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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 870 Documents
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Pemanfaataan Penginderaan Jauh dan Sistem Informasi Geografis Berbasis Transformasi Spektral Indeks Vegetasi Untuk Estimasi Produksi Tanaman Teh Natzratul Zahira; Muhammad Ismail; Wikan Jaya Prihantarto; Triyatno Triyatno; Dilla Angraina
Journal of Information System Research (JOSH) Vol 7 No 4 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i4.10315

Abstract

Tea is a leading plantation commodity in Indonesia, but production estimation through manual field surveys has limitations in terms of cost, time, and accuracy, and is less able to describe spatial variations between blocks representatively. This study aims to estimate tea production in Afdeling B PTPN IV Danau Kembar using PlanetScope imagery with the Transformed Vegetation Index (TVI) approach, and to test the accuracy of the estimation compared to actual production data. The methods used include image pre-processing (radiometric calibration), TVI calculation, field data collection through sample plots, simple linear regression analysis, and production estimation at the block level using zonal aggregation and Jenks Natural Breaks classification. The results show that the TVI value ranges from 0.79–1.13 with a productive land area reaching 240 ha (84.21%) of the total 285 ha. The regression analysis yielded a coefficient of determination (R²) of 0.7933 with the equation y = 1388.8x – 1458.4, while the model validation results showed an R² of 0.7005. The total estimated tea shoot production in Afdeling B reached 119.5 tons, with the highest production in block 24 (8.80 tons) and the lowest in block 46 (0.48 tons). Although the model displayed good accuracy results at the sample scale, the resulting estimate was less precise compared to company data due to differences, namely the temporality of the data. However, the approach using TVI based on PlanetScope imagery has proven to have advantages in presenting spatial information on the distribution of tea plant productivity per block that cannot be obtained from conventional methods, thus supporting more efficient and sustainable spatial data-based tea plantation management. The contribution of this research is to provide a TVI- and PlanetScope-based tea production estimation model applied to the highland tea plantations of West Sumatra, while also generating a spatial productivity distribution map per block as a basis for more practical plantation management decision-making.
Optimasi Rute Distribusi Spare Part Motor Menggunakan Ant Colony Optimization untuk Efisiensi Jarak Tempuh Nani Agustina; Entin Sutinah; Martini Martini
Journal of Information System Research (JOSH) Vol 7 No 4 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i4.9620

Abstract

Logistics efficiency in goods distribution has become a major challenge for distribution companies in reducing operational costs. This study aims to optimize the distribution routes of motorcycle spare parts at PD. Tri Jaya Motor using the Ant Colony Optimization (ACO) algorithm. The main issue faced by the company is the use of unsystematic manual route determination, which results in inefficient travel distances. The methodology employed in this research involves modeling the Traveling Salesman Problem (TSP) across six distribution points using parameters of alpha = 1, beta = 1, and rho = 0.1. The simulation results demonstrate that the ACO algorithm successfully identified the optimal route with a total distance of 206.6 km, resulting in significant savings compared to the initial route. This study contributes by providing a metaheuristic-based decision-making strategy for medium-scale distribution systems.
Prediksi Promosi Pegawai dengan Stacking Ensemble dengan SMOTE-ENN dan SHAP Andri Yudha Pratama; Arief Hermawan; Donny Avianto
Journal of Information System Research (JOSH) Vol 7 No 4 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i4.9879

