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Journal of Information Systems and Technology Research
Published by Ali Institute of Research and Publication
ISSN : 28283864     EISSN : 28282973     DOI : https://doi.org/10.55537/jistr
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
JISTR is a periodical journal that aims to provide scientific literature, especially applied research studies in information systems (IS) / information technology (IT), and an overview of the development of theories, methods, and applied sciences related to these subjects Focus and Scope Artificial intelligence Autonomous reasoning Bio-inspired algorithms Bio-informatics Cloud computing Data science Data mining Data visualization Decision support systems Deep learning Evolutionary computation Fuzzy logic Human-Computer Interaction Hybrid intelligent systems, Adaptation and Learning Systems IoT and smart environments Knowledge mining Machine learning Neural networks Pattern recognition Soft computing Prediction systems Signal and image processing System modeling and optimization Time series prediction Web intelligence
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 89 Documents
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Data Mining of Rural Digital Technology Adoption Factors Using Apriori Algorithm Wanda Windary; Abdul Halim Hasugian
Journal of Information Systems and Technology Research Vol. 4 No. 3 (2025): September 2025
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v4i3.1324

Abstract

Digital technology adoption in rural communities remains a major challenge due to limited infrastructure, weak internet connectivity, and low levels of digital literacy, which contribute to persistent gaps in digital inclusion. This study aims to analyze the socio-economic factors that influence technology adoption in Kuta Baru Village by applying data mining techniques with the Apriori algorithm within the Knowledge Discovery in Database (KDD) framework. A survey was conducted on 50 respondents selected using purposive sampling, and variables such as education, income, occupation, and internet access were encoded into binary items for analysis. The Apriori algorithm was executed with a minimum support threshold of 15% and a minimum confidence threshold of 60% to extract association rules. Results show that the strongest rule was “Low Internet Access ⇒ Weak Signal” with 100% confidence and 30% support, highlighting infrastructure as the most critical barrier. Another key finding revealed that respondents with education levels above high school had an 85% confidence of using the internet, while those with monthly incomes greater than IDR 3 million demonstrated a 78% confidence of adopting digital technologies. Furthermore, formal sector occupations were associated with consistent internet usage at 72% confidence. These findings suggest that improving infrastructure must be complemented by strengthening socio-economic conditions, particularly education and income, to accelerate rural digital transformation. The study provides empirical evidence and practical implications that can inform policymakers in designing targeted programs to bridge the rural digital divide.
Design and Implementation of a Dual-Cloud IoT Air Quality Monitoring System Using Fuzzy Mamdani Method Fiqih Qodri Ramadani; Yusuf Ramadhan Nasution
Journal of Information Systems and Technology Research Vol. 4 No. 3 (2025): September 2025
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v4i3.1326

Abstract

Air pollution continues to be a critical environmental issue that negatively impacts human health, ecosystems, and urban sustainability. Therefore, reliable air quality monitoring systems are urgently required to provide real-time and accurate information for both communities and decision-makers. This study presents the design and implementation of an Internet of Things (IoT)-based air quality monitoring system that integrates environmental sensors with an ESP32 microcontroller. A key novelty of this research is the adoption of a dual-cloud architecture, combining ThingSpeak and Blynk, to enhance data accessibility, visualization, and system reliability compared to conventional single-cloud approaches. The Fuzzy Mamdani method is applied to classify air quality levels into three categories: Good, Moderate, and Poor, using input variables of temperature, humidity, and gas concentration. Methodologically, the system was tested under multiple environmental conditions, and fuzzy membership functions and rules were carefully designed to reflect realistic thresholds. The results show that the dual-cloud system enables more robust and flexible monitoring, with faster data synchronization and higher reliability in remote visualization. Quantitatively, the system achieved a 92% expert validation score and demonstrated a 15% improvement in responsiveness compared to previous single-cloud implementations reported in the literature. The discussion highlights that dual-cloud visualization provides an effective solution to overcome downtime risks and single-point failures, while also improving user experience in accessing real-time air quality information. This research contributes to the growing body of work on IoT-based environmental monitoring and can serve as a foundation for future smart city applications.
Web-Based Decision Support System for Superior Corn Seed Selection Using FMADM and AHP Algorithms Donny Dwi Putra; Abdul Halim Hasugian
Journal of Information Systems and Technology Research Vol. 4 No. 3 (2025): September 2025
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v4i3.1331

