[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118658-id":3,"doc-seo-118658-113":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},118658,8796095027276,"Valentina","https://avatar.qwps.com/avatar/d3BzX2FwX3Rlc3RfMjUxMTI2XzAxODA=",54,"Penelitian & Laporan","Prediksi Peringkat Akreditasi BAN PT Program Studi Sarjana Rumpun Ilmu Komputer Menggunakan Klasifikasi","Peringkat akreditasi menjadi indikator penting bagi calon mahasiswa dalam memilih program studi perguruan tinggi, namun data menunjukkan hanya sekitar 5% prodi rumpun Ilmu Komputer yang memperoleh peringkat Unggul dan A di LLDikti Wilayah III Jakarta. Penelitian ini bertujuan mengidentifikasi faktor-faktor yang memengaruhi nilai peringkat akreditasi melalui pendekatan machine learning. Metode K-Nearest Neighbors (KNN) dibandingkan dengan Gaussian Naïve Bayes, Decision Tree, dan Logistic Regression. Hasil menunjukkan model KNN dengan 2 variabel input memiliki AUC tertinggi 84,38% dan didukung cross validation 10-fold dengan akurasi 80%, sehingga mampu memprediksi peringkat akreditasi program studi khususnya rumpun Ilmu Komputer.","DOI [http://dx.doi.org/10.36722/sst.v10i2.3089](http://dx.doi.org/10.36722/sst.v10i2.3089)  \nPrediksi Peringkat Akreditasi BAN PT Program Studi Sarjana Rumpun Ilmu Komputer Menggunakan Klasifikasi  \nMachine Learning  \nBudi Aribowo 1, Budi Tjahjono 1*, Gerry Firmasnyah 1, Agung Mulyo Widodo1  \n1Magister Ilmu Komputer, Fakultas Ilmu Komputer, Universitas Esa Unggul Jl. Arjuna Utara No. 9, Jakarta, 11510.  \n[Penulis untuk Korespondensi/E-mail: ](Penulis untuk Korespondensi/E-mail: budi.tjahono@esaunggul.ac.id)[budi.tjahono@esaunggul.ac.id](Penulis untuk Korespondensi/E-mail: budi.tjahono@esaunggul.ac.id)  \nAbstract – Accreditation ranking is one of the causes and indicators chosen by prospective students when choosing a study program in higher education. From the data collected, only 5% of study programs in the Computer Science group have a Superior accreditation rating and an A accreditation rating in LLDikti Region III Jakarta. So it is necessary to know the factors that influence the accreditation ranking. The machine learning methodology used in this approach is K-Nearest Neighbors (KNN) and from the data obtained there are 6 factors that can be strongly suspected to influence the study program accreditation value. The four machine learning models, namely KNN, Gaussian Naïve Bayes Decision Tree and Logistic Regression, it was found that the KNN machine learning model with 2 input variables had the highest AUC value, namely 84.38%. Meanwhile, from the model simulation run by KNN machine learning, 2 input variables can produce relatively accurate prediction results. And the results of cross validation with 10 folds support the selected machine learning with an accuracy level of 80%. In general, the KNN machine learning model with 2 input variables was able to predict the accreditation rating of Study Programs, especially from the Computer Science Cluster.  \nAbstrak – Peringkat akreditasi merupakan salah satu sebab dan indikator yang dipilih oleh calon mahasiswa dalam memilih suatu program studi di perguruan tinggi. Berdasarkan data yang dikumpulkan hanya terdapat 5% program studi pada rumpun Ilmu Komputer yang memiliki peringkatakreditasi Unggul dan peringkat akreditasi A di LLDikti Wilayah III Jakarta. Sehingga demikian perlu diketahui faktor-faktor yang mempengaruhi peringkat akreditasi. Metodologi machine learning yang digunakan dalam pendekatan ini adalah K-Nearest Neighbors dan dari data yang berhasil diperoleh terdapat 6 faktor yang dapat diduga kuat mempengaruhi