Abstract

The paradigm of human resource management in the digital era demands an objective and data-driven employee promotion process. However, the extreme class imbalance (ratio 10.74:1) has the potential to introduce bias against minority groups that deserve promotion. This study proposes a stacking ensemble classification framework consisting of Random Forest, XGBoost, and LightGBM as base learners and Logistic Regression as a meta-learner, with the integration of SMOTE-ENN and two-level SHAP interpretability. This study shows that the application of SMOTE-ENN before cross-validation can result in a biassed performance estimate of up to +110% on the F1-Score; thus, the use of imblearn.Pipeline is proposed, which restricts resampling only to the training fold. Based on the evaluation using 10-fold stratified cross-validation free from data leakage, the stacking ensemble model achieved an accuracy of 0.9022, precision of 0.4342, recall of 0.4889, F1-score of 0.4598, and AUC-ROC of 0.8053. Although it did not achieve the highest F1 score, this model attained the best recall value among competitive models, making it relevant for contexts sensitive to false negative errors. SHAP analysis identifies avg_training_score, age, and performance_index as the main determinants of promotion decisions. The proposed framework provides a methodological contribution to model evaluation on imbalanced data while offering a more transparent and accountable decision support system to support the implementation of meritocracy in both government and corporate organisations.
Integrasi Quick Response Dinamis dan Algoritma Geofencing pada Sistem Presensi Terpadu untuk Validasi Kehadiran Tiara Imanuela Putri Tehamen; Lisye Gladis Mengi; Tanjung Maharani Teksar; Herry Setiawan Langi; Steven Johny Runtuwene
Journal of Information System Research (JOSH) Vol 7 No 4 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i4.10003

Abstract

The integrity of student attendance data in educational settings often faces serious challenges, particularly related to manipulation loopholes such as the use of absenteeism and inconsistencies in attendance locations. In a case study at SMK Kristen Imanuel Laikit, the existing attendance recording method was not fully capable of simultaneously validating location points and identities, potentially triggering administrative fraud. Responding to these problems, this study aims to propose the integration of geofencing and dynamic quick response (QR) algorithms into an integrated attendance system to minimize attendance validation manipulation. Technically, the system generates dynamic QR tokens whose patterns are continuously regenerated every few seconds through a time-based token generation mechanism and a random string combination. This automatic update is strictly designed to prevent reuse of authentication tokens, including the practice of sharing QR code screenshots with other students. Meanwhile, the geofencing mechanism is implemented through the Haversine algorithm calculation to validate the user's device position based on the school's specified radius tolerance limit. This system also integrates an autonomous discipline management mechanism that processes student attendance status based on the timeliness of attendance. Based on software testing results, the system is capable of precisely executing authentication, location validation, QR token synchronization, access rights control, and invalid attendance rejection in an integrated manner. This research contributes to the development of a secure attendance system that is free from location manipulation and QR code duplication. Furthermore, this system is designed to independently manage student discipline to create transparent and accountable academic governance.
Optimasi Pengenalan Plat Nomor Kendaraan Berbasis Mobile Menggunakan Google ML Kit: Implementasi dan Analisis Akurasi Mohamamd Hanan Gaffari; Chairani Fauzi
Journal of Information System Research (JOSH) Vol 7 No 4 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i4.10159

Abstract

Manual vehicle identification in parking systems and access control is still slow, error-prone, and inefficient, while the number of vehicles in Indonesia continues to increase. This study aims to design and implement a mobile-based vehicle license plate recognition system using Google ML Kit Text Recognition on the Android platform. Google ML Kit is used as an OCR SDK with a pre-trained model; therefore, this study does not retrain a CNN model and does not claim to develop a deep learning architecture from scratch. The contribution of this study is to provide an Android-based license plate OCR implementation workflow, integrate recognition results with a Supabase database, and evaluate character-level accuracy on license plate images. The system workflow consists of license plate image acquisition, lightweight client-side preprocessing, on-device text recognition, OCR result normalization, database matching, and character-level accuracy evaluation. The test data consist of 25 license plate images collected from a public Kaggle dataset. The results show that 20 out of 25 plates were read perfectly, while five samples contained partial recognition errors. Of 201 tested characters, 194 were correctly recognized, resulting in an average character recognition accuracy of 96.5%. Recognition errors were mainly affected by similar character shapes, image quality, light reflection, capture angle, and blur. These results indicate that Google ML Kit can be applied as a practical mobile OCR solution, although the validation remains limited by the small number and limited variation of test samples.
Combining Generative AI and Scheduling Algorithms for Personalized Learning Powered by TELISIK Artamananda Artamananda; Eogenie Lakilaki; Syakillah Nachwa; Muhammad Gilang Ramadhan; Amaliah Sobli; Annisa Fatihah Salsabila; Bagus Ramadhan
Journal of Information System Research (JOSH) Vol 7 No 4 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i4.10356

