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JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH)
Published by Forum Kerjasama Pendidikan TInggi
ISSN : -     EISSN : 2686228X     DOI : -
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
Artikel yang dimuat melalui proses Blind Review oleh Jurnal JOSH, dengan mempertimbangkan antara lain: terpenuhinya persyaratan baku publikasi jurnal, metodologi riset yang digunakan, dan signifikansi kontribusi hasil riset terhadap pengembangan keilmuan bidang teknologi dan informasi. Fokus Journal of Information System Research (JOSH)
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 795 Documents
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Implementasi Sistem Informasi Berbasis Website Pada Gereja Ichtus Puildon Menggunakan Metode Waterfall Daniel, Gilberth Patrick; Fajri, Ika Nur; Prisyanto, Yoga
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): January 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6671

Abstract

This research focuses on the development of a web-based information system for Ichtus Puildon Church, which faces issues in the management of congregation data that is still done manually. This process causes data to be easily lost and hard to access, as well as requiring more time and effort for report generation. The aim of this research is to design and implement an information system that can improve the efficiency of congregation data management. The approach used in this research is the Waterfall method, which includes problem identification, data collection through questionnaires, system analysis, and implementation. Testing is carried out using Black Box, White Box, and System Usability Scale (SUS) methods to evaluate the system's performance and user satisfaction. The results show that the application achieved a score of 74.2%, which falls into the "Good" category. Functional testing of login, menu access, and content management by the admin indicates that the system works well, provides a satisfactory user experience, and has the potential to improve the church's digital services.
Aplikasi Pemesanan Online Kue Tradisional Berbasis Web Menggunakan Metode Rapid Aplication Development Wulandari, Fitri; Pramata, Adhitya
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): January 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6673

Abstract

Traditional cakes are one of Indonesia's culinary cultural heritages that need to be preserved. However, small business actors, especially MSMEs, often face obstacles in reaching consumers widely due to limited marketing access. This study aims to design and build a website-based traditional cake online ordering application using the Rapid Application Development (RAD) method. This method was chosen because of its iterative approach and direct user involvement, so that application development can be carried out quickly and according to needs. This application is designed to provide key features such as product catalogs, online ordering systems, and digital payments. Web technology is used so that the application can be accessed through various devices, both desktop and mobile. The development process includes four stages: needs planning, design, planning, and implementation. The test results show that the application has achieved a 100% functional success rate, with all key features running smoothly. In addition, User Acceptance Testing (UAT) testing using the Likert scale obtained an average score of 4.5 out of 5, reflecting a very high level of user satisfaction with ease of use, ordering, and product completeness. This application is considered effective in supporting the digitalization of micro, small, and medium enterprises (MSMEs) and preserving local culinary through technological innovation. This application not only makes it easier for consumers to order traditional cakes online, but also helps MSMEs manage orders and expand market reach. This research contributes to supporting the digitalization of MSMEs, especially in the traditional culinary sector, and has the potential to be a solution to maintain the existence of traditional cakes in the digital era.
Implementasi Algoritma A Star Untuk Pemetaan Fasilitas Umum Berbasis Mobile GIS Pada Kabupaten Serdang Bedagai Sari, Fani Panca; Ikhwan, Ali; Suendri, Suendri
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): January 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6695

Abstract

The rapid advancement of technology has significantly simplified the process of accessing essential information. Alongside the increasing population in Serdang Bedagai Regency, there has been a consistent expansion in the construction of public facilities. However, the extensive geographical area of this region poses challenges in providing accessible routes to these facilities. This study aims to implement the A* (A-Star) algorithm within a mobile-based Geographic Information System (GIS) to optimize the mapping and routing of public facilities in Serdang Bedagai Regency. By leveraging the A* algorithm, the application provides users with the most efficient and shortest routes to their desired destinations. This application is designed for easy integration with commonly used smartphones, allowing residents and tourists to navigate public facilities with greater convenience. The GIS-based platform also serves as a valuable tool for local governments to manage and analyze the distribution and accessibility of public infrastructure. The results demonstrate that the implementation of the A* algorithm effectively enhances route optimization by considering both distance and heuristic evaluations. This research contributes to improving public access to essential facilities while addressing geographic challenges within the regency.
Prototype Pengembangan Sistem Informasi E-Commerce Berbasis Website Hapsah, Dido Muhammad; Hanif, Isa Faqihuddin
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): January 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6706

