Prediksi Jumlah Sampah di TPSA Menggunakan Pendekatan Machine Learning
DOI:
https://doi.org/10.30865/mib.v8i1.7278Keywords:
Machine Learning, Prediction, Linear Regression, Support Vector Regression, Random ForestAbstract
The amount of waste at landfills is increasing along with the growing population and human activities. Predicting the amount of waste has become one of the ways to address waste management issues. The quantity of upcoming waste can be determined through waste prediction, providing essential information for solving waste-related problems. This research involves modeling daily waste predictions using three machine learning algorithms: Linear Regression, Support Vector Regression, and Random Forest Regressor. The data used in this study is the waste data at Banyuurip landfill, Magelang City, covering the period from 2019 to 2022. In the data processing stage, attributes for data usage are selected, daily waste summation is performed, missing values are handled, and normalization is carried out using min-max normalization. The three machine learning algorithms are employed in the prediction modeling stage to obtain optimal parameters. The prediction model is evaluated by calculating the MSE. The results of the three waste prediction models using Linear Regression show a model with an MSE-train of 0.0086 and an MSE-test of 0.0083, while the RMSE-train is 0.0930 and the RMSE-test is 0.0915. The optimal SVR prediction model is obtained with hyperparameter combination C = 1, gamma = 1, and epsilon = 0.05, yielding MSE-train of 0.0030 and MSE-test of 0.0089, with RMSE-train at 0.0556 and RMSE-test at 0.0943. The Random Forest Regressor model results in a model with n_estimators of 500, random_state of 1, without the hyperparameter max_depth, and has MSE-train of 0.0012 and MSE-test of 0.0081, along with RMSE-train at 0.0353 and RMSE-test at 0.0901. Based on these three models, it is concluded that the best model is the Random Forest Regressor with the smallest MSE and RMSE values.
References
D. Fauzaan, B. N. Sari, dan I. Maulana, “Komparasi Algoritma Regresi Linear Klasik Dan Bayes Dalam Mengestimasi Lahan Tempat Pembuangan Sampah,†J. Lentera, vol. 21, no. 2, 2022.
Y. Ruslinda, I. K. Asyura, dan R. Aziz, “Pengaruh Pandemi Covid-19 Terhadap Jumlah Sampah di Tempat Pemrosesan Akhir Regional Kota Payakumbuh,†Serambi Eng., vol. 6, no. 4, hal. 2430–2440, 2021, doi: 10.32672/jse.v6i4.3519.
N. D. Maulana, B. D. Setiawan, dan C. Dewi, “Implementasi Metode Support Vector Regression (SVR) Dalam Peramalan Penjualan Roti (Studi Kasus : Harum Bakery),†J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 3, no. 3, hal. 2986–2995, 2019.
W. Santoso, Maimunah, dan P. Sukmasetya, “Prediksi Volume Sampah di TPSA Banyuurip Menggunakan Metode Backpropagation Neural Network,†J. Media Inform. Budidarma, vol. 7, no. 1, hal. 464–472, 2023, doi: 10.30865/mib.v7i1.5499.
Gunawansyah, R. H. Laluma, dan A. Prasetya, “Prediksi Volume Dan Ritasi Pengelolaan Sampah Di Kota Bandung Dengan Metode Regresi Linear,†Techno-Socio Ekon., vol. 15, no. 1, hal. 49, 2022, doi: 10.32897/techno.2022.15.1.1195.
S. Rahmawati, R. A. Nugroho, dan D. K. Eni A, “Perbandingan Prediksi Harga Saham menggunakan Metode SVR, RFR, dan DTR,†Pros. Semin. Pendidik. Mat. dan Mat., vol. 6, 2022, doi: 10.21831/pspmm.v6i2.250.
S. Fachid dan A. Triayudi, “Perbandingan Algoritma Regresi Linier dan Regresi Random Forest Dalam Memprediksi Kasus Positif Covid-19,†J. Media Inform. Budidarma, vol. 6, no. 1, hal. 68, 2022, doi: 10.30865/mib.v6i1.3492.
A. D. Sidik dan A. Ansawarman, “Prediksi Jumlah Kendaraan Bermotor Menggunakan Machine Learning,†Formosa J. Multidiscip. Res., vol. 1, no. 3, hal. 559–568, 2022, doi: 10.55927/fjmr.v1i3.745.
