[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125928-id":3,"doc-seo-125928-113":31,"detail-sidebar-cat-0-id-113":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},125928,2336474459895,"Aria","https://ap-avatar.wpscdn.com/avatar/22000baeef7a5ed0655?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786071322749376916",54,"Penelitian & Laporan","PREDIKSI PERMINTAAN BATU BARA MENGGUNAKAN MACHINE LEARNING","Peramalan kebutuhan permintaan batu bara penting untuk meminimalkan biaya operasional. Peramalan mendukung penentuan jumlah dan waktu pemesanan batu bara dari pemasok secara lebih tepat serta membantu pengelolaan persediaan dan gudang. Penelitian menerapkan ARIMA, Exponential Smoothing, Support Vector Regression (SVR), Recurrent Neural Network (RNN), dan Long Short-Term Memory (LSTM), dievaluasi dengan MAE dan MAPE. Data 1097 hari (Jan 2021–Des 2022) diuji stasioner menggunakan Augmented Dickey-Fuller (ADF). Hasil menunjukkan Exponential Smoothing memiliki error paling kecil di antara model lain.","PREDIKSI PERMINTAAN BATU BARA MENGGUNAKAN  \nMACHINE LEARNING  \nKristina Febriani1) dan Chastine Fatichah 2)  \n1) Manajemen Teknologi, Institut Teknologi Sepuluh Nopember  \nJalan Cokroaminoto 12A, Surabaya  \n2)Teknik Informatika, Institut Teknologi Sepuluh Nopember  \nSukolilo, Surabaya  \n[e-mail: kristfe312@gmail.com](e-mail: kristfe312@gmail.com1)[1](e-mail: kristfe312@gmail.com1)), [chastine@if.its.ac.id](chastine@if.its.ac.id2)[2](chastine@if.its.ac.id2))  \nABSTRAK  \nPeramalan kebutuhan permintaan batu bara penting untuk dilakukan agar dapat meminimalkan biaya operasional. Dengan adanyaperamalan akan membantu perusahaan dalam menentukanjumlah dan waktu yang tepat untuk pemesanan batu bara daripemasok. Penelitian tentang peramalan batu bara di Indonesia umumnya menggunakan pendekatan statistika dan belum melakukan analisis kinerja model peramalanyang lain. Penelitian ini bertujuan melakukan peramalan kebutuhanbatu bara dengan menggunakan metode statistika dan machine learning yaitu ARIMA, Exponential Smoothing, Support Vector Regression (SVR), Recurrent Neural Network (RNN) dan Long Short-Term Memory (LSTM). Metode evaluasi yang digunakan untuk menganalisis kinerjaperamalan yaitu Mean Absolute Error (MAE) dan Mean Absolute Percentage Error (MAPE). Data permintaan baru bara yang digunakan sebanyak 1097 data harian diambil dari Januari 2021 sampai dengan Desember 2022 yang berbentuk timeseries dan bersifat stasioner yang telah diuji menggunakan Augmented Dickey-Fuller (ADF). Hasil uji coba menunjukkan bahwa model ARIMA dengan nilaiMAPE 5.11%, MAE 2.91 dan R-Square 0.925, Exponential Smoothing MAPE 1. 07%, MAE 0.55 dan R-Square 0.997, SVR dengan nilai MAPE 5.48%, MAE 3.16 dan R-Square 0.88, RNNdengan nilai MAPE 5.19%, MAE 2.91 dan R-Square 0.896, LSTM dengan nilaiMAPE 4.83%, MAE 2.84 dan RSquare 0.897. Dari hasil pengujian didapatkan bahwa exponential smoothing memiliki nilai error yang paling kecil diantara model lain. Dengan hasil peramalan yang memiliki tingkat error yang kecil maka dapat membantu manajemen dalampengambilan keputusan untuk dapat meminimalkan biaya dalam pemesanan batu bara dan manajemen pergudangan  \n.  \nKata kunci: Peramalan, ARIMA, Exponential smoothing, SVR, RNN,LSTM  \nCOAL DEMAND PREDICTION MODEL USING MACHINE  \nLEARNING METHODS  \nKristina Febriani1), dan Chastine Fatichah 2)  \n1) Technology Management, Institut Teknologi Sepuluh Nopember  \nJalan Cokroaminoto 12A, Surabaya  \n2)Informatics, Institut Teknologi Sepuluh Nopember  \nSukolilo, Surabaya  \n[e-mail: kristfe312@gmail.com](e-mail: kristfe312@gmail.com1)[1](e-mail: kristfe312@gmail.com1)), [chastine@if.its.ac.id](chastine@if.its.ac.id2)[2](chastine@if.its.ac.id2))  \nABSTRACT  \nForecasting coal demand needs is important to minimize operational costs. Forecasting will help companies determine the right amount and time to order coal from suppliers. Research on coal forecasting in Indonesia generally uses a statistical approach and has not analyzed the performance of other forecasting models. This research aims to forecast coal demand using statistical and machine learning methods, namely ARIMA, Exponential Smoothing, Support Vector Regression (SVR), Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM). The evaluation methods used to analyze forecasting performance are Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). The new coal demand data used is 1097 daily data taken from January 2021 to December 2022 in the form of a timeseries and is stationary which has been tested using Augmented Dickey-Fuller (ADF). The test results show that the ARIMA model has MAPE value of 5. 