Abstract

This study develops a website-based e-commerce information system for the Ruang Style store. Several challenges faced by the store include limited marketing efforts and the lack of an online sales platform, which can lead to a decline in customers. The process of purchasing goods is still done manually, where customers must visit the store in person to buy products. Additionally, the sales and inventory data management is still done manually in the form of written records without an integrated database. The revenue from all sold items is also calculated manually, which increases the potential for errors in the calculation process. The goal of this research is to design a website-based e-commerce information system to help the store owner manage inventory and sales data, as well as facilitate customers in making transactions online so that they no longer need to visit the store physically. The research was conducted at Ruang Style store from April 2024 to December 2024. The prototype methodology was used, as the mockups were designed based on user evaluations. The study utilized PHP, MySQL, HTML, JavaScript, and CSS programming languages. The result of this research is the design of a website-based e-commerce information system. The conclusion of this study is that the design of the website-based store management information system can be successfully implemented on desktop browsers, and it successfully simplifies the marketing process for the store and facilitates customers in making transactions.
Analisa Penerimaan Aplikasi Mypertamina dengan Model Unified Theory of Acceptance and Use of Technology 2 dan Information Systems Success Model Siahaan, Mangapul; Suwarno, Suwarno; Lorenz, Chintya
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): January 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6712

Abstract

This study aims to analyze the factors that influence the acceptance of the MyPertamina application in Batam City using the UTAUT2 model approach and the IS Success Model. This research model includes variables such as performance expectancy, effort expectancy, hedonic motivation, facilitating conditions, information quality, system quality, service quality, behavioral intention, user satisfaction, and use behavior. Data were collected from 177 respondents who used the MyPertamina application in Batam City and analyzed using the PLS-SEM and SEM (Amos) methods. The results showed that factors such as performance expectancy, effort expectancy, supporting conditions, information quality, service quality, user satisfaction, and behavioral intention had a significant influence on the acceptance of the MyPertamina application. In contrast, hedonic motivation and system quality did not show a significant influence. This study provides important insights into the factors that influence the acceptance of technology-based applications and provides recommendations for the development of similar applications in the future.
Perancangan Sistem Deteksi dan Pengendalian Kebakaran Menggunakan Sensor Gas, Api, Asap, Arduino, dan Blynk Hanif, Muhammad Irfanil; Sinduningrum, Estu
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): January 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6714

Abstract

This study develops an Internet of Things (IoT)-based fire detection and control system with two-mode monitoring (offline and online) to provide solutions for early fire prevention and handling. The system integrates multiple sensors (MQ-2, MQ-135, and flame sensor) to detect flammable gas, smoke, and fire, and is equipped with actuators in the form of servos, fans, water pumps, and buzzers as automatic responses. Monitoring is carried out via I2C LCD for offline mode and Blynk application for online mode. The research method uses an experimental approach with stages of literature study, system design, prototype development, testing, data analysis, and improvement and optimization. The test results show that the system is able to detect fire up to a distance of 30 cm, gas with a concentration above 300 ppm, and smoke with a concentration above 200 ppm. The actuators work according to the designed programming. This system successfully integrates early detection, automatic response, and remote monitoring to provide a comprehensive solution in fire prevention and handling.
Analisis Kualitas Website Kompas.com Menggunakan Metode Webqual 4.0 Perdana, Cilma Fresha; Widodo, Suprih
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): January 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6743

Abstract

This study analyzes the quality of the Kompas.com website using the WebQual 4.0 method, which encompasses three main dimensions: usability, information quality, and service interaction quality. The research employed a quantitative approach, involving 40 respondents selected through purposive sampling. Data were collected via questionnaires using a four-point Likert scale and analyzed using statistical techniques. The findings indicate that all three website quality dimensions are categorized as "Good," with usability satisfaction at 79%, information quality at 81%, and service interaction quality at 76%. These results suggest that the Kompas.com website generally meets user expectations, although there is room for improvement, particularly in the aspect of service interaction.
Identifikasi Citra Motif Kain Tenun Sumbawa (Kre Alang) Menggunakan Metode Convolutional Neural Network Arsitektur MobileNetV2 Dianda, Nandita; Rachman, A Sjamsjiar; Yadnya, Made Sutha
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): January 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6774

