[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124960-id":3,"doc-seo-124960-113":31,"detail-sidebar-cat-0-id-113":92},{"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},124960,2336474459895,"Aria","https://ap-avatar.wpscdn.com/avatar/22000baeef7a5ed0655?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786071322749376916",54,"Penelitian & Laporan","Evaluasi Komparatif Metode Machine Learning Untuk Memprediksi Perubahan Harga Saham - Jurnal Ilmiah Teknik Informatika","Memprediksi tren harga di pasar saham merupakan tugas yang kompleks karena banyak faktor ketidakpastian dan variabel yang memengaruhi nilai pasar. Penelitian ini melakukan evaluasi komparatif tiga metode machine learning populer, yaitu Random Forest, K-Nearest Neighbors (KNN), dan XGBoost, untuk memprediksi perubahan harga saham. Random Forest menghasilkan ROC tertinggi 0.98, sedangkan XGBoost unggul pada akurasi, recall, dan presisi. Metode windowing diterapkan untuk mengatasi overfitting serta meningkatkan keandalan pembagian data training dan testing.","ANTIVIRUS: Jurnal Ilmiah Teknik Informatika (p – ISSN: 1978 – 5232; e – ISSN: 2527 – 337X)  \nVol. 17 No. 2 Mei 2023, pp. 278 – 285  \nEVALUASI KOMPARATIF METODE MACHINE LEARNING UNTUK MEMPREDIKSI PERUBAHAN  \nHARGA SAHAM  \nDiterima Redaksi: 18 Juni 2023; Revisi Akhir:4 Mei 2024; Diterbitkan Online:14 Mei 2024  \nGalih Adhi Putratama1), Satya Maulana Fahreza2), Yudhistira Rakha Ramandhani3)  \n1, 2, 3) Program Studi Informatika, Fakultas Teknologi Informasi dan Sains Data, Universitas Sebelas Maret  \n1, 2, 3) Jl. Ir Sutami No.36, Kentingan, Kec. Jebres, Kota Surakarta, Jawa Tengah, kode pos : 57126  \ne-mail: [galihadhip@student.uns.ac.id](galihadhip@student.uns.ac.id1)[1](galihadhip@student.uns.ac.id1)), [fahryreza13@student.uns.ac.id](fahryreza13@student.uns.ac.id2)[2](fahryreza13@student.uns.ac.id2)), [rakharamandhani@student.uns.ac.id](rakharamandhani@student.uns.ac.id3)[3](rakharamandhani@student.uns.ac.id3))  \nAbstrak: Memprediksi tren harga di pasar saham adalah tantanganyang kompleks karena banyakfaktor ketidakpastian danvariabel yang mempengaruhi nilai pasar. Studi ini melakukan evaluasi komparatif terhadap tiga metode machine learning populer, yaitu Random Forest, K-Nearest Neighbors (KNN), dan XGBoost, untuk memprediksi perubahan hargasaham. Hasil penelitian menunjukkan bahwa Random Forest memiliki nilai ROC yang paling tinggi, yaitu 0.98, sedangkan XGBoost memiliki performa yang lebih baik dalam hal akurasi, recall, dan presisi berturut-turut yaitu 0.93, 0.69, 0.77. Metode Windowing juga diterapkan pada dataset untuk mengatasi permasalahan overfitting. Penelitian ini memberikan wawasan penting bagipraktisi dan peneliti di bidang prediksi harga saham untuk memilih model terbaik berdasarkan matriks evaluasi yang lebih diutamakan.  \nKata Kunci—KNN, Prediksi, Random Forest, Windowing, XGBoost  \nAbstract: Forecasting price patterns in the stock market poses a complicated and intricate task due to numerous uncertain factors and variables that influence market value. This study conducts a comparative evaluation of three popular computational learning approaches, namely Random Forest, K-Nearest Neighbors (KNN), and XGBoost, for predicting stock price changes. The research findings indicate that Random Forest achieves highest ROC scores, which is 0.98, while XGBoost exhibits superior performance in relation to accuracy, recall, and precision of 0.93, 0.69, 0. 77, respectively. The Windowing method is also applied to the dataset to address overfitting issues. This study offers valuable knowledge for professionals and researchers in the domain of stock price prediction, enabling them to choose the optimal model based on preferred evaluation metrics.  \nKeywords—KNN, Random Forest, Stock price, XGBoost, Windowing  \nI. PENDAHULUAN  \nMEMPREDIKSItren harga di pasar saham adalah tantangan yang kompleks karenabanyak faktor  \nketidakpastian dan variabel yang mempengaruhi nilai pasar setiap harinya, seperti kondisi  \nekonomi, sentimen investor terhadap perusahaan tertentu, peristiwa politik, dan lain sebagainya [1] . Pasar saham rentan terhadap perubahan yang cepat, yang menyebabkan fluktuasi acak dalam hargasaham [2] . Secara umum, pasar saham bersifat dinamis, non-parametrik, dan tidak teratur [3] . Olehkarena itu, pergerakan harga saham sering dianggap sebagai proses acak dengan fluktuasi yang lebih jelas dalam jangka pendek. Namun, beberapa saham cenderung mengikuti trend linier dalam jangka waktu yang lebih panjang. Investasi di