Analisis Sentimen Pengguna Transportasi Online Maxim Pada Instagram Menggunakan Naïve Bayes Classifier dan K-Nearest Neighbor

Authors

  • Dzul Asfi Warraihan Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru
  • Inggih Permana Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru
  • Mustakim Mustakim Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru
  • Rice Novita Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru

DOI:

https://doi.org/10.30865/mib.v7i3.6336

Keywords:

Sentiment Analysis, Instagram, K-Nearest Neighbor, Maxim, Naive Bayes Classifier

Abstract

Online transportation is a form of internet-based transportation that covers all aspects of the transaction process, including booking, route tracking, payment, and service assessment of the online transportation. Maxim is one of the popular online transportation providers in Indonesia so it will continue to improve its services to serve the needs of the entire community. In making developments, Maxim needs user opinions regarding its application or services. This research conducts sentiment analysis of Maxim users' opinions on Instagram using Naive Bayes Classifier (NBC) and K-Nearest Neighbor (KNN) algorithms. Opinions are divided into 3 classes: negative, neutral, and positive. This research also uses the Random Over Sampling method and data sharing with 10-Fold Cross Validation. The accuracy results on sentiment data related to applications using the NBC algorithm are 81.03% and in the KNN algorithm with a value of k = 3 which is 80.72%. Meanwhile, sentiment data related to services produces an accuracy value in the NBC algorithm, namely 94% and the KNN algorithm with k = 3, namely 84%. It can be concluded that the NBC model is better than the KNN model in testing application-related sentiment data and service-related sentiment data after the Random Over Sampling method.

References

R. M. Oroh, A. S. Sembel, and J. C. Mandey, “Pengaruh Keterjangkauan Trayek Angkutan Kota Terhadap Peningkatan Transportasi Online di Kota Tomohon,†Sabua J. Lingkung. Binaan dan Arsit., vol. 11, no. 1, pp. 21–30, 2022, [Online]. Available: https://ejournal.unsrat.ac.id/index.php/SABUA/article/view/41227.

E. Setyaningsih, E. Ismawan, and T. Hidayat, “Analisa Tingkat Kepuasan Pelanggan Transportasi Online Maxim di Balikpapan,†STMIK Borneo Int., vol. 3, no. 1, pp. 33–38, 2019.

R. A. Tsalisa, S. P. Hadi, and D. Purbawati, “Pengaruh Kualitas Pelayanan dan Harga terhadap Kepuasan Pelanggan Pengguna Jasa Transportasi Online Maxim di Kota Semarang,†J. Ilmu Adm. Bisnis, vol. 11, no. 4, pp. 822–829, 2022, doi: 10.14710/jiab.2022.35970.

M. N. Akbar, N. Hasanahlmar, and M. H. Hasrul, “Sentiment Analysis Terhadap Review Aplikasi Maxim di Google Play Store Menggunakan Support Vector Machine ( SVM ),†AGENTS J. Artif. Intell. Data Sci., vol. 2, no. 2, pp. 1–8, 2022.

Taximaxim.com, “Maxim Ekspansi Jangkauan Layanan, Tambah 11 Kota di Indonesia,†https://id.taximaxim.com, 2023. https://id.taximaxim.com/id/2093-jakarta/blog/2023/01/2106-maxim-ekspansi-jangkauan-layanan-tambah-11-kota-di-indonesia/.

F. Tempola, M. Muhammad, and A. Khairan, “Perbandingan Klasifikasi Antara KNN dan Naive Bayes pada Penentuan Status Gunung Berapi dengan K-Fold Cross Validation,†J. Teknol. Inf. dan Ilmu Komput., vol. 5, no. 5, p. 577, 2018, doi: 10.25126/jtiik.201855983.

D. Pratmanto, R. Rousyati, F. F. Wati, A. E. Widodo, S. Suleman, and R. Wijianto, “App Review Sentiment Analysis Shopee Application in Google Play Store Using Naive Bayes Algorithm,†J. Phys. Conf. Ser., vol. 1641, no. 1, pp. 1–7, 2020, doi: 10.1088/1742-6596/1641/1/012043.

M. Tri Anjasmoros, D. Fitri Marisa, and Istiadi, “Analisis Sentimen Aplikasi Go-Jek Menggunakan Metode Svm Dan Nbc (Studi Kasus: Komentar Pada Play Store),†Conf. Innov. Appl. Sci. Technol. (CIASTECH 2020), no. Ciastech, pp. 489–498, 2020.

T. Qurahman, Mustakim, and A. Jaini, “Penerapan Algoritma Naïve Bayes Classifier Dan Probabilistic Neural Network Untuk Klasifikasi Nasabah Bank Dalam Membayar Kredit,†… Komun. dan Ind., no. November, pp. 205–213, 2019, [Online]. Available: http://ejournal.uin-suska.ac.id/index.php/SNTIKI/article/view/7999.

A. Rahman, E. Utami, and S. Sudarmawan, “Sentimen Analisis Terhadap Aplikasi pada Google Playstore Menggunakan Algoritma Naïve Bayes dan Algoritma Genetika,†J. Komtika (Komputasi dan Inform., vol. 5, no. 1, pp. 60–71, 2021, doi: 10.31603/komtika.v5i1.5188.

