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All Journal Pelita Teknologi : Jurnal Ilmiah Informatika, Arsitektur dan Lingkungan
al fiyan
Universitas Pelita Bangsa
Civil Engineering, Building, Construction & Architecture Computer Science & IT

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Pengujian Akurasi Data Potensi Kepuasan Pelanggan Kereta Commuterline (KRL) Dengan Algoritma C4.5 al fiyan; Muhamad Fatchan; Irfan Afriantoro; Putri Anggun Sari; Endah Yaodah Kodratilah
Jurnal Pelita Teknologi Vol 16 No 1 (2021): Maret 2021
Publisher : DPPM Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (312.129 KB) | DOI: 10.37366/pelitatekno.v16i1.308

Abstract

PT. KAI Commuter line (KRL) is a transportation service provider that was founded in 2008. There are many ways that transportation service companies can win the competition, including by increasing transportation customer satisfaction. This study aims to analyze the potential for KRL customer satisfaction by using data mining techniques with the C4.5 algorithm. The research instrument obtained data in the form of a questionnaire, the attributes of potential customer satisfaction in this study included price, facilities, services, and loyalty. This study uses 2 scales, namely the nominal scale in the form of codes or labels, and the interval scale in the form of weights in the answers to questions. In this study, the results obtained from several attributes produce a cause-and-effect relationship in classifying satisfied and dissatisfied customers. This research is hoped to be able to help the KRL party in increasing customer satisfaction to retain customers and increase the profit of the KRL company. The classification results using the C4.5 algorithm obtained an accuracy of 91.67%, which indicates that the C4.5 algorithm is suitable for measuring the potential for KRL customer satisfaction..
Perbandingan Dalam Memprediksi Penyakit Liver Menggunakan Algoritma Naïve Bayes Dan K-Nearest Neighbor al fiyan; Muhamad Fatchan; Nanang Tedi Kurniadi; Edy Widodo
Jurnal Pelita Teknologi Vol 16 No 1 (2021): Maret 2021
Publisher : DPPM Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (292.728 KB) | DOI: 10.37366/pelitatekno.v16i1.309

Abstract

Along with the rapid development of information technology, and also the increasing need for information in various fields including health sector. Based on data from the World Health Organization (WHO), chronic hepatitis B attacks 300 million people in the world including Southeast Asia and Africa which causes the death of more than 1 million people each year. So far, a lot of data in the hospital has not been used, even though this data can be used to predict liver disease if used. The purpose of this study was to determine the comparison of the accuracy value of the Naïve Bayes algorithm and K-Nearest Neighbor. One of the classifications is to use the Naïve Bayes and K-Nearest Neighbor algorithms and use the Rapid Miner tools in the tests used. The results of this study indicate that the Naïve Bayes algorithm has a higher accuracy rate of 84.00% in diagnosing liver disease compared to the K-Nearest Neighbor algorithm which only gets a value of 80.57%. From this research it can be concluded that the Naïve Bayes algorithm is 3.43% greater than K-Nearest Neighbor.
Co-Authors Edy Widodo Endah Yaodah Kodratilah Irfan Afriantoro Muhamad Fatchan Nanang Tedi Kurniadi Nasution, Annio Indah Lestari