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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 950 Documents
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Sentiment and Topic Analysis of Digital Community Application Gamer Reviews using SVM-LDA and CRISP-DM Ary Pratama, Muhammad Mayda; Kurniawan, Dedy; Rifai, Ahmad; Tania, Ken Ditha
Sistemasi: Jurnal Sistem Informasi Vol 15, No 1 (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.v15i1.5746

Abstract

Impatient behavior among gamers is often reflected in sharp and emotionally charged digital reviews, particularly in the use of community applications such as Discord. This study explores expressions of impatience through sentiment and topic analysis. By adopting the CRISP-DM framework, a total of 10,000 Indonesian-language reviews collected from the Google Play Store were analyzed. The analytical process begins with sentiment labeling using IndoBERT, followed by polarity classification using the Support Vector Machine (SVM) algorithm, and topic exploration through the Latent Dirichlet Allocation (LDA) method. The results indicate that 57.4% of the reviews express positive sentiment, primarily related to voice communication quality and community interaction features. In contrast, 42.6% of the negative reviews commonly convey frustration regarding login issues and verification processes. The SVM model optimized using Bayesian Optimization achieved an accuracy of 90.46%. This study highlights that Discord serves not only as a communication platform but also as a reflection of users’ high expectations for system speed and stability. The main contribution of this research lies in the integration of SVM–LDA methods within the CRISP-DM framework to better understand the digital behavior of Indonesian gamers. The practical implications of these findings provide strategic insights for developers to improve authentication reliability and community features in alignment with user characteristics.
Design of the 'Abdimas' Marketplace System: A Digital Platform for PKM Collaboration (Version 0.0) Fadhilah, M Rizki; Wandri, Rizky; Fadhilla, Mutia; Gunawan, Dwi Fiqri; Labellapansa, Ause; Gunawan, Hendra
Sistemasi: Jurnal Sistem Informasi Vol 15, No 1 (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.v15i1.5545

Abstract

Community Service (Pengabdian kepada Masyarakat or PkM) is one of the three core responsibilities (Tridharma) of higher education in Indonesia, alongside education and research. However, many lecturers especially those new to academia face difficulties in identifying suitable community partners. This study addresses that issue through the design of a digital platform called “Abdimas”, intended to function as a marketplace system for matching lecturers with PkM partners. Applying the Design Thinking methodology, focusing on the conceptual and architectural design of the “Abdimas” platform, this study implemented a user-centered design approach. Data from interviews with lecturers, partners, and university administrators were analyzed using thematic analysis to identify core requirements. Based on these findings, the system was designed to support lecturer registration, partner discovery, and need-based matching. Senior lecturers are also supported in exploring new, underserved areas, while partners can publicly express their needs to attract suitable academic collaborators. University administrators can monitor the distribution of PkM activities over time to ensure equity and effectiveness. Unlike existing administrative platforms that often function as one-way reporting tools, the “Abdimas” marketplace introduces a bidirectional matching mechanism that allows partners to actively broadcast their specific community needs, bridging the information gap for lecturers. The system design includes use case diagrams, UI/UX prototypes, an Entity Relationship Diagram (ERD), and blackbox test scenarios to validate functionality. Although still in the design phase (Version 0.0), “Abdimas” has the potential to scale beyond academic users by supporting Corporate Social Responsibility (CSR) initiatives and facilitating student-level community services. This research contributes a structured and scalable system design to improve collaboration and outreach in community service programs within higher education.
Optimization of the Linear Regression Algorithm using GridSearchCV for Rice Crop Production Prediction Imel, Imel; M.Kom (SCOPUS ID: 57216417658), Norhikmah; Wulandari, Irma Rofni; Mustofa, Ali; Larasati, Niken; subektiningsih, subektiningsih
Sistemasi: Jurnal Sistem Informasi Vol 15, No 1 (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.v15i1.5877

