Perbandingan Metode Artificial Neural Network dan Convolutional Neural Network untuk Memprediksi Jumlah Distribusi Air di PDAM Kota Denpasar
DOI:
https://doi.org/10.23887/jstundiksha.v12i2.47800Keywords:
Artificial Neural Network, Convolutional Neural Network, Distribusi Air, Sequential Forward SelectionAbstract
Kelangsungan hidup di dunia pasti tidak akan bisa terlepas dari penggunaan air. Seiring dengan berkembangnya zaman tentu akan diikuti dengan bertambahnya jumlah penduduk yang juga akan berdampak terhadap meningkatnya kebutuhan air bersih. PDAM merupakan salah satu instansi yang melayani ketersediaan air bersih. Tujuan diadakannya penelitian ini untuk melakukan peramalan terhadap jumlah distribusi air di PDAM Kota Denpasar dengan membandingkan metode ANN (Artificial Neural Network) serta CNN (Convolutional Neural Network) yang juga melibatkan proses seleksi fitur menggunakan metode SFS (Sequential Forward Selection). Pendekatan kuantitatif digunakan untuk melakukan proses peramalan terhadap jumlah distribusi air (Y) dengan melibatkan fitur jumlah produksi (X1), kebocoran (X2), pembelian (X3), dan pelanggan air (x4). Hasil seleksi fitur dengan metode SFS menujukkan bahwa jumlah produksi air (X1) dan kebocoran air (X2) dengan tingkat kesalahan sebesar 0.031069 tepat digunakan untuk membentuk model peramalan jumlah distribusi air dengan metode ANN yang menghasilkan nilai MSE dan MAPE berturut-turut sebesar 0.040 serta 2.02%. Berdasarkan hasil yang diperoleh, model yang dikembangkan termasuk model peramalan yang sangat baik, sehingga tepat digunakan untuk melakukan proses peramalan jumlah distribusi air.
References
Abiodun, O. I., Jantan, A., Omolara, A. E., Dada, K. V., Mohamed, N. A. E., & Arshad, H. (2018). State-of-the-art in artificial neural network applications: A survey. Heliyon, 4(11), e00938. https://doi.org/10.1016/j.heliyon.2018.e00938.
Agga, A., Abbou, A., Labbadi, M., & El Houm, Y. (2021). Short-term self consumption PV plant power production forecasts based on hybrid CNN-LSTM, ConvLSTM models. Renewable Energy, 177, 101–112. https://doi.org/10.1016/j.renene.2021.05.095.
Altan, A., Karasu, S., & Bekiros, S. (2019). Digital currency forecasting with chaotic meta-heuristic bio-inspired signal processing techniques. Chaos, Solitons and Fractals, 126, 325–336. https://doi.org/10.1016/j.chaos.2019.07.011.
Attoue, N., Shahrour, I., & Younes, R. (2018). Smart building: Use of the Artificial Neural Network Approach for Indoor Temperature Forecasting. Energies, 11(2), 1–12. https://doi.org/10.3390/en11020395.
Bhakti, H. D. (2019). Aplikasi Artificial Neural Network (ANN) untuk Memprediksi Masa Studi Mahasiswa Program Studi Teknik Informatika Universitas Muhammadiyah Gresik. Eksplora Informatika, 9(1), 88–95. https://doi.org/10.30864/eksplora.v9i1.234.
Castangia, M., Aliberti, A., Bottaccioli, L., Macii, E., & Patti, E. (2021). A Compound of Feature Selection Techniques to Improve Solar Radiation Forecasting. Expert Systems with Applications, 178. https://doi.org/10.1016/j.eswa.2021.114979.
Freire, P. K. de M. M., Santos, C. A. G., & Silva, G. B. L. da. (2019). Analysis of the use of discrete wavelet transforms coupled with ANN for short-term streamflow forecasting. Applied Soft Computing Journal, 80, 494–505. https://doi.org/10.1016/j.asoc.2019.04.024.