Abstract

Indonesia as an agricultural country still faces challenges in meeting national corn demand due to dependency on imports. One critical issue is the inaccurate selection of superior seeds that suit local conditions. This study aims to develop a web-based decision support system (DSS) for superior corn seed selection using the Fuzzy Multi-Attribute Decision Making (FMADM) algorithm combined with the Analytical Hierarchy Process (AHP) method.The research was conducted in Sei Tembo Village, Langkat Regency, with data obtained through observation, interviews with farmers, and literature review. The AHP method was applied to determine the weights of five criteria: water content, pest resistance, productivity, fruit size, and harvest time. Consistency testing produced a CR value of 0.028, indicating reliable weighting. The FMADM method was then used to rank 142 seed alternatives based on these weights.The results showed that the proposed system successfully ranked Srikandi Putih 1 (A32) as the best alternative with a score of 0.950, while Bima5 Bantimurung (A130) had the lowest score of 0.632. Productivity was identified as the dominant factor (weight = 0.484) in determining superior seeds.These findings demonstrate that the web-based DSS can improve accuracy and objectivity in seed selection, helping farmers reduce trial-and-error decisions. Practically, this system supports agricultural productivity improvement and contributes to strengthening national food security by reducing reliance on corn imports.
Integration of AHP and TOPSIS in a Web-Based Decision Support System for Wedding Package Selection Cindya Putri Hidayat Siregar; M. Fakhriza
Journal of Information Systems and Technology Research Vol. 4 No. 3 (2025): September 2025
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v4i3.1332

Abstract

The rapid growth of information technology has significantly influenced service industries, including the wedding sector, where customers often face difficulties in selecting the most suitable package from many available alternatives. To address this challenge, this study designed a web-based Decision Support System (DSS) by integrating the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The research was conducted at Putri Darma Make Up, Medan, Indonesia, using customer preference records collected between January and May 2025. AHP was applied to determine the weights of decision criteria, yielding Facilities (0.521) and Decoration (0.297) as the most important, while Makeup Quality (0.058) received the lowest weight. TOPSIS was then used to evaluate and rank the alternatives. Results showed that the wedding package priced at 25 million IDR achieved the highest preference score (Ci = 0.937), followed by the 22 million IDR package (Ci = 0.662), while the 12 million IDR package had the lowest score (Ci = 0.301). These findings demonstrate consistency between AHP weight priorities and TOPSIS rankings, confirming the reliability of the system. The study contributes theoretically by applying multi-criteria decision-making to the wedding service industry and practically by providing a transparent tool that reduces subjectivity, accelerates decision-making, and improves customer satisfaction.
X-Means Clustering Algorithm in Property Customer Payment Pattern Fortuna, Edelin; Dwi Arman Prasetya; Hindrayani, Kartika Maulida
Journal of Information Systems and Technology Research Vol. 5 No. 1 (2026): January 2026
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v5i1.1228

Abstract

Understanding customer behavior is essential for ensuring the sustainability and competitiveness of property businesses. This study aims to segment customers of PT X based on installment payment patterns using the X-Means clustering algorithm, which automatically determines the optimal number of clusters. From 9,615 transaction records, 386 customer profiles were analyzed using four features: number of transactions, number of late payments, payment differences, and payment status. The analysis produced five customer clusters with a silhouette score of 0.571, reflecting good cluster separation and internal consistency. The results reveal distinct payment behaviors, such as customers who consistently pay on time, those frequently late, and those who have fully completed their payments. These clusters provide practical insights that can support targeted communication, billing, and retention strategies. Furthermore, the study highlights the effectiveness of adaptive clustering techniques in improving segmentation accuracy. The findings contribute to data-driven decision-making in customer management, offering valuable guidance for enhancing operational efficiency and supporting long-term business performance.
ProManageTI Integrated System for Managing Internships, Proposals, and Final Projects in Informatics Engineering Students Faldza Fadhillah, Muhammad; Yulisa Geni, Bias
Journal of Information Systems and Technology Research Vol. 5 No. 1 (2026): January 2026
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v5i1.1274