Abstract

Weaving is a cultural product that reflects the identity of the people who make it, with each region having its patterns, beauty, and distinctive features of its weaving motifs. However, identifying the origin of the region based on woven fabric motifs is often difficult to do due to the unique and diverse characteristics of the motifs. This paper aims to evaluate the performance of the MobileNetV2 architectural model in classifying the motif image of Sumbawa woven fabrics. This model was tested using a dataset of woven fabric images that included various motifs from Sumbawa. The results showed that the model managed to achieve the highest accuracy of 98.14% in the 20th and 25th epochs, with a training time of less than 1 hour. In the training data, the model obtained an accuracy of 99.71% with a loss of 12.99%, which indicates that the model can recognize images with a very high level of accuracy. However, in the validation data, the accuracy of the model was recorded at 92.71% with a loss of 41.98%, which shows that despite the decrease in accuracy, the model is still able to generalize well on data that has never been encountered before. In addition, the model showed excellent results in terms of precision (98.14%), recall (100%), and f1-score (99%). These findings confirm the effectiveness of the MobileNetV2 model in recognizing Sumbawa woven fabric motifs and provide a solid basis for the development of an automated system in supporting the preservation and promotion of regional weaving culture. This paper also shows the importance of model optimization to improve accuracy on validation data and reduce the gap between training data and unseen data. As a next step, the research can be directed to expand the dataset with more variations of motifs and regions to improve the model's ability to generalize to different types of woven fabric motifs.
Evaluating Deep Learning Models for HIV/AIDS Classification: A Comparative Study Using Clinical and Laboratory Data Airlangga, Gregorius
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): January 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6783

Abstract

The accurate classification of HIV/AIDS status is critical for effective diagnosis, treatment planning, and disease management. This study evaluates the performance of four deep learning models: Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) on a comprehensive clinical and laboratory dataset derived from the AIDS Clinical Trials Group Study 175. The dataset includes features such as demographic information, treatment history, and immune markers like CD4 and CD8 counts. To address class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied, followed by stratified 10-fold cross-validation to ensure robust evaluation. Each model's performance was assessed using metrics including accuracy, precision, recall, F1-score, and ROC-AUC. GRU emerged as the most effective model, achieving the highest accuracy (71.04%) and ROC-AUC (57.72%), demonstrating its robustness in handling sequential data. CNN and LSTM showed competitive performance, particularly in balancing precision and recall. However, all models faced challenges in recall, highlighting difficulties in identifying minority-class samples. The findings underscore the potential of GRU for HIV/AIDS classification while identifying limitations in current approaches to handling class imbalance. Future work will explore advanced architectures, such as attention mechanisms and hybrid models, to further improve sensitivity and robustness. This study contributes to the growing body of research on applying deep learning to healthcare, with implications for improving diagnostic accuracy and patient outcomes.
Performance Evaluation of Machine Learning Models for HIV/AIDS Classification Airlangga, Gregorius
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): January 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6790

Abstract

Accurate and early diagnosis of HIV/AIDS is critical for effective treatment and reducing disease transmission. This study evaluates the performance of several machine learning models, including Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naive Bayes, for classifying HIV/AIDS infection status. A dataset comprising 50,000 samples was used, and models were assessed based on accuracy, precision, recall, and F1 score using stratified ten-fold cross-validation to ensure robust evaluation. The results reveal significant trade-offs between sensitivity and specificity across the models. Gradient Boosting achieved the highest accuracy (70.85%) and precision (57.81%), making it suitable for confirmatory testing where minimizing false positives is critical. Conversely, Naive Bayes demonstrated the highest recall (57.99%) and F1 score (51.04%), emphasizing its effectiveness in early-stage diagnostics where sensitivity is paramount. SVM exhibited the highest precision (59.87%) but struggled with recall (11.28%), reflecting its conservative nature in classifying positive cases. These findings underscore the importance of selecting models tailored to specific diagnostic objectives. While Naive Bayes is ideal for comprehensive screening programs, Gradient Boosting and SVM are better suited for confirmatory testing. This research provides valuable insights into the strengths and limitations of machine learning models for medical diagnostics, paving the way for developing more robust, hybrid approaches to optimize sensitivity and specificity in HIV/AIDS classification.

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