Güven, İ., & Şimşir, F. (2020). Demand forecasting with color parameter in retail apparel industry using artificial neural networks (ANN) and support vector machines (SVM) methods. Computers and Industrial Engineering, 147. https://doi.org/10.1016/j.cie.2020.106678.
Haq, A. U., Zeb, A., Lei, Z., & Zhang, D. (2021). Forecasting Daily Stock Trend Using Multi-Filter Feature Selection and Deep Learning. Expert Systems with Applications, 168, 114444. https://doi.org/10.1016/j.eswa.2020.114444.
Huang, C. J., Chen, Y. H., Ma, Y., & Kuo, Huan, P. (2020). Multiple-Input Deep Convolutional Neural Network Model for COVID-19 Forecasting in China. MedRxiv. https://doi.org/10.1101/2020.03.23.20041608.
Kurniawan, M. A., Fitriani, H., & Hadinata, F. (2021). Analisis Kebutuhan Penyediaan Air Bersih di Kota Palembang City. Jurnal Saintis, 21(02), 105–112. https://doi.org/10.25299/saintis.2021.vol21(02).7611.
La Foucade, A. D., Gabriel, S., Scott, E., Theodore, K., & Metivier, C. (2019). A Survey of Selected Grey Forecasting Models with Application to Medical Tourism Forecasting. Theoretical Economics Letters, 09(04), 1079–1092. https://doi.org/10.4236/tel.2019.94070.
Lestari, F., Susanto, T., & Kastamto, K. (2021). Pemanenan Air Hujan Sebagai Penyediaan Air Bersih Pada Era New Normal Di Kelurahan Susunan Baru. SELAPARANG Jurnal Pengabdian Masyarakat Berkemajuan, 4(2), 427. https://doi.org/10.31764/jpmb.v4i2.4447.
Li, H., Zhao, W., Zhang, Y., & Zio, E. (2020). Remaining Useful Life Prediction Using Multi-Scale Deep Convolutional Neural Network. Applied Soft Computing Journal, 89, 106113. https://doi.org/10.1016/j.asoc.2020.106113.
Martín-Vázquez, R., Aler, R., & Galván, I. M. (2018). Wind Energy Forecasting at Different Time Horizons with Individual and Global Models. IFIP Advances in Information and Communication Technology, 519, 240–248. https://doi.org/10.1007/978-3-319-92007-8_21.
Oktafiansyah, E., & Uperiati, A. (2021). Prediksi Pendistribusian Air di Perusahaan Daerah Air Minum ( PDAM ) dengan Metode Adaptive Neuro Fuzzy Inference System ( ANFIS ) ( Studi Kasus : PDAM Tirta Kepri Tanjungpinang ). Jurnal Hasil Penelitian Dan Industri Terapan, 10(01), 32–36. https://doi.org/10.31629/sustainable.v10i1.1404.
Ozoegwu, C. G. (2019). Artificial Neural Network Forecast of Monthly Mean Daily Global Solar Radiation of Selected Locations Based on Time Series and Month Number. Journal of Cleaner Production, 216, 1–13. https://doi.org/10.1016/j.jclepro.2019.01.096.
Pan, H., & Zhou, H. (2020). Study on Convolutional Neural Network and Its Application in Data Mining and Sales Forecasting for E-Commerce. Electronic Commerce Research, 20(2), 297–320. https://doi.org/10.1007/s10660-020-09409-0.
Prayudani, S., Hizriadi, A., Lase, Y. Y., Fatmi, Y., & Al-Khowarizmi. (2019). Analysis Accuracy of Forecasting Measurement Technique on Random K-Nearest Neighbor (RKNN) Using MAPE and MSE. Journal of Physics: Conference Series, 1361(1). https://doi.org/10.1088/1742-6596/1361/1/012089.
Priliani, E. M., Putra, A. T., & Muslim, M. A. (2018). Forecasting Inflation Rate Using Support Vector Regression (SVR) Based Weight Attribute Particle Swarm Optimization (WAPSO). Scientific Journal of Informatics, 5(2), 118–127. https://doi.org/10.15294/sji.v5i2.14613.
