ANALISIS USER SENTIMENT APLIKASI GOOGLE MAPS, MAPS.ME DAN WAZE MENGGUNAKAN METODE SUPPORT VECTOR MACHINE

  • Ilham Fariz Asya Mubarok Universitas Buana Perjuangan Karawang
  • Baenil Huda Universitas Buana Perjuangan Karawang
  • Agustia Hananto Universitas Buana Perjuangan Karawang
  • Tukino Tukino Universitas Buana Perjuangan Karawang
  • Huban Kabir Universitas Subang

Abstract

Nowadays, the routing app is often used by many people, this app is very useful for users to find the best route by just entering the address code, this app can provide travel routes which can be taken by different kinds of vehicles. In Indonesia itself, there are several widely used route guidance apps with various positive and negative reviews. In this study, different types of apps namely Google Maps, Maps.me and Waze were used and the data is from user feedback through an online survey. The purpose of this study is to find out the users' ratings for each application which was used as the material for the study. Support Vector Machine method was used to process the data. For each app, 750 comments were received and the final result of maps.me was the app with the highest score based on 86.40% accuracy, 86.55% precision and 99.69% recall. The maps.me app received 68% positive reviews, followed by Waze with 29% and Google Maps with 3%. This makes maps.me the app with the highest score based on positive reviews.

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Keywords: Google Play, Analisis Sentiment, Google Play Support Vector Machine

References

A. P. Giovani, A. Ardiansyah, T. Haryanti, L. Kurniawati, and W. Gata, “Analisis Sentimen Aplikasi Ruang Guru Di Twitter Menggunakan Algoritma Klasifikasi,” J. Teknoinfo, vol. 14, no. 2, p. 115, 2020, doi: 10.33365/jti.v14i2.679.

A. L. Hananto, B. Priyatna, and A. Y. Rahman, “Penerapan Algoritma Djikstra Pada Sistem Monitoring Petugas Lapangan Pemkab Bekasi Berbasis Android,” JOINTECS (Journal Inf. Technol. Comput. Sci., vol. 4, no. 3, p. 95, 2019, doi: 10.31328/jointecs.v4i3.1078.

N. Herlinawati, Y. Yuliani, S. Faizah, W. Gata, and S. Samudi, “Analisis Sentimen Zoom Cloud Meetings di Play Store Menggunakan Naïve Bayes dan Support Vector Machine,” CESS (Journal Comput. Eng. Syst. Sci., vol. 5, no. 2, p. 293, 2020, doi: 10.24114/cess.v5i2.18186.

F. Trapsilawati, T. Wijayanto, and E. S. Jourdy, “Human-computer trust in navigation systems: Google maps vs Waze,” Commun. Sci. Technol., vol. 4, no. 1, pp. 38–43, 2019, doi: 10.21924/cst.4.1.2019.112.

N. Yolanda, I. H. Santi, D. Fanny, and H. Permadi, “Analisis Sentimen Popularitas Aplikasi Moodle Dan Edmodo Menggunakan Algoritma Support Vector Machine,” vol. 3, no. 1, pp. 48–59, 2022.

R. Tineges, A. Triayudi, and I. D. Sholihati, “Analisis Sentimen Terhadap Layanan Indihome Berdasarkan Twitter Dengan Metode Klasifikasi Support Vector Machine (SVM),” J. Media Inform. Budidarma, vol. 4, no. 3, p. 650, 2020, doi: 10.30865/mib.v4i3.2181.

R. Kusnadi, Y. Yusuf, A. Andriantony, R. Ardian Yaputra, and M. Caintan, “Analisis Sentimen Terhadap Game Genshin Impact Menggunakan Bert,” Rabit J. Teknol. dan Sist. Inf. Univrab, vol. 6, no. 2, pp. 122–129, 2021, doi: 10.36341/rabit.v6i2.1765.

M. Diki Hendriyanto, A. A. Ridha, and U. Enri, “Analisis Sentimen Ulasan Aplikasi Mola Pada Google Play Store Menggunakan Algoritma Support Vector Machine Sentiment Analysis of Mola Application Reviews on Google Play Store Using Support Vector Machine Algorithm,” J. Inf. Technol. Comput. Sci., vol. 5, no. 1, pp. 1–7, 2022.

B. W. Sari and F. F. Haranto, “Implementasi Support Vector Machine Untuk Analisis Sentimen Pengguna Twitter Terhadap Pelayanan Telkom Dan Biznet,” J. Pilar Nusa Mandiri, vol. 15, no. 2, pp. 171–176, 2019, doi: 10.33480/pilar.v15i2.699.

F. Bei and S. Sudin, “Analisis Sentimen Aplikasi Tiket Online Di Play Store Menggunakan Metode Support Vector Machine (Svm),” Sismatik, vol. 01, no. 01, pp. 91–97, 2021.

R. Wahyudi and G. Kusumawardana, “Analisis Sentimen pada Aplikasi Grab di Google Play Store Menggunakan Support Vector Machine,” J. Inform., vol. 8, no. 2, pp. 200–207, 2021, doi: 10.31294/ji.v8i2.9681.

S. I. Nurhafida, F. Sembiring, J. Raya, and C. No, “Analisis Sentimen Aplikasi Novel Online Di Google Play Store Menggunakan Algoritma Support Vector Machine ( SVM ),” vol. 6, pp. 317–327, 2022.

A. M. Siregar, “Klasifikasi Untuk Prediksi Cuaca Menggunakan Esemble Learning,” Petir, vol. 13, no. 2, pp. 138–147, 2020, doi: 10.33322/petir.v13i2.998.

F. Fatmawati and M. Affandes, “Klasifikasi Keluhan Menggunakan Metode Support Vector Machine (SVM) Pada Akun Facebook Group iRaise Helpdesk,” J. CoreIT J. Has. Penelit. Ilmu Komput. dan Teknol. Inf., vol. 3, no. 1, p. 24, 2018, doi: 10.24014/coreit.v3i1.3552.

A. Salim, W. Gata, M. Hilman Fakhriza, C. Sri Rhayu, A. Budiarto, and P. Studi Magister Ilmu Komputer STMIK Nusa Mandiri Jakarta, “Analisis Sentiment Instagram Menggunakan Metode Support Vector Machine (SVM) Berbasis Grid Search Algorithm (GSA),” pp. 466–472, 2022, [Online]. Available: https://ejournal.poltektegal.ac.id/index.php/smartcomp/article/view/3899.

M. I. Fikri, T. S. Sabrila, and Y. Azhar, “Perbandingan Metode Naïve Bayes dan Support Vector Machine pada Analisis Sentimen Twitter,” Smatika J., vol. 10, no. 02, pp. 71–76, 2020, doi: 10.32664/smatika.v10i02.455.

Published
2023-01-10
How to Cite
[1]
I. Mubarok, B. Huda, A. Hananto, T. Tukino, and H. Kabir, “ANALISIS USER SENTIMENT APLIKASI GOOGLE MAPS, MAPS.ME DAN WAZE MENGGUNAKAN METODE SUPPORT VECTOR MACHINE”, rabit, vol. 8, no. 1, pp. 69-74, Jan. 2023.
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Articles
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