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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 38 Documents
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Search results for , issue "Vol 7 No 2 (2026): January 2026" : 38 Documents clear
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.
Prediksi Harga Penutupan Saham Gojek-Tokopedia Menggunakan Model Hybrid GARCH-LSTM Pramudito, Farhan Wegig; Arianto, Kezia Jazzlyn; Thoyib, Najma Humairoh; Arvintyani, Risquina Angelica; Herlambang, Yudhistira Jalu; Zuhdi, Shaifudin
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.v7i2.8911

Abstract

This study proposes the application of a hybrid GARCH–LSTM model to predict GoTo stock prices in the context of Indonesia's rapidly growing digital economy. GoTo stock prices are characterized by high volatility and a non-linear time series pattern, making them difficult to model using conventional approaches. Daily closing price data from 2022 to November 2025 are transformed into logarithmic returns to meet the stationarity assumption. The GARCH(1,1) model is used to estimate conditional volatility, which represents short-term risk dynamics and the volatility clustering phenomenon. Furthermore, historical returns and conditional volatility are used as additional features in the LSTM model to predict the next period's stock returns, which are then converted back into closing price predictions. The estimation results show that all GARCH parameters are statistically significant, indicating the persistence of volatility in GoTo stock data. Evaluation of the performance of the hybrid model on the test data produces an RMSE value of 3.126, an MAE of 2.245, and a coefficient of determination (R²) of 0.899, indicating that the model is able to represent stock price movement patterns well. These findings indicate that the hybrid GARCH–LSTM approach is effective in modeling stock price dynamics under highly volatile market conditions.
Perancangan Aplikasi Tiket Wisata Air Terjun Berbasis Web Menggunakan Metode Agile Scrum Syarifah, Hafa Leny Tahta; Swastyastu, Cempaka Ananggadipa; Shanty, Ratna Nur Tiara
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.v7i2.8920

Abstract

The rapid advancement of information technology has become a key driver of digital transformation across various sectors, including tourism. The utilization of technology in managing tourist destinations can enhance service efficiency and improve the quality of information provided to visitors. One of the challenges faced by Dlundung Waterfall Tourism is the ticket booking process, which is still carried out manually, resulting in long queues, data recording errors, and the risk of information loss during data recap. These conditions lead to inefficient data management and hinder efforts to improve service quality for tourists. This study aims to design and develop a web-based ticket booking application for Dlundung Waterfall Tourism to assist administrators in automating service processes. In its development, the Agile Scrum methodology is employed, as it can quickly adapt to changing requirements through iterative stages. Additionally, the Laravel framework is chosen as the primary development foundation because it offers a robust architectural structure, strong security features, and convenience in data management and organized feature development. The application includes several main features, such as the home page, online ticket booking, destination information, payment, my tickets, and contact.
Sistem Lingkungan Pintar Solusi Cerdas Pengelolaan Sampah Menggunakan Adaptasi Machine Learning dan Internet of Things Widiyono, Widiyono; Amalia, Nurul; Ismanto, Bambang
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.v7i2.8941

Abstract

Indonesia's waste generation increased from 28.59 million tons per year in 2021 to 34.21 million tons per year in 2024, a 19.67% increase. However, in 2024, only 46.1% of the total waste generation was successfully managed.. This condition highlights the need for a more efficient waste management solution, particularly at Temporary Disposal Sites (TPS), which still rely on manual monitoring and often experience waste overflow. This study aims to develop a Smart Environmental System based on the Internet of Things (IoT) and Machine Learning to monitor waste levels in real time and predict disposal patterns using historical data. The research uses a qualitative approach through field observations, interviews with the Environmental Agency, and literature studies to identify system requirements. System design was carried out using UML diagrams, followed by the development of an IoT device using ESP32 and an Android application built with Flutter, integrated with Firebase. The Machine Learning model employs the Random Forest algorithm to classify waste-level conditions. System testing included unit testing, integration testing, performance testing, and user evaluation using the PIECES method. The results show that the Performance, Information, Control, and Efficiency aspects scored above 80%, indicating that the system effectively provides sensor information, ensures data security, and improves operational efficiency. However, the Economic and Service aspects still require optimization, particularly in reducing operational costs and improving system maintenance routines. Overall, the system demonstrates strong potential in supporting smarter, faster, and more efficient waste management, and is suitable for further development.
Pengembangan Aplikasi Notulensi Rapat Berbasis Web Pada Rumah Sakit Menggunakan Motode Waterfall Febri, Febri; Wardani, Muhammad; Astri, Lola Yorita; Surya, Chandy Ophelia; Rofi'i, Imam
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.v7i2.8953

Abstract

Baiturrahim Hospital in Jambi routinely holds coordination and evaluation meetings as part of its efforts to improve the quality of hospital services and management. However, the process of recording meeting minutes is still done manually using separate documents, resulting in delays in report preparation, difficulties in searching meeting archives, and an increased risk of data loss. This study aims to develop a web-based meeting minutes application that can support the process of recording, storing, and presenting meeting reports in an integrated manner. The software development method used is the Waterfall method, which includes the stages of needs analysis, system design, implementation, testing, and maintenance. Data collection was carried out through workflow observations and interviews with administrative staff and related units at Baiturrahim Hospital in Jambi. The results of functional testing indicate that the application can accelerate the process of creating meeting minutes, improve the ease of searching and managing meeting archives, and assist in monitoring the follow-up of meeting results in a more structured manner. Thus, this web-based meeting minutes application is considered suitable for use as a supporting tool for meeting administration and has the potential to increase the effectiveness and efficiency of meeting management at Baiturrahim Hospital in Jambi.

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