Comparison of Algorithms for Sentiment Analysis of Operator Satisfaction Level for Increasing Neo Feeder Applications in PDDikti Higher Education LLDIKTI Region VI Semarang Central Java
DOI:
10.33395/sinkron.v8i4.12907Keywords:
Decision Tree; K-Nearest Neighbor; Multinomial Naïve Bayes; Neo; Oversampling; Random Forest; Sentiment Analysis; Support Vector Machine;Abstract
Sentiment analysis on the satisfaction level of PDDikti operators is very important to find out how PDDikti operators feel after the version of the academic reporting application for higher education was upgraded, namely Neo Feeeder. The increase in the version of this application causes some of the features in it to not function properly. So some academic reporting activities from tertiary institutions experience problems. As a result of this condition, the most felt impact is students, where students experience delays in graduation. Then it is necessary to evaluate through sentiment analysis from PDDikti operators to find out the response from operators and be able to provide positive suggestions to developers from the PDDikti reporting application. This study applies several classification methods for sentiment analysis at once, including the Random Forest algorithm, the Support Vector Machine algorithm, the Multinomial Naïve Bayes algorithm, the Decision Tree algorithm, and the K-Nearest Neighbor algorithm. Of the 5 methods applied, the results of their performance accuracy will be compared. The performance of the highest classification algorithm is the K-Nearest Neighbor (K-NN) algorithm which produces an accuracy value when testing data, which is up to 90% using the oversampling technique in unbalanced classes. While the lowest classification accuracy performance value is in the Multinomial Naïve Bayes (MNB) algorithm with a value of 76%. It is proven that oversampling can help the performance of the classification algorithm to be more optimal. Thus, it should be noted that the balance of data classes is an important factor when applying the classification method.
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Asro’i, A., & Februariyanti, H. (2022). Analisis Sentimen Pengguna Twitter Terhadap Perpanjangan Ppkm Menggunakan Metode K-Nearest Neighbor. Jurnal Khatulistiwa Informatika, 10(1), 17–24. https://doi.org/10.31294/jki.v10i1.12624
Bogor, W. (2023). ScienceDirect ScienceDirect Sentiment analysis of Indonesian police chief using multi-level Sentiment analysis of Indonesian police chief using multi-level ensemble model ensemble model. Procedia Computer Science, 216(2022), 620–629. https://doi.org/10.1016/j.procs.2022.12.177
Ciptady, K., Harahap, M., Jonvin, J., Ndruru, Y., & Ibadurrahman, I. (2022). Prediksi Kualitas Kopi Dengan Algoritma Random Forest Melalui Pendekatan Data Science. Data Sciences Indonesia (DSI), 2(1). https://doi.org/10.47709/dsi.v2i1.1708
Desiani, A., Akbar, M., Irmeilyana, & Amran, A. (2022). Implementasi Algoritma Naïve Bayes dan Support Vector Machine ( SVM ) Pada Klasifikasi Penyakit Kardiovaskular. Jurnal Teknik Elektro Dan Komputasi (ELKOM), 4(2), 207–214.
Fandi, F. Y. P., & Sephia Dwi Arma Putri. (2023). Komparasi Metode Smote Dan Adasyn Untuk Penanganan Data Tidak Seimbang Multiclass. Jurnal Informatika Polinema, 9(3), 331–338. https://doi.org/10.33795/jip.v9i3.1330
Giovani, A. P., Ardiansyah, A., Haryanti, T., Kurniawati, L., & Gata, W. (2020). Analisis Sentimen Aplikasi Ruang Guru Di Twitter Menggunakan Algoritma Klasifikasi. Jurnal Teknoinfo, 14(2), 115. https://doi.org/10.33365/jti.v14i2.679
Hendriyanto, M. D., Ridha, A. A., & Enri, U. (2022). Analisis Sentimen Ulasan Aplikasi Mola Pada Google Play Store Menggunakan Algoritma Support Vector Machine. INTECOMS: Journal of Information Technology and Computer Science, 5(1), 1–7. https://doi.org/10.31539/intecoms.v5i1.3708
Husada, H. C., & Paramita, A. S. (2021). Analisis Sentimen Pada Maskapai Penerbangan di Platform Twitter Menggunakan Algoritma Support Vector Machine (SVM). Teknika, 10(1), 18–26. https://doi.org/10.34148/teknika.v10i1.311
Informatika, D., Azhima, T., Siswa, Y., Pranoto, W. J., Studi, P., Informatika, T., Muhammadiyah, U., & Timur, K. (2023). IMPLEMENTASI SELEKSI FITUR INFORMATION GAIN RATIO PADA ALGORITMA RANDOM FOREST UNTUK MODEL DATA KLASIFIKASI. 15(1), 42–49.