D. A. I. C. Dewi dan D. A. K. Pramita, “Analisis Perbandingan Metode Elbow dan Silhouette pada Algoritma Clustering K-Medoids dalam Pengelompokan Produksi Kerajinan Bali,†Matrix J. Manaj. Teknol. dan Inform., vol. 9, no. 3, hal. 102–109, 2019, doi: 10.31940/matrix.v9i3.1662.
W. Sudrajat dan I. Cholid, “K-NEAREST NEIGHBOR (K-NN) UNTUK PENANGANAN MISSING VALUE PADA DATA UMKM,†vol. 1, no. 2, hal. 54–63, 2023.
R. E. Wahyuni, “Optimasi Prediksi Inflasi Dengan Neural Network Pada Tahap Windowing: Adakah Pengaruh Perbedaan Window Size?,†Technologia, vol. 12, no. 3, hal. 176, 2021, doi: 10.31602/tji.v12i3.5181.
S. Wulandari, “Clustering Kecamatan Di Kota Bandung Berdasarkan Indikator Jumlah Penduduk Dengan Menggunakan Algoritma K-Means,†Semnas Ristek (Seminar Nas. Ris. dan Teknol., hal. 128–132, 2020, [Daring]. Tersedia pada: https://www.proceeding.unindra.ac.id/index.php/semnasristek/article/view/1688%0Ahttps://www.proceeding.unindra.ac.id/index.php/semnasristek/article/download/1688/235.
C. Hung, J. F. Wijaya, V. Victor, I. A. Pardosi, dan F. M. Sinaga, “Prediksi Fluktuasi Harga Bitcoin Dengan Menggunakan Random Forest Classifier,†J. Sifo Mikroskil, vol. 24, no. 2, hal. 95–108, 2023, doi: 10.55601/jsm.v24i2.1024.
H. K. Pambudi, P. G. A. Kusuma, F. Yulianti, dan K. A. Julian, “Prediksi Status Pengiriman Barang Menggunakan Metode Machine Learning,†J. Ilm. Teknol. Infomasi Terap., vol. 6, no. 2, hal. 100–109, 2020, doi: 10.33197/jitter.vol6.iss2.2020.396.
G. N. Ayuni dan D. Fitrianah, “Penerapan Metode Regresi Linear Untuk Prediksi Penjualan Properti pada PT XYZ,†J. Telemat., vol. 14, no. 2, hal. 79–86, 2019, [Daring]. Tersedia pada: https://journal.ithb.ac.id/telematika/article/view/321.
A. B. Raharjo, Z. Z. Dinanto, D. Sunaryono, dan D. Purwitasari, “Prediksi Akumulasi Kasus Terkonfirmasi Covid-19 Di Indonesia Menggunakan Support Vector Regression,†Techno.COM, vol. 20, no. 3, hal. 372–381, 2021, doi: 10.33633/tc.v20i3.5062.
M. E. Bastian, B. Rahayudi, dan D. E. Ratnawati, “Prediksi Trend Harga Saham Jangka Pendek berdasarkan Fitur Technical Analysis dengan menggunakan Algoritma Random Forest,†J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 5, no. 10, hal. 4536–4542, 2021, [Daring]. Tersedia pada: http://j-ptiik.ub.ac.id.
Hernadewita, Y. K. Hadi, M. J. Syaputra, dan D. Setiawan, “Peramalan Penjualan Obat Generik Melalui Time Series Forecasting Model Pada Perusahaan Farmasi di Tangerang: Studi Kasus,†J. Ind. Eng. Manag. Res. ( Jiemar), vol. 1, no. 2, hal. 35–49, 2020, [Daring]. Tersedia pada: https://jiemar.org/index.php/jiemar/article/view/38.
A. S. B. Karno, “Prediksi Data Time Series Saham Bank BRI Dengan Mesin Belajar LSTM (Long Short Term Memory),†J. Inform. Inf. Secur., vol. 1, no. 1, hal. 1–8, 2020, doi: 10.31599/jiforty.v1i1.133.
A. A. A. Purnamaswari, I. K. G. D. Putra, dan I. M. S. Putra, “Komparasi Metode Neural Network Backpropagation dan Support Vector Machines dalam Prediksi Volume Sampah TPA Suwung,†JITTER J. Ilm. Teknol. dan Komput., vol. 3, no. 1, hal. 853–861, 2022, [Daring]. Tersedia pada: https://ojs.unud.ac.id/index.php/jitter/article/view/83024/43066.
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