi: Jurnal Sistem Informasi Vol 14, No 6 (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.v14i6.5258

Abstract

This study aims to compare the performance of four machine learning algorithms Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR), and K-Nearest Neighbors (KNN) in predicting lung cancer severity based on patient medical data. The dataset includes clinical information with the target variable categorized into three severity levels: low, medium, and high. Experiments were conducted using an 80:20 train-test split without feature scaling. The results show that RF achieved 100% accuracy, LR 99%, KNN 82%, and SVM 43%. The superior performance of Random Forest can be attributed to its ensemble of decision trees, which mitigates overfitting in medium-dimensional numerical features, whereas SVM (kernel = RBF, C = 1.0, gamma = "scale") failed to adapt due to the absence of scaling and hyperparameter tuning. Recall, precision, and F1-score further confirm the dominance of RF and LR. This study provides insights into the effectiveness of machine learning algorithms in lung cancer diagnosis and highlights the contribution of a multi-algorithm approach. The findings recommend using RF as the primary model and LR as a complementary control within clinical decision support systems, enabling physicians to make earlier, more personalized treatment decisions and ultimately improve lung cancer patient prognosis.
Adaptive Integration Optimization of Naïve Bayes and Information Gain for Diabetes Complication Prediction Fitriyadi, Farid; Indriyati, Indriyati
Sistemasi: Jurnal Sistem Informasi Vol 14, No 6 (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.v14i6.5666

Abstract

Diabetes mellitus is a major global health issue with a steadily increasing prevalence in Indonesia, including the Surakarta region. Diabetes complications are among the leading causes of mortality and reduced quality of life in patients, while also imposing a significant economic burden. This situation highlights the need for an accurate predictive model to identify both high- and low-risk patients, particularly in primary healthcare facilities that still face limitations in conducting comprehensive risk prediction. This study aims to develop a predictive model for diabetes complications by optimizing the adaptive integration of the Naïve Bayes algorithm with Information Gain for feature selection. The dataset used includes demographic variables (age, gender), clinical data (blood glucose level, HbA1c, blood pressure), medical history (family history of diabetes), and lifestyle factors (physical activity, dietary patterns). The results indicate that the pure Naïve Bayes algorithm achieved an accuracy of 75%. After applying Information Gain for feature selection, the accuracy improved to 87.5%, representing a 12.5% increase. These findings demonstrate that integrating Naïve Bayes with Information Gain can produce a more accurate prediction model for diabetes complications, making it a potentially effective decision-support tool for healthcare professionals in preventing complications and reducing the burden on the healthcare system.
Analysis of the Performance Comparison between Random Forest and SVM RBF in Detecting Cyberbullying on Imbalanced Data with the SMOTE Approach Amalina, Inna Nur; Norhikmah, Norhikmah; Ashari, Wahid Miftahul
Sistemasi: Jurnal Sistem Informasi Vol 14, No 6 (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.v14i6.5574

Abstract

Cyberbullying has emerged as a growing threat with the widespread adoption of social media, creating significant risks to online safety. Automatic detection of such behavior remains challenging, particularly when the training dataset is highly imbalanced. This study presents a comparative analysis of Random Forest and Support Vector Machine with Radial Basis Function kernel (SVM RBF) for cyberbullying detection, incorporating the Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance. The experiments utilized a publicly available, manually annotated dataset containing 47,693 English-language tweets from global users, labeled as cyberbullying or non-cyberbullying. Performance was evaluated using accuracy, precision, recall, and F1-score. Results indicate that Random Forest achieved the highest performance before SMOTE (accuracy = 88.52%, precision = 89.07%, recall = 94.00%, F1-score = 91.49%), while SMOTE improved recall for both algorithms but reduced accuracy and precision. These findings highlight that the choice of algorithm and effective handling of class imbalance are critical for enhancing the reliability of automated cyberbullying detection systems, thereby enabling more effective content moderation and safer online environments.
User Satisfaction Analysis of PDAM’s CATER System using Heuristic-SUS Evaluation Seno Purdayanto, Stefanus Adin; Tanaem, Penidas Fiodinggo
Sistemasi: Jurnal Sistem Informasi Vol 14, No 6 (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.v14i6.5484

