SENTIMENT ANALYSIS OF ONLINE DATING APPS USING SUPPORT VECTOR MACHINE AND NAÏVE BAYES ALGORITHMS

Urip Hadi Laksono; Ryan Randy Suryono

doi:10.52436/1.jutif.2025.6.1.2105

Urip Hadi Laksono Information System, Universitas Teknokrat Indonesia, Indonesia
Ryan Randy Suryono Information System, Universitas Teknokrat Indonesia, Indonesia

DOI: https://doi.org/10.52436/1.jutif.2025.6.1.2105

Keywords: Dating Apps, Naïve Bayes, Sentimen Analysis, Support Vector Machine, SMOTE Optimization

Abstract

In daily life, the use of digital applications is increasingly widespread, making dating apps increasingly popular and an important part of modern social interaction. This research aims to analyze user sentiment towards online dating apps, specifically Tinder, using Support Vector Machine (SVM) and Naïve Bayes algorithms. The problem underlying the importance of this research is the lack of balance between positive and negative sentiments in Tinder app users, which can affect user experience and the quality of service provided by Tinder. Utilizing the CRISP-DM framework, this research involves six stages, from data collection to evaluation. The results showed a significant imbalance between the number of positive and negative sentiments before optimization, but after the application of the SMOTE technique, there was a balancing between the two sentiment categories. SVM achieved 85% accuracy, while Naïve Bayes achieved 84%, with similar performance in identifying positive and negative sentiments. While both models performed satisfactorily, SVM appeared more stable in recognizing both positive and negative sentiments, suggesting the potential to be a superior choice in the context of dating apps. As such, this research makes an important contribution to the understanding of users' views on Timder apps and provides a basis for further development.

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References

A. P. Meiliani and I. Fuady, “Pengaruh Motivasi Pengguna Aplikasi Kencan Terhadap Kesehatan Mental Pada Mahasiswa,” Hum. J. Sosiohumaniora Nusant., vol. 1, no. 1, pp. 43–55, 2023, doi: 10.62180/yp2gp136.

V. A. Shania, M. F. Tri Palupi, and H. Kusumaningrum, “2747-File Utama Naskah-5038-1-10-20230718,” pp. 176–185, 2021, [Online]. Available: http://repository.untag-sby.ac.id/id/eprint/25766

M. Mafruh and Q. N. Wijayani, “Pendekatan Terhadap Interaksi Penguna Tinder Dalam Konteks Kencan Digital,” Madani J. Ilm. Multidisiplin, vol. 1, no. 11, pp. 842–845, 2023.

F. A. Madarina, “Budaya Hook-Up pada Online Dating Tinder,” Asketik, vol. 4, no. 2, pp. 187–196, 2020, doi: 10.30762/ask.v4i2.2420.

A. J. Pendidikan, S. Humaniora, N. Februari, Y. Sarah, A. Agustang, and M. R. S. Ahmad, “Aplikasi Tinder Sebagai Media Mencari Pasangan Dalam Membangun Interaksi Pada Kalangan Remaja Di Kota Makassar,” vol. 2, no. 1, 2024.

D. Kusumo and R. Afandi, “Table Of Content Article information ............................................ Rechtsidee,” Indones. J. Innov. Stud., vol. 13, no. 1, pp. 1–12, 2020, doi: 10.21070/acopen.9.2024.5394.

M. O. R. Nakano and S. Sumardjijati, “Resepsi Perempuan Surabaya terhadap Pemberitaan Negatif Aplikasi Kencan Online Tinder,” JIIP - J. Ilm. Ilmu Pendidik., vol. 7, no. 1, pp. 320–326, 2024, doi: 10.54371/jiip.v7i1.3642.

C. T. Saputri, S. Nursanti, and F. O. Lubis, “Proses Keberhasilan Hubungan Pengguna Aplikasi Kencan OnlineTinder Generasi Z,” Pendidik. Tambusai, vol. 7, no. 3, pp. 23081–23087, 2023, [Online]. Available: https://jptam.org/index.php/jptam/article/view/10258/8246

A. W. V. Hutabarat, N. L. S. S. Adnyani, and K. Suryadi, “Analisis Sentimen Data Ulasan Pengguna MyPertamina di Twitter dengan Metode Text Mining,” J. Rekayasa Sist. Ind., vol. 13, no. 1, pp. 145–154, 2024.

F. N. Hasan and M. Dwijayanti, “Analisis Sentimen Ulasan Pelanggan Terhadap Layanan Grab Indonesia Menggunakan Multinominal Naïve Bayes Classifier,” J. Linguist. Komputasional, vol. 4, no. 2, pp. 52–58, 2021, doi: https://doi.org/10.26418/jlk.v4i2.61.

Y. Ardian Pradana, I. Cholissodin, and D. Kurnianingtyas, “Analisis Sentimen Pemindahan Ibu Kota Indonesia pada Media Sosial Twitter menggunakan Metode LSTM dan Word2Vec,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 7, no. 5, pp. 2389–2397, 2023, [Online]. Available: http://j-ptiik.ub.ac.id

Friska Aditia Indriyani, Ahmad Fauzi, and Sutan Faisal, “Analisis sentimen aplikasi tiktok menggunakan algoritma naïve bayes dan support vector machine,” TEKNOSAINS J. Sains, Teknol. dan Inform., vol. 10, no. 2, pp. 176–184, 2023, doi: 10.37373/tekno.v10i2.419.

I. Sugiyarto et al., “Sentimen Analisis Pengguna Aplikasi Grab Menggunakan Algoritma Naive Bayes Classifier dan Support Vector Machine,” vol. 18, no. x, pp. 331–341, 1978.

