Analisis Sentimen dan Prediksi Ulasan Pada Aplikasi Info BMKG
DOI:
https://doi.org/10.37859/jf.v15i2.9746
Abstract
Aplikasi Info BMKG menyediakan informasi cuaca dan iklim bagi masyarakat Indonesia. Ulasan pengguna di Google Play Store merefleksikan kepuasan dan kritik yang dapat dianalisis untuk peningkatan layanan. Penelitian ini bertujuan mengklasifikasikan sentimen dan memprediksi volume ulasan menggunakan Recurrent Neural Network (RNN) dan Long Short-Term Memory (LSTM). Sebanyak 3000 ulasan diperoleh melalui web scraping dan setelah diproses dengan pembersihan data, tokenisasi, stemming, penghapusan stopword, dan labelling, maka jumlahnya menjadi 2645 ulasan. Hasil menunjukkan LSTM unggul pada klasifikasi sentimen dengan akurasi 90% dan F1-score 0,90, sedangkan RNN memperoleh akurasi 87% dan F1-score 0,82. Pada prediksi jumlah ulasan negatif, RNN lebih baik (MSE: 104,97; MAE: 7,61; R²: 0,22), sementara kedua model kurang optimal untuk kategori positif (R² negatif). Temuan ini menunjukkan LSTM lebih efektif untuk klasifikasi, sedangkan RNN lebih unggul dalam prediksi ulasan negatif
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B. T. Ariyanto, Y. Ruldeviyani, G. S. B. Dharmawan, and M. I. F. Bahar, “Optimizing User Satisfaction: A ComprehensiveEvaluation of the Info BMKG App Using UEQ+ and IPA Methods,” International Journal of Economics (IJEC), vol. 3, no. 1, pp. 139–156, 2024, doi: 10.58291/ijec.v3i1.242.
I. M. Karo Karo, J. A. Karo Karo, Y. Yunianto, H. Hariyanto, M. Falah, and M. Ginting, “Analisis Sentimen Ulasan Aplikasi Info BMKG Di Google Play Menggunakan TF-IDF Dan Support Vector Machine,” Multitek Indonesia, vol. 4, no. 4, pp. 1423–1430, 2023, doi: 10.47065/josh.v4i4.3943.
D. Pratmanto, F. F. D. Imaniawan, and V. Maarif, “Analisis Sentimen Pada Ulasan Pengguna Aplikasi Identitas Kependudukan Digital Dengan Metode Naive Bayes Dan K- Nearest,” Computatio: Journal of Computer Science and Information Systems, vol. 7, no. 2, pp. 155–166, 2023, doi: 10.24912/computatio.v7i2.26322.
T. A. Zuraiyah, M. M. Mulyati, and G. H. F. Harahap, “Perbandingan Metode Naïve Bayes, Support Vector Machine Dan Recurrent Neural network Pada Analisis Sentimen Ulasan Produk E-Commerce,” Multitek Indonesia, vol. 17, no. 1, pp. 28–44, 2023, doi: 10.24269/mtkind.v17i1.7092.
M. V. Shyahrin, Y. Sibaroni, and D. Puspandari, “Penerapan Metode Long Short-Term Memory Dan Word2Vec Dalam Analisis Sentimen Ulasan Pada Aplikasi Ferizy,” Techno.Com, vol. 22, no. 4, pp. 833–842, 2023, doi: 10.33633/tc.v22i4.9205.
A. Ligthart, C. Catal, and B. Tekinerdogan, “Systematic Reviews in Sentiment Analysis: A Tertiary Study,” Artificial Intelligence Review, vol. 54, no. 7, pp. 4997–5053, 2021, doi: 10.1007/s10462-021-09973-3.
A. R. Harungguan, H. Napitupulu, and F. Firdaniza, “Analisis Sentimen Dengan Metode Klasifikasi Naïve Bayes DanSeleksi Fitur Chi-Square,” In Search (Informatic, Science, Entrepreneur, Applied Art, Research, Humanism), vol. 22, no.2, pp. 332–339, 2023, doi: 10.37278/insearch.v22i2.762.
I. Gede Bintang Arya Budaya, L. Putu Safitri Pratiwi, and D. Panji Agustino, “Klasifikasi Sentimen Untuk Analisis Kepuasan Pelayanan Puskesmas Berbasis Arsitektur LSTM,” Smart Comp: Jurnalnya Orang Pintar Komputer, vol. 12, no.4, 2023, doi: 10.30591/smartcomp.v12i4.5361.
S. Jurnalis Pipin and H. Kurniawan, “Analisis Sentimen Kebijakan MBKM Berdasarkan Opini Masyarakat Di Twitter Menggunakan LSTM,” Jurnal SIFO Mikroskil, vol. 23, no. 2, pp. 197–208, 2022, doi: 10.55601/jsm.v23i2.900.
A. Nurian, M. S. Ma’arif, I. N. Amalia, and C. Rozikin, “Analisis Sentimen Pengguna Aplikasi Shopee Pada Situs Google Play Menggunakan Naive Bayes Classifier,” Jurnal Informatika dan Teknik Elektro Terapan (JITET), vol. 12, no. 1, 2024, doi: 10.23960/jitet.v12i1.3631.
