[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"ssr-repo-dspace000004654":3,"ssr-repo-divisions":63,"menu-footer-id":249,"menu-main-id":310},{"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":53,"abstract":60,"abstract_html":61,"url":62},"repo-dspace000004654","Optimasi Metode Support Vector Machine Menggunakan Particle Swarm Optimization (PSO) untuk Analisis Sentimen Terhadap Ulasan Pengguna Aplikasi Membaca Komik pada Google Play",[7],"Faradila Wahyu Wijayanti",[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},"2141764158","faradila-wahyu-wijayanti",[],"Politeknik Negeri Malang","Malang","Indonesia","Skripsi (D4)","skripsi-d4",2024,"D-IV SISTEM INFORMASI BISNIS",null,[50,51,52],"Analisis Sentimen","Support Vector Machine","Particle Swarm Optimization",[54,56,58],{"name":50,"slug":55},"analisis-sentimen",{"name":51,"slug":57},"support-vector-machine",{"name":52,"slug":59},"particle-swarm-optimization","Di Indonesia, komik memiliki sejumlah penggemar yang signifikan, terutama di kalangan remaja. Keuntungan membaca komik secara digital adalah kemudahan akses, fleksibilitas, dan mengurangi ruang untuk penyimpanan media-media cetak tersebut. Melalui hal ini maka akan dilakukan penelitian mengenai analisis sentimen dengan mengklasifikasikan ulasan pengguna aplikasi membaca komik dari Google Play. Data ulasan diambil melalui aplikasi Webtoon, MangaToon, Manga Plus, dan Kakao Webtoon. Penelitian ini menganalisis sentimen pengguna aplikasi membaca komik di Google Play menggunakan metode Support Vector Machine (SVM) dan SVM berbasis Particle Swarm Optimization (PSO). Tahapan dalam pengolahan data yaitu preprocessing, pelabelan dengan TextBlob dan manual, split data, TF-IDF, dan penerapan permodelan. Tujuan penelitian ini adalah untuk mengklasifikasikan ulasan pengguna aplikasi membaca komik berdasarkan analisis sentimen dan membandingkan kinerja model SVM dan SVM berbasis PSO. Hasilnya menunjukkan bahwa SVM dengan PSO secara signifikan dapat meningkatkan akurasi klasifikasi, terutama pada pelabelan TextBlob, dengan akurasi mencapai 91.75% menggunakan kernel RBF. Di sisi lain, SVM tanpa PSO mencapai akurasi 90.61% dengan kernel Linear. Berdasarkan klasifikasi terbaik kata kunci divisualisasikan melalui wordcloud untuk mengidentifikasi kata positif seperti \"read\", \"good\", dan \"comic\", yang mencerminkan kepuasan pengguna, sementara kata negatif seperti \"read\", \"ad\", dan \"please\" menandakan adanya keluhan tentang gangguan iklan dalam aplikasi. Meskipun PSO berhasil meningkatkan performa, penggunaan metode ini pada dataset dengan pelabelan manual tidak memberikan hasil yang sama baiknya. Ini menunjukkan bahwa memilih metode pelabelan dan kernel yang tepat sangat penting untuk mencapai klasifikasi yang optimal.","\u003Cp>Di Indonesia, komik memiliki sejumlah penggemar yang signifikan, terutama di kalangan remaja. Keuntungan membaca komik secara digital adalah kemudahan akses, fleksibilitas, dan mengurangi ruang untuk penyimpanan media-media cetak tersebut. Melalui hal ini maka akan dilakukan penelitian mengenai analisis sentimen dengan mengklasifikasikan ulasan pengguna aplikasi membaca komik dari Google Play. Data ulasan diambil melalui aplikasi Webtoon, MangaToon, Manga Plus, dan Kakao Webtoon. Penelitian ini menganalisis sentimen pengguna aplikasi membaca komik di Google Play menggunakan metode Support Vector Machine (SVM) dan SVM berbasis Particle Swarm Optimization (PSO). Tahapan dalam pengolahan data yaitu preprocessing, pelabelan dengan TextBlob dan manual, split data, TF-IDF, dan penerapan permodelan. Tujuan penelitian ini adalah untuk mengklasifikasikan ulasan pengguna aplikasi membaca komik berdasarkan analisis sentimen dan membandingkan kinerja model SVM dan SVM berbasis PSO. Hasilnya menunjukkan bahwa SVM dengan PSO secara signifikan dapat meningkatkan akurasi klasifikasi, terutama pada pelabelan TextBlob, dengan akurasi mencapai 91.75% menggunakan kernel RBF. Di sisi lain, SVM tanpa PSO mencapai akurasi 90.61% dengan kernel Linear. Berdasarkan klasifikasi terbaik kata kunci divisualisasikan melalui wordcloud untuk mengidentifikasi kata positif seperti "read", "good", dan "comic", yang mencerminkan kepuasan pengguna, sementara kata negatif seperti "read", "ad", dan "please" menandakan adanya keluhan tentang gangguan iklan dalam aplikasi. Meskipun PSO berhasil meningkatkan performa, penggunaan metode ini pada dataset dengan pelabelan manual tidak memberikan hasil yang sama baiknya. 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