Analisis Sentimen Ulasan Aplikasi Info BMKG di Google Play Menggunakan TF-IDF dan Support Vector Machine

Ichwanul Muslim Karo Karo; Justaman Arifin Karo Karo; Yunianto Yunianto; Hariyanto Hariyanto; Miftahul Falah; Manan Ginting

doi:10.47065/josh.v4i4.3943

Ichwanul Muslim Karo Karo * Universitas Negeri Medan, Medan, Indonesia
Justaman Arifin Karo Karo Politeknik Teknologi Kimia Industri, Medan, Indonesia
Yunianto Yunianto Politeknik Teknologi Kimia Industri, Medan, Indonesia
Hariyanto Hariyanto Politeknik Teknologi Kimia Industri, Medan, Indonesia
Miftahul Falah Politeknik Teknologi Kimia Industri, Medan, Indonesia
Manan Ginting Politeknik Teknologi Kimia Industri, Medan, Indonesia

(*) Corresponding Author

DOI: https://doi.org/10.47065/josh.v4i4.3943

Keywords: Info BMKG; TF-IDF; SVM; Sentiment Analysis; Text Preprocessing

Abstract

Posting online reviews has become one of the most popular ways to express opinions and sentiments towards service applications. The Meteorology, Climatology and Geophysics Agency (BMKG) Info application is an Android and iOS-based mobile application that provides information on weather, climate, air quality, and earthquakes that occur in various regions in Indonesia. The information contained in this application is very important but has a worse value than other forecasting applications. Sentiment analysis is the process of classifying text into several classes such as positive sentiment, negative or not containing both. This research aims to analyze user reviews on the BMKG Info application from the Google Play website. The benefits obtained are as consideration for developers to improve the shortcomings of the application. The classification process uses Term Frequency-Inverse Document Frequency (TF-IDF) and the Support Vector Machine (SVM) algorithm. This research successfully collected 2500 reviews from users of the BMKG Info application on the Google PlayStore website using the web scraping method. Text preprocessing of the reviews used case folding, symbolic and stopword removal, tokenization, normalization, and stemming. User ratings help in identifying the sentiment label of a review, 66% of reviews are positive while the rest are negative. The most frequently reviewed topics with sentiment value are "application", "information", "update". This research conducted three experimental scenarios based on the composition of training data and test data. Based on the prediction model, the scenario with 75%:25% split data has the highest accuracy rate of 79%.

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References

Q. A. Chairunnisa, Y. Herdiyeni, M. K. D. Hardhienata, and J. Adisantoso, “Analisis Sentimen Pengguna Twitter Terhadap Program Vaksinasi Covid-19 di Indonesia Menggunakan Algoritme Support Vector Machine,” Jurnal Ilmu Komputer dan Agri-Informatika, vol. 9, no. 1, 2022, doi: 10.29244/jika.9.1.79-89.

J. A. Septian, T. M. Fachrudin, and A. Nugroho, “Analisis Sentimen Pengguna Twitter Terhadap Polemik Persepakbolaan Indonesia Menggunakan Pembobotan TF-IDF dan K-Nearest Neighbor,” Journal of Intelligent System and Computation, vol. 1, no. 1, 2019, doi: 10.52985/insyst.v1i1.36.

Y. Cahyono, “Analisis Sentiment Tweets Berbahasa Sunda Menggunakan Naive Bayes Classifier dengan Seleksi Feature Chi Squared Statistic,” vol. 4, no. 3, 2019, [Online]. Available: http://openjournal.unpam.ac.id/index.php/informatika

K. Sailunaz and R. Alhajj, “Emotion and sentiment analysis from Twitter text,” J Comput Sci, vol. 36, p. 101003, Sep. 2019, doi: 10.1016/j.jocs.2019.05.009.

A. Novantirani, M. K. Sabariah, and V. Effendy, “Analisis Sentimen pada Twitter untuk Mengenai Penggunaan Transportasi Umum Darat Dalam Kota dengan Metode Support Vector Machine,” e-Proceeeding of Engineering, vol. 2, no. 1, 2015.

P. A. Fajriyah, “PENGARUH PENGGUNAAN APLIKASI INFO BMKG TERHADAP SIKAP TANGGAP BENCANA MAHASISWA UNIVERSITAS MATARAM PASCA GEMPA BUMI LOMBOK 2018,” JCommsci - Journal of Media and Communication Science, vol. 2, no. 1, 2019, doi: 10.29303/jcommsci.v2i1.23.

D. Darwis, N. Siskawati, and Z. Abidin, “PENERAPAN ALGORITMA NAIVE BAYES UNTUK ANALISIS SENTIMEN REVIEW DATA TWITTER BMKG NASIONAL,” Jurnal Tekno Kompak, vol. 15, no. 1, 2021, doi: 10.33365/jtk.v15i1.744.

