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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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Group Decision Making Using Mean SAW Borda and Decision Maker-Based Criteria Weighting Utomo, Pradityo; Amadi, Dwi Nor
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.v7i2.1132

Abstract

In an organization, unfair decisions are often a problem. Therefore, this study creates a group decision-making model to optimize the achievement of fair decisions. This study combines the Mean and Simple Additive Weighting Borda (SAW Borda) methods. The combination of these methods is called Mean SAW Borda. The Mean calculation is used to obtain the average value of decision-makers. The mean value can be the weight of the group decision-making criteria. The weight is the reference for the SAW Borda calculation. This aims to optimize fair decisions. The SAW Borda calculation provides group decisions. This study uses data on the election of a university's head of the Informatics Engineering study program. In the study program, 24 decision-makers gave scores to choose the head of the study program. The Means SAW Borda method calculates the assessment, and the result is that A4 has the highest decision value (1923.26573). The results of the Mean SAW Borda method are the same as those of the conventional election. The conventional method chooses A4 to be the elected candidate. Based on these results, the Mean SAW Borda method can produce fair decisions and is agreed upon by decision-makers.
From Traditional Marketplace to Online Shop: Shifting Shopping Patterns among University Students in Bangladesh Balaly, Md. Habibullah; Islam, Md. Rabiul; Makhdum, Niaz; Shah, A.M.M. Mubassher; Banna, Hasanul
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.v7i2.1137

Abstract

The explosive rise of e-commerce has largely changed shopping habits around the world, and university students are one of the biggest groups to have changed their ways of shopping. In Bangladesh, the change from conventional to digital shopping has been visible with the help of mobile technology, the impact of social media, and also due to the ease of online shopping. The study identifies the drivers of online shopping acceptance among university students in Bangladesh through the lens of theoretical framework based on TAM, UTAUT and TPB. Quantitative method was employed, and the data were collected using simple random sampling from 384 students, determined based on 95% confidence level and 5% margin of error, from three different universities in Bangladesh. The results suggest that the convenience, quickness, and the assortment of the product are the key motivations that drive students to online shopping. Besides, social media networks, mainly Facebook and Instagram, play an incredibly significant role in deciding to buy the students. However, issues like the delay in delivery, high delivery charges, and the question of products' authenticity have proven to be the barriers to the online shopping experience. The research advocates that a reduction in delivery fees, better logistics operations, and providing student discounts will lead to an increase in adoption of e-commerce in Bangladesh. Besides, it is essential to instill customer trust in e-commerce platforms by using secure payment systems and trustworthy products and delivery services.
Agile-Scrum Methodology for Hospital Information System Development Zulkifli, Zulkifli; Ratnasari, Ratnasari; Arifin, Yulyani; Habib, Cahya
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.v7i2.1148

Abstract

Hospitals face significant challenges in managing large and complex data, and Hospital Information Systems (SIRS) are essential for supporting hospital operations. However, many SIRS projects experience delays and failures due to rigid development approaches. Agile-Scrum is proposed as a more flexible and adaptive solution, emphasizing collaboration and iterative processes to enhance the quality of healthcare services. This qualitative case study, conducted in a hospital with an internal development team, used observations, document analysis, and semi-structured interviews with 10 participants, including developers, a Scrum Master, and key hospital stakeholders. The findings indicate that implementing Agile-Scrum led to a 35% increase in team collaboration, a 40% improvement in responsiveness to changing requirements, and a 30% boost in overall project efficiency. The study highlights the effectiveness of Agile-Scrum in managing the complexities of SIRS development, especially through backlog organization, sprint planning, and stakeholder feedback. The study suggests further research to assess the long-term impact of Agile-Scrum in other information system development contexts.
Customer Continuance Usage of Digital Banking: A Systematic Review of Influencing Factors Christy, Cathrine Abigael; Lisana, Lisana
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.v7i2.1151