Isnain, A. R., Supriyanto, J., & Kharisma, M. P. (2021). Implementation of K-Nearest Neighbor (K-NN) Algorithm For Public Sentiment Analysis of Online Learning. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 15(2), 121. https://doi.org/10.22146/ijccs.65176
Kewsuwun, N., & Kajornkasirat, S. (2022). A sentiment analysis model of agritech startup on Facebook comments using naive Bayes classifier. International Journal of Electrical and Computer Engineering, 12(3), 2829–2838. https://doi.org/10.11591/ijece.v12i3.pp2829-2838
Khotimah, A. C., Utami, E., Universitas, I., Yogyakarta, A., Teknik, M., Universitas, I., & Yogyakarta, A. (2022). COMPARISON NAÏVE BAYES CLASSIFIER , K-NEAREST NEIGHBOR AND SUPPORT VECTOR MACHINE IN THE CLASSIFICATION OF INDIVIDUAL ON PERBANDINGAN ALGORITMA NAÏVE BAYES CLASSIFIER , K-NEAREST NEIGHBOR DAN SUPPORT VECTOR MACHINE DALAM KLASIFIKASI. 3(3), 673–680.
Laurensz, B., & Eko Sediyono. (2021). Analisis Sentimen Masyarakat terhadap Tindakan Vaksinasi dalam Upaya Mengatasi Pandemi Covid-19. Jurnal Nasional Teknik Elektro Dan Teknologi Informasi, 10(2), 118–123. https://doi.org/10.22146/jnteti.v10i2.1421
Morama, H. C., Ratnawati, D. E., & Arwani, I. (2022). Analisis Sentimen berbasis Aspek terhadap Ulasan Hotel Tentrem Yogyakarta menggunakan Algoritma Random Forest Classifier. 6(4), 1702–1708.
Nurmawiya, & Harvian, K. A. (2021). Public sentiment towards face-to-face activities during the COVID-19 pandemic in Indonesia. Procedia Computer Science, 197(2021), 529–537. https://doi.org/10.1016/j.procs.2021.12.170
Oktafani, M., & Prasetyaningrum, P. T. (2022). Implementasi Support Vector Machine Untuk Analisis Sentimen Komentar Aplikasi Tanda Tangan Digital. Jurnal Sistem Informasi Dan Bisnis Cerdas, 15(1), 10–19. https://doi.org/10.33005/sibc.v15i1.2697
Prasetyo, K. G., & Pahlevi, S. M. (2019). Analisis Perbandingan Algoritma Decision Treedengan Support Vector Machineuntuk Mendeteksi Kompetensi Mahasiswakonsentrasi Informatika Komputerstudi Kasus : Politeknik Lp3I Jakarta, Kampus Depok. Jurna Lentera ICT, 5(2), 11–26.
Ramon, E., Nazir, A., Novriyanto, N., Yusra, Y., & Oktavia, L. (2022). Klasifikasi Status Gizi Bayi Posyandu Kecamatan Bangun Purba Menggunakan Algoritma Support Vector Machine (Svm). Jurnal Sistem Informasi Dan Informatika (Simika), 5(2), 143–150. https://doi.org/10.47080/simika.v5i2.2185
Sastypratiwi, H., Muhardi, H., & Noveanto, M. (2022). Klasifikasi Emosi Pada Lirik Lagu Menggunakan Algoritma Multiclass SVM dengan Tuning Hyperparameter PSO. 6, 2279–2286. https://doi.org/10.30865/mib.v6i4.4609
Shah, P., Swaminarayan, P., & Patel, M. (2022). Sentiment analysis on film review in Gujarati language using machine learning. International Journal of Electrical and Computer Engineering, 12(1), 1030–1039. https://doi.org/10.11591/ijece.v12i1.pp1030-1039
Sholeha, E. W., Yunita, S., Hammad, R., Hardita, V. C., & Kaharuddin, K. (2022). Analisis Sentimen Pada Agen Perjalanan Online Menggunakan Naïve Bayes dan K-Nearest Neighbor. JTIM : Jurnal Teknologi Informasi Dan Multimedia, 3(4), 203–208. https://doi.org/10.35746/jtim.v3i4.178
Simanjuntak, T. H., Mahmudy, W. F., & Sutrisno Sutrisno. (2017). Implementasi Modified K-Nearest Neighbor Dengan Otomatisasi Nilai K Pada Pengklasifikasian Penyakit Tanaman Kedelai. Pengembangan Teknologi Informasi Dan Ilmu Komputer, 1, No.2(2), 75–79. http://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/15/21
Siringoringo, R. (2018). Klasifikasi data tidak Seimbang menggunakan algoritma SMOTE dan k-nearest neighbor. Jurnal ISD, 3(1), 44–49.
Sulastomo, H., Gibran, K., Maryansyah, E., & Tegar, A. (2022). Analisis Sentimen Pada Twitter @Ovo_Id dengan Metode Support Vectore Machine (SVM). Jurnal Sains Komputer & Informatika (J-SAKTI, 6(2), 1050–1056.
Turjaman, R. M., & Budi, I. (2022). Analisis Sentimen Berbasis Aspek Marketing Mix Terhadap Ulasan Aplikasi Dompet Digital (Studi Kasus: Aplikasi Linkaja Pada Twitter). Jurnal Darma Agung, 30(2), 266. https://doi.org/10.46930/ojsuda.v30i2.1672
Verawati, I., & Audit, B. S. (2022). Algoritma Naïve Bayes Classifier Untuk Analisis Sentiment Pengguna Twitter Terhadap Provider By.u. Jurnal Media Informatika Budidarma, 6(3), 1411. https://doi.org/10.30865/mib.v6i3.4132
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