Rachman, R. (2018). Penerapan Metode Moving Average Dan Exponential Smoothing Pada Peramalan Produksi Industri Garment. Jurnal Informatika, 5(2), 211–220. https://doi.org/10.31311/ji.v5i2.3309.
Sagheer, A., & Kotb, M. (2019). Time Series Forecasting of Petroleum Production using Deep LSTM Recurrent Networks. Neurocomputing, 323, 203–213. https://doi.org/10.1016/j.neucom.2018.09.082.
Shafiee, S., Lied, L. M., Burud, I., Dieseth, J. A., Alsheikh, M., & Lillemo, M. (2021). Sequential Forward Selection and support vector regression in comparison to LASSO regression for spring wheat yield prediction based on UAV imagery. Computers and Electronics in Agriculture, 183(1432), 106036. https://doi.org/10.1016/j.compag.2021.106036.
Suryadibrata, A., & Chandra, D. P. (2020). Implementasi Jaringan Saraf Tiruan Backpropagation untuk Pengenalan Karakter pada Dokumen Tercetak. Ultima Computing : Jurnal Sistem Komputer, 11(2), 81–89. https://doi.org/10.31937/sk.v11i2.1456.
Syarifudin, M. A., Rini Novitasari, D. C., Marpaung, F., Wahyudi, N., Hapsari, D. P., Supriyati, E., Farida, Y., Amin, F. M., Nugraheni, R. D., Ilham, Nariswari, R., & Setiawan, F. (2021). Hotspot Prediction Using 1D Convolutional Neural Network. Procedia Computer Science, 179(2019), 845–853. https://doi.org/10.1016/j.procs.2021.01.073.
Toğa, G., Atalay, B., & Toksari, M. D. (2021). COVID-19 prevalence forecasting using Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANN): Case of Turkey. Journal of Infection and Public Health, 14(7), 811–816. https://doi.org/10.1016/j.jiph.2021.04.015.
Trach, R., Trach, Y., & Lendo‐siwicka, M. (2021). Using ANN to Predict the Impact of Communication Factors on the Rework Cost in Construction Projects. Energies, 14(14), 1–15. https://doi.org/10.3390/en14144376.
Xu, X., Cao, D., Zhou, Y., & Gao, J. (2020). Application of Neural Network Algorithm in Fault Diagnosis of Mechanical Intelligence. Mechanical Systems and Signal Processing, 141, 106625. https://doi.org/10.1016/j.ymssp.2020.106625.
Yan, R., Liao, J., Yang, J., Sun, W., Nong, M., & Li, F. (2021). Multi-Hour and Multi-Site Air Quality Index Forecasting in Beijing Using CNN, LSTM, CNN-LSTM, and Spatiotemporal Clustering. Expert Systems with Applications, 169(November 2020), 114513. https://doi.org/10.1016/j.eswa.2020.114513.
Yazici, I., Beyca, O. F., & Delen, D. (2022). Deep-learning-based short-term electricity load forecasting: A real case application. Engineering Applications of Artificial Intelligence, 109, 104645. https://doi.org/10.1016/J.ENGAPPAI.2021.104645.
Zhao, X., Wei, H., Wang, H., Zhu, T., & Zhang, K. (2019). 3D-CNN-based feature extraction of ground-based cloud images for direct normal irradiance prediction. Solar Energy, 181(September 2018), 510–518. https://doi.org/10.1016/j.solener.2019.01.096.
Zhu, R., Liao, W., & Wang, Y. (2020). Short-term prediction for wind power based on temporal convolutional network. Energy Reports, 6, 424–429. https://doi.org/10.1016/j.egyr.2020.11.219
Downloads
Published
Issue
Section
License
Copyright (c) 2023 Putu Agung Bayupati, Anak Agung Ayu Sintya Dewi, Ni Kadek Ayu Wirdiani
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with the Jurnal Sains dan Teknologi (JST) agree to the following terms:
- Authors retain copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work. (See The Effect of Open Access)
