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All Journal PROCEEDING IC-ITECHS 2014 Jurnal Sistem Komputer & Kecerdasan Buatan Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Digital Transformation Technology (Digitech) Malcom: Indonesian Journal of Machine Learning and Computer Science Amorti: Jurnal Studi Islam Interdisipliner Ahsana: Jurnal Penelitian dan Pengabdian kepada Masyarakat Journal of Computer Science Advancements
saifulloh Saifulloh, saifulloh
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Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Library & Information Science Nursing Public Health Social Sciences Other

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Forecasting Waste Generation with Increment Linear Regression Technique: A Case Study of SIMASKOT Application Jayadi, Puguh; Hidayati, Nasrul Rofiah; Saifulloh, Saifulloh; Hamid, Suhardi; Shuib, Salehuddin; Ismail, Siti Nurbaya
Journal of Computer Science Advancements Vol. 2 No. 5 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v2i5.1369

Abstract

This research aims to develop a prediction system for urban waste generation using the Incremental Linear Regression method on SIMASKOT. This method is applied to deal with the limitations of historical data, where the prediction results from the previous year are used as training data to predict the next year. The problem faced is the lack of sufficient data to create accurate and reliable prediction models in the long term. The purpose of this study is to improve the accuracy of waste generation prediction using an incremental regression approach. The experimental methodology involves the use of waste generation data from several waste categories during the period 2019 to 2022, which is then used to predict data until 2026. Model evaluation was carried out using the metrics Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R². The results show that this incremental prediction model is able to provide more accurate predictions than conventional models, especially for more volatile waste categories such as wood twigs and metals. The conclusion of this study shows that the Incremental Linear Regression technique is effective to be used in waste generation prediction, and can be integrated in long-term prediction-based monitoring applications.
Co-Authors Aldida, Jovanza Denis Astuti, Anggun Yuli Bakir, Bakir Eka, Muhammad Rendy Hamid, Suhardi Harly, Daniswara Andhika Putra Hoiriyah, Hoiriyah Ismail, Siti Nurbaya Jamaludin, Muhammad Aizat Jayadi, Puguh Joang Ipmawati Kartika, Betania Kusnawi Kusnawi Nasrul Rofiah Hidayati, Nasrul Rofiah Nita, Sekreningsih Pratiwi Susanti Razali, Mohamad Afiq bin Shuib, Salehuddin Sulistyo, Dwimas Budi Susanti, Pratiwi Welasih, Rusti Dewi Yessi Yunitasari