Analisis Sentimen Berbasis Aspek Menggunakan Support Vector Machine dan Binary Relevance Terhadap Aplikasi Access by KAI
Kata Kunci:
analisis sentimen, analisis sentimen berbasis aspek, RCA, Root Cause Analysis, Support Vector Machine, SVM, BR, Binary Relevance, GridsearchCV, Access by KAIAbstrak
Naskah ini akan di terbitkan pada Konferensi Internasional ICOMMIT
Referensi
Fikria, N., 2018. Analisis Klasifikasi Sentimen Review Aplikasi E-Ticketing Menggunakan Metode Support Vector Machine dan Asosiasi. Universitas Islam Indonesia, 1(2018-05–15).
Google, 2023. Access By KAI Google Play Store. [online] Available at: <https://play.google.com/store/apps/details?id=com.kai.kaiticketing> [Accessed 8 September 2023].
Herrera, F., Charte, F., Rivera, A.J. and del Jesus, M.J., 2016. Multilabel Classification. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-41111-8.
Iskandar, J.W. and Nataliani, Y., 2021. Perbandingan Naïve Bayes, SVM, dan K-NN untuk Analisis Sentimen Gadget Berbasis Aspek. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 5(6), pp.1120–1126. https://doi.org/10.29207/resti.v5i6.3588
Kowalczyk, A., 2017. Support Vector Machines Succinctly. Journal of Chemical Information and Modeling, 53(9).
Luaces, O., Díez, J., Barranquero, J., del Coz, J.J. and Bahamonde, A., 2012. Binary Relevance Efficacy for Multilabel Classification. Progress in Artificial Intelligence, 1(4). https://doi.org/10.1007/s13748-012-0030-x.
Monika, I.P. and Furqon, M.T., 2018. Penerapan Metode Support Vector Machine (SVM) pada Klasifikasi Penyimpangan Tumbuh Kembang Anak. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2(10).
Mustakim, H. and Priyanta, S., 2022. Aspect Based Sentiment Analysis of KAI Access Reviews Using NBC and SVM. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 16(2). https://doi.org/10.22146/ijccs.68903.
Nurfikri, F.S. and Adiwijaya, 2019. A Comparison of Neural Network and SVM on The Multi-Label Classification of Quran Verses Topic in English Translation. In: Journal of Physics: Conference Series. https://doi.org/10.1088/1742-6596/1192/1/012030.
Radiena, G. and Nugroho, A., 2023. Analisis Sentimen Berbasis Aspek pada Ulasan Aplikasi KAI Access Menggunakan Metode Support Vector Machine. Jurnal Pendidikan Teknologi Informasi (JUKANTI), 6(1). https://doi.org/10.37792/jukanti.v6i1.836.
Rahmawati, D., Suprihardjo, R., Santoso, E.B., Setiawan, R.P., Pradinie, K. and Yusuf, M., 2016. Penerapan Metode Root Cause Analysis (RCA) dalam Pengembangan Kawasan Wisata Cagar Budaya Kampung Kemasan, Gresik. Jurnal Penataan Ruang, 11(1). https://doi.org/10.12962/j2716179x.v11i1.5211.
Tao, J. and Fang, X., 2020. Toward Multi-Label Sentiment Analysis: A Transfer Learning Based Approach. Journal of Big Data, 7(1). https://doi.org/10.1186/s40537-019-0278-0.
Fikria, N., 2018. Analisis Klasifikasi Sentimen Review Aplikasi E-Ticketing Menggunakan Metode Support Vector Machine dan Asosiasi. Universitas Islam Indonesia, 1(2018-05–15).
Google, 2023. Access By KAI Google Play Store. [online] Available at: <https://play.google.com/store/apps/details?id=com.kai.kaiticketing> [Accessed 8 September 2023].
Herrera, F., Charte, F., Rivera, A.J. and del Jesus, M.J., 2016. Multilabel Classification. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-41111-8.
Iskandar, J.W. and Nataliani, Y., 2021. Perbandingan Naïve Bayes, SVM, dan K-NN untuk Analisis Sentimen Gadget Berbasis Aspek. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 5(6), pp.1120–1126. https://doi.org/10.29207/resti.v5i6.3588
Kowalczyk, A., 2017. Support Vector Machines Succinctly. Journal of Chemical Information and Modeling, 53(9).
Luaces, O., Díez, J., Barranquero, J., del Coz, J.J. and Bahamonde, A., 2012. Binary Relevance Efficacy for Multilabel Classification. Progress in Artificial Intelligence, 1(4). https://doi.org/10.1007/s13748-012-0030-x.
Monika, I.P. and Furqon, M.T., 2018. Penerapan Metode Support Vector Machine (SVM) pada Klasifikasi Penyimpangan Tumbuh Kembang Anak. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2(10).
Mustakim, H. and Priyanta, S., 2022. Aspect Based Sentiment Analysis of KAI Access Reviews Using NBC and SVM. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 16(2). https://doi.org/10.22146/ijccs.68903.
Nurfikri, F.S. and Adiwijaya, 2019. A Comparison of Neural Network and SVM on The Multi-Label Classification of Quran Verses Topic in English Translation. In: Journal of Physics: Conference Series. https://doi.org/10.1088/1742-6596/1192/1/012030.
Radiena, G. and Nugroho, A., 2023. Analisis Sentimen Berbasis Aspek pada Ulasan Aplikasi KAI Access Menggunakan Metode Support Vector Machine. Jurnal Pendidikan Teknologi Informasi (JUKANTI), 6(1). https://doi.org/10.37792/jukanti.v6i1.836.
Rahmawati, D., Suprihardjo, R., Santoso, E.B., Setiawan, R.P., Pradinie, K. and Yusuf, M., 2016. Penerapan Metode Root Cause Analysis (RCA) dalam Pengembangan Kawasan Wisata Cagar Budaya Kampung Kemasan, Gresik. Jurnal Penataan Ruang, 11(1). https://doi.org/10.12962/j2716179x.v11i1.5211.
Tao, J. and Fang, X., 2020. Toward Multi-Label Sentiment Analysis: A Transfer Learning Based Approach. Journal of Big Data, 7(1). https://doi.org/10.1186/s40537-019-0278-0.
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