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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 795 Documents
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Sistem Monitoring Volume Sampah Medis Berdasarkan Ketinggian Menggunakan Sensor Ultrasonik Berbasis IoT dengan Notifikasi Telegram Ramdani, Fauzi; Nuroji, Nuroji
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.v7i2.9236

Abstract

This research aims to develop an Internet of Things (IoT)-based medical waste height monitoring system as an effort to overcome the problem of manual medical waste monitoring in small-scale health facilities. This condition has the potential to cause delays in waste transportation, thereby increasing health risks and environmental pollution. The developed system utilizes a NodeMCU ESP8266 microcontroller as the main control unit, an HC-SR04 ultrasonic sensor to measure the height of medical waste piles, and a 16x2 I2C LCD as a real-time information display medium. If the waste height reaches a predetermined limit, the system automatically sends a warning notification via the Telegram application. The research method used is Research and Development (R&D) with an experimental approach that includes the stages of problem identification, system design, implementation, and tool testing. The test results show that the ultrasonic sensor has an accuracy level of 98% with an average error difference of ±2 cm, and the Telegram notification delivery time is less than 5 seconds after the threshold is reached. The contribution of this research lies in the design and implementation of a simple, real-time, and easy-to-implement IoT-based medical waste volume monitoring system, so that it can support safer, more effective, and more responsive medical waste management in small-scale health facilities.
Penerapan Random Forest dan Content-Based Filtering pada Alokasi Tenaga Kesehatan Hipertensi Asri, Jefry Sunupurwa; Aryani, Diah; Fannya, Puteri; Dewi, Ratna
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.v7i2.9251

Abstract

Hypertension is a major health issue in DKI Jakarta requiring efficient resource distribution to overcome inter-regional access inequalities. This research aims to design and implement a web-based decision support system (DSS) integrating Geographic Information System (GIS) to optimize health worker allocation and determine hypertension priority areas precisely. The novelty lies in integrating a Random Forest machine learning model to predict service coverage until 2030 with Content-Based Filtering (CBF). The CBF method utilizes intrinsic regional features, including service percentages, geographical locations, and prediction trends, to generate objective health worker quota recommendations. The Random Forest model was validated using 5-Fold Cross Validation with excellent performance, showing an average R² value of 0.86 and an accurate Mean Absolute Error (MAE) of 6.7%. The system is implemented using Streamlit and Folium frameworks for geographical visualization. Research results provide contributions through priority area maps, adaptive health worker quota recommendations, and Mobile Health Clinic route simulations supporting data-driven decision-making. Through this system, policymakers can perform strategic planning to improve hypertension intervention effectiveness in Jakarta. With an integrated predictive and recommendation approach, this study is expected to become a reference in the digital transformation of public health resource allocation more equitably and accurately.
Analisis Komparasi Algoritma Machine Learning Untuk Klasifikasi Kualitas Udara Indoor Berbasis Sensor Low-Cost Prasetyo, Stefanus Eko; Hansen, Irvan; Haeruddin, Haeruddin
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.v7i2.9024

Abstract

Indoor Air Quality (IAQ) has a significant impact on occupants’ health and comfort; however, limitations of conventional monitoring systems and the high cost of commercial devices have hindered the widespread implementation of indoor air quality monitoring. Sensor-based IAQ monitoring using low-cost devices provides an affordable solution; however, the resulting data often exhibit variability and noise, making direct interpretation challenging. This study presents a comparative analysis of several machine learning algorithms for indoor air quality classification using sensor data. The dataset was collected from DHT22 and MQ-135 sensors measuring temperature, humidity, and air pollutant levels, resulting in 18,000 samples evenly distributed across three air quality classes: Good, Moderate, and Poor. The proposed methodology includes data preprocessing through median imputation and feature standardization, stratified dataset splitting with a ratio of 70% training, 15% validation, and 15% testing data, and model training using four supervised learning algorithms: Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Gaussian Naive Bayes. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. Experimental results indicate that all evaluated models achieved high classification performance, with KNN outperforming other algorithms by achieving an F1-score of 1.00 on the test dataset, while the lowest-performing model still achieved an F1-score above 0.96, indicating a relatively narrow yet consistent performance range among the evaluated algorithms. These findings demonstrate the effectiveness of machine learning approaches for indoor air quality classification using low-cost sensor data under controlled experimental conditions.
Penerapan Digital Forensic Research Workshop Framework pada Layanan Virtual Machine Asruddin, Asruddin; Riadi, Imam; Umar, Rusydi
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.v7i2.9034

Abstract

ICMP flooding is a denial-of-service attack that overwhelms a target with high-rate ICMP packets, degrading service availability. End-to-end network forensic reporting from identification to evidence presentation remains limited. This study applies the Digital Forensic Research Workshop (DFRWS) process model - Identification, Preservation, Collection, Examination, Analysis, and Presentation - to investigate ICMP flooding in a controlled virtualized environment. Primary artifacts consist of baseline PCAPs (5 runs) and attack PCAPs (5 runs) analyzed using capinfos to extract capture duration (T), packet count (N), average et rate (pps), and file size. Results indicate that the baseline traffic (normal system activity in the VM laboratory) at 9 pps over 58.91 s with approximately 66 kB file size, while attack traffic reaches 2,000 pps over 6.39 s with an average file size of approximately 18.2 MB. Comparison of both conditions yields a packet-rate amplification of F = 2000/9 = 222× and a file-size increase of approximately 280× (18.2 MB versus 66 kB). The extreme pps spike observed during the attack condition reflects a volumetric attack pattern that operationally correlates with resource exhaustion and reduced service availability, indicating that the PCAP artifacts support not only statistical anomaly detection but also event-level evidence of a denial-of-service incident. All attack runs exceed 1,000 pps (5/5; 100%), and all baseline runs remain stable at 9 pps (5/5; 100% [1]), indicating consistent volumetric evidence. Preservation procedures using read-only storage and SHA-256 hashing ensure artifact integrity and traceability, thereby supporting the admissibility of the PCAPs as valid digital evidence in controlled virtual machine experiments.
Integrasi Feature Engineering dan SMOTE pada Algoritma Random Forest untuk Prediksi Kerusakan Chip RFID di Industri Sel Surya Haeruddin, Haeruddin; Winata, Franklin; Tresnawan, Muhammad Ilham Ashiddiq; Wijaya, Gautama; Wijayanto Aripradono, Heru
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.v7i2.9038

Abstract

The electronics industry, particularly solar cell manufacturing, demands production processes that are fast, precise, and supported by high data integrity. One critical component in the production flow is the chip embedded in the flower basket, which functions to store and transmit data through an RFID system. Damage to the chip can lead to information loss, tag reading failures, and disruptions in production efficiency and continuity. This study aims to predict chip status, classified as either normal or damaged, based on various process parameters, including immersion temperature, ambient humidity, process pressure, machine vibration, drying speed, heating and cooling duration, firing temperature, usage frequency, and RFID reading conditions. A feature engineering approach is applied to construct more representative derived features, while SMOTE is utilized to address class imbalance in the dataset. This study focuses on developing a predictive model using the Random Forest method to identify the most influential process variables related to chip damage risk. The data used in this study are obtained from historical production process records of a solar cell manufacturing plant. The results indicate that combinations of multiple process parameters significantly contribute to the potential risk of chip damage, and the Random Forest model demonstrates good predictive performance in classifying chip conditions. These findings suggest that the proposed model can serve as an early warning system to detect chip damage risks before they impact production processes. With proper implementation, the predictive model is expected to support preventive actions, enhance data integrity, and minimize disruptions in the solar cell manufacturing workflow.

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