pasar saham memiliki risiko tinggi karena sifat kacau dan tidak stabilnya perilaku saham [4]. Untuk mengurangi risiko tersebut, pengetahuan mendalam tentang pergerakan hargasaham di masa depan sangat penting. Para trader cenderung membeli saham yang diprediksi akanmeningkat nilainya di masa depan, sementara mereka cenderung menghindari saham yang diprediksi akan turun nilainya [1] . Oleh karena itu, prediksi tren harga pasar saham yang akurat menjadi krusialuntuk memaksimalkan keuntungan dan meminimalkan kerugi","cbCaimMq3UyVDO1u","https://ap.wps.com/l/cbCaimMq3UyVDO1u","pdf",585364,4,1,8,"Indonesian","id",113,"# Pendahuluan\n## Latar belakang dan pentingnya prediksi harga saham\n## Permasalahan pada penelitian terdahulu\n## Tujuan dan kontribusi penggunaan Windowing\n# Tinjauan Pustaka\n## Random Forest","[{\"question\":\"Penelitian ini membandingkan metode machine learning apa saja untuk memprediksi perubahan harga saham?\",\"answer\":\"Random Forest, K-Nearest Neighbors (KNN), dan XGBoost dibandingkan untuk memprediksi perubahan harga saham.\"},{\"question\":\"Bagaimana hasil performa berdasarkan metrik ROC, akurasi, recall, dan presisi?\",\"answer\":\"Random Forest memperoleh nilai ROC tertinggi sebesar 0.98. XGBoost menunjukkan performa lebih baik pada akurasi, recall, dan presisi berturut-turut 0.93, 0.69, dan 0.77.\"},{\"question\":\"Mengapa metode Windowing diterapkan pada dataset dalam penelitian ini?\",\"answer\":\"Windowing digunakan untuk mengatasi permasalahan seperti overfitting, data leakage, serta kesalahan pembagian dataset antara training dan testing.\"}]","Evaluasi Komparatif Metode Machine Learning Untuk Memprediksi Perubahan Harga Saham - Jurnal Ilmiah Teknik Informatika | PDF",1785895633,12,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":29},"comparative-evaluation-of-machine-learning-methods-for-predicting-stock-price-changes-journal-of-computer-science","",{"@graph":37,"@context":86},[38,54,69],{"@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":20},"https://docshare.wps.com/id/document/comparative-evaluation-of-machine-learning-methods-for-predicting-stock-price-changes-journal-of-computer-science/124960/",{"url":53,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-18","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Penelitian ini membandingkan metode machine learning apa saja untuk memprediksi perubahan harga saham?","Question",{"text":76,"@type":77},"Random Forest, K-Nearest Neighbors (KNN), dan XGBoost dibandingkan untuk memprediksi perubahan harga saham.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Bagaimana hasil performa berdasarkan metrik ROC, akurasi, recall, dan presisi?",{"text":81,"@type":77},"Random Forest memperoleh nilai ROC tertinggi sebesar 0.98. XGBoost menunjukkan performa lebih baik pada akurasi, recall, dan presisi berturut-turut 0.93, 0.69, dan 0.77.",{"name":83,"@type":74,"acceptedAnswer":84},"Mengapa metode Windowing diterapkan pada dataset dalam penelitian ini?",{"text":85,"@type":77},"Windowing digunakan untuk mengatasi permasalahan seperti overfitting, data leakage, serta kesalahan pembagian dataset antara training dan testing.","https://schema.org",{"og:url":53,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,99,103,107,111,115,117,121,125,129,133],{"id":95,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},55,"Agama & Spiritualitas",60,"religion-spirituality",{"id":100,"doc_module":4,"doc_module_name":47,"category_name":101,"show_sort_weight":97,"slug":102},48,"Cerita & Novel","story-novel",{"id":104,"doc_module":4,"doc_module_name":47,"category_name":105,"show_sort_weight":97,"slug":106},56,"Gaya Hidup","lifestyle",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":97,"slug":110},51,"Komik","comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":97,"slug":114},53,"Layanan Kesehatan","healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":97,"slug":116},"research-report",{"id":118,"doc_module":4,"doc_module_name":47,"category_name":119,"show_sort_weight":97,"slug":120},49,"Sastra","literature",{"id":122,"doc_module":4,"doc_module_name":47,"category_name":123,"show_sort_weight":97,"slug":124},52,"Teknologi","technology",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":127,"show_sort_weight":97,"slug":128},50,"Ujian","exam",{"id":130,"doc_module":4,"doc_module_name":47,"category_name":131,"show_sort_weight":97,"slug":132},57,"Umum","general",{"id":134,"doc_module":4,"doc_module_name":47,"category_name":135,"show_sort_weight":4,"slug":136},181,"Formulir","formulir"]