Abstract

The Regional Drinking Water Company (PDAM) of Salatiga City, as a provider of clean water services, has implemented a web-based Meter Recording System (CATER) to enhance operational efficiency. However, the system's effectiveness has not been comprehensively evaluated in terms of usability, despite this factor being crucial for employee productivity and service quality. This study aims to assess the usability level of the CATER system by combining the Heuristic Evaluation (HE) and System Usability Scale (SUS) methods to identify the system's strengths and weaknesses and provide evidence-based recommendations for improvement. The evaluation results show that the system generally demonstrates good usability, with the highest HE scores in Visibility of System Status (89%) and Aesthetic Design (88%), indicating an informative and consistent interface. However, weaknesses were identified in the areas of Recognition Rather Than Recall (68%) and Help Documentation (75%), suggesting a need for workflow simplification and more intuitive user guidance. The average SUS score of 75 (classified as Acceptable) supports the finding that the system is generally well-received by users, though there is still room for improvement. Based on these findings, the study recommends improvements focusing on workflow simplification, the development of more contextual help features, and optimization of error prevention mechanisms. This study’s results are not only valuable for the further development of the CATER system but also serve as a reference for evaluating similar systems in other public utility sectors.
Development of Waste Management Application using GIS Ardianti, Mifta; Suakanto, Sinung; Zulkarnaen, Rizky Zaki
Sistemasi: Jurnal Sistem Informasi Vol 14, No 6 (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.v14i6.5680

Abstract

Waste management is a serious challenge in modern society due to rapid population growth and urbanization. The inefficiency of conventional systems such as limited coordination, infrastructure, and reporting calls for a technology-based approach. This study develops a web-based waste management application integrated with Geographic Information System (GIS) to improve waste collection, monitoring, and reporting. The development method used is prototyping, allowing iterative improvement based on user feedback. The application includes key features such as waste bin location data entry, interactive map visualization with Leaflet.js, and transaction reporting. The result showed that the app is able to provide real-time visualization of waste bin locations, display waste statistics, and improve the transparency and efficiency of waste management operations. This research contributes to the development of environmental information systems that support data-driven decision-making and environmental conservation.
Analysis of Student Job Readiness in Facing AI Transformation Towards Society 5.0 in XYZ University Students Rahman, Aura; Lattu, Arny; Permana, M. Anton
Sistemasi: Jurnal Sistem Informasi Vol 14, No 6 (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.v14i6.5712

Abstract

This study analyzes the factors influencing job readiness among XYZ University students in the context of Society 5.0. The novelty of this research lies in its application of Soft Systems Methodology (SSM) combined with quantitative questionnaire analysis, enabling both systemic and measurable problem identification—an approach still rarely applied in studies of student job readiness in Indonesia. A survey was conducted with 355 active students across five study programs and analyzed using IBM SPSS 2.5 to ensure data reliability. The findings reveal that AI literacy, psychological readiness, and the use of digital technologies have a significant impact, while human-centered skills such as communication and empathy serve as key reinforcing factors. Practically, this study encourages universities to integrate technology-based technical skills with soft skills in their curricula and training programs to produce graduates who are more adaptive, competitive, and prepared for the demands of Society 5.0. The study’s contribution lies in its use of SSM to map complex issues and formulate strategies for improving job readiness. The implication is that universities should embed technical competencies, soft skills, and AI-based training into adaptive learning ecosystems to better prepare graduates for the challenges of Society 5.0.
Heuristic Analysis of Functionality and UI/UX in Five Digital Finance Applications Piisi, Agrianto Timotheus; Kristianto, Budhi
Sistemasi: Jurnal Sistem Informasi Vol 14, No 6 (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.v14i6.5657

Abstract

Digital financial applications, such as e-wallets and mobile banking services, have grown significantly in Indonesia as a result of advancements in information technology. However, the user interface (UI) and user experience (UX) of these applications still face several challenges, particularly in navigation, information clarity, and transaction efficiency. Using a heuristic evaluation method, this study aims to compare the functionality and UI/UX quality of five popular digital finance applications in Indonesia: DANA, OVO, LinkAja, myBCA, and Jenius. Three data collection techniques were employed: heuristic evaluation by trained evaluators, analysis of user comments from the Google Play Store, and semi-structured interviews with active users. The most frequently violated heuristic principle was “recognition rather than recall,” followed by issues related to Error Prevention and Flexibility and Efficiency of Use. Among the evaluated applications, LinkAja exhibited the highest number of usability violations, while DANA and OVO recorded the most critical usability issues. Overall, the findings from the three methods were consistent and mutually reinforcing, indicating that usability problems have a direct impact on user experience. This study demonstrates that a triangulated methodological approach is highly effective in identifying and understanding usability issues comprehensively and in providing actionable design recommendations to improve the UI/UX quality of digital financial applications.
Graduation Prediction for Prospective University Students using Stacking Ensemble Learning Swastikawati, Claudia; Utami, Ema
Sistemasi: Jurnal Sistem Informasi Vol 14, No 6 (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.v14i6.5535

