COMPARISON OF K-NEAREST NEIGHBORS AND NAÏVE BAYES CLASSIFIER ALGORITHMS IN SENTIMENT ANALYSIS OF USER REVIEWS FOR INTERMITTENT FASTING APPLICATIONS

Muhammad Varhan Kusuma; Safitri Juanita

doi:10.52436/1.jutif.2024.5.5.2345

Muhammad Varhan Kusuma Information Systems, Faculty of Information Technology, Universitas Budi Luhur, Indonesia
Safitri Juanita Information Systems, Faculty of Information Technology, Universitas Budi Luhur, Indonesia

DOI: https://doi.org/10.52436/1.jutif.2024.5.5.2345

Keywords: CRISP-DM, intermittent fasting, k-nearest neighbors, naïve bayes classifier, sentiment analysis

Abstract

Applications that focus on health, especially obesity prevention, are scattered in the Google Play Store, one of which is the "Intermittent Fasting" application, which, according to the developer, aims to help users maintain a healthy lifestyle and regulate eating habits. With the increasing number of similar health applications, this research focuses on sentiment analysis of user reviews of "Intermittent Fasting" to find out how users respond. The purpose of this research is to find the best algorithm to analyze sentiment on user reviews on the Google Play Store against the "Intermittent Fasting" application, as well as provide recommendations for new or old users and for application developers based on the results of processing review data. The data mining methodology used in this research is CRISP-DM, using a dataset collected on user reviews on the Google Play Store for five years (2019-2024), which is annotated with three sentiment labels (positive, negative, and neutral) based on user ratings, then modeling using two algorithms K-Nearest Neighbors (KNN) and Naïve Bayes Classifier (NBC). The contribution of this research is to test, evaluate, and compare the two algorithms (KNN and NBC) using two testing models (Split and K-Fold Cross Validation) and then provide recommendations for the best algorithm. The research concludes that the NBC algorithm is superior to KNN with an accuracy value of 80%, while the KNN algorithm has an accuracy value of only 71.43%. In addition, the K-Fold Cross Validation testing model is more optimal in improving the accuracy of the algorithm's performance than the Split model.

Downloads

Download data is not yet available.

References

H. E. Ardiani, T. A. E. Permatasari, and S. Sugiatmi, “Obesitas, Pola Diet, dan Aktifitas Fisik dalam Penanganan Diabetes Melitus pada Masa Pandemi Covid-19,” Muhammadiyah Journal of Nutrition and Food Science (MJNF), vol. 2, no. 1, Jul. 2021, doi: 10.24853/mjnf.2.1.1-12.

F. Aulia Rahman, T. Roekmantara, N. Romadhona Prodi Pendidikan Kedokteran, F. Kedokteran, and U. Islam Bandung, “Pengaruh Obesitas terhadap Kejadian Penyakit Jantung Koroner (PJK) pada Populasi Dewasa,” Bandung Conference Series: Medical Science, vol. 2, no. 1, pp. 1002–1008, 2022, doi: 10.29313/bcsms.v2i1.1979.

S. Rose, E. R. Noer, M. Muniroh, and A. Kartini, “Literatur Review: Pembatasan energi untuk peningkatan umur panjang. Manajemen alternatif terhadap metabolik obesitas,” AcTion: Aceh Nutrition Journal, vol. 8, no. 1, p. 139, Mar. 2023, doi: 10.30867/action.v8i1.602.

A. Valentina Millenia and A. Kurniawan, “Hubungan Antara Citra Tubuh Dengan Sikap Perempuan Terhadap Perilaku Diet,” Berajah Journal, vol. 2, no. 2, pp. 305–314, May 2022, doi: 10.47353/bj.v2i2.93.

D. A. Putri and R. Indryawati, “Body Dissatisfaction Dan Perilaku Diet Pada Mahasiswi,” Jurnal Psikologi, vol. 12, no. 1, pp. 88–97, Jun. 2019, doi: 10.35760/psi.2019.v12i1.1919.

C. Setiawan, “Obesitas, Olahraga, dan Diet: Analisis Sentimen pada Twitter Berbasis Analitik Big Data,” Jurnal Olahraga Kebugaran dan Rehabilitasi, vol. 3, no. 1, pp. 71–81, 2023.

A. Dwiki, A. Putra, and S. Juanita, “Analisis Sentimen Pada Ulasan Pengguna Aplikasi Bibit Dan Bareksa Dengan Algoritma KNN,” Jurnal Teknik Informatika dan Sistem Informasi, vol. 8, no. 2, pp. 636–646, Jun. 2021, [Online]. Available: http://jurnal.mdp.ac.id

A. Oktian Permana and Sudin Saepudin, “Perbandingan algoritma k-nearst neighbor dan naïve bayes pada aplikasi shopee,” Jurnal CoSciTech (Computer Science and Information Technology), vol. 4, no. 1, pp. 25–32, Apr. 2023, doi: 10.37859/coscitech.v4i1.4474.