Abstract

Customer loyalty plays a crucial role in sustaining banking revenue and long-term growth. This study presents a systematic review that aims to provide insights for future studies about the trends of digital banking continuance usage intention. Using Population-Intervention-Comparison-Outcome-Time-Question (PICOTQ) Framework, this research focuses on journal articles published between 2020 and 2025, written in English, featuring a conceptualized research model, and published in peer-reviewed journals. Twenty-nine relevant articles were selected. The Preferred Reporting Items for Systematic Review (PRISMA) Framework guided the review process, revealing 56 variables used in related models. Among these, satisfaction, privacy and security, user experience, ease of use, and customer service and support were the most frequently significant factors influencing continuance usage. Most studies were conducted in Indonesia, India, and Korea, reflecting a variety of country income levels. The findings confirm that digital banking continuance usage intention remains a promising and prospective area for future investigation. Further exploration using diverse moderating variables and alternative analytical methods is encouraged to enrich understanding. Practically, this research offers valuable insights for digital banking stakeholders to strengthen customer loyalty by improving service quality, particularly by enhancing user satisfaction, strengthening data privacy and security, improving interface usability, and delivering responsive customer support.
Applying the Periodic Review System Method in Progressive Web Apps for E-Commerce Inventory Management Siagian, Andika Fadilla; Suendri, Suendri
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.v7i2.1117

Abstract

Retail businesses, particularly hardware stores, often encounter challenges in order management such as delayed deliveries, inaccurate stock tracking, and limited information transparency factors that hinder operational efficiency and customer satisfaction. This study proposes a web-based order management system utilizing Progressive Web Apps (PWA) technology, developed with the Next.js framework. The Periodic Review System (PRS) method is implemented to calculate reorder points based on actual demand and safety stock levels. System development follows the Waterfall model, with data collected through observation, semi-structured interviews, and literature review. Testing confirms that the application enhances stock accuracy, minimizes delivery delays, supports offline access, and meets SEO performance standards. The implementation significantly improves operational efficiency and holds promise for boosting customer loyalty. The study concludes that PWA-based digital systems are practical, scalable solutions for the MSME sector, with future potential for integration of AI, CRM, and real-time analytics.
Examining ICT Interventions for Rural Health System Connectivity: Challenges and Gaps for Improvement: A Systematic Review Mukalere, Justine; Ssembatya, Richard; Habinka, Annabella
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.v7i2.1123

Abstract

Community healthcare interventions in Low- and Lower-Middle-Income Countries (LLMICs) frequently face record management issues that hinder effective linkage between community services and national health systems, contributing to persistently high mortality rates. This study aimed to identify and analyze ICT-based community health interventions implemented in LLMICs, evaluate their effectiveness, and explore challenges limiting their impact. A comprehensive literature search was conducted in June 2024 across ACM Library, PubMed, ScienceDirect, and Google Scholar, focusing on studies published between January 2019 and May 2024. Inclusion criteria targeted ICT-based interventions conducted in LLMICs, available in English, with accessible full texts and clearly defined ICT components. Of the 792 records initially screened, only 9 met the eligibility requirements. Most interventions addressed individual components such as data collection, monitoring, consultations, referrals, and reminders. However, they often lacked integrated systems for data management, continuity of care, and follow-up, limiting their long-term effectiveness. While the review was restricted to open-access studies, the findings offer crucial insights into the design and implementation of ICT-based health solutions. The absence of process integration in current interventions remains a major barrier. Future research and policy development should focus on designing comprehensive, integrated ICT frameworks to strengthen community-to-health system linkages and improve health outcomes in LLMICs.
Enhancing Hate Speech Detection: Leveraging Emoji Preprocessing with BI-LSTM Model Amalia, Junita; Tambunan, Sarah Rosdiana; Purba, Susi Eva Maria; Simanjuntak, Walker Valentinus
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.v7i2.1147

Abstract

Microblogging platforms like Twitter enable users to rapidly share opinions, information, and viewpoints. However, the vast volume of daily user-generated content poses challenges in ensuring the platform remains safe and inclusive. One key concern is the prevalence of hate speech, which must be addressed to foster a respectful and open environment. This study explores the effectiveness of the Emoji Description Method (EMJ DESC), which enhances tweet classification by converting emojis into descriptive text or sentences. These descriptions are then encoded into numerical vector matrices that capture the meaning and emotional tone of each emoji. Integrated into a basic text classification model, these vectors help improve detection performance. The research examines how different emoji preprocessing strategies affect the performance of a BI-LSTM model for hate speech classification. Results show that removing emojis significantly reduces accuracy (68%) and weakens the model’s ability to distinguish between hate and non-hate speech, due to the loss of valuable semantic context. In contrast, retaining emoji semantics either through textual descriptions or embeddings boosts classification accuracy to 93% and 94%, respectively. The highest performance is achieved through emoji embedding, highlighting its ability to capture subtle non-verbal cues critically for accurate hate speech detection. Overall, the findings emphasize the importance of incorporating emoji-aware preprocessing techniques to enhance the effectiveness of social media content classification.
Integration of Hash Encoding Technique with Machine Learning for Employee Turnover Prediction Kamila, Ahya Radiatul; Andry, Johanes Fernandes; Lee, Francka Sakti; Tampinongkol, Felliks F.
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.v7i2.1129

