[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-231377-113":3,"doc-detail-231377-id":80,"detail-sidebar-cat-0-id-113":97},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":73,"head_meta":75,"extra_data":77,"updated_unix":79},113,"id","prediction-of-graduation-path-for-university-admissions-in-banda-aceh-using-support-vector-machine-algorithm-svm-classification-research-with-weka","PREDIKSI KELULUSAN JALUR MASUK PERGURUAN TINGGI BANDA ACEH MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE - Penelitian Klasifikasi SVM Berbantuan WEKA","","Penelitian ini memprediksi kelulusan jalur masuk perguruan tinggi di Banda Aceh melalui metode klasifikasi menggunakan algoritma Support Vector Machine (SVM) berbantuan machine learning WEKA. Data yang digunakan berasal dari data mahasiswa baru tahun sekolah 2019. Hasil menunjukkan dua variabel dengan hubungan terbaik, yaitu variabel bimbel seleksi masuk PTN dan variabel jalur minat, dengan nilai korelasi Pearson sebesar -0,180 dan signifikansi 0,002 pada variabel bimbel serta akurasi 0,311 dan signifikansi 0,000 pada variabel jalur minat.",{"@graph":14,"@context":72},[15,34,55],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/id/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/id/document/penelitian-laporan/","Penelitian & Laporan",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/id/document/prediction-of-graduation-path-for-university-admissions-in-banda-aceh-using-support-vector-machine-algorithm-svm-classification-research-with-weka/231377/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/prediction-of-graduation-path-for-university-admissions-in-banda-aceh-using-support-vector-machine-algorithm-svm-classification-research-with-weka/231377.png","ImageObject",300,407,{"name":42,"@type":43},"Connor ","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-09-27","2026-09-10",true,{"@type":52,"interactionType":53,"userInteractionCount":33},"InteractionCounter",{"@type":54},"ViewAction",{"@type":56,"mainEntity":57},"FAQPage",[58,64,68],{"name":59,"@type":60,"acceptedAnswer":61},"Bagaimana tujuan penelitian memprediksi kelulusan jalur masuk PTN?","Question",{"text":62,"@type":63},"Penelitian bertujuan memprediksi kelulusan jalur masuk perguruan tinggi menggunakan metode klasifikasi berbasis SVM yang dibantu WEKA.","Answer",{"name":65,"@type":60,"acceptedAnswer":66},"Apa variabel yang memiliki hubungan paling baik dalam hasil penelitian?",{"text":67,"@type":63},"Variabel bimbel seleksi masuk PTN dan variabel jalur minat menjadi dua variabel dengan hubungan paling baik berdasarkan hasil analisis yang ditampilkan.",{"name":69,"@type":60,"acceptedAnswer":70},"Bagaimana kinerja model SVM dalam pengujian?",{"text":71,"@type":63},"Berdasarkan hasil cross-validation dan percentage split, algoritma SVM menunjukkan akurasi rata-rata sekitar 99% dengan nilai AUC 0,9907.","https://schema.org",{"og:url":32,"og:type":74,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":76,"canonical":32},"index,follow",{"doc_id":78,"site_id":7},231377,1789062986,{"code":4,"msg":81,"data":82},"success",{"doc_id":78,"user_id":83,"nickname":42,"user_avatar":84,"doc_module":4,"category_id":85,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":86,"file_id":87,"file_url":88,"file_type":89,"file_size":90,"view_count":33,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":91,"language":92,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":93,"faqs":94,"seo_title":95,"seo_description":12,"update_tm":79,"read_time":96},687207022233,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",54,"PREDIKSI KELULUSAN JALUR MASUK PERGURUAN TINGGI BANDA ACEH MENGGUNAKAN ALGORITMA SUPPORT VECTOR  \nMACHINE  \nMarhamah  \nProgram Studi Pendidikan Teknologi Informasi Fakultas Tarbiyah dan Keguruan, Universitas Islam Negeri Ar-Raniry Banda Aceh  \n[Email:](Email: marhamahm7@gmail.com)[ ](Email: marhamahm7@gmail.com)[marhamahm7@gmail.com](Email: marhamahm7@gmail.com)  \nAbstract: Nowadays there are several types of entry points for new student admissions at tertiary institutions. There are many ways that every prospective student can prepare to pass the PTN entrance selection exam. Therefore the research aim to predict the graduation of tertiary entrance by classification method using the SVM algorithm (Support Vector Machine) which is assisted by WEKA machine learning, using data of new students in the 2019 school year. The final results in this study there are two variables that have the most relationship both the PTN entry selection tutoring variable and the interest pathway variable, with the PTN entry selection tutoring variable having a Pearson correlation value of-0.180 ** and a significance value of 0.002, the interest pathway having an accuracy value of 0.311 ** and a significance value of 0,000. Then based on the results of cross-validation testing and percentage split SVM algorithm has very good accuracy with an average accuracy of 99% with an AUC (Area Under Curve) value of 0.9907.  \nKeyword: PTN Entry Data, Data Mining, Support Vector Machine, Split Percentage  \nAbstrak : Saat ini ada beberapa jenis jalur masuk penerimaan mahasiswa baru di perguruan tinggi. Banyak cara yang bisa dipersiapkan oleh setiap calon mahasiswa untuk bisa lulus ujianseleksi masuk PTN. Penelitian ini memprediksi kelulusanjalur masuk perguruan tinggi dengan metode klasifikasi menggunakan algoritma SVM yang dibantu dengan machine learning WEKA. Hasil akhir penelitian ini terdapat dua variabel yang memiliki hubungan paling baikyaitu variabel bimbel seleksi masuk PTN dan variabel jalur minat,dengan variabel bimbelseleksi masuk PTN memiliki nilai pearson correlation sebesar-0,180** dan nilai signifikansisebesar 0,002, jalur minat memiliki nilai akurasi sebesar 0,311** dan nilai signifikansi sebesar 0,000. Kemudian berdasarkan hasil pengujian cross-validation dan percentage split algoritma SVM memiliki akurasi yang sangat baik dengan rata-rata akurasi mencapai 99% dengan nilai AUC (Area Under Curve) sebesar 0.9907 dan waktu konsumsi yang dibutuhkan untuk pengujian hanya sebesar 0.01-0.02 second.  \nKata Kunci : Jalur MasukPTN, Data Mining, Support Vector Machine, Percentage Split  \n1. Pendahuluan  \nBanyak cara yang bisa dipersiapkan oleh setiap calon mahasiswa untuk bisalulus ujian seleksi masuk PTN diantaranya adalah dengan mengikuti bimbel, les privat, mempunyai prestasi akademik, dan nilai UN yang memadai. Akan tetapiada kasus dimana calon mahasiswa sudah melakukan persiapantersebut namun hasilnya mereka dinyatakan tidak lulus. Ada juga kasus dimana calon mahasiswatersebut tidak melakukan persiapan yang matang dinyatakan lulus di jalur yang  \ndiminati.Ini menandakan persiapan-persiapan tersebut belum bisa menjamin calon mahasiswa untuk lulus dijalur yang diminati. Metode data mining adalah salah satu cara untuk menganalisis prediksi pada permasalahan tersebut.  \nDalam proses pengolahan data dengan menggunakan data mining, telah banyak di lakukan penelitian sebelumya, diantaranya penelitian tentang “ implementasi data mining untuk memprediksi kelulusan mahasiswa menggunakan metode Naive Bayes, dengan hasil akhir memiliki nilai akurasi sebesar 94%(Syarli & Muin, 2016) . Selain itu penelitian tentang “Analisis kinerja Metode Naïve Bayes Dan SVM Untuk Penentuan Pola Kelompok Penyakit”, hasil yang di dapatkan dengan metode SVM dengan nilai akurasi mencapai 99%, dan metode Naïve Bayes dengan nilai akurasi mencapai 93% . Dari nilai akurasi yang di dapatkan menunjukkan metode SVM lebih akurat daripada metode naive bayes (SITANGGANG, 2017) . Untuk mencapai pe","cbCainBNBQ7dUtHs","https://ap.wps.com/l/cbCainBNBQ7dUtHs","pdf",1016213,11,"Indonesian","# Pendahuluan\n# Kajian Pustaka\n## Jalur Masuk Perguruan Tinggi\n## Data Mining","[{\"question\":\"Bagaimana tujuan penelitian memprediksi kelulusan jalur masuk PTN?\",\"answer\":\"Penelitian bertujuan memprediksi kelulusan jalur masuk perguruan tinggi menggunakan metode klasifikasi berbasis SVM yang dibantu WEKA.\"},{\"question\":\"Apa variabel yang memiliki hubungan paling baik dalam hasil penelitian?\",\"answer\":\"Variabel bimbel seleksi masuk PTN dan variabel jalur minat menjadi dua variabel dengan hubungan paling baik berdasarkan hasil analisis yang ditampilkan.\"},{\"question\":\"Bagaimana kinerja model SVM dalam pengujian?\",\"answer\":\"Berdasarkan hasil cross-validation dan percentage split, algoritma SVM menunjukkan akurasi rata-rata sekitar 99% dengan nilai AUC 0,9907.\"}]","PREDIKSI KELULUSAN JALUR MASUK PERGURUAN TINGGI BANDA ACEH MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE - Penelitian Klasifikasi SVM Berbantuan WEKA | PDF",17,{"code":4,"msg":81,"data":98},[99,104,108,112,116,120,122,126,130,134,138],{"id":100,"doc_module":4,"doc_module_name":25,"category_name":101,"show_sort_weight":102,"slug":103},55,"Agama & Spiritualitas",60,"religion-spirituality",{"id":105,"doc_module":4,"doc_module_name":25,"category_name":106,"show_sort_weight":102,"slug":107},48,"Cerita & Novel","story-novel",{"id":109,"doc_module":4,"doc_module_name":25,"category_name":110,"show_sort_weight":102,"slug":111},56,"Gaya Hidup","lifestyle",{"id":113,"doc_module":4,"doc_module_name":25,"category_name":114,"show_sort_weight":102,"slug":115},51,"Komik","comic",{"id":117,"doc_module":4,"doc_module_name":25,"category_name":118,"show_sort_weight":102,"slug":119},53,"Layanan Kesehatan","healthcare",{"id":85,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":102,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":102,"slug":125},49,"Sastra","literature",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":102,"slug":129},52,"Teknologi","technology",{"id":131,"doc_module":4,"doc_module_name":25,"category_name":132,"show_sort_weight":102,"slug":133},50,"Ujian","exam",{"id":135,"doc_module":4,"doc_module_name":25,"category_name":136,"show_sort_weight":102,"slug":137},57,"Umum","general",{"id":139,"doc_module":4,"doc_module_name":25,"category_name":140,"show_sort_weight":4,"slug":141},181,"Formulir","formulir"]