Analisis Sentimen Menggunakan Algoritma Naïve Bayes, KNN, dan Decision Tree Terhadap Ulasan Aplikasi KitaLulus


  • Rodyah Mulyani Harun * Mail Universitas Airlangga, Surabaya, Indonesia
  • Faisal Fahmi Universitas Airlangga, Surabaya, Indonesia
  • (*) Corresponding Author
Keywords: Sentiment Analysis; Naïve Bayes; KNN; Decision Tree; KitaLulus

Abstract

The advent of digital technologies has transformed interaction dynamics between companies and potential employees by creating job search platforms. KitaLulus, one of the leading platforms in Indonesia, facilitates the job search process by providing various vacancies from various companies on one platform. However, there are several complaints from users, such as a complex job application process, inefficient file storage, and poor user interface (UI) and user experience (UX). On the other hand, Twitter is one of the places that contains user reviews, both in the form of satisfaction or disappointment, so that it can be used to identify public sentiment towards the KitaLulus application. Since it is important for the current generation, it is necessary to have a quality job search application, where recommendations for improving the quality of the application can be obtained from sentiment analysis. Therefore, sentiment analysis was conducted to identify public sentiment towards the KitaLulus application. The analysis in this study used 600 review data from Twitter which were then classified by sentiment based on Naïve Bayes, KNN, and Decision Tree algorithms. This research consists of six stages, namely data collection, data cleaning, data labelling, data preprocessing starting from SMOTE, split data, transform cases, tokenize, filter stopwords, and filter tokens (by length), sentiment classification, and finally results and evaluation. The results, after SMOTE was applied at the preprocessing stage, showed that KNN was the best algorithm with accuracy of 83.33%, precision of 80.36%%, and recall of 71.09%, followed by Naïve Bayes and Decision Tree respectively.

Downloads

Download data is not yet available.

References

H. Jang, S. Kim, J. Jeon, and J. Oh, “Voice of Employee: Impact of Online Reviews on Company and Job Seeker Matching Performance,” in 2023 IEEE International Conference on Big Data (Big Data), Institute of Electrical and Electronics Engineers Inc., 2023, pp. 6172–6174. doi: 10.1109/BigData59044.2023.10386598.

KitaLulus, “Platform Pencarian Kerja Berorientasi Komunitas Terbesar dan Teraman di Indonesia.” [Online]. Available: https://www.kitalulus.com/tentang-kitalulus

G. Y. Rahayu and A. Indrati, “Perancangan Ulang Antarmuka Portal KitaLulus dengan Menggunakan Metode user Centered Design (UCD),” JURNAL JUIT, vol. 3, no. 1, pp. 66–73, 2024, doi: https://doi.org/10.56127/juit.v3i1.1163.

V. Pavani, N. M. Pujitha, P. V. Vaishnavi, K. Neha, and D. S. Sahithi, “Feature Extraction based Online Job Portal,” in Proceedings of the International Conference on Electronics and Renewable Systems, ICEARS 2022, Institute of Electrical and Electronics Engineers Inc., 2022, pp. 1676–1683. doi: 10.1109/ICEARS53579.2022.9752295.

G. S. Al-Husna, D. Asmarajati, I. A. Ihsanuddin, and R. Mahmudati, “Perbandingan Metode Naïve Bayes dan Support Vector Machine untuk Analisis Sentimen pada Ulasan Pengguna Aplikasi LinkedIn,” STORAGE – Jurnal Ilmiah Teknik dan Ilmu Komputer, vol. 3, no. 2, pp. 139–144, 2024, doi: 10.55123.

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

A. S. Teguh, A. Gandhi, and A. P. Kurniati, “Analisis Kualitas Sistem dan Kualitas Informasi Terhadap Kepuasan Pelanggan (Studi Kasus KitaLulus),” e-Proceeding of Engineering, vol. 10, no. 3, pp. 3704–3714, Jun. 2023.

S. Styawati, A. Nurkholis, A. A. Aldino, S. Samsugi, E. Suryati, and R. P. Cahyono, “Sentiment Analysis on Online Transportation Reviews Using Word2Vec Text Embedding Model Feature Extraction and Support Vector Machine (SVM) Algorithm,” in 2021 International Seminar on Machine Learning, Optimization, and Data Science, ISMODE, Institute of Electrical and Electronics Engineers Inc., 2022, pp. 163–167. doi: 10.1109/ISMODE53584.2022.9742906.

D. Fristtikasari, S. Alam, and I. Kurniawan, “Analisis Sentimen Pengguna Aplikasi Kitalulus pada Ulasan Google Play Store Menggunakan Metode Naïve Bayes,” Jurnal Teknologi Informatika dan Komputer, vol. 10, no. 2, pp. 458–473, Sep. 2024, doi: 10.37012/jtik.v10i2.2244.

S. Rahayu, B. R. Asmoro, and E. Rinaldi, “Classification of Congestion in Jakarta Using KNN, Naïve Bayes and Decision Tree Method,” Jurnal Syntax Admiration, vol. 4, no. 7, pp. 928–952, Jul. 2023, doi: 10.46799/jsa.v4i7.654.

M. Iqbal, A. D. Wiranata, R. Suwito, and R. F. Ananda, “Perbandingan Algoritma Naïve Bayes, KNN, dan Decision Tree terhadap Ulasan Aplikasi Threads dan Twitter,” KLIK: Kajian Ilmiah Informatika dan Komputer, vol. 4, no. 3, pp. 1799–1807, 2023, doi: 10.30865/klik.v4i3.1402.

