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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 15 Documents
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Search results for , issue "Vol. 5 No. 1 (2026): January 2026" : 15 Documents clear
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.
Learning Rate and Epoch Analysis for Medicinal Plant Identification Using GLCM and BPNN Nurqolbiah, fatihani; Absharina, Eriene Dheanda; Utari, Aspirani; Febriady, Mukhlis; Saputra, Tommy
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.v5i1.1413

Abstract

Accurate identification of medicinal plants is essential for pharmacology and biodiversity conservation. However, traditional methods rely heavily on subjective visual inspection, which is prone to misclassification due to subtle differences in leaf textures. A primary challenge that remains unaddressed is the understanding of hyperparameter sensitivity within limited datasets, particularly when the subjects exhibit extremely high visual similarity. This study proposes an automated identification approach using Gray-Level Co-occurrence Matrix (GLCM) and Backpropagation Neural Network (BPNN) to classify three Indonesian medicinal species: white ginger, mango ginger, and yellow turmeric. The distinctive focus of this research lies in its attempt to differentiate these specific plants, which possess leaf texture characteristics so similar that they are often indistinguishable to the human eye. This approach involves a systematic analysis of learning rate and epoch parameters to optimize convergence for these nearly identical texture features. A dataset of 63 images was transformed into five GLCM statistical features to serve as the primary inputs for the BPNN. Experimental results demonstrate that classification performance is highly sensitive to parameter tuning. The system achieved its peak accuracy of 65.03% using a learning rate of 0.1 and 100 epochs. The findings reveal that smaller learning rates and limited training iterations facilitate more stable convergence when processing data with high feature similarity. While the accuracy indicates potential for further development, this study provides a significant contribution to creating objective identification methods for visually similar plants and offers empirical insights into optimal parameter selection for texture-based neural network architectures.
User Interface Design and UML-Based Modeling for an Internship Monitoring and Evaluation Information System Nurul Zafirah; Ikhsan Pratama; Samsudin
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.v5i1.1432

Abstract

Conventional internship monitoring workflows frequently suffered from critical inefficiencies, including data fragmentation, delayed reporting, and subjective performance evaluations. Furthermore, existing literature on system design often prioritized backend logical structures while neglecting frontend visual usability, resulting in functional but difficult-to-use applications. This study aimed to address these specific gaps by designing a comprehensive internship monitoring and evaluation system that explicitly integrated strict Unified Modeling Language architecture with high-fidelity user interface design at the conceptual level. The methodology utilized a qualitative descriptive approach, employing specific structural diagrams including use case, activity, and sequence diagrams to enforce role-based access control and user-centered design principles. The results demonstrated that the proposed blueprint successfully ensured data integrity and atomicity. Validated through black box testing, the conceptual models were confirmed to be translated into a functional design without logical errors, enabling real-time activity tracking and objective assessment. This study contributed to information system design research by bridging strict data security standards with minimalist usability heuristics, providing a matured visual and structural foundation. The findings offered a concrete basis for future implementation and empirical validation using user acceptance testing in operational environments.
A Web-Based Decision Support System for Inventory Procurement Optimisation Using Pareto Analysis Fajar , Ibnu; Rachmawati Yahya, Sitti; Bohani, Farah Aqilah; Yusof , Nor Nadiah
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.v5i1.1447

Abstract

Existing research and practical applications of multi-objective optimization in this domain continue to rely mainly on manual Pareto analysis. Typically, decision makers analyze trade-off curves or a collection of candidate solutions before making subjective configuration choices. This method is time-consuming, difficult to replicate, and subject to bias or inconsistency among evaluators. Furthermore, many publications stop at creating the Pareto front without giving a systematic mechanism for automated selection or assessing the effectiveness of the produced front in comparison to alternative tactics. Data for fast-moving product categories with high profit margins can be processed in a computerized application. These two parameters will provide the best recommendations according to the Pareto principle, which states that 80% of the best income comes from 20% of sources. Pareto Method optimization has proven to narrow the focus of work on the parts that have a significant effect (benefit) for the pharmacy. The manual process used before the research was conducted resulted in one item recommendation in 6 minutes and 20 seconds, while the computerized DSS could process a large amount of item data in just 3 minutes and 15 seconds, with an average gross profit for the top 10 recommended items of 32.1%. This study presents an automated Pareto optimization and selection methodology, which eliminates the need for manual inspection. The system not only creates candidates for Pareto-optimal solutions, but also ranks and selects them based on quantitative criteria. In addition, the framework includes comparative benchmarking, which allows for performance evaluation against baseline methodologies, heuristics, or existing decision procedures. This results in an objective, repeatable, data-driven decision pipeline.
An Intelligence-Oriented System Architecture for Integrated Pharmaceutical Data Analytics and Decision Support Ningsiah; Aminudin, Nur; Ariyanti, Septika; Abbasov, Ramil
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.v5i1.1461

Abstract

This study proposes and evaluates an intelligence-oriented hybrid information system architecture for pharmaceutical data analytics and decision support. Unlike conventional approaches that treat analytics as an external component, the proposed framework embeds analytical intelligence directly into the core system architecture through an integrated, multi-layer design. The study adopts an experimental and system development methodology using a large-scale public pharmaceutical dataset consisting of 240,591 records and 10 attributes. Supervised machine learning models are implemented to support data classification and intelligence generation, and system performance is evaluated using accuracy, precision, recall, and F1-score. The results demonstrate that the proposed hybrid system consistently outperforms baseline and non-integrated approaches, achieving higher predictive stability and analytical consistency. The main contribution of this study lies in its system-level integration model, which enables the transformation of raw pharmaceutical data into actionable decision-support intelligence. The findings confirm that embedding analytics within information system architecture significantly enhances both analytical performance and decision-making capability in pharmaceutical information systems.

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