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INDONESIA
Journal of Information Systems and Informatics
Published by Asosiasi Doktor Sistem Informasi Indonesia
ISSN : 26565935     EISSN : 26564882     DOI : 10.63158/journalisi
Core Subject : Science,
Computer Science & IT
Journal-ISI is a scientific article journal that is the result of ideas, great and original thoughts about the latest research and technological developments covering the fields of information systems, information technology, informatics engineering, and computer science, and industrial engineering which is summarized in one publisher. Journal-ISI became one of the means for researchers to publish their great works published two times in one year, namely in March and September with e-ISSN: 2656-4882 and p-ISSN: 2656-5935.
Arjuna Subject : -
Articles 653 Documents
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Strategic IS/IT Planning for Enhanced Competitiveness and Operational Efficiency at PT. Songgo Jati Baru: Applying the Ward and Peppard Method Firmansya, Andika Jati; Amelia, Putri
Journal of Information System and Informatics Vol 7 No 2 (2025): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i2.1105

Abstract

This study designs an integrated IS/IT strategy to enhance PT. Songgo Jati Baru's operational efficiency and global competitiveness in the trading and services sector. A qualitative approach was employed, utilizing data collection methods such as focus group discussions, document analysis, observation, and interviews. Analysis was conducted using the robust Ward and Peppard method, which incorporates SWOT analysis, Gap analysis, and the McFarlan Strategic Grid. The findings revealed that the company faced significant challenges, including a lack of system integration, limited data analytics capabilities, and suboptimal digital marketing strategies. To address these, the research recommends a cloud-based Enterprise Resource Planning (ERP) system for comprehensive business process integration, a Vendor Management System (VMS) for efficient collaboration, and a Customer Relationship Management (CRM) system for data-driven marketing, with a phased implementation planned for 2026-2028. This comprehensive strategy, underpinned by robust cloud infrastructure and continuous staff training, is poised to not only significantly enhance PT. Songgo Jati Baru's operational efficiency and global market reach but also to solidify its competitive position and ensure sustainable growth in the dynamic trade and services sector.
Measuring Tiktok Shop Service Quality Using The E-ServQual Method And Importance Performance Analysis (IPA) Method Marlindawati, Marlindawati; Sahfitri, Vivi; Rosalinda, Rosalinda
Journal of Information System and Informatics Vol 7 No 2 (2025): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i2.1108

Abstract

Utilizing online shop services is an example of applying technology that aims to increase product sales, one of which is TikTok Shop. This research aims to measure the quality of service provided by TikTok Shop in order to offer appropriate recommendations for improving service features and other aspects. The method used is e-servqual, which includes variables such as efficiency, compliance, reliability, privacy, responsiveness, compensation, and contact which are used to identify service quality factors that require repair, maintenance, or improvement. The Importance Performance Analysis (IPA) method is used to assess the performance of services provided to consumers compared to desired expectations. The results of measurement research using the e-ServQual method show a satisfaction index of 0.925274, which means TikTok Shop users are very satisfied, because the service quality score is close to 1. Measurements using the IPA method reveal three attributes that require performance improvement. These three attributes have the highest priority for improved to increase user satisfaction. Additionally, seven attributes can be retained because their performance exceeds the user's importance level. It is hoped that the results of this research can provide valuable contributions or insights for relevant stakeholders.
A Comprehensive Review of Energy Optimization Techniques in the Internet of Things Isong, Bassey; Moeti, Kedibone
Journal of Information System and Informatics Vol 7 No 2 (2025): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i2.1110

Abstract

The advancement of energy efficiency in the Internet of Things (IoT) and wireless sensor networks (WSNs) is an important research effort, given their rapid application expansion across smart cities and homes, healthcare, agriculture, and industrial automation. This paper conducted a comprehensive survey of existing innovative solutions to challenges focusing on hardware-based, software-driven, and network optimization approaches, alongside artificial intelligence-driven and demand-side energy management, and security-enhanced frameworks. 82 peer-reviewed journal articles and conference papers published between 2021 and 2025 were reviewed, using sources such as IEEE Xplore, ScienceDirect, Web of Science, SpringerLink, and Google Scholar. It identifies significant developments in energy-efficient techniques, including ultra-low-power hardware, adaptive scheduling, bio-inspired clustering, and energy harvesting. Others include intelligent optimization methods(e.g. machine, quantum-inspired heuristics), and blockchain-enhanced security. A structured evaluation process is implemented, following PRISMA guidelines, categorizing studies, and synthesizing findings to highlight technological progress, challenges, and future research directions. The findings show a growing trend towards integrated, multi-objective routing and cross-layer energy optimizations, with significant progress in minimizing energy use, network lifetime and improving security mechanisms. However, challenges like scalability, computational overhead and real-world deployment issues persist. Our study offers valuable insights for sustainable energy management in IoT and WSNs and helps guide future development toward more resilient, adaptable and sustainable energy-aware systems.
Decision Support System for Job Applicant Recommendation Using ROC and ORESTE Methods Mu'arif, Risdani; Putri, Raissa Amanda
Journal of Information System and Informatics Vol 7 No 2 (2025): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i2.1113

