[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-id-113":3,"doc-seo-127897-113":53,"doc-detail-127897-id":132},{"code":4,"msg":5,"data":6},0,"success",[7,13,17,21,25,29,33,37,41,45,49],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},55,"Document","Agama & Spiritualitas",60,"religion-spirituality",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":11,"slug":16},48,"Cerita & Novel","story-novel",{"id":18,"doc_module":4,"doc_module_name":9,"category_name":19,"show_sort_weight":11,"slug":20},56,"Gaya Hidup","lifestyle",{"id":22,"doc_module":4,"doc_module_name":9,"category_name":23,"show_sort_weight":11,"slug":24},51,"Komik","comic",{"id":26,"doc_module":4,"doc_module_name":9,"category_name":27,"show_sort_weight":11,"slug":28},53,"Layanan Kesehatan","healthcare",{"id":30,"doc_module":4,"doc_module_name":9,"category_name":31,"show_sort_weight":11,"slug":32},54,"Penelitian & Laporan","research-report",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":11,"slug":36},49,"Sastra","literature",{"id":38,"doc_module":4,"doc_module_name":9,"category_name":39,"show_sort_weight":11,"slug":40},52,"Teknologi","technology",{"id":42,"doc_module":4,"doc_module_name":9,"category_name":43,"show_sort_weight":11,"slug":44},50,"Ujian","exam",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":47,"show_sort_weight":11,"slug":48},57,"Umum","general",{"id":50,"doc_module":4,"doc_module_name":9,"category_name":51,"show_sort_weight":4,"slug":52},181,"Formulir","formulir",{"code":4,"msg":54,"data":55},"ok",{"site_id":56,"language":57,"slug":58,"title":59,"keywords":60,"description":61,"schema_data":62,"social_meta":125,"head_meta":127,"extra_data":129,"updated_unix":131},113,"id","machine-learning-based-univariate-time-series-imputation-method-for-estimating-missing-values-in-non-stationary-data","Metode Machine Learning-Based Univariate Time Series Imputation untuk Estimasi Nilai Hilang pada Data Non-Stasioner","","Penanganan nilai hilang pada data deret waktu sangat penting karena nilai yang hilang dapat merusak analisis dan interpretasi. Nilai hilang berurutan pada time series univariat umumnya lebih kompleks dibandingkan nilai hilang acak dan sering dikaitkan dengan mekanisme Missing Not at Random (MNAR). Penelitian ini mengevaluasi metode Machine Learning-Based Univariate Time Series Imputation (MLBUI) menggunakan Random Forest Regression dan Support Vector Regression untuk mengestimasi nilai hilang pada data non-stasioner. Pengujian dilakukan pada data suhu rata-rata Kabupaten Bogor dengan skenario 6%, 10%, dan 14% serta dibandingkan dengan beberapa metode Kalman dan interpolasi. Hasil menunjukkan performa MLBUI kurang baik pada data non-stasioner, meskipun MAPE tetap di bawah 10%.",{"@graph":63,"@context":124},[64,81,103],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,72,75,78],{"item":68,"name":69,"@type":70,"position":71},"https://docshare.wps.com","Home","ListItem",1,{"item":73,"name":9,"@type":70,"position":74},"https://docshare.wps.com/id/document/",2,{"item":76,"name":31,"@type":70,"position":77},"https://docshare.wps.com/id/document/penelitian-laporan/",3,{"item":79,"name":59,"@type":70,"position":80},"https://docshare.wps.com/id/document/machine-learning-based-univariate-time-series-imputation-method-for-estimating-missing-values-in-non-stationary-data/127897/",4,{"url":79,"name":59,"@type":82,"image":83,"author":88,"headline":59,"publisher":91,"fileFormat":94,"inLanguage":57,"description":61,"dateModified":95,"datePublished":96,"encodingFormat":94,"isAccessibleForFree":97,"interactionStatistic":98},"DigitalDocument",{"url":84,"@type":85,"width":86,"height":87},"https://docshare.wps.com/thumbnails/machine-learning-based-univariate-time-series-imputation-method-for-estimating-missing-values-in-non-stationary-data/127897.png","ImageObject",300,407,{"name":89,"@type":90},"Noah","Person",{"url":68,"name":92,"@type":93},"DocShare","Organization","application/pdf","2026-09-19","2026-08-05",true,{"@type":99,"interactionType":100,"userInteractionCount":102},"InteractionCounter",{"@type":101},"ViewAction",8,{"@type":104,"mainEntity":105},"FAQPage",[106,112,116,120],{"name":107,"@type":108,"acceptedAnswer":109},"Mengapa