Prediction of Organic Waste Deposits in Compost Houses using LSTM and ARIMA Algorithms
DOI:
10.33395/sinkron.v9i1.14271Keywords:
Machine Learning, Smart Waste Management, ARIMA, LSTM, Predictive ModelsAbstract
Indonesia faces a significant waste problem and is becoming a global challenge, mainly due to inadequate food waste management. In Kendal District, the Environmental Agency struggles to optimize waste collection and predict the volume of organic waste. To address this issue, this study explores the application of predictive technology and data analysis to improve the efficiency of waste management. Two predictive models, ARIMA and Long Short-Term Memory (LSTM), were developed and compared by collecting historical data from Kendal Organic Compost House from 2020-2024 while for train and test data using data from January 2, 2023, to December 30, 2023. The ARIMA model showed better accuracy, capturing stable trends and seasonal patterns in the time series data, with an MSE of 72,799.49. Meanwhile, the LSTM model, although capable of handling non-linear and complex patterns, performed poorly with an MSE of 54,711,498,631,770.58, indicating a failure to accommodate sharp fluctuations in the data. These findings highlight the suitability of ARIMA for data with low volatility and strong seasonality, making it more reliable for short-term predictions. The results of this study are expected to assist the Kendal District Environmental Agency in planning efficient waste management strategies, optimizing compost house operations, and improving resource allocation. Future research should focus on the integration of external variables, such as weather and population dynamics, and explore hybrid models for better prediction.
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References
Amansyah, I., Indra, J., Nurlaelasari, E., & Juwita, A. R. (2024). Prediksi Penjualan Kendaraan Menggunakan Regresi Linear: Studi Kasus pada Industri Otomotif di Indonesia. *Innovative Journal*, 4, 1199–1216. https://j-innovative.org/index.php/InnovativePrediksi
Ariefahnoor, D., Hasanah, N., & Surya, A. (2020). Pengelolaan Sampah Desa Gudang Tengah Melalui Manajemen Bank Sampah. *Jurnal Kacapuri: Jurnal Keilmuan Teknik Sipil*, 3(1), 14. https://doi.org/10.31602/jk.v3i1.3594
Chaerul, M., & Zatadini, S. U. (2020). Perilaku Membuang Sampah Makanan dan Pengelolaan Sampah Makanan di Berbagai Negara: Review. *Jurnal Ilmu Lingkungan*, 18(3), 455–466. https://doi.org/10.14710/jil.18.3.455-466
Ferdinandus, Y. R. M., Kusrini, K., & Hidayat, T. (2023). Gold Price Prediction Using the ARIMA and LSTM Models. *Sinkron*, 8(3), 1255–1264. https://doi.org/10.33395/sinkron.v8i3.12461
Hasibuan, M. R. R. (2023). Manfaat Daur Ulang Sampah Organik dan Anorganik untuk Kesehatan Lingkungan. *Jurnal Ilmiah Lingkungan*, 2(3), 1–11.
Karyadi, Y. (2022). Prediksi Kualitas Udara Dengan Metoda LSTM, Bidirectional LSTM, dan GRU. *JATISI (Jurnal Teknik Informatika dan Sistem Informasi)*, 9(1), 671–684. https://doi.org/10.35957/jatisi.v9i1.1588
Milniadi, A. D., & Adiwijaya, N. O. (2023). Analisis Perbandingan Model ARIMA dan LSTM dalam Peramalan Harga Penutupan Saham (Studi Kasus: 6 Kriteria Kategori Saham Menurut Peter Lynch). *SIBATIK JOURNAL: Jurnal Ilmiah Bidang Sosial, Ekonomi, Budaya, Teknologi, dan Pendidikan*, 2(6), 1683–1692. https://doi.org/10.54443/sibatik.v2i6.798
Purnama, J., & Juliana, A. (2020). Analisa Prediksi Indeks Harga Saham Gabungan Menggunakan Metode ARIMA. *Cakrawala Management Business Journal*, 2(2), 454. https://doi.org/10.30862/cm-bj.v2i2.51
Putri, N. W., Rahmah, S. P., Tafsia, S. I., & Putri, V. Y. (2022). Edukasi Daur Ulang Sampah Organik Menjadi Pupuk Kompos di Kelurahan Pasar Ambacang Kecamatan Kuranji Kota Padang. *Jurnal Hilirisasi IPTEKS*, 5(2), 109–117. https://doi.org/10.25077/jhi.v5i2.606
Rini, W. N. E., Aswin, B., & Hidayati, F. (2021). Pelatihan Pembuatan Kompos dari Sampah Organik Rumah Tangga dengan Komposter Ember. *Jurnal Karya Abdi*, 5(3), 116–121.
Riyantoni, N. H., Bahreisy, M. F., Hakim, I., & Rolliawati, D. (2023). Komparasi Support Vector Machine (SVM) dan Autoregressive Integrated Moving Average (ARIMA) pada Peramalan Hujan di Daerah Albury, Australia. *Jurnal Sistem Informasi dan Informatika (Simika)*, 6(1), 59–68. https://doi.org/10.47080/simika.v6i1.2412
Roring, H. N., Tulusan, F. M. G., & Kolondam, H. F. (2023). Sinergitas Pemerintah dan Masyarakat dalam Penanganan Sampah di Pasar Pinasungkulan Karombasan Kota Manado. *Jurnal Administrasi Publik*, 9(3), 44–51.
Siahaan, I. H., Jonoadji, N., & Lourentius, S. (2023). Pemanfaatan Rumah Kompos sebagai Sarana Upgrading Keterampilan Pembuatan Pupuk Kompos. *Prima Abdika: Jurnal Pengabdian Masyarakat*, 3(4), 398–408. https://doi.org/10.37478/abdika.v3i4.3252
Suhardono, S., Sari, M. M., Afifah, A. S., & Suryawan, I. W. K. (2024). Performa Fasilitas Rumah Kompos di Kabupaten Kendal, Jawa Tengah. *Journal of Sustainable Infrastructure*, 3(1). https://doi.org/10.61078/jsi.v3i1.25
Tita Lattifia, Putu Wira Buana, & NI Kadek Dwi Rusjayanthi. (2022). Model Prediksi Cuaca Menggunakan Metode LSTM. *JITTER-Jurnal Ilmiah Teknologi dan Komputer*, 3(1).
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