[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123705-id":3,"doc-seo-123705-113":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},123705,962084925502,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",54,"Penelitian & Laporan","Sistem Tinjauan Literatur Sistematis Tantangan Ketidakseimbangan Kelas dalam Machine Learning - Abstrak dan Pendahuluan","Pertumbuhan data yang sangat cepat menimbulkan tantangan besar dalam penyimpanan, pengelolaan, dan analisis. Dalam banyak kasus, data untuk klasifikasi machine learning bersifat tidak seimbang antara kelas mayoritas dan minoritas, sehingga model cenderung mempelajari pola kelas mayoritas dan mengabaikan pola kelas minoritas. Ketidakseimbangan ini berpotensi menurunkan akurasi dan kinerja model. Tinjauan literatur sistematis ini bertujuan memetakan perkembangan terbaru, metode penanganan, serta teknik evaluasi untuk mengatasi data imbalance agar hasil klasifikasi lebih tidak bias, akurat, dan konsisten.","SYSTEMATIC LITERATURE REVIEW OF THE CLASS IMBALANCE CHALLENGES  \nIN MACHINE LEARNING  \nRifqi Fitriadi*1, Deni Mahdiana2  \n1Computer Science Master's Study Program, Faculty of Information Technology, Universitas Budi Luhur,  \nJakarta, Indonesia  \n2Information Systems Study Program, Faculty of Information Technology, Universitas Budi Luhur, Jakarta,  \nIndonesia  \n[Email:](Email:1rifqi0587@gmail.com)[1](Email:1rifqi0587@gmail.com)[rifqi0587@gmail.com](Email:1rifqi0587@gmail.com), [2](2deni.mahdiana@budiluhur.ac.id)[deni.mahdiana@budiluhur.ac.id](2deni.mahdiana@budiluhur.ac.id)  \n(Article received: March 24, 2023; Revision: April 28, 2023; published: October 15, 2023)  \nAbstract  \nThe significant growth of data poses its own challenges, both in terms of storing, managing, and analyzing the available data. Untreated and unanalyzed data can only provide limited benefits to its owner. In many cases, the data we analyze is imbalanced. An example of natural data imbalance is in detecting financial fraud, where the number of non-fraudulent transactions is usually much higher than fraudulent ones. This imbalance issue can affect the accuracy and performance of machine learning classification models. Many machine learning classification models tend to learn more general patterns in the majority class. As a result, the model may overlook patterns that exist in the minority class. Various research has been conducted to address the problem of imbalanced data. The objective of this systematic literature review is to provide the latest developments regarding the cases, methods used, and evaluation techniques in handling imbalanced data. This research successfully identifies new methods and is expected to provide more choices for researchers so that imbalanced data can be properly handled, and classification models can produce unbiased, accurate, and consistent results.  \nKeywords: Class Imbalance, Handling Method, Machine Learning, Systematic Literature Review.  \nTINJAUAN LITERATUR SISTEMATIS DARI TANTANGANKETIDAKSEIMBANGAN KELAS DALAM MACHINE LEARNING  \nAbstrak  \nPertumbuhan data secara signifikan menjadikan tantangan tersendiri, baik dalam hal menyimpan, mengelola dan menganalisis data yang tersedia. Data yang tidak diolah dan dianalisis hanya sebatas data saja, tidak bisamemberikan manfaat bagi pemiliknya. Dalam banyak kondisi, data yang kita analisa bersifat imbalance. Salah satu contoh ketidakseimbangan data yang terjadi secara alami adalah data untuk mendeteksi kecurangan (fraud) di bidang keuangan, dimanajumlah transaksi yang tidak curang biasanya jauh lebih banyak dibandingkan transaksi yang curang. Masalah ketidakseimbangan data dapat mempengaruhi akurasi dan kinerja model klasifikasi machine learning. Banyak model klasifikasi machine learning cenderung mempelajari pola yang lebih umum pada kelas mayoritas. Hasilnya, model mungkin mengabaikan pola yang terdapat