Abstract

The increasing competitiveness of the Computer Based Written Examination for National Selection Based on Test (UTBK-SNBT) necessitates adaptive and sustainable learning support. This study proposes a web based intelligent learning platform that integrates Artificial Intelligence (AI) to facilitate examination preparation through Automatic Question Generation (AQG), an AI Tutor, automated solution generation, and a scheduler driven question generation mechanism. The platform adopts a client server architecture and employs a Large Language Model (LLM) to generate examination questions tailored to the characteristics of each UTBK-SNBT subtest. The system was evaluated from two perspectives: the learning performance of 30 students across seven UTBK-SNBT subtests and the quality of 1.663 AI generated questions using a Jaccard Similarity based deduplication approach. The results demonstrate that the proposed platform provides continuous and personalised practice beyond conventional static question banks. Acceptable question generation rates reached 96.3% for Quantitative Knowledge, 92.9% for Mathematical Reasoning, and 91.4% for General Knowledge and Comprehension, whereas reading-intensive subtests exhibited higher duplication rates due to the limitations of lexical similarity measurement. Overall, the findings confirm the feasibility of the proposed platform as an intelligent and scalable solution for UTBK-SNBT preparation, while highlighting semantic similarity techniques as a promising direction for improving the quality of text-based question generation.
Improved Genetic Algorithm with Adaptive Operators and Elitism for Random Forest Feature Selection in Heart Disease Classification Rahma Dhea Safitri; Solikhun Solikhun; Timbo Faritcan P. Siallagan
Journal of Information System Research (JOSH) Vol 7 No 4 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i4.10372

Abstract

Heart disease is one of the leading causes of mortality worldwide, and accurate prediction models are essential to support early diagnosis. However, conventional Random Forest classifiers generally utilize all available features, although not all features contribute equally to classification performance, resulting in unnecessary model complexity. This study proposes an Improved Genetic Algorithm (IGA) that extends the conventional Genetic Algorithm through elitism, adaptive crossover, and adaptive mutation operators to optimize feature selection for Random Forest-based heart disease classification. The proposed method was evaluated using the Cardiovascular Disease Dataset from Kaggle, which consisting of 1,000 records and 14 variables, where 12 predictor features were used for model development. The experimental procedure included data preprocessing, train-test splitting, class imbalance handling using SMOTE on the training set, feature normalization, Random Forest modeling, feature selection using the proposed IGA, and model evaluation. The proposed IGA selected six important features slope, chestpain, restingBP, restingelectro, oldpeak, and gender. The optimized Random Forest model achieved an accuracy of 99.50%, precision of 99.15%, recall of 100.00%, F1-score of 99.57%, and AUC-ROC of 99.90%. These findings indicate that feature selection can simplify the model without compromising classification performance, making the Random Forest + IGA approach a viable alternative for developing more efficient heart disease prediction models.
Pengembangan Aplikasi Web Smart Waste Management Berbasis IoT dan Dashboard Spasial Untuk Optimasi Rute Pengangkutan Sampah Muhammad Ammar Fariz Baihaqi; Kurniawan Dwi Irianto
Journal of Information System Research (JOSH) Vol 7 No 4 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i4.10425

