[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"ssr-repo-dspace000006362":3,"ssr-repo-divisions":75,"menu-footer-id":261,"menu-main-id":322},{"id":4,"title":5,"authors":6,"files":8,"advisors":35,"authors_detail":36,"advisors_detail":40,"publisher":41,"publish_place":42,"language":43,"type":44,"type_slug":45,"year":46,"department":47,"study_program":48,"keywords":49,"keywords_detail":57,"abstract":72,"abstract_html":73,"url":74},"repo-dspace000006362","Perbandingan Algoritma Support Vector Machine, Multinomial Naïve Bayes, dan Logistic Regression untuk Klasifikasi Multi-Label pada Tag Berita Online",[7],"Diva Gracia Salmanta Candra",[9,13,16,19,23,26,29,32],{"chapter":10,"requires_login":11,"label":12,"available":11},"abstrak",false,"Abstrak",{"chapter":14,"requires_login":11,"label":15,"available":11},"bab1","Bab I",{"chapter":17,"requires_login":11,"label":18,"available":11},"bab2","Bab II",{"chapter":20,"requires_login":21,"label":22,"available":11},"bab3",true,"Bab III",{"chapter":24,"requires_login":21,"label":25,"available":11},"bab4","Bab IV",{"chapter":27,"requires_login":21,"label":28,"available":11},"bab5","Bab V",{"chapter":30,"requires_login":21,"label":31,"available":11},"full","Dokumen Lengkap",{"chapter":33,"requires_login":11,"label":34,"available":21},"open","Akses Terbuka",[],[37],{"name":7,"nim":38,"slug":39},"2141762071","diva-gracia-salmanta-candra",[],"Politeknik Negeri Malang","Malang","Indonesia","Skripsi (D4)","skripsi-d4",2025,"D-IV SISTEM INFORMASI BISNIS",null,[50,51,52,53,54,55,56],"Klasifikasi Multi-Label","Support Vector Machine","Multinomial Naive Bayes","Logistic Regression","Tag Berita Online","Segmentasi Audiens","Text Mining",[58,60,62,64,66,68,70],{"name":50,"slug":59},"klasifikasi-multi-label",{"name":51,"slug":61},"support-vector-machine",{"name":52,"slug":63},"multinomial-naive-bayes",{"name":53,"slug":65},"logistic-regression",{"name":54,"slug":67},"tag-berita-online",{"name":55,"slug":69},"segmentasi-audiens",{"name":56,"slug":71},"text-mining","Perkembangan pesat media digital telah mendorong kebutuhan sistem klasifikasi teks yang efisien guna mengelompokkan tag berita online sesuai dengan segmentasi audiens. Penelitian ini bertujuan untuk mengevaluasi kinerja tiga algoritma klasifikasi teks, yaitu Support Vector Machine, Multinomial Naive Bayes, dan Logistic Regression dalam pendekatan klasifikasi multi-label pada data tag artikel online. Proses penelitian mencakup praproses teks, transformasi fitur menggunakan TF-IDF, pelatihan model multi-label dengan pendekatan Binary Relevance, serta pelatihan dan pengujian model dengan pembagian data sebesar 90:10. Evaluasi dilakukan menggunakan metrik akurasi, hamming loss, presisi, recall, f1-score, dan waktu prediksi. Hasil menunjukkan bahwa ketiga algoritma menghasilkan kinerja yang cukup kompetitif setelah dilakukan hyperparameter tuning. Support Vector Machine mencapai nilai akurasi sebesar 0.709, hamming loss 0.0357, presisi 0.89, recall 0.76, dan f1-score 0.82, dengan waktu prediksi berkisar dari 0.0595 detik hingga 0.2177 detik. Multinomial Naïve Bayes menunjukkan nilai akurasi 0.719, hamming loss 0.0364, presisi 0.89, recall 0.75, dan f1-score 0.82, dengan waktu prediksi berkisar dari 0.0049 detik hingga 0.0081 detik. Logistic Regression mencapai akurasi sebesar 0.702, hamming loss 0.0359, presisi 0.89, recall 0.76, dan f1-score 0.82, dengan waktu prediksi berkisar dari 0.0043 detik hingga 0.0080 detik. Berdasarkan hasil tersebut, Multinomial Naïve Bayes merupakan algoritma terbaik dengan akurasi tertinggi (0.719) dan waktu prediksi tercepat (0.0049 - 0.0081 detik) untuk klasifikasi multi-label ini.","\u003Cp>Perkembangan pesat media digital telah mendorong kebutuhan sistem klasifikasi teks yang efisien guna mengelompokkan tag berita online sesuai dengan segmentasi audiens. Penelitian ini bertujuan untuk mengevaluasi kinerja tiga algoritma klasifikasi teks, yaitu Support Vector Machine, Multinomial Naive Bayes, dan Logistic Regression dalam pendekatan klasifikasi multi-label pada data tag artikel online. Proses penelitian mencakup praproses teks, transformasi fitur menggunakan TF-IDF, pelatihan model multi-label dengan pendekatan Binary Relevance, serta pelatihan dan pengujian model dengan pembagian data sebesar 90:10. Evaluasi dilakukan menggunakan metrik akurasi, hamming loss, presisi, recall, f1-score, dan waktu prediksi. Hasil menunjukkan bahwa ketiga algoritma menghasilkan kinerja yang cukup kompetitif setelah dilakukan hyperparameter tuning. Support Vector Machine mencapai nilai akurasi sebesar 0.709, hamming loss 0.0357, presisi 0.89, recall 0.76, dan f1-score 0.82, dengan waktu prediksi berkisar dari 0.0595 detik hingga 0.2177 detik. Multinomial Naïve Bayes menunjukkan nilai akurasi 0.719, hamming loss 0.0364, presisi 0.89, recall 0.75, dan f1-score 0.82, dengan waktu prediksi berkisar dari 0.0049 detik hingga 0.0081 detik. 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