[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126021-id":3,"doc-seo-126021-113":31,"detail-sidebar-cat-0-id-113":93},{"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},126021,2336474466412,"Ezra","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",54,"Penelitian & Laporan","Komparasi Algoritma Machine Learning untuk Menganalisis Sentimen Ulasan pada Aplikasi Digital Korlantas Polri","Aplikasi Digital Korlantas Polri menghadirkan kemudahan bagi masyarakat dalam memperpanjang SIM, sehingga ulasan pengguna menjadi sumber penting untuk memahami persepsi publik terhadap layanan. Penelitian ini mengevaluasi lima algoritma machine learning—Support Vector Machine (SVM), Naive Bayes, Random Forest, K-Nearest Neighbors (KNN), dan Logistic Regression—dalam analisis sentimen ulasan. Sebanyak 10.000 ulasan berlabel diproses melalui validasi ahli bahasa, pemrosesan ulang, serta pembobotan kata, dengan penerapan SMOTE sebelum pembagian data. Hasil menunjukkan Random Forest dan SVM memberikan kinerja terbaik berdasarkan akurasi, presisi, recall, dan F1.","KomparasiAlgoritma Machine Learning Untuk Menganalisis SentimenUlasan PadaAplikasi Digital Korlantas Polri  \nSiti Delimasari1􀀍 , Kusrini2  \n1, 2 Informatika, PJJ MTI, Universitas Amikom Yogyakarta, Indonesia  \n\n| Informasi Artikel\u003Cbr>Riwayat Artikel\u003Cbr>Diserahkan : 01-08-2024\u003Cbr>Direvisi : 15-08-2024\u003Cbr>Diterima : 25-08-2024\u003Cbr>Kata Kunci:\u003Cbr>Analisis Sentimen;\u003Cbr>Aplikasi Digital Korlantas  Polri; Machine Learning \u003Cbr>Keywords :\u003Cbr>Sentiment Analysis, Digital Korlantas Polri Application, Machine Learning | ABSTRAK |\n| --- | --- |\n|  | Aplikasi Digital Korlantas Polri merupakan aplikasi mobile yang memberikan kemudahan bagi masyarakat dalam memperpanjang SIM. Analisis sentimen terhadap ulasan pengguna membantu Korlantas Polri mengidentifikasi persepsi publik terhadap layananyang diberikan. Penelitian ini bertujuan mengevaluasi lima algoritma machine learning mana yang paling baik performanya dari Support Vector Machine (SVM), Naive Bayes, Random Forest, K-Nearest Neighbors (KNN), dan Logistic Regression dalam proses analisis sentimen. Evaluasidilakukan dengan mengukur akurasi, presisi, recall dan F1 measure. Terdapat 10.000 ulasan diberi label dengan validasi ahli bahasa, diproses ulang, diberi bobot kata setelah data dikumpulkan. Teknik over-sampling minoritas sintetis (SMOTE) diterapkan sebelum pembagian data untuk pelatihan dan pengujian. Hasil evaluasi menunjukkan Random Forest dan SVM melakukannya dengan paling baik. Random Forest memiliki akurasi 90,77%, recall 90,77%, dan nilai F1 tertinggi yaitu 90,79% . SVM memiliki presisi tertinggi dengan 91,14% di antara algoritma lainnya, yang menunjukkan potensi besar kedua algoritmaini menganalisis sentimen ulasan aplikasi digital Korlantas Polri. |\n|  | ABSTRACT |\n|  | Korlantas Polri Digital Application is one of the mobile applications that provides ease for the public in extending the driving license. Sentiment analysis of user reviews can help korlantas polri identify public perception of the given service. The study aims to evaluate which of the five machine learning algorithms performed bestfrom Support Vector Machine (SVM), Naive Bayes, Random Forest, K-Nearest Neighbors (KNN), and Logistic Regression in sentiment analysis. The evaluation was done by measuring accuracy, precision, recall and F1 measure. There were 10,000 reviews labelled with linguistic validation, re-processed, and word weighted after data was collected. Synthetic minority