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JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH)
Published by Forum Kerjasama Pendidikan TInggi
ISSN : -     EISSN : 2686228X     DOI : -
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
Artikel yang dimuat melalui proses Blind Review oleh Jurnal JOSH, dengan mempertimbangkan antara lain: terpenuhinya persyaratan baku publikasi jurnal, metodologi riset yang digunakan, dan signifikansi kontribusi hasil riset terhadap pengembangan keilmuan bidang teknologi dan informasi. Fokus Journal of Information System Research (JOSH)
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 754 Documents
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Artificial Intelligence Recommendation System for Optimizing Steam Power Plant Heat Rate: A Conceptual Design Ardiansyah, Lulu; Rohayani, Hetty
Journal of Information System Research (JOSH) Vol 7 No 1 (2025): Oktober 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i1.7858

Abstract

Steam power plants are one of the major electricity generation units in many countries around the world. The thermal efficiency of power plants is primarily dependent on decision making by the operator on real time process parameters. This decision-making process currently utilizes human expertise, in conjunction with static setpoints and operating procedures. However, variability in human operator performance and plant operating conditions often leads to non-optimal heat rate values. The purpose of this paper is to develop a conceptual framework for an artificial intelligence-based operator decision-support system for real-time heat rate optimization, integrating Model-Based Design (MBD) and Design Science Research (DSR) principles. The framework presented in this paper is informed by past high efficiency operational experience and machine learning methodology to describe the necessary steps in generating actionable, explainable recommendations for process parameter adjustments. The conceptual framework presented, which incorporates both predictive capabilities as well as domain expertise, is intended to bridge the gap between the development of predictive models and their eventual deployment as prescriptive operational support systems by providing a high-level blueprint of a system design that is expected to lead to more robust and consistent decision making. The key functional components of the framework include data capture, preprocessing, inference modeling and, ultimately, presentation of recommendations on a human-machine interface. An initial, theoretical appraisal of the proposed framework suggests promising potential for improving operational efficiency, reducing fuel consumption, and lowering emissions, and it is expected to serve as a useful reference for ongoing and future development efforts.
Analisis Sentimen dan Pemodelan Topik Terhadap Ulasan Aplikasi Mobile JKN Menggunakan SVM dan LDA Arisa, Nursanti Novi; Himawan, Kevin
Journal of Information System Research (JOSH) Vol 7 No 1 (2025): Oktober 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.
Sistem Pembersih Kaca dengan Mekanisme Gerak Motorik Pada Smart Aquarium Berbasis Internet of Things (IoT) Hidayat, Dody; Ramli, Ramli
Journal of Information System Research (JOSH) Vol 7 No 1 (2025): Oktober 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i1.8409

Abstract

Aquariums are decorative elements that not only serve to beautify a room, but also as artificial habitats for fish and other aquatic creatures. One of the main problems in maintaining an aquarium is keeping the glass clean from algae and dirt that can interfere with the aesthetics and comfort of the creatures inside. Previous research has focused on Internet of Things (IoT)-based water control and lighting systems, but has not yet covered the aspect of automatic glass cleaning. Manual cleaning requires time and effort, and if not done regularly, can lead to a decline in the quality of the aquarium environment. This study aims to design and develop a glass cleaning system with a motorized mechanism for an IoT-based smart aquarium. The system is designed to operate automatically or via remote control using a smartphone. The cleaning mechanism is activated based on the detection of dirt through a turbidity sensor that measures the level of clarity or turbidity of the water due to algae build up. The turbidity sensor is also used as the main trigger for glass cleaning. The hardware used includes a drive motor (stepper), turbidity sensor, and NodeMCU ESP32 microcontroller connected to the Blynk Cloud platform for control and monitoring via a mobile application. The results of this study are a prototype of an efficient, responsive, and easy-to-operate IoT-based aquarium glass cleaning system that can improve the convenience of aquarium maintenance automatically and in real-time.
Implementasi K-Means Clustering Berbasis RapidMiner untuk Optimalisasi Segmentasi Penjualan Produk dalam Meningkatkan Efektivitas Strategi Pemasaran Butsianto, Sufajar; Siswandi, Arif
Journal of Information System Research (JOSH) Vol 7 No 1 (2025): Oktober 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i1.8439

Abstract

The Indonesian electronic retail industry is experiencing rapid growth along with digital transformation. However, available sales data is often only stored as transaction records without further analysis, so it has not been optimally utilized for marketing decision making or customer segmentation. This study aims to implement the RapidMiner-based K-Means Clustering algorithm to analyze segmentation patterns of electronic products at XYZ Store. The dataset used includes the variables Transaction_ID, Product_ID, Product_Name, Category, Quantity, Unit_Price, Revenue, and Recency. The research stages include data collection, preprocessing (filtering, aggregation, and Z-Score normalization), K-Means application, and interpretation of clustering results. Determination of the number of clusters in this study uses the Elbow Method, which shows an optimal point at K = 3, so that number of clusters is chosen for the data grouping process. Based on the results of the application of the K-Means algorithm with the three clusters, the following are obtained: (1) Cluster 0 (High Sales & High Revenue) consisting of Smartphones, Laptops, and Tablets as superior products with a contribution of almost 60% of total revenue; (2) Cluster 1 (Medium Sales & Moderate Revenue) includes Televisions, Refrigerators, and Smartwatches with a stable contribution of around 27%; and (3) Cluster 2 (Low Sales & Low Revenue) contains Washing Machines, Speakers, Headphones, and Printers with a low contribution of only 14%. These findings provide a strategic basis for management in making business decisions, such as procurement priorities, seasonal promotions, product bundling, and clearance strategies. This study proves that the application of data mining with K-Means Clustering is effective in increasing operational efficiency and supporting the competitiveness of the electronics retail business in Indonesia.

Page 76 of 76 | Total Record : 754

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