Abstract

This study presents the design and development of ProManageTI, a web-based integrated system for managing and monitoring Internship (KP), Final Project Proposals (Proposal TA), and Final Projects (TA) in the Informatics Engineering Study Program at Universitas Dian Nusantara. The research problem addressed is the inefficiency and lack of integration in the manual process using Google Workspace tools, which led to fragmented data, delays in academic workflows, and limited real-time progress tracking. The objective of this study is to provide a structured digital platform that enhances effectiveness, transparency, and accountability in academic process management. The system was developed using the Agile methodology with the Scrum framework, implemented with PHP, MySQL, and Bootstrap, and designed through Unified Modeling Language (UML) diagrams. It integrates features such as title submission, supervision monitoring, scheduling, document verification, notifications, and centralized document storage. Functional testing applied the Black Box method with Equivalence Partition and Boundary Value Analysis techniques, confirming that all features met requirements. The findings indicate that ProManageTI improves operational efficiency, data accuracy, and coordination among students, supervisors, and program administrators. The contribution and novelty of this research are the provision of a scalable and adaptable model for integrated academic management, enabling structured, accountable, and responsive academic services that can be replicated in broader higher education contexts.  
The Adaptive Medical Image Compression Based On A Hybrid Neural Network With Built-In ROI Detection aldeen A.Khalid, Noor; hameed, aymen; A.Jassim, Arkan
Journal of Information Systems and Technology Research Vol. 5 No. 1 (2026): January 2026
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v5i1.1325

Abstract

This study addresses the critical challenge of efficiently compressing the rapidly growing volume of medical images while preserving essential diagnostic details, particularly within the Regions of Interest (ROI). Traditional compression techniques, whether lossless or lossy, often struggle to balance high compression efficiency with image quality lossless methods offer limited data reduction, while lossy techniques risk removing vital clinical information. To overcome these limitations, a comprehensive hybrid compression framework is developed, integrating segmentation and compression within a single deep neural network. The system employs Convolutional Neural Networks (CNNs) to accurately segment medical images and identify ROIs, while an autoencoder-based compression module performs selective encoding applying near-lossless compression for ROI regions to maintain diagnostic fidelity and lossy compression for non-ROI (NROI) areas to maximize storage savings. This unified design eliminates the need for separate processing stages, reduces computational complexity, and enhances compression performance. The proposed framework was validated using the CLEF MED X-ray and BRATS MRI datasets, demonstrating high effectiveness and adaptability across different modalities. Experimental results achieved a Peak Signal-to-Noise Ratio (PSNR) of 56.07 dB for ROI and 45.12 dB for NROI, with an overall compression ratio of 6.73, confirming its strong balance between data reduction and image quality.
The Integration of HSV and GLCM Features with LDA for Classification of Breadfruit Maturity Levels Pratama, Hamdan; Khairina, Nurul; Novita, Nanda; Firdaus, Muhammad Huda; Rumapea, Yolanda Y.P.
Journal of Information Systems and Technology Research Vol. 5 No. 1 (2026): January 2026
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v5i1.1377

