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Technology Sciences Insights Journal
Published by MID Publisher International
ISSN : -     EISSN : 30636523     DOI : -
Core Subject : Science, Engineering,
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
Technology Sciences Insights Journal (TSIJ) is a distinguished peer-reviewed publication aimed at fostering advancements in the dynamic field of technology sciences. TSIJ provides an inclusive platform for scholars, researchers, industry practitioners, and policymakers to share their original research findings, transformative ideas, and novel technological innovations. By facilitating the exchange of knowledge and expertise across a wide range of technology-related disciplines, TSIJ strives to push the boundaries of technological possibilities and contribute to global progress and sustainable development.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 22 Documents
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Penerapan Algoritma K-Nearest Neighbor (K-NN) untuk Klasifikasi Status Monitoring Automatic Pump Water Machine Studi Kasus: Industri Manufaktur Indra, Indra Mora; Adlian, Adlian Jefiza
Technology Sciences Insights Journal Vol. 2 No. 2 (2025): Technology Sciences Insights Journal
Publisher : MID Publisher International

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60036/zb0dph85

Abstract

In the modern industrial world, real-time monitoring of system conditions is  crucial to maintain efficiency and prevent equipment damage. This research aims to classify industrial system conditions based on sensor data using the K Nearest Neighbors (KNN) algorithm. The data used consists of four main  parameters namely pressure, flow rate, voltage, and engine speed (RPM),  which are then classified into three conditions: Alert, Critical, and Normal.  Preprocessing is done with Min-Max normalization and division of data into  training and test data. The evaluation results show that the KNN method is  able to achieve an accuracy of 58% with a mean squared error (MSE) value of 1.06 and an average cross-validation accuracy of 64%. These results show that  KNN is effective enough to be used as an initial method for industrial system  condition detection, although the classification performance for the Critical  category can still be improved. 
Industrial Equipment Monitoring Dataset for Predictive Maintenance Analysis Pradana, Kreshna Lucky; Jefiza, Adlian
Technology Sciences Insights Journal Vol. 2 No. 2 (2025): Technology Sciences Insights Journal
Publisher : MID Publisher International

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60036/t9mr4w17

Abstract

This study develops and evaluates a Support Vector Machine (SVM) model using a Radial Basis Function (RBF) kernel to detect faulty conditions in systems based on sensor data (temperature, pressure, vibration, humidity). The data is processed through normalization and split into training and testing sets. The evaluation results show an overall model accuracy of 0.93. The model is highly effective in identifying normal conditions (precision 0.93, recall 1.00), but less optimal in detecting faulty conditions (precision 0.96, recall 0.30), indicating a high number of false negatives and a low F1-score (0.45) for this class. The ROC AUC score of 0.892 indicates good overall discriminative ability. This performance gap is likely due to class imbalance. Enhancing faulty detection through class imbalance handling or further model optimization is recommended for critical applications.

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