A. I. Tanggraeni and M. N. N. Sitokdana, “Analisis Sentimen Aplikasi E-Government Pada Google Play Menggunakan Algoritma Naïve Bayes,” Jurnal Teknik Informatika dan Sistem Informasi, vol. 9, no. 2, pp. 785–795, Jun. 2022.

D. Asfi Warraihan, I. Permana, R. Novita, and A. Marsal, “Analisis Sentimen Pengguna Transportasi Online Maxim Pada Instagram Menggunakan Naïve Bayes Classifier dan K-Nearest Neighbor,” Jurnal Media Informatika Budidarma, vol. 7, no. 3, pp. 1134–1143, Jul. 2023, doi: 10.30865/mib.v7i3.6336.

S. Syafrizal, M. Afdal, and R. Novita, “Analisis Sentimen Ulasan Aplikasi PLN Mobile Menggunakan Algoritma Naïve Bayes Classifier dan K-Nearest Neighbor,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 4, no. 1, pp. 10–19, Dec. 2023, doi: 10.57152/malcom.v4i1.983.

S. Mulyani and R. Novita, “Implementation Of The Naive Bayes Classifier Algorithm For Classification Of Community Sentiment About Depression On Youtube,” Jurnal Teknik Informatika (Jutif), vol. 3, no. 5, pp. 1355–1361, Oct. 2022, doi: 10.20884/1.jutif.2022.3.5.374.

K. M. Elistiana, Bagus Adhi Kusuma, P. Subarkah, and H. A. Awal Rozaq, “Improvement Of Naive Bayes Algorithm In Sentiment Analysis Of Shopee Application Reviews On Google Play Store,” Jurnal Teknik Informatika (Jutif), vol. 4, no. 6, pp. 1431–1436, Dec. 2023, doi: 10.52436/1.jutif.2023.4.6.1486.

D. Tualang Ksatria, Y. Yunefri, and L. F. Lhaura Van, “Analisis Sentimen Ulasan Pengguna Aplikasi Mypertamina Pada Google Playstore menggunakan K-Nearest Neighbor dan Naïve Bayes,” Seminar Nasional Teknologi Informasi & Ilmu Komputer (SEMASTER), vol. 2, no. 1, pp. 213–227, 2023.

G. K. Locarso, “Analisis Sentimen Review Aplikasi Peduli Lindungi Pada Google Play Store Menggunakan NBC,” Jurnal Teknik Informatika Kaputama (JTIK), vol. 6, no. 2, 2022.

C. Schröer, F. Kruse, and J. M. Gómez, “A systematic literature review on applying CRISP-DM process model,” in Procedia Computer Science, Elsevier B.V., 2021, pp. 526–534. doi: 10.1016/j.procs.2021.01.199.

D. Nurmalasari, I. Hermanto, and I. Ma’ruf Nugroho, “Perbandingan Algoritma SVM, KNN dan NBC Terhadap Analisis Sentimen Aplikasi Loan Service,” Jurnal Media Informatika Budidarma, vol. 7, no. 3, pp. 1521–1530, Jul. 2023, doi: 10.30865/mib.v7i3.6427.

H. D. Abubakar and M. Umar, “Sentiment Classification: Review of Text Vectorization Methods: Bag of Words, Tf-Idf, Word2vec and Doc2vec,” SLU Journal of Science and Technology, vol. 4, no. 1 & 2, pp. 27–33, Aug. 2022, doi: 10.56471/slujst.v4i.266.

D. Sandi and E. Utami, “Analisis Sentimen Publik Terhadap Elektabilitas Ganjar Pranowo di Tahun Politik 2024 di Twitter dengan Algoritma KNN dan Naïve Bayes,” Jurnal Media Informatika Budidarma, vol. 7, no. 3, pp. 1097–1108, Jul. 2023, doi: 10.30865/mib.v7i3.6298.

V. R. Joseph and A. Vakayil, “Split: An Optimal Method for Data Splitting,” Technometrics, vol. 64, no. 2, pp. 166–176, 2022, doi: 10.1080/00401706.2021.1921037.

I. K. Nti, O. Nyarko-Boateng, and J. Aning, “Performance of Machine Learning Algorithms with Different K Values in K-fold CrossValidation,” International Journal of Information Technology and Computer Science, vol. 13, no. 6, pp. 61–71, Dec. 2021, doi: 10.5815/ijitcs.2021.06.05.