cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Mesran
Contact Email
jurnal.josh@gmail.com
Phone
+6282161108110
Journal Mail Official
jurnal.josh@gmail.com
Editorial Address
Sekretariat Forum Kerjasama Pendidikan Tinggi (FKPT) Jalan Sisingamangaraja No. 338, Medan, Sumatera Utara
Location
Kota medan,
Sumatera utara
INDONESIA
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 795 Documents
 74   75   76   77   78 
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): October 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): 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.
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): October 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): October 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.
Comparative Analysis of Ahmad-Yusoff and Jaro-Winkler Approaches for Javanese Language Stemming Andira, Aysza Belia Auly; Ahda, Fadhli Almu'iini; Sulistyo, Danang Arbian
Journal of Information System Research (JOSH) Vol 7 No 2 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i2.7879

Abstract

This research presents a performance comparison between two approaches for identifying the base form of affixed Javanese words: the Ahmad Yusoff Sembok (AYS) rule-based stemming algorithm and the Jaro-Winkler (JW) string similarity approach. Javanese was selected as the focus because of its complex morphological structure, encompassing prefixes, suffixes, infixes, and confixes, along with significant speech-level and dialectal variation, which together pose challenges for natural language processing. The dataset comprises 720 manually annotated word lemma pairs. Evaluation was carried out using precision, recall, F1-score, accuracy, and Cohen’s Kappa, complemented by error analysis on over-stemming and under-stemming cases. Results indicate that JW achieves higher overall performance (83.19% accuracy, 83% F1-score) compared to AYS (73.19% accuracy, 73% F1-score), with AYS producing more over-stemming errors (88 cases) and JW showing more under-stemming errors (47 cases). These outcomes suggest that similarity-based approaches are more effective in addressing Javanese morphological complexity, while also contributing a benchmark dataset of manually annotated Javanese word lemma pairs, a comparative evaluation framework between rule-based and similarity-based approaches, and practical insights for the development of stemming tools in regional languages that currently lack NLP resources.
Sistem Pakar Untuk Menentukan Departemen Sesuai Kepribadian Calon Karyawan dengan Menggunakan Metode Forward Chaining Andriyanto, Lely Panca; Wahyu, Meidy Fajar
Journal of Information System Research (JOSH) Vol 7 No 2 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i2.7889

Abstract

Human Resource Management (HRD) is an important aspect in ensuring the success of an organization, where the placement of employees who match their personality and skills is a key factor in increasing productivity and reducing turnover rates. This study aims to build a desktop-based expert system using the Forward Chaining method, where the inference process begins by matching personality trait facts from test results to rules (IF-THEN knowledge base) to produce conclusions in the form of recommendations for the most suitable department. as an objective tool in the process of placing new employees based on personality test results, where the system is designed to match the personality characteristics (Sanguine, Melancholy, Choleric, Phlegmatic) of prospective employees with the specific needs of each department. The results of functional testing and validation show that the system built has an accuracy rate of 92% from 50 employee data. The system built is able to provide employee placement recommendations faster with a time efficiency rate of 93.33%. The implementation of this system is a significant contribution in increasing the effectiveness of HRD decision making through the use of artificial intelligence technology.
Implementasi Metode Long Short-Term Memory (LSTM) untuk Klasifikasi Berita Online Berdasarkan Konten Teks Kusmanto, Indar; KH, Musliadi; Hidayat, Hidayat; Kristian, Kristian
Journal of Information System Research (JOSH) Vol 7 No 2 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i2.8628

Abstract

This study aims to classify Indonesian-language news using the Long Short-Term Memory (LSTM) method and to evaluate its performance through accuracy, precision, recall, and F1-score metrics. The dataset consists of 48,634 news titles collected from various national and regional portals, covering five main categories: finance, travel, health, food, and sports. The research process involves several text preprocessing stages-tokenization, stop-word removal, normalization, and stemming-followed by feature representation using word embedding and the design of the LSTM model architecture. The model's performance is assessed using a confusion matrix along with additional validation through cross-validation to ensure result consistency. The LSTM model demonstrates strong performance, achieving 90% accuracy, 89% precision, 88% recall, and 89% F1-score, indicating its capability to capture semantic patterns and contextual dependencies in textual data effectively. In addition, LSTM outperforms the baseline method with a 6% increase in accuracy, reinforcing its reliability for Indonesian text classification tasks. Overall, the findings confirm that the combination of optimal preprocessing techniques and a well-designed LSTM architecture enhances the performance of the news classification system and offers significant potential for various text analysis applications in the digital information era.
Implementasi YOLO (You Only Look Once) untuk Klasifikasi Kesegaran Daging Ayam Berdasarkan Citra Digital Pasya, Joanna Andini Prabaningrum; Fachrie, Muhammad
Journal of Information System Research (JOSH) Vol 7 No 2 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i2.8709