11%, MAE 2.91 and R-Square 0.925, Exponential Smoothing MAPE 1. 07%, MAE 0.55 and R-Square 0.997, SVR with MAPE value of 5.48%, MAE 3.16 andR-Square 0.88, RNN with MAPE value of 5. 19%, MAE 2.91 andR-Square 0.896, LSTM with MAPE value of 4.83%, MAE 2.84 and R-Square 0.897. From the test results it was found that exponential smoothing had the smallest error ","cbCainhLQpNAwDqC","https://ap.wps.com/l/cbCainhLQpNAwDqC","pdf",935498,6,1,13,"Indonesian","id",113,"# Abstrak\n# Kata Kunci\n# I. Pendahuluan","[{\"question\":\"Tujuan penelitian ini apa?\",\"answer\":\"Melakukan peramalan kebutuhan batu bara menggunakan metode statistik dan machine learning serta menganalisis kinerja model peramalan.\"},{\"question\":\"Metode peramalan apa saja yang dibandingkan?\",\"answer\":\"ARIMA, Exponential Smoothing, Support Vector Regression (SVR), Recurrent Neural Network (RNN), dan Long Short-Term Memory (LSTM).\"},{\"question\":\"Bagaimana kinerja model dievaluasi?\",\"answer\":\"Kinerja dianalisis menggunakan Mean Absolute Error (MAE) dan Mean Absolute Percentage Error (MAPE). Hasil menunjukkan Exponential Smoothing memiliki error paling kecil dibanding model lain.\"}]","PREDIKSI PERMINTAAN BATU BARA MENGGUNAKAN MACHINE LEARNING | PDF",1785902081,20,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"coal-demand-prediction-using-machine-learning","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/id/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/id/document/penelitian-laporan/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/id/document/coal-demand-prediction-using-machine-learning/125928/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-18","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"Tujuan penelitian ini apa?","Question",{"text":77,"@type":78},"Melakukan peramalan kebutuhan batu bara menggunakan metode statistik dan machine learning serta menganalisis kinerja model peramalan.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Metode peramalan apa saja yang dibandingkan?",{"text":82,"@type":78},"ARIMA, Exponential Smoothing, Support Vector Regression (SVR), Recurrent Neural Network (RNN), dan Long Short-Term Memory (LSTM).",{"name":84,"@type":75,"acceptedAnswer":85},"Bagaimana kinerja model dievaluasi?",{"text":86,"@type":78},"Kinerja dianalisis menggunakan Mean Absolute Error (MAE) dan Mean Absolute Percentage Error (MAPE). Hasil menunjukkan Exponential Smoothing memiliki error paling kecil dibanding model lain.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,100,104,108,112,116,118,122,126,130,134],{"id":96,"doc_module":4,"doc_module_name":47,"category_name":97,"show_sort_weight":98,"slug":99},55,"Agama & Spiritualitas",60,"religion-spirituality",{"id":101,"doc_module":4,"doc_module_name":47,"category_name":102,"show_sort_weight":98,"slug":103},48,"Cerita & Novel","story-novel",{"id":105,"doc_module":4,"doc_module_name":47,"category_name":106,"show_sort_weight":98,"slug":107},56,"Gaya Hidup","lifestyle",{"id":109,"doc_module":4,"doc_module_name":47,"category_name":110,"show_sort_weight":98,"slug":111},51,"Komik","comic",{"id":113,"doc_module":4,"doc_module_name":47,"category_name":114,"show_sort_weight":98,"slug":115},53,"Layanan Kesehatan","healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":98,"slug":117},"research-report",{"id":119,"doc_module":4,"doc_module_name":47,"category_name":120,"show_sort_weight":98,"slug":121},49,"Sastra","literature",{"id":123,"doc_module":4,"doc_module_name":47,"category_name":124,"show_sort_weight":98,"slug":125},52,"Teknologi","technology",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":128,"show_sort_weight":98,"slug":129},50,"Ujian","exam",{"id":131,"doc_module":4,"doc_module_name":47,"category_name":132,"show_sort_weight":98,"slug":133},57,"Umum","general",{"id":135,"doc_module":4,"doc_module_name":47,"category_name":136,"show_sort_weight":4,"slug":137},181,"Formulir","formulir"]