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All Journal Prosiding National Conference for Community Service Project JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH)
Hansen, Irvan
Unknown Affiliation
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education

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PERANCANGAN DAN IMPLEMENTASI JARINGAN VLAN BERBASIS MIKROTIK DI PT. BATAM CIPTA INDUSTRI Hansen, Irvan
National Conference for Community Service Project (NaCosPro) Vol. 7 No. 01 (2025): The 7th National Conference for Community Service Project 2025
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat Universitas Internasional Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37253/nacospro.v7i01.11159

Abstract

Unstructured computer networks often lead to problems such as IP conflicts, unstable connections, and lack of user segmentation. PT. Batam Cipta Industri experienced these issues due to its conventional system without centralized network management. This community service project involved designing and implementing a VLAN-based network using Mikrotik RouterOS. The activity stages included network observation, configuration simulation using pNetLab, and direct implementation in the office. Each entity within the building—BCI, WPRP, and SDCA—was assigned a dedicated VLAN segment with separate DHCP servers and gateways. The implementation results showed successful IP assignment, more stable internet connection, and isolated communication across VLANs. This activity had a direct impact on improving network efficiency and serves as a simple model for SME-scale network design. Further recommendations include integrating firewall systems and bandwidth management tailored to each entity’s needs.
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.
Co-Authors Haeruddin Haeruddin Prasetyo, Stefanus Eko