Abstract

Manual assessment of chicken meat freshness is prone to subjectivity, limited sensory perception, and inconsistent environmental conditions, leading to inaccuracy in freshness determination and potential risks to consumer health and safety. The quality of chicken meat that is not properly maintained can negatively impact consumer health and reduce trust in food businesses. This study aims to develop a chicken meat freshness classification system using the Convolutional Neural Network (CNN) algorithm with the YOLOv8 model approach. The dataset of fresh and non-fresh chicken meat images was obtained through manual documentation and processed using Roboflow platform for augmentation and data splitting. The CNN model was trained using YOLOv8 with a configuration of 50 epochs and an image size 416x416 pixels. The model was then implemented into a web-based application system using the Streamlit framework. The classification result are presented visually (bounding box and class label), along with an automatic conclusion and confidence score that the YOLOv8-based CNN model can accurately classify chicken meat freshness with an accuracy of 98,71%, demonstrating its potential as a rapid and objective food quality assessment tool.
Peramalan Curah Hujan Menggunakan Metode Holt-Winters Exponential Smoothing Putra, Dzulfidho Wijianto; Setiawan, Ahmad Fahrudi; Vendyansyah, Nurlaily
Journal of Information System Research (JOSH) Vol 7 No 2 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i2.8820

Abstract

Rainfall is a crucial climatological parameter for agriculture, tourism, and water resource management. Its seasonal and fluctuating nature requires accurate forecasting methods to capture historical patterns. This study forecasts monthly rainfall using data from Ngaglik, Temas, and Tinjumoyo stations between January 2021 and December 2024, totaling 48 observations. The Holt–Winters Exponential Smoothing Additive method was chosen due to stable annual seasonal patterns. Model accuracy was assessed with Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). Results show varying optimal parameters across stations. Ngaglik achieved the best performance with α = 0, β = 0, γ = 0.81, yielding MAE 64.39 mm and RMSE 90.84 mm. Temas recorded MAE 69.25 mm and RMSE 102.19 mm with γ = 0.78, while Tinjumoyo produced MAE 73.95 mm and RMSE 109.42 mm with γ = 0.56. This study highlights the effectiveness of Holt–Winters Additive forecasting and provides accuracy evaluations to support data-driven decisions in rainfall-dependent sectors.
Sentiment Analysis on X, TikTok, and Instagram on Indonesian Capital relocation using Support Vector Machine Jayanto, Syawalian Rais Dwi; Suprihadi, Suprihadi
Journal of Information System Research (JOSH) Vol 7 No 2 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i2.8839

Abstract

This study examines public sentiment toward Indonesia’s new capital city, Ibu Kota Nusantara (IKN), across three major social media platforms: X, TikTok, and Instagram. The research aims to identify how public perceptions differ across platforms and to understand their implications for policy communication. A total of approximately 6,000 user comments collected up to March 2025 were processed through standard text-mining procedures, including cleaning, tokenization, stop-word removal, and stemming. The text data were converted into numerical features using the Term Frequency–Inverse Document Frequency (TF-IDF) technique and classified using a linear Support Vector Machine (SVM) model. Model evaluation with a 20% hold-out test set yielded an accuracy of 90.23% and a macro F1-score of 0.8905. The analysis shows that overall sentiment toward IKN is predominantly positive, with Instagram and TikTok generating more supportive narratives, while X displays a higher concentration of critical or negative comments. These findings highlight significant platform-specific differences that can inform more effective public communication strategies regarding the IKN project.

Page 76 of 80 | Total Record : 795

 74   75   76   77   78 

Filter by Year

2019 2026


Reset
Filter By Issues
All Issue Vol 7 No 2 (2026): January 2026 Vol 7 No 1 (2025): October 2025 Vol 6 No 4 (2025): July 2025 Vol 6 No 3 (2025): April 2025 Vol 6 No 2 (2025): January 2025 Vol 6 No 1 (2024): Oktober 2024 Vol 5 No 4 (2024): Juli 2024 Vol 5 No 3 (2024): April 2024 Vol 5 No 2 (2024): Januari 2024 Vol 5 No 1 (2023): Oktober 2023 Vol 4 No 4 (2023): Juli 2023 Vol 4 No 3 (2023): April 2023 Vol 4 No 2 (2023): January 2023 Vol 4 No 1 (2022): October 2022 Vol 3 No 4 (2022): Juli 2022 Vol 3 No 3 (2022): April 2022 Vol 3 No 2 (2022): Januari 2022 Vol 3 No 1 (2021): Oktober 2021 Vol 2 No 4 (2021): Juli 2021 Vol 2 No 3 (2021): April 2021 Vol 2 No 2 (2021): Januari 2021 Vol 2 No 1 (2020): Oktober 2020 Vol 1 No 4 (2020): JULI 2020 Vol 1 No 3 (2020): April 2020 Vol 1 No 2 (2020): Januari 2020 Vol 1 No 1 (2019): Oktober 2019 More Issue