Abstract

Employee turnover refers to the replacement of employees within an organization, which can lead to losses such as recruitment costs and decreased productivity. Predicting turnover is crucial for companies to anticipate and take appropriate actions to retain potential employees. This study aims to optimize the employee turnover prediction model by integrating hash encoding techniques and machine learning. The dataset used in this study is an open-source dataset obtained from Kaggle dataset. It consists of 14,994 rows and 10 columns (features) representing employee-related information such as satisfaction level, evaluation score, number of projects, average monthly hours, and whether the employee left the company. Among these features, some are of object data type. Since machine learning algorithms generally cannot work directly with object-type features, the use of hash encoding is proposed. This technique converts object-type data into numerical data. It is part of the preprocessing stage, aiming to reduce memory usage, speed up data preprocessing, and improve model performance. After preprocessing is completed, the prediction model is trained using the Random Forest algorithm to predict employee turnover. The evaluation is conducted using accuracy, recall, precision, and F1-score metrics, which yielded results of 0.988, 0.961, 0.988, and 0.974, respectively. These results indicate that the integration of hash encoding techniques and machine learning can produce a well-performing model for predicting employee turnover.
Data Warehousing for Optimizing Healthcare Resource Allocation in Botswana Mabina, Alton thuo; Malema, Gabofetswe; Kombe, Cleverence
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.v7i2.1149

Abstract

Healthcare resource allocation remains a persistent challenge in Botswana, primarily due to inefficiencies in data management that obstruct equitable distribution and evidence-based decision-making. Traditional allocation approaches in Botswana exhibit severe fragmentation, low interoperability, and an absence of real-time data analytics factors that contribute to service delivery disparities, especially in rural and underserved areas. In contrast, developed countries have leveraged data warehousing to optimize healthcare resource planning, offering Botswana a proven yet untapped strategic opportunity. This study designs and validates a context-sensitive data warehouse methodology, applying the Kimball Lifecycle model as the guiding framework. A mixed-methods design was adopted, incorporating qualitative interviews with 24 healthcare practitioners and administrators across public and private health facilities, along with quantitative surveys assessing the state of 12 existing health data systems. Results reveal systemic shortcomings in data accuracy (average error rates of 22%), timeliness (with a median data update lag of 14 days), and accessibility (only 38% of facilities had centralized access). Post-implementation of the prototype data warehouse, significant improvements were noted: data accuracy increased by 47%, data accessibility across departments rose to 85%, and decision turnaround time was reduced by 33%. The warehousing also demonstrated cost-effectiveness, reducing redundant data handling expenses by an estimated 18% over six months. In conclusion, this study presents a robust, scalable, and locally adaptable data warehousing framework that effectively addresses Botswana’s systemic challenges in healthcare resource allocation.
Sentiment Analysis of Consumer Acceptance of Honda’s Digital Marketing Strategy Using Lexicon-Based Algorithm Dias, Bartolomius; Khoirunnisa’, Asma’; Budiman, Yosef; Setiyawami, Setiyawami
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.v7i2.1150

Abstract

This study analyzes customer sentiment toward Honda’s digital marketing strategy via the Wahana Honda application. A total of 2,000 customer reviews were collected from the Google Play Store using web-scraping techniques. Text data underwent preprocessing (e.g. cleansing, tokenization, stop-word removal, stemming, and translation into English). Sentiment classification using a lexicon-based approach revealed that 56.7% of reviews were positive, 20.8% neutral, and 22.5% negative. The model demonstrated high precision in identifying negative sentiment, though it showed limitations in classifying neutral opinions due to linguistic ambiguity. These findings highlight the need for more adaptive sentiment models and offer strategic insights for Honda’s digital marketing. Specifically, the analysis can help prioritize improvements in app functionality, excellence service priority, enhance personalized customer engagement, and shape targeted digital marketing strategies based on real user feedback. Leveraging these insights enables Honda to optimize user experience, increase retention, and align digital campaigns with customer expectations.

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