[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123327-id":3,"doc-seo-123327-113":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},123327,2336475104362,"Eden","https://ap-avatar.wpscdn.com/avatar/22000c4c46a41b752dd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786595829695023868",54,"Penelitian & Laporan","Kesimpulan dan Saran - Pemanfaatan Machine Learning untuk Memprediksi Dropout Mahasiswa","Bab ini merangkum hasil penelitian tentang pemanfaatan algoritma machine learning untuk memprediksi kemungkinan dropout mahasiswa. Implementasi beberapa metode seperti Random Forest, KNN, Naïve Bayes, dan SVM menunjukkan variasi akurasi yang dipengaruhi jenis algoritma serta kondisi data. Random Forest menjadi yang paling efektif dengan akurasi tertinggi, termasuk pada skenario tanpa modifikasi, dengan hyperparameter tuning, maupun saat ditambah noise. Analisis feature importance mengidentifikasi variabel kunci seperti keaktifan di perpustakaan, penggunaan LMS, poin mahasiswa, dan jumlah SKS semester 2.","BAB 6  \n# KESIMPULAN DAN SARAN\n\n## 6.1 Kesimpulan\n\nAlgoritma prediksi berbasis machine learning seperti Random Forest,KNN, Naïve Bayes, dan SVM telah berhasil diimplementasikan untukmemprediksi kemungkinan dropout mahasiswa. Dari hasil penelitian yangdilakukan, Random Forest terbukti paling efektif dalam memprediksi dropout,menunjukkan tingkat akurasi dan kemampuan diskriminatif yang tinggi terhadapkelas target. Algoritmainiefisiendalam menangani data yang tidakseimbang danmemiliki robustness terhadap variasi data, terutama ketika dilakukanhyperparameter tuning dan ketika menghadapi noise tambahan dalam dataset.  \nTingkat akurasi algoritma prediksi dalam penerapan prediksi dropoutmahasiswa sangat bervariasi tergantung pada jenis algoritma dan kondisi data.Random Forest menunjukkan akurasi sangat tinggi yaitu 0.99725 tanpamodifikasi, 0.99718 dengan hyperparameter tuning dan 0.99127 denganpenambahan noise. KNN dan Naïve Bayes juga menunjukkan peningkatanperforma dengan hyperparameter tuning. Sementara itu, SVM, meskipunmenunjukkan peningkatan dengan tuning, tetap memiliki performa yang lebihrendah dibandingkan algoritma lainnya. Ini menunjukkan bahwa beberapaalgoritma lebih sensitif terhadap perubahan dalam data dan membutuhkanpenyesuaian parameter yanglebih cermat untuk mencapai optimalisasiprediksi.  \nAnalisis feature importance yang dilakukan menunjukkan bahwavariabelsepertikeaktifan mahasiswadi perpustakaan, penggunaan LMS, poin mahasiswa,dan jumlah SKS semester 2 adalah faktor kunci dalam prediksi dropout.Penemuan ini memberikan landasan untuk intervensi yang lebih ditargetkan danperencanaan akademik yang lebih efisien, sehingga potensi dropout dapatdiminimalisir.  \n## 6.2 Saran\n\nBerdasarkan hasil penelitian ini, beberapa saran untuk penelitian selanjutnya adalahmelakukan penelitian tentang:  \n1. Pengaruh variabel lain yang mungkin mempengaruhi dropout, sepertidukungan sosial, kesehatan mental, dan faktor ekonomi, untuk memperkayadalamprediksi mahasiswa dropout.  \n2. Eksplorasi dan penerapan bentuk prediksi lain seperti Deep Learning atauensemble methods untuk melihat apakah ada peningkatan dalam akurasi danefektivitas.  \n3. Kemungkinan integrasi algoritma prediksi ke dalam sistem informasimahasiswa yang ada untuk fasilitasi peringatan dini dan intervensi yang lebihefektif.  \n# DAFTAR PUSTAKA\n\n[1] Bashir Barthos, Perguruan Tinggi Swastadi Indonesia: Proses PendirianPenyelenggaraan & Dunia.  \n[2] “Pangkalan Data Pendidikan Tinggi.” Accessed: May 06, 2024. [Online] .  \nAvailable:  \nhttps://pddikti.kemdikbud.go.id/data_pt/OEZCMjVDN0MtQUZBQS00MDM4LTg5NDktODQ1RDU3NEZEQUVF  \n[3] T. Dharmawan, H. Ginardi, and A. Munif, “Dropout Detection Using Non -Academic Data,” 2018.  \n[4] B. Pérez, C. Castellanos, and D. Correal, “Predicting student drop-out ratesusing data mining techniques: A case study,” in Communications inComputer and Information Science, Springer Verlag, 2018, pp. 111–125.doi: 10.1007/978-3-030-03023-0_ 10.  \n[5] M. Vaarma and H. Li, “Predicting student dropouts with machine learning:An empirical study in Finnish higher education,” Technol Soc, vol. 76,Mar. 2024, doi: 10.1016/j.techsoc.2024.102474 .  \n[6] S. R. Sihare, “Student Dropout Analysis in Higher Education and Retentionby Artificial Intelligence and Machine Learning,” SN Comput Sci, vol. 5,no. 2, Feb. 2024, doi: 10.1007/s42979-023-02458-w.  \n[7] M. M. Tamada, R. Giusti, and J. F. de M. Netto, “Predicting Students atRisk of Dropout in Technical Course Using LMS Logs,” Electronics(Switzerland), vol. 11, no. 3, Feb. 2022, doi: 10.3390/electronics11030468 .  \n[8] M. M. Rofi, F. A. Setiawan, and F. Riana, “Perbandingan Metode K-NnDan Random Forest Pada Klasifikasi Mahasiswa Berpotensi Dropout,”INFOTECH journal, vol. 10, no. 1, pp. 84–89, Mar. 2024, doi:  \n10.31949/infotech.v10i1.8856 .  \n[9] D. Andrade-Girón et al., “Predicting Student Dropout based on MachineLearning and Deep Learning: A Systematic Review,”EAI EndorsedTransactions on Scalable Information Systems, vol","cbCaitxtow3GtHmF","https://ap.wps.com/l/cbCaitxtow3GtHmF","pdf",271047,4,1,7,"Indonesian","id",113,"# Kesimpulan dan Saran\n## Kesimpulan\n## Saran\n# Daftar Pustaka","[{\"question\":\"Algoritma mana yang paling efektif untuk memprediksi dropout mahasiswa?\",\"answer\":\"Random Forest terbukti paling efektif dengan tingkat akurasi sangat tinggi dan kemampuan diskriminatif terhadap kelas target.\"},{\"question\":\"Bagaimana pengaruh hyperparameter tuning terhadap performa model?\",\"answer\":\"Sebagian algoritma menunjukkan peningkatan performa setelah hyperparameter tuning, meskipun sensitivitas terhadap perubahan data berbeda antar algoritma.\"},{\"question\":\"Variabel apa yang menjadi faktor kunci dalam prediksi dropout berdasarkan feature importance?\",\"answer\":\"Variabel kunci meliputi keaktifan mahasiswa di perpustakaan, penggunaan LMS, poin mahasiswa, dan jumlah SKS semester 2.\"}]","Kesimpulan dan Saran - Pemanfaatan Machine Learning untuk Memprediksi Dropout Mahasiswa | PDF",1785815957,11,{"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},"conclusion-and-recommendations-using-machine-learning-to-predict-student-dropout","",{"@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/conclusion-and-recommendations-using-machine-learning-to-predict-student-dropout/123327/",{"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","2026-08-17","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},"Algoritma mana yang paling efektif untuk memprediksi dropout mahasiswa?","Question",{"text":76,"@type":77},"Random Forest terbukti paling efektif dengan tingkat akurasi sangat tinggi dan kemampuan diskriminatif terhadap kelas target.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Bagaimana pengaruh hyperparameter tuning terhadap performa model?",{"text":81,"@type":77},"Sebagian algoritma menunjukkan peningkatan performa setelah hyperparameter tuning, meskipun sensitivitas terhadap perubahan data berbeda antar algoritma.",{"name":83,"@type":74,"acceptedAnswer":84},"Variabel apa yang menjadi faktor kunci dalam prediksi dropout berdasarkan feature importance?",{"text":85,"@type":77},"Variabel kunci meliputi keaktifan mahasiswa di perpustakaan, penggunaan LMS, poin mahasiswa, dan jumlah SKS semester 2.","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"]