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All Journal Majalah Ilmiah Teknologi Elektro
Putu Agus Santika Santika
Universitas Telkom
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy

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Teknik Machine Learning dengan Metode SVM untuk Deteksi Anomali pada Kendala Jaringan FTTH Komang Agus Putra Kardiyasa; Putu Agus Santika Santika
Majalah Ilmiah Teknologi Elektro Vol 24 No 1 (2025): ( Januari - Juni ) Majalah Ilmiah Teknologi Elektro
Publisher : Study Program of Magister Electrical Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MITE.205.v24i01.P07

Abstract

FTTH is a technology that is important in providing high-speed internet services to customers. However, interruptions or anomalies in the FTTH network may cause service interruptions that impact user experience. Anomaly data from FTTH telecommunications networks are collected and processed using preprocessing techniques to prepare data before being used in SVM model training. The training process is carried out by using training data to classify data as normal or anomaly. After the training, an evaluation of the performance of the SVM model was carried out using test data that had never been seen before. The results of the analysis show the ability of the SVM model to detect anomalies in FTTH telecommunication networks with high accuracy and good performance. The conclusion of this study is that machine learning techniques with the SVM method have great potential in anomaly detection in FTTH telecommunication networks.
Co-Authors Komang Agus Putra Kardiyasa