I. P. Rahayu, A. Fauzi, and J. Indra, “Analisis Sentimen Terhadap Program Kampus Merdeka Menggunakan Naive Bayes Dan Support Vector Machine,” J. Sist. Komput. dan Inform., vol. 4, no. 2, p. 296, 2022, doi: 10.30865/json.v4i2.5381.

Tania Puspa Rahayu Sanjaya, Ahmad Fauzi, and Anis Fitri Nur Masruriyah, “Analisis sentimen ulasan pada e-commerce shopee menggunakan algoritma naive bayes dan support vector machine,” INFOTECH J. Inform. Teknol., vol. 4, no. 1, pp. 16–26, 2023, doi: 10.37373/infotech.v4i1.422.

A. Amalia, M. S. Lydia, S. D. Fadilla, and M. Huda, “Perbandingan Metode Klaster dan Preprocessing Untuk Dokumen Berbahasa Indonesia,” J. Rekayasa Elektr., vol. 14, no. 1, pp. 35–42, 2018, doi: 10.17529/jre.v14i1.9027.

F. Rahman, “WAKTU MENGGUNAKAN BINNING DAN SYNTHETIC MINORITY,” vol. 4, no. 1, pp. 29–35, 2024.

J. Ipmawati, S. Saifulloh, and K. Kusnawi, “Analisis Sentimen Tempat Wisata Berdasarkan Ulasan pada Google Maps Menggunakan Algoritma Support Vector Machine,” MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 4, no. 1, pp. 247–256, 2024, doi: 10.57152/malcom.v4i1.1066.

P. Widyantara et al., “Analisis Sentimen pada Teks Berbahasa Bali Menggunakan Metode Multinomial Naive Bayes dengan TF-IDF dan BoW,” Jnatia, vol. 2, no. 1, pp. 37–46, 2023.

Y. Julianto, D. H. Setiabudi, and S. Rostianingsih, “Analisis Sentimen Ulasan Restoran Menggunakan Metode SVM,” J. Infra, vol. 10, no. 1, 2022.

W. Eko Saputro, H. Yuana, and W. Dwi Puspitasari, “Analisis Sentimen Pengguna Dompet Digital Dana Pada Kolom Komentar Google Play Store Dengan Metode Klasifikasi Support Vector Machine,” JATI (Jurnal Mhs. Tek. Inform., vol. 7, no. 2, pp. 1151–1156, 2023, doi: 10.36040/jati.v7i2.6842.

A. M. Nur, N. Nurhidayati, and I. Fathurrahman, “Penerapan Metode Naïve Bayes Untuk Penentuan Penerima Beasiswa Program Indonesia Pintar (PIP).,” Infotek J. Inform. dan Teknol., vol. 7, no. 1, pp. 93–102, 2024, doi: 10.29408/jit.v7i1.23995.

A. I. Tanggraeni and M. N. N. Sitokdana, “Analisis Sentimen Aplikasi E-Government pada Google Play Menggunakan Algoritma Naïve Bayes,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 9, no. 2, pp. 785–795, 2022, doi: 10.35957/jatisi.v9i2.1835.

D. Wijaya, R. A. Saputra, and F. Irwiensyah, “KLIK: Kajian Ilmiah Informatika dan Komputer Analisis Sentimen Ulasan Aplikasi Samsat Digital Nasional Pada Google Playstore Menggunakan Algoritma Naïve Bayes,” Media Online, vol. 4, no. 4, pp. 2369–2380, 2024, doi: 10.30865/klik.v4i4.1738.

O. I. Gifari, M. Adha, F. Freddy, and F. F. S. Durrand, “Analisis Sentimen Review Film Menggunakan TF-IDF dan Support Vector Machine,” J. Inf. Technol., vol. 2, no. 1, pp. 36–40, 2022, doi: 10.46229/jifotech.v2i1.330.

I. T. Julianto, D. Kurniadi, M. R. Nashrulloh, and A. Mulyani, “Comparison of Classification Algorithm and Feature Selection in Bitcoin Sentiment Analysis,” J. Tek. Inform., vol. 3, no. 3, pp. 739–744, 2022, [Online]. Available: https://doi.org/10.20884/1.jutif.2022.3.3.343%0Ahttp://jutif.if.unsoed.ac.id/index.php/jurnal/article/view/343

N. Suarna and W. Prihartono, “PENGGUNAAN NAIVE BAYES DALAM MENGANALISIS SENTIMEN ULASAN APLIKASI MCDONALD ’ S DI INDONESIA,” vol. 8, no. 2, pp. 1949–1956, 2024.

K. D. Indarwati and H. Februariyanti, “Analisis Sentimen Terhadap Kualitas Pelayanan Aplikasi Gojek Menggunakan Metode Naive Bayes Classifier,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 10, no. 1, 2023, doi: 10.35957/jatisi.v10i1.2643.

U. Kusnia, F. Kurniawan, and S. Artikel, “Analisis Sentimen Review Aplikasi Media Berita Online Pada Google Play menggunakan Metode Algoritma Support Vector Machines (SVM) Dan Naive Bayes INFO ARTIKEL ABSTRAK,” Explor. IT, vol. 4, no. 36, pp. 24–28, 2022, [Online]. Available: https://doi.org/10.35891/explorit

D. Ananda and R. R. Suryono, “Analisis Sentimen Publik Terhadap Pengungsi Rohingya di Indonesia dengan Metode Support Vector Machine dan Naïve Bayes,” vol. 8, no. April, pp. 748–757, 2024, doi: 10.30865/mib.v8i2.7517.

P. K. Sari and R. R. Suryono, “Komparasi Algoritma Support Vector Machine Dan Random Forest Untuk Analisis Sentimen Metaverse,” J. Mnemon., vol. 7, no. 1, pp. 31–39, 2024, doi: 10.36040/mnemonic.v7i1.8977.