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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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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.
Lung Cancer Classification using the Naïve Bayes Method with SMOTE Akbar, Ananda Ikhwana Khairur; Astuti, Yani Parti
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.v14i6.5607

Abstract

The primary challenges addressed in this study include delays in the early detection of lung cancer due to non-specific initial symptoms, the limitations of the Naïve Bayes algorithm in processing categorical data such as symptoms, gender, and smoking habits, as well as class imbalance issues in the dataset that can affect model accuracy. To overcome these challenges, the SMOTE (Synthetic Minority Over-sampling Technique) method was applied to improve classification performance. This study aims to implement the Naïve Bayes algorithm for lung cancer classification and compare its performance on imbalanced data versus data balanced using SMOTE. The methodology consists of data preprocessing, encoding, applying SMOTE for balancing, and classification using Naïve Bayes. Evaluation was performed using three data split ratios: 80:20, 70:30, and 60:40. The results show that applying SMOTE led to performance improvements, with the most significant gains observed at the 60:40 split ratio. In this case, model accuracy improved from 88.29% to 93.19%. For the “Yes” (positive) class, precision remained at 0.96, recall at 0.91, and F1-score at 0.93. However, for the “No” (negative) class, precision improved from 0.40 to 0.90, recall from 0.60 to 0.96, and F1-score from 0.48 to 0.93. Conversely, slight decreases in accuracy were observed for the 80:20 and 70:30 ratios after SMOTE application. These findings demonstrate that SMOTE significantly enhances model performance at the 60:40 ratio, not only in terms of accuracy but also in recall and F1-score, which are crucial for reducing false negatives in the minority (“Yes”) class. This is especially critical in early detection, as correctly identifying actual cancer cases is more important than merely maintaining overall accuracy. Although SMOTE did not always improve accuracy at other ratios, it still contributed to better cancer case detection. Therefore, its application should be considered carefully, balancing overall accuracy with clinically meaningful metrics.
CNN-Based Model for Classifying Regional Types on Shipping Label Images Widodo, Wahyu Kurniawan Ade Nur; Triyanto, Wiwit Agus; Setiaji, Pratomo
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.v14i6.5584

Abstract

The rapid growth of the e-commerce sector has led to a significant surge in shipping volumes in Indonesia. In logistics systems, a shipping receipt serves as a crucial document containing destination information such as address, city/regency, and postal code. Errors or delays in classifying destination regions not only generate additional operational costs (e.g., reshipment fees and service penalties) but may also reduce customer satisfaction and harm the reputation of service providers. This study proposes the implementation of a Convolutional Neural Network (CNN) model to automatically classify region types in shipping receipt images, aiming to minimize manual errors and accelerate processing time. CNN was chosen for its ability to recognize complex visual patterns in digital documents without requiring manual feature extraction. The dataset used in this study consists of 1,540 shipping receipt images from various courier services, labeled as REG_JAWA and REG_LUARJAWA. The research process includes image preprocessing (resizing, normalization, augmentation), CNN architecture design, model training with early stopping, and performance evaluation using accuracy, precision, recall, and F1-score metrics. The results demonstrate that the model achieved a testing accuracy of 99.87%, precision of 99.71%, and recall of 100%, highlighting its strong potential for real-world implementation in logistics systems to improve efficiency and reliability of deliveries.

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