Abstract

Breadfruit is a perennial plant that has historically been distributed throughout Southeast Asia as a food source. Breadfruit that has entered the harvest period or has fallen on its own has several levels of maturity, namely raw, unripe, ripe, and rotten. Breadfruit that has been separated from the tree will have the same characteristics, namely green and slightly yellowish or brownish in colour. The research problem centres on the trouble buyers and sellers have when determining the maturity level of breadfruit. Based on this problem, the purpose of this study is to classify the maturity level of breadfruit using the LDA method. With image classification, it is hoped that the maturity level of breadfruit can be identified more accurately. The research gap in this study lies in the limited number of feature extraction methods used simultaneously, as well as the infrequent use of LDA methods for classification. In this study, Linear Discriminant Analysis is applied together with GLCM and HSV-based feature extraction. The LDA is a statistical method used for classification. LDA focuses on finding lines that separate two or more classes in a dataset by maximizing the distance between class averages and minimizing variance within classes. GLCM feature extraction is an image-processing technique used to evaluate texture. The contribution of this research lies in its improved classification performance and greater accuracy compared to previous studies. It offers a statistical description of how pairs of gray levels are distributed within an image, helping to reveal texture patterns and characteristics. The results of this study show that the classification of maturity levels in breadfruit images is good. This is measured by an accuracy of 89.9333%, precision of 90.1732%, recall of 89.3333%, and an F1-score of 89.7513%.
Web-Based Satisfaction Measurement System with Automated Index Computation and Role-Based Analytics Ardiansyah; Yafiz, Muhammad; Vorfi Lama, Alma
Journal of Information Systems and Technology Research Vol. 5 No. 1 (2026): January 2026
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v5i1.1410

Abstract

Measuring service satisfaction is essential for evaluating institutional performance; however, manual survey processes often cause delays in data compilation, duplicate entries, and limited analytical capability. This study aims to design and implement a web-based Satisfaction Measurement Information System that automates survey distribution, validation, index computation, and reporting. The system was developed using a structured system development methodology and implemented with modern web technologies that support centralized data management, automated index calculation, and role-based reporting. System evaluation was conducted through User Acceptance Testing (UAT) involving 45 respondents from 12 organizational units. The results show a UAT score of 88.6%, indicating high usability and functional suitability. In addition, the average data processing time was reduced from approximately five days (manual tabulation) to less than 10 minutes through automated computation. The system successfully managed 1,250 survey responses without duplicate records through validation mechanisms. These mechanisms and findings indicate that the proposed system improves the accuracy, processing speed, and accessibility of satisfaction data. This study contributes a practical model of automated satisfaction measurement with centralized analytics to support data-driven decision-making in higher education institutions
Examining the Impact of Virtual Tour Service Quality on Visitor Satisfaction in Digital Museum Environments Rizal, Chairul; Erni Marlina Saari
Journal of Information Systems and Technology Research Vol. 5 No. 1 (2026): January 2026
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v5i1.1411

Abstract

Research on virtual museums has expanded globally; however, empirical evidence from Indonesian digital cultural heritage institutions remains limited, particularly regarding how service quality shapes visitor satisfaction in fully virtual environments. Addressing this gap, this study investigates the influence of virtual tour service quality on visitor satisfaction at the Museum Kebangkitan Nasional, Indonesia, and examines the applicability of established service quality frameworks within a digital heritage context. Using a quantitative research design, data were collected through a structured survey from 97 users of the museum’s virtual tour platform. Measurement instruments were adapted from SERVQUAL and e-SERVQUAL models, incorporating digital-specific dimensions such as interactivity, system usability, interface aesthetics, and accessibility. Data analysis employed descriptive statistics, correlation analysis, and multiple regression techniques. The results reveal that virtual tour service quality has a significant positive effect on visitor satisfaction, explaining 41.6% of the variance, with respondents reporting high levels of perceived service quality and overall satisfaction. These findings demonstrate the novelty of extending traditional service quality models to virtual museum environments, where technological performance and user interface design emerge as critical experiential determinants. Theoretically, the study contributes to service quality and digital heritage literature by validating hybrid service quality constructs in a virtual cultural setting. Practically, it provides actionable insights for museum managers and cultural institutions in Indonesia to enhance digital engagement through user-centered design, platform reliability, and continuous technological innovation.

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