Abstract

Student graduation is an important indicator in accreditation and serves as part of quality management strategies in higher education. Therefore, early prediction of student graduation is necessary to improve the effectiveness of data-driven admission decision-making. Differences in student graduation rates are influenced by a combination of academic, demographic, economic, and family factors. This study applies the Stacking Ensemble Learning method by combining Random Forest, K-Nearest Neighbors, and Support Vector Machine, with XGBoost serving as the meta-learner. The dataset used integrates student admission records and graduation status reports from the NeoFeeder PDDikti system, covering 16 academic and non-academic feature variables. The model was evaluated using accuracy, precision, recall, F1-score, and Area Under the Curve (AUC). The results show that the stacking ensemble model outperformed single models, achieving 82% accuracy, a weighted F1-score of 80%, and an AUC of 87.15% on the test data. These findings contribute both the selected feature set and the implementation of an ensemble model for building a machine learning–based prediction system, particularly in addressing data imbalance and improving classification accuracy.
WhatsApp-Based Drug Reminder Information System Fatah, Haerul; Fauziah, Ari Zainal; Sidik, Akmal; Khofifah, Shofia; Wahyuni, Tri; Ermawati, Erni; Indriyanti, Indriyanti; Ichsan, Nurul
Sistemasi: Jurnal Sistem Informasi Vol 14, No 6 (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.v14i6.5610

Abstract

Timely and accurate medication use is a critical factor in the success of treatment, especially for patients with chronic illnesses such as diabetes, hypertension, and heart disease. However, patient non-adherence remains a major challenge. Based on observations at Panglayungan Community Health Center (Puskesmas), Tasikmalaya City, data recording and visit reminders are still handled manually. This often leads to service delays, data errors, and high rates of patient absenteeism. To address these issues, this study designed and developed a WhatsApp-based medication reminder information system capable of sending automated notifications to patients according to schedules set by healthcare staff. The application includes features such as patient data management, visit scheduling, and automated message delivery via the WhatsApp API. The system was developed using the waterfall software engineering model, starting from requirements analysis, system design, implementation, and testing. Test results demonstrated that the system successfully delivered medication reminders on time and effectively reached patients. Moreover, integrating WhatsApp as a reminder medium proved to be user-friendly and widely accepted, as it does not require users to install additional applications. This system not only improves patient adherence to prescribed therapies but also assists healthcare providers in data management and patient monitoring more efficiently. Furthermore, the system was designed with a lightweight, responsive web-based interface to ensure ease of use for health center staff. With a structured database, it helps reduce administrative workload and minimizes the risk of data loss.
Development of a Website-based Inventory Information System at Konveksi Mitra Jaya Abadi Indrianita, Dian; Napianto, Riduwan
Sistemasi: Jurnal Sistem Informasi Vol 14, No 6 (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.v14i6.5419

Abstract

The utilization of information technology has increased the demand for more efficient and accurate inventory management systems, particularly for businesses such as Mitra Jaya Abadi Garment Company, which previously relied on manual record-keeping. This study aims to design and implement a web-based inventory information system using the Extreme Programming (XP) methodology. XP was chosen for its flexibility in software development and its support for direct user involvement in the iterative process. The system was developed using the CodeIgniter framework and applied the Model-View-Controller (MVC) architecture to ensure a clear separation between application logic, user interface, and data management. Compared to the previous system, the new system provides more flexible reporting features, including filters by date and item category, as well as report export capabilities. Unlike the waterfall approach, the XP methodology allows for faster iterations, better adaptability to changing requirements, and stronger user engagement. System testing based on the ISO 25010 standard demonstrated excellent feasibility, achieving an overall score of 95.36%. The system is expected to accelerate and simplify inventory management processes, reduce errors in data recording, and improve the efficiency of decision-making.

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