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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 811 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.
Implementasi Algoritma Kriptografi RSA untuk Keamanan Transmisi Data pada Sistem Monitoring Energi Listrik Berbasis IoT Rajawali Rajawali; Syamsul Bahri; Kasliono Kasliono
Journal of Information System Research (JOSH) Vol 7 No 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i6.8232

Abstract

Data security is a crucial issue in Internet of Things (IoT) systems used to monitor electricity consumption. This study aims to enhance the security of data transmission in an IoT-based electricity monitoring system by implementing the Rivest–Shamir–Adleman (RSA) cryptographic algorithm. Data from the PZEM-004T sensor is encrypted using the RSA public key and verified with a digital signature before being transmitted to the server. The system was tested under two conditions: without encryption and with RSA encryption, including a simulated ARP spoofing attack using Ettercap. The results show that the system successfully rejected manipulated data, with a packet loss rate of 2.08%, which is categorized as “very good” based on the TIPHON standard, and achieved a throughput of approximately 9.88 bit/s. The implementation of RSA proved effective in maintaining data integrity and authenticity, thereby improving the reliability of the IoT-based electricity monitoring system.
Implementasi Model Deep Learning MobileNetV2 untuk Klasifikasi Citra Melanoma Berbasis Web Deva Safara Alfan; Intan Kumalasari
Journal of Information System Research (JOSH) Vol 7 No 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i6.8848

Abstract

Melanoma is one of the most aggressive types of skin cancer with a high mortality rate if not detected at an early stage. In primary healthcare facilities, the lack of dermoscopy equipment causes examinations to rely solely on visual assessment, which may lead to diagnostic errors, particularly false negatives. This study aims to develop a web-based early melanoma detection system as a tool to assist initial screening. The proposed method implements a deep learning model based on the MobileNetV2 architecture using a transfer learning approach with pre-trained ImageNet weights. The dataset used in this study consists of melanoma and notmelanoma images from HAM10000, while the nonskin class is obtained from CIFAR-10 to help the model distinguish between skin lesion images and non-skin images. The dataset is divided into 70% training data, 20% validation data, and 10% testing data. Evaluation results show that the model achieves an accuracy of 90% in multiclass classification, while binary evaluation focusing on melanoma detection yields an accuracy of 90.48%, precision of 81.75%, recall of 91.96%, and an F1-score of 86.50% on the test data. The model is then implemented in a web-based system capable of displaying skin lesion classification results along with a confidence score in real time. The findings indicate that the developed system can perform automated image analysis and has the potential to be used as a supporting tool for early melanoma screening.
Implementasi Metode SAW pada Sistem Seleksi Siswa Baru Berbasis Web Nabil Ahyan Annakhief; Sri Lestari
Journal of Information System Research (JOSH) Vol 7 No 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i6.9352

Abstract

New student admission is a crucial process in educational institution management because it determines the quality of accepted students. The Mathlaul Anwar Foundation offers several selection pathways: scholarships, report card grades, achievement pathways, and transfer pathways. Currently, the selection process is still conducted manually, resulting in various problems such as delays in data processing, potential calculation errors, lack of objectivity, and low transparency of selection results. This research aims to develop a web-based New Student Selection System using the Simple Additive Weighting (SAW) method as a decision support system to assist in the ranking process and determine student graduation objectively and measurably. The research methods used include observation, interviews, and documentation. The system development utilizes the Waterfall model, which consists of the stages of needs analysis, design, implementation, testing, and maintenance. The implementation results show that the system is able to reduce the selection process time from an average of 5 days to 2 days (a time efficiency of 60%). The process of calculating grades and ranking, which was previously done manually for approximately 120 minutes for 100 applicants, can be accelerated to approximately 15 minutes using the system (an efficiency increase of 87.5%). System testing using the Black Box method on 20 test scenarios showed a 100% functional success rate according to user requirements. In addition, the results of the SAW method calculation validation showed 100% accuracy compared to manual calculations. Thus, the application of the SAW method in the web-based new student selection system has been proven to be able to increase the efficiency, accuracy, objectivity, and transparency of the selection process at the Mathlaul Anwar Foundation.
Penerapan Metode Association Rule Mining Menggunakan Algoritma Equivalence Class Transformation Dalam Menganalisis Pola Stok Obat Aniq Astofa
Journal of Information System Research (JOSH) Vol 7 No 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i6.9432

Abstract

Poorly planned drug inventory management often leads to imbalances between patient needs and the availability of medicines in clinics. This issue generally arises because transaction data has not been optimally utilized as a basis for decision-making. The purpose of this study is to identify patterns of drug associations by applying Association Rule Mining techniques using the Equivalence Class Transformation (ECLAT) algorithm. The research adopts a quantitative approach, utilizing one year of drug transaction data. The analysis reveals several combinations of medicines that are frequently prescribed together by healthcare providers. These association patterns provide valuable insights into prescribing tendencies within the clinic. By understanding the most common combinations, managers can plan drug procurement more accurately and efficiently. The information obtained not only helps anticipate the risk of stock shortages but also prevents excessive inventory that could result in waste. Thus, the application of the ECLAT algorithm proves effective in enhancing drug inventory management. Furthermore, the findings of this study can serve as a foundation for developing more efficient procurement strategies, ultimately improving the quality of healthcare services in clinics. Overall, leveraging transaction data through Association Rule Mining contributes significantly to evidence-based decision-making. This demonstrates that integrating data analysis techniques with inventory management can create a healthcare system that is more responsive, efficient, and patient-centered.
Evaluation of Service Quality Gaps in Pos Express Services Using the SERVQUAL Method Muhamad Alif Fitrah Adriansyah; Rahayu Amalia; Ari Muzakir
Journal of Information System Research (JOSH) Vol 7 No 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i6.9466

Abstract

This study aims to analyze the service quality of Pos Express in South Sumatra by applying the SERVQUAL method to identify gaps between customer expectations and perceptions. A quantitative approach was employed by distributing structured questionnaires to 120 respondents selected through purposive sampling. The measurement instrument was developed based on five SERVQUAL dimensions: tangibles, reliability, responsiveness, assurance, and empathy. The results indicate that customer expectations were consistently higher than perceived service performance across all dimensions. The largest negative gap values were found in the responsiveness (-0.73) and reliability (-0.72) dimensions, indicating weaknesses in service response time, complaint handling, delivery punctuality, and information accuracy. Meanwhile, the empathy dimension recorded the smallest gap (-0.29), suggesting relatively positive interpersonal interactions between staff and customers. To support data processing and analysis, a web-based evaluation system was developed to automate SERVQUAL calculations and reporting. The system facilitated efficient data management and improved the accuracy of service quality analysis. Overall, the findings highlight the need for service improvement, particularly in enhancing operational reliability and responsiveness. This study provides empirical evidence to support service quality management and decision-making in regional postal services.

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