nilai akreditasi. Berdasarkan keempat model machine learning yaitu KNN, Gaussian Naïve Bayes, Decision Tree dan Regresi Logistik didapatkan model machine learning KNN 2 variabel input memiliki nilai AUC tertinggi yaitu sebesar 84,38%. Sertadari simulasi model yang dijalankan machine learning KNN 2 variabel input dapat menghasilkan hasilprediksi yang relatif tepat, dan hasil cross validation dengan 10 fold mendukung machine learning yang terpilih dengan tingkat akurasi sebesar 80%. Secara umum model machine learning KNN dengan 2 variabel input dapat memprediksi peringkat akreditasi Program Studi terutama dari Rumpun Ilmu Komputer.  \nKeywords –Accreditation, Area Under Curve (AUC), Department of School, Kfold Cross Validation, Machine Learning.  \nPENDAHULUAN  \nAkreditasi Program Studi (APS)  \nsuatu kegiatan untuk menjaminsuatu program studi di sebuah  \nmerupakan kelayakan universitas  \nsebagaimana yang dinyatakan pada Peraturan Menteri Pendidikan dan Kebudayaan Republik Indonesia Nomor 5 Tahun 2020 tentang Akreditasi Perguruan Tinggi ayat (2) yang berbunyi:“Akreditasi Program Studi adalah kegiatan penilaian  \nuntuk menentukan kelayakan Program Studi”. Saatini menemukan suatu pendidikan tinggi yang layak merupakan tantangan tersendiri bagi kebanyakanorang tua dan siswa [1] .  \nHasil penelitian [2] menjelaskan bahwa kulturkualitas dan kinerja suatu perguruan tinggi akan mempengaruhi nilai akreditasi baik secara langsung maupun tidak langsung. Nilai akreditasi program","cbCailghtJ9OEtcG","https://ap.wps.com/l/cbCailghtJ9OEtcG","pdf",324866,4,1,6,"Indonesian","id",113,"# Pendahuluan\n## Akreditasi Program Studi (APS)\n# Metode\n## Pengumpulan Data\n# Hasil dan Pembahasan\n## Model Klasifikasi dan Evaluasi\n# Kesimpulan","[{\"question\":\"Mengapa peringkat akreditasi penting bagi calon mahasiswa dalam memilih program studi?\",\"answer\":\"Peringkat akreditasi menjadi salah satu sebab dan indikator pilihan calon mahasiswa saat menentukan program studi di perguruan tinggi.\"},{\"question\":\"Metode machine learning apa yang digunakan untuk memprediksi peringkat akreditasi?\",\"answer\":\"Penelitian menggunakan K-Nearest Neighbors (KNN) dan membandingkannya dengan Gaussian Naïve Bayes, Decision Tree, dan Logistic Regression.\"},{\"question\":\"Bagaimana performa model KNN yang dipilih berdasarkan hasil AUC dan validasi silang?\",\"answer\":\"Model KNN dengan 2 variabel input menghasilkan AUC tertinggi sebesar 84,38% dan cross validation 10-fold mendukung akurasi sebesar 80%.\"}]","Prediksi Peringkat Akreditasi BAN PT Program Studi Sarjana Rumpun Ilmu Komputer Menggunakan Klasifikasi | 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peringkat akreditasi penting bagi calon mahasiswa dalam memilih program studi?","Question",{"text":76,"@type":77},"Peringkat akreditasi menjadi salah satu sebab dan indikator pilihan calon mahasiswa saat menentukan program studi di perguruan tinggi.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Metode machine learning apa yang digunakan untuk memprediksi peringkat akreditasi?",{"text":81,"@type":77},"Penelitian menggunakan K-Nearest Neighbors (KNN) dan membandingkannya dengan Gaussian Naïve Bayes, Decision Tree, dan Logistic Regression.",{"name":83,"@type":74,"acceptedAnswer":84},"Bagaimana performa model KNN yang dipilih berdasarkan hasil AUC dan validasi silang?",{"text":85,"@type":77},"Model KNN dengan 2 variabel input menghasilkan AUC tertinggi sebesar 84,38% dan cross validation 10-fold mendukung akurasi sebesar 80%.","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 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