over-sampling techniques (SMOTE) are applied before data splitting for training and testing. The evaluation shows that Random Forest and SVM do the best. Random Forest has an accuracy of 90.77%, recall 90.77%, and its highest F1 rating is 90.79%. SVM has the highest precision with 91.14% among other algorithms, which shows the great potential of both of these algorítms in the analysis of sentiment reviews of digital applications Korlantas Polri. |\n| Corresponding Author :\u003Cbr>Siti Delimasari\u003Cbr>Teknik Informatika, PJJ MTI, Universitas Amikom Yogyakarta, Indonesia\u003Cbr>Jl. Ring Road Utara, Ngringin, Condongcatur, Kec. Depok, Kabupaten Sleman, DIY Email: [sari@swu.ac.id](sari@swu.ac.id) |  |\n\nPENDAHULUAN  \nAplikasi mobile semakin populer dalam kehidupan sehari-hari di era digital, termasuk layanan pemerintah seperti perpanjangan SIM. Platform resmi untuk memperpanjang SIM adalahaplikasi digital yang disediakan oleh Korlantas Polri (Kaburuan & Setiawan, 2023) . Aplikasi tersebut saat ini telah diunduh oleh banyak pengguna di Google Play Store, dengan rating 3,6 dan 103 ribu ulasan positif, negatif, dan netral berdasarkan peringkat bintang atau rating yang diberikan pengguna aplikasi. rating bintang memberikan gambaran umum tentang kepuasan pengguna (Bahtiar et al., 2023) .  \nDalam konteks aplikasi digital Korlantas Polri, analisis sentimen dapat membantu mengidentifikasi persepsi publik terhadap layanan yang diberikan melalui ulasan pengguna.(Jardim & Mora, 2021) .Ulasan Pengguna dievaluasi menggunakan beberapa algoritma machine learning un","cbCails6uV7YdR57","https://ap.wps.com/l/cbCails6uV7YdR57","pdf",354638,7,1,9,"Indonesian","id",113,"# Pendahuluan\n## Latar belakang aplikasi digital Korlantas Polri\n## Urgensi analisis sentimen pada ulasan pengguna\n## Penelitian terdahulu dan celah penelitian\n## Tujuan dan kontribusi studi","[{\"question\":\"Algoritma apa saja yang dibandingkan dalam penelitian analisis sentimen ulasan aplikasi Digital Korlantas Polri?\",\"answer\":\"Penelitian membandingkan Support Vector Machine (SVM), Naive Bayes, Random Forest, K-Nearest Neighbors (KNN), dan Logistic Regression.\"},{\"question\":\"Bagaimana evaluasi performa model dilakukan?\",\"answer\":\"Evaluasi dilakukan menggunakan metrik akurasi, presisi, recall, dan F1 measure.\"},{\"question\":\"Apa hasil utama yang menunjukkan algoritma terbaik?\",\"answer\":\"Random Forest dan SVM menunjukkan kinerja terbaik, dengan Random Forest memiliki akurasi 90,77% dan F1 tertinggi 90,79%, sedangkan SVM memiliki presisi tertinggi 91,14%.\"}]","Komparasi Algoritma Machine Learning untuk Menganalisis Sentimen Ulasan pada Aplikasi Digital Korlantas Polri | 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apa saja yang dibandingkan dalam penelitian analisis sentimen ulasan aplikasi Digital Korlantas Polri?","Question",{"text":77,"@type":78},"Penelitian membandingkan Support Vector Machine (SVM), Naive Bayes, Random Forest, K-Nearest Neighbors (KNN), dan Logistic Regression.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Bagaimana evaluasi performa model dilakukan?",{"text":82,"@type":78},"Evaluasi dilakukan menggunakan metrik akurasi, presisi, recall, dan F1 measure.",{"name":84,"@type":75,"acceptedAnswer":85},"Apa hasil utama yang menunjukkan algoritma terbaik?",{"text":86,"@type":78},"Random Forest dan SVM menunjukkan kinerja terbaik, dengan Random Forest memiliki akurasi 90,77% dan F1 tertinggi 90,79%, sedangkan SVM memiliki presisi tertinggi 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