S. Sisnawati, R. Astuti, and F. Muhamad basysyar, “Analisis Sentimen Pada Aplikasi Kfcku Di Google PlaystoreMenggunakan Naïve Bayes,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 8, no. 3, pp. 3010–3016, 2024, doi:10.36040/jati.v8i3.8382.
D. P. Prabowo, R. A. Pramunendar, and R. A. Megantara, “Prediksi Sentimen Masyarakat Terhadap Penggunaan Vaksin Covid 19 Menggunakan RNN,” Jurnal Informatika UPGRIS, vol. 8, no. 1, pp. 42–45, 2022, doi: 10.26877/jiu.v8i1.11599.
A. R. Isnain, H. Sulistiani, B. M. Hurohman, A. Nurkholis, and S. Styawati, “Analisis Perbandingan Algoritma LSTM DanNaive Bayes Untuk Analisis Sentimen,” Jurnal Edukasi dan Penelitian Informatika (JEPIN), vol. 8, no. 2, p. 299, 2022, doi:10.26418/jp.v8i2.54704.
A. Fuad and M. Al-Yahya, “Analysis and Classification of Mobile Apps Using Topic Modeling: A Case Study on GooglePlay Arabic Apps,” Complexity, vol. 2021, no. 1, 2021, doi: 10.1155/2021/6677413.
S. Wang, J. Ren, and R. Bai, “A Regularized Attribute Weighting Framework for Naive Bayes,” IEEE Access, vol. 8,pp. 225639–225649, 2020, doi: 10.1109/ACCESS.2020.3044946.
A. S. Berliana and M. Mustikasari, “Analisis Sentimen Pada Ulasan Aplikasi Jakartanotebook Di Google Play Menggunakan Metode Recurrent Neural network (RNN),” Jurnal Informatika dan Teknik Elektro Terapan (JITET), vol. 12, no. 3, 2024, doi: 10.23960/jitet.v12i3.5067.
C. K. Aridas, S. Karlos, V. G. Kanas, N. Fazakis, and S. B. Kotsiantis, “Uncertainty Based Under-Sampling for Learning Naive Bayes Classifiers under Imbalanced Data Sets,” IEEE Access, vol. 8, pp. 2122–2133, 2020, doi: 10.1109/ACCESS.2019.2961784.
H.-C. Kuo et al., “Penggunaan Software Orange Data Mining Pada Implementasi Text Mining Dalam Analisis SentimenNetizen Di Twitter Terhadap Kelangkaan Minyak Goreng,” Jurnal Ilmiah Informatika (JIF), vol. 4, no. 1, p. 1, 2023, doi: 10.33884/jif.v11i01.6611.
M. S. Islam et al., “Challenges and Future in Deep learning for Sentiment Analysis: A Comprehensive Review and aProposed Novel Hybrid Approach,” Artificial Intelligence Review, vol. 57, no. 3, 2024, doi: 10.1007/s10462-023-10651- 9.
V. Mironov, A. Gusarenko, and G. Tuguzbaev, “Graphic Documents Parametric Personalization for Information Support of Educational Design Using Situation-Oriented Databases,” 2020, doi: 10.2991/aisr.k.201029.050.
V. Balakrishnan, Z. Shi, C. L. Law, R. Lim, L. L. Teh, and Y. Fan, “A Deep learning Approach in Predicting Products’ Sentiment Ratings: A Comparative Analysis,” Journal of Supercomputing, vol. 78, no. 5, pp. 7206–7226, 2021, doi: 10.1007/s11227-021-04169-6.
S. J. Pipin, R. Purba, and H. Kurniawan, “Prediksi Saham Menggunakan Recurrent Neural network (RNN-LSTM)Dengan Optimasi Adaptive Moment Estimation,” J. Comput. Syst. Informatics, vol. 4, no. 4, pp. 806–815, 2023, doi:10.47065/josyc.v4i4.4014.
N. Moch Farryz Rizkilloh and N. Sri Widiyanesti, “Prediksi Harga Cryptocurrency Menggunakan Algoritma Long ShortTermMemory (LSTM),” J. RESTI (Rekayasa Sist. Dan Teknol. Informasi), vol. 6, no. 1, pp. 25–31, 2022, doi:10.29207/resti.v6i1.3630.
Y. Kryvenchuk, N. Horishna, and N. None, “Creation of Salary Prediction System,” Herald of Khmelnytskyi NationalUniversity. Technical Sciences, vol. 317, no. 1, pp. 276–279, 2023, doi: 10.31891/2307-5732-2023-317-1-276-279.
N. Selle, N. Yudistira, and C. Dewi, “Perbandingan Prediksi Penggunaan Listrik Dengan Menggunakan Metode Long Short Term Memory (LSTM) Dan Recurrent Neural network (RNN),” J. Teknol. Inf. Dan Ilmu Komput., vol. 9, no. 1, pp.155–162, 2022, doi: 10.25126/jtiik.2022915585.
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