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All Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Bulletin of Computer Science Research
Nursanti Novi Arisa
Institut Teknologi Kalimantan, Balikpapan
Computer Science & IT Decision Sciences, Operations Research & Management

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Implementasi Scrum untuk Meningkatkan Adaptivitas Pengembangan Sistem Business Intelligence Pada Perusahaan Distributor Alat Kesehatan Nursanti Novi Arisa; Indrayanto Dwicaksono; Is Riosena Nur Soffa
Bulletin of Computer Science Research Vol. 6 No. 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i3.1018

Abstract

In the Industry 4.0 era, optimizing data utilization has become an important factor in enhancing organizational monitoring effectiveness and decision-making processes. PT Promedika Mitra Utama and PT Promedika Mitra Farma have digitalized various operational aspects, including employee activities, correspondence management, risk management, and weekly reporting. However, the generated data have not been optimally integrated to support managerial analysis. This study aims to design and implement a dashboard-based Business Intelligence (BI) system to improve monitoring effectiveness and managerial information accessibility. The development process includes performance metric identification, data collection and cleansing, data integration, and centralized data storage, with visualization implemented using Google Looker Studio. The Scrum method was applied to accommodate evolving variables and visualization requirements throughout iterative development and stakeholder feedback. The system development was completed in three sprints with a 100% backlog completion rate. Evaluation through sprint reviews and stakeholder validation demonstrated that the dashboard successfully accommodated changing requirements and supported a more systematic and integrated monitoring process. The resulting dashboard consists of three main reports, namely an integrated operational report and weekly reports for each company. The findings indicate the effectiveness of the Scrum approach in developing an adaptive BI system.
Analisis Sentimen dan Pemodelan Topik Terhadap Ulasan Aplikasi Mobile JKN Menggunakan SVM dan LDA Nursanti Novi Arisa; Kevin Himawan
Journal of Information System Research (JOSH) Vol 7 No 1 (2025): October 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i1.8029

Abstract

In 2024, the number of internet users in Indonesia reached 221.56 million, accounting for 79.5% of the population an increase of 1.4% from the previous year (APJII). This growth has driven digital transformation in various sectors, including healthcare. To support this, the government launched the Mobile JKN app as part of the digitalization of the National Health Insurance (JKN) program, aimed at expanding access to services, especially in remote areas. Despite over 50 million downloads, the app still faces technical issues such as difficulties with registration, verification, and frequent updates that disrupt user experience. This study analyzes user complaints using sentiment analysis with the Support Vector Machine (SVM) algorithm and topic modeling via Latent Dirichlet Allocation (LDA). A total of 285,661 reviews from the Google Play Store (June 2016–December 2024) were collected and pre-processed. Of these, 181,657 reviews were analyzed—80% used for training (145,615) and 20% for testing (36,042). The SVM model showed strong performance, achieving 90% accuracy, 90% precision, 89% recall, and an F1-score of 89%. It classified 12,965 reviews as positive and 23,077 as negative. Topic modeling of negative reviews revealed five key themes with a coherence score of 0.5064: app usage, login and registration, data verification, online services and data changes, and app updates. Further analysis of version 4.12.0 informed improvement recommendations, particularly regarding phone number verification, login, and facial recognition issues.
Co-Authors Indrayanto Dwicaksono Is Riosena Nur Soffa Kevin Himawan