M. Yusuf Hidayatulloh, A. Sunanto, M. Farrell Afelino Gevin, and D. Dwi Saputra, “Optimasi Sentimen Analisis Informatif dan Tidak Informatif dari Tweet di BMKG Menggunakan Algoritma Naive Bayes dan Metode Teknik Pengambilan Sampel Minoritas Sintetis,” Jurnal Sains Komputer & Informatika (J-SAKTI, vol. 7, no. 1, pp. 1–12, 2023.

R. Wahyudi and G. Kusumawardana, “Analisis Sentimen pada Aplikasi Grab di Google Play Store Menggunakan Support Vector Machine,” Jurnal Informatika, vol. 8, no. 2, 2021, doi: 10.31294/ji.v8i2.9681.

M. D. Hendriyanto, A. A. Ridha, and U. Enri, “Analisis Sentimen Ulasan Aplikasi Mola Pada Google Play Store Menggunakan Algoritma Support Vector Machine,” INTECOMS: Journal of Information Technology and Computer Science, vol. 5, no. 1, 2022, doi: 10.31539/intecoms.v5i1.3708.

F. Bei and S. Sudin, “Analisis Sentimen Aplikasi Tiket Online Di Play Store Menggunakan Metode Support Vector Machine (Svm),” Sismatik, vol. 01, no. 01, 2021.

I. M. Karo Karo, M. F. M. Fudzee, S. Kasim, and A. A. Ramli, “Sentiment Analysis in Karonese Tweet using Machine Learning,” Indonesian Journal of Electrical Engineering and Informatics, vol. 10, no. 1, pp. 219–231, Mar. 2022, doi: 10.52549/ijeei.v10i1.3565.

S. vanden Broucke and B. Baesens, Practical Web Scraping for Data Science. 2018. doi: 10.1007/978-1-4842-3582-9.

F. Alghifari and D. Juardi, “PENERAPAN DATA MINING PADA PENJUALAN MAKANAN DAN MINUMAN MENGGUNAKAN METODE ALGORITMA NAÏVE BAYES,” JURNAL ILMIAH INFORMATIKA, vol. 9, no. 02, 2021, doi: 10.33884/jif.v9i02.3755.

I. M. Karo Karo, M. Farhan, M. Fudzee, S. Kasim, and A. A. Ramli, “Karonese Sentiment Analysis: A New Dataset and Preliminary Result,” JOIV: International Journal on Informatics Visualization, vol. 6, no. 2–2, pp. 523–530, 2022, [Online]. Available: www.joiv.org/index.php/joiv

S. Cheng, B. Liu, Y. Shi, Y. Jun, and B. Li, Data Mining and Big Data. 2015. doi: 10.1007/978-3-319-40973-3.

Yuyun, Nurul Hidayah, and Supriadi Sahibu, “Algoritma Multinomial Naïve Bayes Untuk Klasifikasi Sentimen Pemerintah Terhadap Penanganan Covid-19 Menggunakan Data Twitter,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 5, no. 4, 2021, doi: 10.29207/resti.v5i4.3146.

A. Fauzi, E. B. Setiawan, and Z. K. A. Baizal, “Hoax News Detection on Twitter using Term Frequency Inverse Document Frequency and Support Vector Machine Method,” in Journal of Physics: Conference Series, 2019. doi: 10.1088/1742-6596/1192/1/012025.

I. M. Karo Karo, R. Romia, S. Dewi, and P. M. Fadilah, “Hoax Detection on Indonesian Tweets using Naïve Bayes Classifier with TF-IDF,” Journal of Information System Research (JOSH), vol. 4, no. 3, pp. 914–919, Apr. 2023, doi: 10.47065/josh.v4i3.3317.

E. Muningsih, “KOMBINASI METODE K-MEANS DAN DECISION TREE DENGAN PERBANDINGAN KRITERIA DAN SPLIT DATA,” Jurnal Teknoinfo, vol. 16, no. 1, 2022, doi: 10.33365/jti.v16i1.1561.

I. M. Karo Karo and H. Hendriyana, “Klasifikasi Penderita Diabetes menggunakan Algoritma Machine Learning dan Z-Score,” Jurnal Teknologi Terpadu, vol. 8, no. 2, pp. 94–99, 2022.

T. Joachims, Learning to Classify Text Using Support Vector Machines. 2002. doi: 10.1007/978-1-4615-0907-3.

I. M. K. Karo, R. Ramdhani, A. W. Ramadhelza, and B. Z. Aufa, “A Hybrid Classification Based on Machine Learning Classifiers to Predict Smart Indonesia Program,” in Proceeding - 2020 3rd International Conference on Vocational Education and Electrical Engineering: Strengthening the framework of Society 5.0 through Innovations in Education, Electrical, Engineering and Informatics Engineering, ICVEE 2020, 2020. doi: 10.1109/ICVEE50212.2020.9243195.

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