Abstract

This study developed a Decision Support System (DSS) to assist the employee selection process for an agricultural company located in North Sumatra by utilizing a combination of the Rank Order Centroid (ROC) and ORESTE methods. The system was designed to address the limitations of the manual selection process, which had been time-consuming and inefficient. The ROC method was used to objectively determine the weights of each selection criterion, while the ORESTE method was applied to rank candidates based on their closeness to the company’s ideal profile. The study evaluated ten candidates based on key aspects such as educational background, competencies, work experience, and completeness of documents. The testing results demonstrated that the system was capable of producing accurate rankings consistent with manual calculations and was able to reduce the selection time from approximately two months to just a few minutes. The implementation of this system improved the objectivity and efficiency of the selection process while minimizing the risk of subjectivity in recruitment decision-making.
Designing Customer Analytics Dashboard in Smart Device Retail Using Power BI Rahutomo, Reza
Journal of Information System and Informatics Vol 7 No 2 (2025): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i2.1114

Abstract

The adoption of data analytics has led to a paradigm shift in business decision-making, moving from intuition-based to data-driven strategies. Specifically in customer analytics, metrics such as Net Promoter Score (NPS), Customer Satisfaction Score (CSS), and Repeat Purchase Rate (RPR) are widely used to formulate customer retention strategies. Although dashboard applications like Microsoft Power BI support the visualization of these metrics, existing designs lack integrated filtering capabilities based on demographic characteristics such as gender and age group. This study aims to propose a Power BI dashboard application design that integrates NPS, CSS, and RPR with demographic filters to effectively convey customer loyalty, satisfaction, and advocacy. The research methodology includes four stages which are Power BI understanding, data acquisition, data pre-processing, and metric modeling. The dataset was collected by using an online questionnaire in January 2025 (N = 542). It must be validated and transformed before being modeled by using DAX. The proposed dashboard design offers an interactive interface, allowing users to explore insights through chart elements such as bars and pie slices. This design enhances user experience and supports intuitive analysis, making it a valuable tool for smart device retailers and manufacturers to make data-driven decisions. Additionally, the dashboard is adaptable to other business contexts with similar analytical needs. For real-world implementation, the inclusion of Key Performance Indicators (KPIs) for each metric is recommended to ensure that insights are actionable and aligned with business objectives.
A Novel UX-Centered ITSM Framework for Technology Startups: Beyond Traditional Service Management Marcel, Marcel; Marzuqi, Tubagus Ahmad
Journal of Information System and Informatics Vol 7 No 2 (2025): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i2.1118

Abstract

This research explores the integration of User Experience (UX) principles into IT Service Management (ITSM) frameworks within resource-constrained B2B SaaS technology startups. Through a comprehensive qualitative case study methodology involving semi-structured interviews with seven stakeholders, participatory observation across 12 sessions, and systematic document analysis at a Jakarta-based startup serving SMEs, we uncovered a critical paradox: companies selling superior UX solutions to clients often neglect these principles in internal IT management. The primary contribution is a novel adaptive UX-Centered ITSM conceptual model featuring three interconnected layers: Core Principles, Implementation Domains, and Operational Elements, designed for incremental implementation based on startup capacity. Unlike rigid existing ITSM frameworks, this model introduces a prioritized approach with "Must Have," "Should Have," and "Can Be Added" categorizations specifically tailored for startup contexts. The research identified five contextual factors influencing implementation: organizational culture, leadership structure, resource limitations, team dynamics, and SME client characteristics. Findings reveal that UX-centered ITSM not only addresses internal operational challenges but creates strategic alignment between internal practices and external value propositions, forming the foundation for market credibility and business sustainability. This framework provides startup managers and IT practitioners with an actionable roadmap for transforming ad-hoc internal systems into user-centered services that support operational excellence while enhancing competitive positioning in digital transformation markets.
Comparative Analysis of Classification Algorithms for Predicting Membership Churn in Fitness Centers: Case Study and Predictive Modeling at EightGym Indonesia Mu'ti, Dewi Lestari; Prasetyaningrum, Putri Taqwa
Journal of Information System and Informatics Vol 7 No 2 (2025): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i2.1120