E. N. Halim, B. Huda, and A. Elanda, “Perbandingan KNN, Decision Tree Dan Naïve Bayes Untuk Analisis Sentimen Marketplace Bukalapak,” CESS (Journal of Computing Engineering, System and Science), vol. 8, no. 1, pp. 71–79, 2023, doi: https://doi.org/10.24114/cess.v8i1.41385.

E. Apriliyanto and Y. S. Rahayu, “Comparison of Sentiment Analysis from Twitter Data Collection with Naïve Bayes, Decision Tree, and k-Nearest Neighbor Methods,” Jurnal Ilmiah SINUS, vol. 22, no. 2, p. 1, Jul. 2024, doi: 10.30646/sinus.v22i2.833.

N. N. Wilim and R. S. Oetama, “Sentiment Analysis about Indonesian Lawyers Club Television Program Using K-Nearest Neighbor, Naïve Bayes Classifier, and Decision Tree,” IJNMT (International Journal of New Media Technology), vol. 8, no. 1, p. 50, 2021, doi: https://doi.org/10.31937/ijnmt.v8i1.1965.

M. B. Husna and W. Gata, “Analisis Sentimen Terhadap Layanan Aplikasi Jenius di Media Sosial Menggunakan Alogritma Long Short-Term Memory,” Progresif: Jurnal Ilmiah Komputer, vol. 20, no. 2, pp. 793–805, 2024, doi: 10.35889/progresif.v20i2.1957.

D. S. Putri, N. Sulistiyowati, and A. Voutama, “Analisis Sentimen dan Pemodelan Ulasan Aplikasi AdaKami Menggunakan Algoritma SVM dan KNN,” Journal Sensi, vol. 9, no. 2, pp. 209–225, 2023, doi: https://doi.org/10.33050/sensi.v9i2.2914.

D. A. Kristiyanti, D. A. Putri, E. Indrayuni, A. Nurhadi, and A. H. Umam, “E-Wallet Sentiment Analysis Using Naïve Bayes and Support Vector Machine Algorithm,” J Phys Conf Ser, Nov. 2020, doi: 10.1088/1742-6596/1641/1/012079.

R. Puspita and A. Widodo, “Perbandingan Metode KNN, Decision Tree, dan Naïve Bayes Terhadap Analisis Sentimen Pengguna Layanan BPJS,” Jurnal Informatika Universitas Pamulang, vol. 5, no. 4, p. 646, Dec. 2021, doi: 10.32493/informatika.v5i4.7622.

M. F. Fakhrezi, A. F. Rochim, and D. M. K. Nugraheni, “Comparison of Sentiment Analysis Methods Based on Accuracy Value Case Study: Twitter Mentions of Academic Article,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 7, no. 1, pp. 161–167, Feb. 2023, doi: 10.29207/resti.v7i1.4767.

P. A. R. Devi, “Klasifikasi Penyakit Gagal Ginjal Kronis Dengan Metode KNN (Studi Kasus RS Di Kab Gresik,” JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), vol. 9, no. 3, pp. 1739–1748, Sep. 2024, doi: 10.29100/jipi.v9i3.6226.

A. Fatkhudin, F. A. Artanto, N. A. Safli, and D. Wibowo, “Decision Tree Berbasis SMOTE dalam Analisis Sentimen Penggunaan Artificial Intelligence untuk Skripsi,” Remik: Riset dan E-Jurnal Manajemen Informatika Komputer, vol. 8, no. 2, pp. 495–505, 2024, doi: 10.33395/remik.v8i2.13531.

M. Maharani and F. Fathoni, “Analisis Sentimen Pengguna Terhadap Faktor Penggunaan PayPal Menggunakan Metode Decision Tree,” Jurnal Ilmiah Teknologi Informasi Asia, vol. 18, no. 1, 2024, doi: https://doi.org/10.32815/jitika.v18i1.1002.

S. G. Barus, “Klasifikasi Sentimen Data Tidak Seimbang Menggunakan Algoritma SMOTE dan K-Nearest Neighbor Pada Ulasan Pengguna Aplikasi PeduliLindungi,” Seminar Nasional Mahasiswa Ilmu Komputer dan Aplikasinya (SENAMIKA), vol. 3, no. 2, pp. 162–173, 2022.

E. Eviyanti, B. Irawan, and A. Bahtiar, “Penggunaan Algoritma Naïve Bayes Dalam Menganalisis Sentimen Ulasan Aplikasi AdaKami Di Google Play Store,” Jurnal Mahasiswa Teknik Informatika, vol. 7, no. 6, pp. 3879–3885, 2023, doi: https://doi.org/10.36040/jati.v7i6.8272.


Bila bermanfaat silahkan share artikel ini

Berikan Komentar Anda terhadap artikel Analisis Sentimen Menggunakan Algoritma Naïve Bayes, KNN, dan Decision Tree Terhadap Ulasan Aplikasi KitaLulus

Dimensions Badge
Article History
Submitted: 2024-11-29
Published: 2024-12-30
Abstract View: 59 times
PDF Download: 32 times
How to Cite
Harun, R., & Fahmi, F. (2024). Analisis Sentimen Menggunakan Algoritma Naïve Bayes, KNN, dan Decision Tree Terhadap Ulasan Aplikasi KitaLulus. Building of Informatics, Technology and Science (BITS), 6(3), 2033-2042. https://doi.org/10.47065/bits.v6i3.6367
Issue
Section
Articles