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All Journal Information Management For Educators And Professionals (IMBI) Jurnal Informasi dan Komputer JURSIMA (Jurnal Sistem Informasi dan Manajemen) Jurnal Informatika Teknologi dan Sains (Jinteks) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Journal of Artificial Intelligence and Engineering Applications (JAIEA) JURSIMA
Arif Rinaldi Dikananda
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Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Library & Information Science Mathematics Social Sciences Other

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Sales Data Analysis using Linear Regression Algorithm on Raw Water Sales Rohayati, Eti; Martanto; Arif Rinaldi Dikananda; Dede Rohman
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.809

Abstract

This study aims to assess the effectiveness of linear regression algorithm in predicting raw water demand by considering customer transaction data, raw water volume, and seasonal variables. The method used is Knowledge Discovery in Databases (KDD), including data selection, preprocessing, transformation, data mining, and result evaluation. The dataset is divided 80% for training and 20% for testing. The analysis results show that the linear regression model has a coefficient of determination (R²) of 0.77, which means that the model can explain 77% of the data variability. The prediction error value is low, with Mean Absolute Error (MAE) 0.06, Mean Squared Error (MSE) 0.01, and Root Mean Squared Error (RMSE) 0.08, indicating good accuracy. In the comparison between actual and predicted values, for actual data of 7,000 liters, the model predicts 7,984.70 liters. The variable number of customer transactions has the greatest influence on raw water demand, with a coefficient of 16,940.46, while seasonal factors have less influence. Based on these findings, it can be concluded that the linear regression algorithm is effective in predicting raw water demand, however further development is required to improve accuracy at extreme values, by adding variables or using more complex algorithms.
Co-Authors Ade Irma Purnamasari Ahmad Faqih Ahmad Rifa’i Aria Pratama Candra, Candra Rahmawati Dadang Sudrajat Dede Rohman Dyana Fitri Ega Salsa Nugraha Eka Wisnu Ramadhona Fahmi Naufal Fatihanursari Dikananda Gifthera Dwilestari Hanifah Nur Aulia Ica, Ica Pandia Irfan Ali Laylatunna'imah Martanto Moch Feri Izulhaq Mulyawan Mulyawan Mulyawan Musofi Musofi Nana Suarna nur syarief abdullah Nurjana Adi Wijaya Odi Nurdiawan Rohayati, Eti Rohman, Dede Rudi Kurniawan Saeful Anwar Saefullah Fasa Santi Nurjulaiha Shofian Yunus Tati Suprapti Tio Prasetya Wiratriyana