penanganan nilai hilang pada data deret waktu penting?","Question",{"text":110,"@type":111},"Nilai hilang dapat menyebabkan distorsi dalam analisis dan interpretasi serta berujung pada kesimpulan yang salah dan model yang tidak akurat.","Answer",{"name":113,"@type":108,"acceptedAnswer":114},"Apa perbedaan nilai hilang berurutan dengan nilai hilang acak pada time series?",{"text":115,"@type":111},"Nilai hilang berurutan dalam deret waktu univariat menjadi tantangan lebih besar karena lebih kompleks dan sering dikaitkan dengan pola tertentu.",{"name":117,"@type":108,"acceptedAnswer":118},"Bagaimana MLBUI bekerja dan metode apa yang dibandingkan?",{"text":119,"@type":111},"MLBUI menggunakan Random Forest Regression (RFR) dan Support Vector Regression (SVR). Studi ini membandingkannya dengan Kalman StructTS, Kalman Auto-ARIMA, spline, stine, dan moving average.",{"name":121,"@type":108,"acceptedAnswer":122},"Bagaimana hasil evaluasi MLBUI pada data non-stasioner?",{"text":123,"@type":111},"MLBUI menunjukkan kinerja kurang baik untuk data non-stasioner, namun nilai MAPE yang diperoleh masih berada di bawah 10%.","https://schema.org",{"og:url":79,"og:type":126,"og:title":59,"og:site_name":92,"og:description":61},"article",{"robots":128,"canonical":79},"index,follow",{"doc_id":130,"site_id":56},127897,1785942792,{"code":4,"msg":5,"data":133},{"doc_id":130,"user_id":134,"nickname":89,"user_avatar":135,"doc_module":4,"category_id":30,"category_name":31,"doc_title":59,"doc_description":61,"doc_content":136,"file_id":137,"file_url":138,"file_type":139,"file_size":140,"view_count":102,"is_deleted":4,"is_public":71,"is_downloadable":71,"audit_status":71,"page_count":141,"language":142,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":143,"faqs":144,"seo_title":145,"seo_description":61,"update_tm":131,"read_time":146},137451207643,"https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2","Machine Learning-Based Univariate Time Series Imputation Method for Estimating Missing Values in Non  \nStationary Data  \nMetode Machine Learning-Based Univariate Time Series Imputation Method untuk Estimasi Nilai Hilang pada Data Non-Stasioner  \nDini Ramadhani1*, Agus Mohamad Soleh2, Erfiani3  \n1,2,3 Pogram Studi Statistika dan Sains Data, Fakultas Matematika dan Ilmu Pengetahuan Alam,  \nInstitut Pertanian Bogor, Indonesia  \n[Email](Email:1diniramadhani@apps.ipb.ac.id)[:](Email:1diniramadhani@apps.ipb.ac.id)[1](Email:1diniramadhani@apps.ipb.ac.id)[diniramadhani@apps.ipb.ac.id](Email:1diniramadhani@apps.ipb.ac.id),[2](2agusms@apps.ipb.ac.id)[agusms@apps.ipb.ac.id](2agusms@apps.ipb.ac.id), [3](3erfiani@apps.ipb.ac.id)[erfiani@apps.ipb.ac.id](3erfiani@apps.ipb.ac.id)  \nAbstrak  \nHandling missing values in time series data is crucial because they can disrupt data analysis and interpretation. Sequentially missing values in time series often pose a more complex challenge compared to randomly missing values. One of the promising recent methods is Machine LearningBased Univariate Time Series Imputation (MLBUI), although it is still not widely used and its accessibility is limited. MLBUI employs Random Forest Regression (RFR) and Support Vector Regression (SVR) algorithms. This study evaluates the performance of MLBUI in addressing missing data scenarios in non-stationary univariate time series data. The data used in this research is the average temperature data from Bogor Regency. The missing data scenarios considered include rates of 6%, 10%, and 14% . Besides MLBUI, five other comparison methods are used:  \nKalman StructTS, Kalman Auto-ARIMA, Spline Interpolation, Stine Interpolation, and Moving Average. The results show that MLBUI performs poorly for non-stationary data, although the obtained Mean Absolute Percentage Error (MAPE) is below 10% .  \nKeywords: Imputation, Non-Stationary Data, Machine Learning, Missing Values  \nAbstract  \nPenanganan nilai hilang dalam data deret waktu adalah krusial karena dapat menyebabkan gangguan dalam analisis dan interpretasi data. Khususnya, nilai-nilai hilang yang terjadi secaraberurutan dalam deret waktu seringkali menjadi tantangan yang lebih kompleks dibandingkandengan nilai-nilai yang hilang secara acak. Salah satu metode terbaru yang menjanjikan adalah Machine Learning-Based Univariate Time Series Imputation (MLBUI), meskipun masih belum banyak digunakan dan aksesibilitasnya masih terbatas. MLBUI menggunakan algoritma Random Forest Regression (RFR) dan Support Vector Regression (SVR) . Dalam studi ini, evaluasidilakukan terhadap kinerja MLBUI dalam mengatasi skenario data yang hilang pada deret waktu univariat yang non-stasioner. Data yang digunakan pada penelitian ini yaitu data suhu rata-rata dari Kabupaten Bogor. Skenario kehilangan data yang dipertimbangkan mencakup tingkat 6%, 10%, dan 14% . Selain MLBUI, lima metode perbandingan lainnya digunakan: Kalman StructTS,  \nKalman Auto-ARIMA, Interpolasi Spline, Interpolasi Stine, dan Moving Average. Hasil penelitian menunjukkan bahwa MLBUI memberikan hasil yang kurang baik untuk data yang non-stasioner walaupun nilai Mean Absolute Percentage Error (MAPE) yang diperoleh berada dibawah 10% .  \nKata kunci: Imputasi, Data Non-Stasioner, Pembelajaran Mesin, Nilai Hilang  \n1. PENDAHULUAN  \nPenanganan nilai hilang pada data deret waktu sangat penting karena nilai hilang dapat menyebabkan distorsi dalam analisis dan interpretasi data. Jika tidak ditangani dengan benar, nilai hilang dapat mengakibatkan kesimpulan yang salah dan model yang tidak akurat. Metode yangumum digunakan untuk menangani nilai hilang meliputi imputasi, interpolasi, dan penggunaan model statistik khusus yang dapat mengakomodasi nilai hilang. Dengan penanganan yang tepat, kita dapat meminimalkan dampak negatif dari nilai hilang dan meningkatkan akurasi analisis sertahasil prediksi.  \nNilai hilang yang muncul secara berturut-turut dalam data deret waktu dapat menjadi tantanganyang ","cbCaipEFd86T48dj","https://ap.wps.com/l/cbCaipEFd86T48dj","pdf",724099,14,"Indonesian","# PENDAHULUAN\n## Tantangan nilai hilang pada time series univariat\n## Mekanisme Missing Not at Random (MNAR)\n## Tinjauan penelitian terdahulu dan metode terkait\n## Kerangka pendekatan Dynamic Time Wrapping (DTW)","[{\"question\":\"Mengapa penanganan nilai hilang pada data deret waktu penting?\",\"answer\":\"Nilai hilang dapat menyebabkan distorsi dalam analisis dan interpretasi serta berujung pada kesimpulan yang salah dan model yang tidak akurat.\"},{\"question\":\"Apa perbedaan nilai hilang berurutan dengan nilai hilang acak pada time series?\",\"answer\":\"Nilai hilang berurutan dalam deret waktu univariat menjadi tantangan lebih besar karena lebih kompleks dan sering dikaitkan dengan pola tertentu.\"},{\"question\":\"Bagaimana MLBUI bekerja dan metode apa yang dibandingkan?\",\"answer\":\"MLBUI menggunakan Random Forest Regression (RFR) dan Support Vector Regression (SVR). Studi ini membandingkannya dengan Kalman StructTS, Kalman Auto-ARIMA, spline, stine, dan moving average.\"},{\"question\":\"Bagaimana hasil evaluasi MLBUI pada data non-stasioner?\",\"answer\":\"MLBUI menunjukkan kinerja kurang baik untuk data non-stasioner, namun nilai MAPE yang diperoleh masih berada di bawah 10%.\"}]","Metode Machine Learning-Based Univariate Time Series Imputation untuk Estimasi Nilai Hilang pada Data Non-Stasioner | PDF",22]