M. N. Muttaqin and I. Kharisudin, “Analisis Sentimen Pada Ulasan Aplikasi Gojek Menggunakan Metode Support Vector Machine dan K Nearest Neighbor,†UNNES J. Math., vol. 10, no. 2, pp. 22–27, 2021, [Online]. Available: http://journal.unnes.ac.id/sju/index.php/ujm.

S. G. Setyorini and Mustakim, “Application of The Nearest Neighbor Algorithm for Classification of Online Taxibike Sentiments In Indonesia In The Google Playstore Application,†in journal of Physics: Conference Series, 2021, pp. 1–7, doi: 10.1088/1742-6596/2049/1/012026.

A. D. Afifaturahman and F. MSN, “Perbandingan Algoritma K-Nearest Neighbour (KNN) dan Naive Bayes pada Intrusion Detection System (IDS),†Innov. Res. Informatics, vol. 3, no. 1, pp. 17–25, 2021, doi: 10.37058/innovatics.v3i1.2852.

S. Mandasari, B. H. Hayadi, and R. Gunawan, “Analisis Sentimen Pengguna Transportasi Online Terhadap Layanan Grab Indonesia Menggunakan Multinomial Naive Bayes Classifier,†J-SISKO TECH (Jurnal Teknol. Sist. Inf. dan Sist. Komput. TGD), vol. 5, no. 2, p. 118, 2022, doi: 10.53513/jsk.v5i2.5635.

S. Nurwahyuni, “Analisis Sentimen Aplikasi Transportasi Online Krl Access Menggunakan Metode Naive Bayes,†Swabumi, vol. 7, no. 1, pp. 31–36, 2019, doi: 10.31294/swabumi.v7i1.5575.

B. Mas Pintoko and K. Muslim, “Analisis Sentimen Jasa Transportasi Online pada Twitter Menggunakan Metode Naïve Bayes Classifier,†e-Proceeding Eng., vol. 5, no. 3, pp. 8121–8230, 2018.

F. R. Irawan, A. Jazuli, and T. Khotimah, “Analisis Sentimen Terhadap Pengguna Gojek Menggunakan Metode K-Nearset Neighbors Sentiment Analysis of Gojek Users Using K-Nearest Neighbor,†JIKO (Jurnal Inform. dan Komputer), vol. 5, no. 1, pp. 62–68, 2022, doi: 10.33387/jiko.

W. Athira Luqyana, I. Cholissodin, and R. S. Perdana, “Analisis Sentimen Cyberbullying pada Komentar Instagram dengan Metode Klasifikasi Support Vector Machine,†J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 11, pp. 4704–4713, 2018, [Online]. Available: http://j-ptiik.ub.ac.id.

M. A. Rizaty, “Indonesia Miliki 97,38 Juta Pengguna Instagram pada Oktober 2022,†https://dataindonesia.id/, 2022. https://dataindonesia.id/digital/detail/indonesia-miliki-9738-juta-pengguna-instagram-pada-oktober-2022 (accessed Apr. 07, 2023).

S. Z. Ulya, “Analisa Sentimen Masyarakat Mengenai Ppkm Di Kota Pekanbaru Pada Instagram Menggunakan Metode Naïve Bayes Classifier,†Universitas Islam Negeri Sultan Syarief Kasim Riau, 2022.

S. Khomsah and Agus Sasmito Aribowo, “Text-Preprocessing Model Youtube Comments in Indonesian,†J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 4, no. 4, pp. 648–654, 2020, doi: 10.29207/resti.v4i4.2035.

N. T. Miko, “Analisis Sentimen pada Kasus Covid19 Menggunakan Convolutional Neural Network dan Word Embedding,†Universitas Komputer Indonesia, 2021.

E. F. Saraswita, D. P. Rini, and A. Abdiansah, “Analisis Sentimen E-Wallet di Twitter Menggunakan Support Vector Machine dan Recursive Feature Elimination,†J. Media Inform. Budidarma, vol. 5, no. 4, p. 1195, 2021, doi: 10.30865/mib.v5i4.3118.

N. Aliyah Salsabila, Y. Ardhito Winatmoko, A. Akbar Septiandri, and A. Jamal, “Colloquial Indonesian Lexicon,†Proc. 2018 Int. Conf. Asian Lang. Process. IALP 2018, pp. 226–229, 2019, doi: 10.1109/IALP.2018.8629151.

V. W. D. Thomas and F. Rumaisa, “Analisis Sentimen Ulasan Hotel Bahasa Indonesia Menggunakan Support Vector Machine dan TF-IDF,†J. Media Inform. Budidarma, vol. 6, no. 3, p. 1767, 2022, doi: 10.30865/mib.v6i3.4218.

R. Aryanti, T. Misriati, and R. Hidayat, “Klasifikasi Risiko Kesehatan Ibu Hamil Menggunakan Random Oversampling Untuk Mengatasi Ketidakseimbangan Data,†KLIK Kaji. Ilm. Inform. dan Komput., vol. 3, no. 5, pp. 409–416, 2023, doi: DOI: https://doi.org/10.30865/klik.v3i5.728.

M. Tripati and A. Taneja, “K-Fold Cross-Validation Machine Learning Approach on Data Imbalance for Wireless Sensor Network,†Int. J. Sci. Res. Eng. Trends, vol. 5, no. 5, pp. 1590–1595, 2019.

Downloads

Published

2023-07-23

Issue

Vol. 7 No. 3 (2023): Juli 2023

Section

Articles

License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License

Authors who publish with this journal agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).