pada kelas minoritas. Berbagai penelitiantelah dilakukan untuk mengatasi permasalahan data yang tidak seimbang. Tujuan dari penelitian Tinjuan Literatur Sistematis ini adalah untuk memberikan gambaran perkembangan terbaru tentang kasus yang terjadi, metode yang digunakan dan teknik evaluasi dalam menangani ketidakseimbangan data. Penelitian ini berhasil mengidentifikasi metode-metode baru dan diharapkan dapat memberikan lebih banyak pilihan bagi para peneliti sehingga data imbalance bisa ditangani dengan baik dan model klasifikasi menghasilkan model yang tidak bias, akurat dankonsisten.  \nKata kunci: Ketidakseimbangan Kelas, Machine Learning, Metode Penanganan, Tinjauan Literatur Sistematis.  \n1. PENDAHULUAN  \nBeberapa dekade terakhir, penggunaan teknologi informasi berkembang sangat pesat [1] . Penggunaan teknologi informasi memberikan banyak  \nkemudahan dalam kehidupan sehari-hari [2] . Teknologi informasi mengubah cara kita dalambekerja, belajar, berperilaku maupun berinteraksi antara satu individu dengan individu yang lain [3] . Penggunaan teknologi informasi telah diterapk","cbCaibQXDFfNkJZ8","https://ap.wps.com/l/cbCaibQXDFfNkJZ8","pdf",459792,3,1,9,"Indonesian","id",113,"# Pendahuluan\n## Latar belakang pertumbuhan data dan Big Data\n## Konsep ketidakseimbangan kelas pada data machine learning\n## Faktor penyebab ketidakseimbangan data","[{\"question\":\"Apa yang dimaksud dengan ketidakseimbangan kelas (class imbalance) dalam machine learning?\",\"answer\":\"Ketidakseimbangan kelas adalah kondisi ketika jumlah data antar kelas tidak seimbang, yaitu kelas mayoritas jauh lebih banyak daripada kelas minoritas.\"},{\"question\":\"Bagaimana class imbalance memengaruhi kinerja model klasifikasi?\",\"answer\":\"Model cenderung mempelajari pola umum pada kelas mayoritas sehingga pola pada kelas minoritas dapat terabaikan, yang berdampak pada akurasi dan performa.\"},{\"question\":\"Apa tujuan tinjauan literatur sistematis dalam dokumen ini?\",\"answer\":\"Memberikan gambaran perkembangan terbaru tentang kasus, metode yang digunakan, dan teknik evaluasi dalam menangani data yang tidak seimbang.\"}]","Sistem Tinjauan Literatur Sistematis Tantangan Ketidakseimbangan Kelas dalam Machine Learning - Abstrak dan Pendahuluan | PDF",1785818106,14,{"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},"systematic-literature-review-of-class-imbalance-challenges-in-machine-learning-abstract-and-introduction","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"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":20},"https://docshare.wps.com/id/document/penelitian-laporan/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/id/document/systematic-literature-review-of-class-imbalance-challenges-in-machine-learning-abstract-and-introduction/123705/",4,{"url":52,"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","2026-08-15","2026-08-04",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},"Apa yang dimaksud dengan ketidakseimbangan kelas (class imbalance) dalam machine learning?","Question",{"text":76,"@type":77},"Ketidakseimbangan kelas adalah kondisi ketika jumlah data antar kelas tidak seimbang, yaitu kelas mayoritas jauh lebih banyak daripada kelas minoritas.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Bagaimana class imbalance memengaruhi kinerja model klasifikasi?",{"text":81,"@type":77},"Model cenderung mempelajari pola umum pada kelas mayoritas sehingga pola pada kelas minoritas dapat terabaikan, yang berdampak pada akurasi dan performa.",{"name":83,"@type":74,"acceptedAnswer":84},"Apa tujuan tinjauan literatur sistematis dalam dokumen ini?",{"text":85,"@type":77},"Memberikan gambaran perkembangan terbaru tentang kasus, metode yang digunakan, dan teknik evaluasi dalam menangani data yang tidak seimbang.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"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"]