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All Journal EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi Jurnal Dakwah Risalah Jurnal Komputasi SENSITEK INOVTEK Polbeng - Seri Informatika
Erna Zuni Astuti
Universitas Dian Nuswantoro
Computer Science & IT Education

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Journal : INOVTEK Polbeng - Seri Informatika

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Application of Random Forest Method for Television Malfunction Prediction Elfira Aulia Septrian; Erna Zuni Astuti
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 1 (2025): Maret
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/ymcj1j22

Abstract

 In repairing a television (TV), it is necessary to understand the symptoms experienced by the TV. Therefore, technicians need to conduct an initial analysis of the causes of these symptoms. Analysis of the causes of TV damage can be predicted using a technological approach, one of which is by using an expert system. This study will focus on developing an expert system to predict the causes of TV damage. This study will apply the Random Forest method to predict TV damage based on historical datasets obtained from company X. Company X is a company engaged in the repair of electronic devices, one of which is TV. The data obtained will be used as training data to create a model that can predict the causes of TV damage. Then the experiment was carried out with a quantitative approach with experiments to optimise the model in increasing prediction accuracy. The model was evaluated using accuracy metrics. The results of the study showed that Random Forest has very good performance in classifying the causes of TV damage with a high level of accuracy reaching 100%. However, this study is only limited to certain historical data and does not consider external factors that influence damage to the TV.
Co-Authors Angga Adetiya Arni Ernawati Benedictta Dinda Permatasari Elfira Aulia Septrian Erlin Dolphina Hanny Haryanto Jouns Candy Felice Lang Muljono, - Rustono - Setia Astuti Surya Tarmiandi Teguh Hartono Patriantoro