Abstract

Rice production in Central Java Province fluctuates annually, affecting food security and agricultural output distribution. Therefore, accurate prediction methods are essential to assist stakeholders in agricultural planning and strategic decision-making. This study applies the Linear Regression algorithm to predict rice production based on historical data from 2014 to 2023 obtained from the official website of the Central Java Provincial Agriculture and Plantation Office. The model is developed using multiple linear regression with variables including planted area, harvested area, and productivity. The novelty of this study lies in the structured application of hyperparameter tuning using GridSearchCV to optimize Linear Regression performance, as well as the integration of a preprocessing pipeline based on data distribution stabilization to improve accuracy and model generalization. The research process includes data collection, preprocessing, modeling, optimization, model evaluation, and deployment as a web-based application using Streamlit Cloud. GridSearchCV optimization results indicate a cross-validation accuracy of 98.26%, confirming the model’s strong predictive capability. Model evaluation shows an R² value of 0.9754, with MAE of 0.0957, MSE of 0.0307, and RMSE of 0.1753, indicating low prediction errors and stable model performance. The optimized model is implemented as a web application via Streamlit Cloud, enabling direct use by end-users. For future research, it is recommended to incorporate additional variables such as rainfall, temperature, and rice variety, or to compare performance with other algorithms such as Random Forest, Support Vector Regression, or Long Short-Term Memory (LSTM) to further enhance prediction accuracy.
Development of Android GIS Applications for Mapping Clean Water Sources in Natural Resource Management in Disaster-Affected Areas Sahrial, Rysa; Aripin, Samsul; Susilawati, Eva; Herdiana, Emil
Sistemasi: Jurnal Sistem Informasi Vol 15, No 1 (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.v15i1.5770

Abstract

This research was motivated by the post-disaster challenges faced by the people of Sarampad Village, Cugenang District, Cianjur Regency, after the 2022 earthquake, which severely damaged vital infrastructure and disrupted access to clean water. The lack of a systematic mapping system for clean water sources highlights the urgent need for technology-based solutions to support effective and sustainable water resource management. The study employed a software development method using an Agile Programming approach, allowing iterative development and adaptation based on user feedback. Data were collected through field surveys, interviews with local communities and village officials, and direct observations of clean water source conditions. The system was designed using the Unified Modelling Language (UML), and the Android application was developed with Flutter Dart via the Visual Studio Code platform. Application functionality was tested using the black-box testing method to ensure performance reliability. The developed Android-based GIS application successfully maps and visualizes clean water sources, providing users with accurate and accessible spatial information. The system enables communities to identify the nearest clean water sources efficiently, particularly in post-disaster conditions. The findings demonstrate that integrating GIS with mobile technology can significantly improve public access to clean water information while promoting community involvement in environmental resource management. This innovation serves as a practical step toward sustainable and participatory water resource management in disaster-affected areas.
Image Forensics Analysis of the Authenticity of Digital Payment Evidence using the K-Nearest Neighbor Algorithm Agusta, Feriyan; Setiaji, Pratomo; Triyanto, Wiwit Agus
Sistemasi: Jurnal Sistem Informasi Vol 15, No 1 (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.v15i1.5728

Abstract

The rapid growth of digital transactions has also increased the risk of digital payment evidence forgery, such as screenshot manipulation or digital image editing. This study aims to develop an automated authenticity validation system for digital payment evidence by integrating Image Processing, Image Forensics, and Optical Character Recognition (OCR) technologies. The processing pipeline begins with image preprocessing, followed by forensic feature extraction and OCR-based text analysis, which are then classified using the K-Nearest Neighbor (KNN) algorithm. This study evaluates 15 experimental scenarios based on combinations of training and testing data ratios (90:10, 80:20, 70:30, 60:40, and 50:50) and random state values (42, 32, and 22). Model performance is assessed using accuracy, precision, recall, and F1-score metrics across a range of k values from 1 to 15. The results indicate that the optimal performance is achieved at k = 7, with an accuracy of 97.1%. The proposed system is able to efficiently distinguish between authentic and manipulated digital payment evidence. The system is implemented as an Android application that allows users to upload payment evidence via the device camera or gallery, after which the system automatically analyzes its authenticity. The findings demonstrate that the integration of image forensic techniques and the K-Nearest Neighbor (KNN) algorithm effectively detects indications of manipulation in digital payment evidence and enhances the efficiency of the verification process within the digital financial services ecosystem.
Prediction of Unpaid Student Fees at Muhammadiyah Ahmad Dahlan University Cirebon using the Random Forest Algorithm Herman, Suherman; Kristomo, Domy
Sistemasi: Jurnal Sistem Informasi Vol 15, No 1 (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.v15i1.5411

Abstract

This study aims to develop a predictive model for student fee payment arrears at Universitas Muhammadiyah Ahmad Dahlan Cirebon using the Random Forest algorithm. The dataset was obtained from the Academic Information System and consisted of 490 student records from four cohorts (2018–2021), which were divided into 80% training data and 20% testing data. The data processing stages included data cleaning, transformation, and feature selection using Recursive Feature Elimination (RFE). The model was optimized using GridSearchCV to obtain the best configuration. The evaluation results indicate strong performance, with an AUC of 0.980, accuracy of 88.8%, precision of 90.4%, recall of 88.8%, and an F1-score of 0.875. Feature importance analysis identified the amount of arrears variable as the most dominant factor influencing prediction outcomes. Strategic recommendations for university implementation include: (1) deploying a data-driven early warning system to identify at-risk students, (2) offering payment relief or installment programs for students with high arrears, and (3) conducting regular financial monitoring through a dashboard to support timely decision-making. Therefore, this study not only produces an effective predictive model but also provides practical solutions for improving university financial management.
Implementation of a Hybrid Filtering Approach in a Website-based Football News Recommendation System Putra, Krissna Haridarma; Rohman, Arif Nur; hikmah, Norhikmah
Sistemasi: Jurnal Sistem Informasi Vol 15, No 1 (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.v15i1.5854