Abstract

Increasing urban waste creates logistical hurdles for the Sleman Waste Services (UPTD). Static collection schedules without actual capacity data lead to operational inefficiencies. This study designs an Internet of Things (IoT)-based Smart Waste Management system integrated with a spatial dashboard to optimize collection efficiency. System development adopted the Waterfall method, encompassing hardware (ESP32 and HC-SR04) and web interfaces. Telemetry transmission utilizes a REST API for real-time visualization. Test results indicated a sensor accuracy of 97.2% with an average network latency of 7.46 seconds. User Acceptance Test (UAT) and black-box tests recorded success rates exceeding 95%. Operationally, the smart routing implementation successfully reduced the daily fleet travel distance from 25 km to 12 km (52% fuel efficiency). The main contribution is twofold. Scientifically, it proposes an integrated architecture combining IoT telemetry and spatial smart routing within a single platform. Practically, the system is proven to reduce fleet travel distance and achieve 52% fuel savings, providing an effective solution to prevent waste overload and realize efficient urban sanitation logistics.
Pengembangan Sistem Informasi Early Warning System Banjir Berbasis Internet of Things Menggunakan REST API dan WhatsApp Gateway Afriza Akhid Khoiruddin
Journal of Information System Research (JOSH) Vol 7 No 4 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i4.10453

Abstract

Flooding is one of the most frequent natural disasters caused by increasing rainfall intensity and overflowing rivers, highlighting the need for a monitoring system capable of providing timely and accurate information. This study aims to develop an Internet of Things (IoT)-based Early Warning System (EWS) for flood monitoring that enables real-time water level observation and automatic early warning notifications. The proposed method involves the design of a hardware system using the A02YYUW ultrasonic sensor and ESP32 microcontroller, the development of a REST API-based backend using PHP Native and MySQL, and a web-based monitoring dashboard integrated with the WhatsApp Gateway through the Fonnte API. Water level data collected by the sensor are processed using a .-based classification method to determine three alert levels: NORMAL, ALERT, and DANGER. The processed data are then transmitted to the server in JSON format via a Wi-Fi network. Subsequently, the backend validates the authentication token, stores the data in the database, displays real-time information on the monitoring dashboard, and automatically sends WhatsApp notifications when the water level reaches the ALERT or DANGER status. The experimental results demonstrate that the proposed system operates successfully in an integrated manner, covering sensor data acquisition, data transmission, database storage, dashboard visualization, and automatic notification delivery. The A02YYUW ultrasonic sensor achieved an average measurement error of 0.89%, indicating high measurement accuracy and stability. Therefore, the developed system can serve as an effective solution for real-time flood monitoring and early warning, providing fast, accurate, and easily accessible information to the community. The main contribution of this study is the development of an integrated flood early warning system that combines the waterproof A02YYUW ultrasonic sensor, ESP32 microcontroller, PHP Native-based REST API, web-based monitoring dashboard, token-based authentication, and WhatsApp Gateway into a unified architecture for secure real-time monitoring and early warning dissemination.
Pengembangan Website Responsive Sebagai Portal Informasi Kegiatan dan Berita Himpunan Mahasiswa Menggunakan Metode Research And Development dengan Model Waterfall Satria Cahya Syaputra; Cucut Hariz Pratomo
Journal of Information System Research (JOSH) Vol 7 No 4 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i4.10463

Abstract

The dissemination of activity information and news within Student Associations is often carried out through various separate platforms, resulting in information fragmentation and reducing the effectiveness of information delivery to students. This condition makes it difficult for students to obtain information due to the increased risk of missing important organizational activities and news. Therefore, a centralized information platform is needed to provide integrated information that can be easily accessed through various devices. This research aims to design and develop a responsive website as a centralized portal for Student Association activities and news using the Next.js framework. This study applies the Research and Development (R&D) method with the Waterfall development model, which consists of several stages, namely requirements analysis, system design, implementation, and testing to ensure that the website operates in accordance with user requirements. The system was developed using Next.js as the main framework and Tailwind CSS to support the implementation of Responsive Web Design, enabling the website to be accessed optimally on desktop, tablet, and smartphone devices. This research resulted in a system that successfully integrates news, activities, organizational profiles, and documentation into a single centralized digital platform. System testing was conducted using the Black Box Testing method by evaluating all major functions based on predetermined input and output scenarios without examining the internal structure of the program code. The test results showed that all functions operated in accordance with the specified functional requirements, achieving a 100% success rate across all testing scenarios. The developed website enables students to access centralized information on Student Association activities and news more conveniently through a wide range of devices.

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