Abstract

The fitness industry in Yogyakarta is experiencing rapid growth accompanied by intense competition among gym service providers. This has led to an increase in membership churn, negatively impacting business sustainability. This study aims to conduct a comparative analysis of three supervised classification algorithms Random Forest, Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) to predict member churn at EightGym Indonesia. The dataset, consisting of 1,287 membership records collected between July 2024 and April 2025, includes features such as visit frequency, subscription duration, membership type, and churn status. The study focuses on predicting members at risk of subscription cancellation using historical data such as visit frequency, subscription duration, membership type, and churn status. The methodology follows the CRISP-DM framework, covering business understanding, data preparation, modeling, evaluation, and deployment stages. Evaluation results indicate that XGBoost delivers the best performance with 95% accuracy, high recall, and F1-score, making it the most effective algorithm for churn prediction in this context. Additionally, the model was implemented in a web-based prototype application to support gym management decision-making. The findings contribute significantly to the application of machine learning for customer retention strategies in the fitness industry and provide a foundation for the future development of predictive decision support systems.
Career Preference-Personality Mismatch: Leveraging the RIASEC Model in IT-Driven Career Guidance Tuhame, Moses Kamondo; Kayondo, Barbara Naluwadda; Habinka, Annabella Dorothy Basaza; Maiga, Gilbert
Journal of Information System and Informatics Vol 7 No 2 (2025): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i2.1121

Abstract

Whereas choosing a career is a critical life decision, career decision-making process among secondary school students involves misalignment between students’ aspirations and their aptitudes. This study examines the mismatch between career preferences and personality profiles of 717 Ugandan Advanced level and university students from 15 secondary schools and 1 university in Central and Western Uganda. Holland's RIASEC model was used to determine career preferences and determined personality through a 42-item inventory. Statistical analysis in SPSS indicated a substantial misalignment: while nearly 50% of students preferred Investigative or Realistic careers such as engineering and medicine, only 28% demonstrated personality congruence with their preferences. Conversely, students with Social-dominant personalities, rarely selected careers matching this orientation. The overall findings demonstrate a weak positive relationship (Kendall's τ = 0.394) between students’ career preferences and personalities. These results challenge conventional personality-driven career guidance systems, demonstrating their limited applicability in Uganda. Our key contribution lies in transforming mismatches into actionable insights, proposing a hybrid framework that dynamically weights RIASEC profiles against local opportunity data and student aspirations, offering a scalable solution for low-resource educational contexts.
A Qualitative Study of Researchers Perspective on the Use and Risks of Open Government Data Emigawaty, Emigawaty; Sukmaningrum, Dinda; Nurastuti, Wiji
Journal of Information System and Informatics Vol 7 No 2 (2025): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i2.1122

Abstract

Open government data has the potential to improve transparency, accountability, public participation, business innovation, and research quality. However, this openness also poses various opportunities for losses or even risks, especially related to low data quality, personal data security issues, data translation errors, and misuse of information. This study aims to review the potential risks of data openness on government data portals from the perspective of researchers as one of the important actors who use data. Using qualitative method with structured interviews, this study involved five potential researchers who actively used open data between May and August 2023. The results of the interviews showed that high data quality, such as accuracy, completeness, and currency, can increase researchers' trust in the data. At the same time, obstacles in accessibility and bureaucracy or data administration requirements can slow down the research process or stages. Security and privacy issues are also important parameters, with strict security policies and good audit processes can reduce the risk of data misuse. Data openness and transparency play a major role in increasing the use of data for public policy and evidence-based research. In addition, data standardization is essential to ensure the efficiency of data use by researchers. This study concludes that to optimize the benefits of data openness, there needs to be proper and measurable management in order to consider data quality, accessibility, security, and standardization.
Predicting Respiratory Conditions Using Random Forest and XGBoost Dhiyaussalam, Dhiyaussalam; Yusuf, Ahmad; Wardiah, Isna; Putri, Nitami Lestari
Journal of Information System and Informatics Vol 7 No 2 (2025): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i2.1124

Abstract

This study examines the performance of Random Forest and XGBoost in predicting the diagnosis and severity of respiratory diseases using a simulated dataset of 2,000 patient records. The models were tested on two classification tasks: identifying disease types (e.g., pneumonia, influenza) and classifying severity levels (mild, moderate, severe). Both models achieved perfect accuracy in severity classification, with 1.0000 ± 0.0000 cross-validation scores, demonstrating strong stability under balanced class distributions. However, in the diagnosis task, Random Forest underperformed on minority classes, particularly pneumonia, with a recall of 0.18 and F1-score of 0.31. XGBoost, on the other hand, achieved superior results across all classes, including minority cases, with 0.9825 ± 0.0170 cross-validation accuracy and perfect test set performance. These findings highlight XGBoost’s robustness in handling imbalanced and multiclass medical data, making it a promising candidate for clinical decision support. Future work should address class imbalance and explore explainability techniques to improve trust and transparency in real-world applications.

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