Abstract

The rapid growth of football news on digital portals has made it increasingly difficult for users to find information that matches their interests. This study develops a web-based news recommendation system by combining Content-Based Filtering and Collaborative Filtering through a feature-level Hybrid Filtering approach. The proposed hybrid approach constitutes the main novelty of this research, as it does not rely on score aggregation methods commonly used in previous studies, making it lighter, simpler, and more suitable for small datasets and limited user interactions. The system employs Term Overlap Matching to measure the similarity between news titles and Cosine Similarity to assess user preference similarity based on bookmark data. The evaluation results show that Content-Based Filtering achieves the best performance, with a Precision of 0.60, Recall of 0.75, and an F1-score of 0.67, while Collaborative Filtering performs poorly due to data sparsity in user interactions, resulting in a Precision of 0, Recall of 0, and an F1-score of 0. Overall, the feature-based hybrid approach is able to provide relevant recommendations from both content and preference perspectives, although system accuracy is still predominantly driven by Content-Based Filtering. These findings indicate that the proposed simple hybrid model can serve as an effective solution for small-scale sports news platforms and has the potential to be further improved through increased data availability, enhanced user interaction, and the adoption of more advanced NLP techniques.
The Influence of Experience-Centric IT Governance on Digital Ethics Perception in Social Commerce Gumay, Naretha Kawadha Pasemah; Afrina, Mira; Indah, Dwi Rosa; Sari, Winda Kurnia; Sartika, Widya
Sistemasi: Jurnal Sistem Informasi Vol 15, No 1 (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.v15i1.5750

Abstract

Implementation of the Apriori Algorithm for Clothing Store Product Recommendations based on Sales Transaction History Saputro, M Ilham; Rohman, Arif Nur
Sistemasi: Jurnal Sistem Informasi Vol 15, No 1 (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.v15i1.5648

Abstract

This study is motivated by the limitations faced by small-scale clothing stores, which generally do not have customer ratings or reviews that can be used as a basis for product recommendations. This condition necessitates an alternative method capable of utilizing available sales transaction data. The objective of this study is to generate product recommendations by identifying consumer purchasing patterns through the application of the Apriori Algorithm. The methodology involves processing sales transaction data consisting of transaction codes, lists of purchased products, and transaction timestamps. Support, confidence, and lift ratio values are calculated to generate and validate association rules among products. The analyzed data are derived from the transaction history of a clothing store and are processed using a web-based system developed with PHP and MySQL. The experimental results indicate that several product combinations achieve confidence values of 50% and lift ratios greater than or equal to 1, suggesting that these patterns can be used as a basis for product recommendations. These findings demonstrate a strong association among items that are frequently purchased together. Based on the results, this study concludes that the Apriori Algorithm is effective in identifying meaningful purchasing patterns that can support product arrangement strategies and inventory management in small-scale clothing stores.
Implementation of User-Centered Design (UCD) in Developing a Mobile Attendance Application to Improve User Experience Andriani (SCOPUS ID: 57208011426), Ria; Pujianto, Ade; Abdullah, Abdullah
Sistemasi: Jurnal Sistem Informasi Vol 15, No 1 (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.v15i1.5778

Abstract

The desktop- or web-based attendance system currently used by Sevenpion is considered to have suboptimal performance, particularly in terms of speed and user convenience. This limitation is primarily due to the system’s reliance on web browsers, internet connection stability, and a user interface that is not fully responsive on mobile devices. This study aims to design a more comfortable, efficient, and adaptive mobile attendance application by improving its user interface (UI) and user experience (UX). The design process follows a User-Centered Design (UCD) approach, which emphasizes user needs through stages including requirements identification, context-of-use analysis, prototyping from low- to high-fidelity, and system evaluation. Usability testing was conducted using the System Usability Scale (SUS) method, involving five participants, yielding an average score of 77, categorized as “Good.” The results indicate that the prototype application enhances comfort, security, and efficiency in the attendance process compared to the existing web-based system. The study highlights the importance of user-centered approaches in mobile application development to ensure optimal usability.

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