Penerapan Metode K-Nearest Neighbors dan Naïve Bayes pada Analisis Sentimen Pengguna Aplikasi Bstation melalui Platform Playstore


  • Sigit Fathu Amrillah * Mail Universitas Amikom Purwokerto, Indonesia
  • Dwi Krisbiantoro Universitas Amikom Purwokerto, Indonesia
  • Agung Prasetyo Universitas Amikom Purwokerto, Indonesia
  • (*) Corresponding Author
Keywords: K-Nearest Neighbors; Naïve Bayes; Sentiment Analysis; Bstation; Playstore

Abstract

Streaming is a method of distributing digital content directly over the internet, which allows users to access media without the need to download files. Bstation is a streaming platform that combines (OGV) and User-Generated Content (UGC). This research assesses the effectiveness of the K-Nearest Neighbors (KNN) and Naïve Bayes algorithms in analyzing sentiment in user reviews of the Bstation application, using a data sample of 5,000 reviews. The problem faced is the large number of users of the Bstation application, so sentiment analysis is needed to measure and understand the public's assessment of the application more accurately. This research aims to analyze the sentiment of Bstation users on Playstore and compare the performance of K-Nearest Neighbors (KNN) and Naïve Bayes to determine the best method for classifying reviews and user sentiment patterns. The findings showed that Naïve Bayes achieved 84% accuracy, surpassing KNN which only achieved 68%. Naïve Bayes showed 86% precision and 88% recall for negative sentiment, while achieving 78% precision and 76% recall for positive sentiment. recall for positive sentiment. In contrast, KNN achieved 80% precision and 66% recall for negative sentiments, and 54% recall for positive sentiments. recall for negative sentiments, and 54% precision and 71% recall for positive sentiments. The F1-Score for Naïve Bayes is also higher, reflecting a better balance overall. better balance overall. The macro average and weighted average weighted average for precision, recall, and F1-score with Naïve Bayes were 82% and 83%, respectively, while KNN recorded a macro average of 0.67. In conclusion, Naïve Bayes is more effective in sentiment analysis than KNN, providing more consistent and accurate performance

Downloads

Download data is not yet available.

Author Biographies

Dwi Krisbiantoro, Universitas Amikom Purwokerto
Agung Prasetyo, Universitas Amikom Purwokerto

References

A. Sampurna, F. Ramadhan, S. Al Azhar Sihombing, A. Balqis, and A. Ridha, “Dampak Integrasi Platform Streaming Online dalam Transformasi Broadcasting Kontemporer,” Jurnal Pendidikan Tambusai, vol. 8(1), pp. 4821–4829, 2024, doi: 10.25139/jkm.v5i1.3637.

Alya Dwi Yuliani and Oji Kurniadi, “Peranan Media Streaming dalam Menggantikan Televisi Konvensional di Kalangan Masyarakat,” Jurnal Riset Manajemen Komunikasi, vol. 3, no. 2, pp. 109–114, Dec. 2023, doi: 10.29313/jrmk.v3i2.3140.

F. Noor Hasan, “Analisis Sentimen Pengguna Aplikasi CapCut Pada Ulasan di Play Store Menggunakan Metode Naïve Bayes,” KLIK, vol. 4, no. 4, 2024, doi: 10.30865/klik.v4i4.1555.

A. Mukti, A. D. Hadiyanti, A. Nurlaela, and J. Panjaitan, “Sistem Analisa Sentiment Bakal Calon Presiden 2024 Menggunakan Metode NLP Berbasis Web,” JURIKOM, vol. 6, no. 1, p. p-ISSN, 2023, doi: 10.30865/jurikom.v9i2.3989.

M. N. Fahriza and N. Riza, “ANALISIS SENTIMEN PADA ULASAN APLIKASI CHAT GENERATIVE PRE-TRAINED TRANSFORMER GPT MENGGUNAKAN METODE KLASIFIKASI K-NEAREST NEIGHBOR(KNN) Sistematic Literature Review,” Jurnal Mahasiswa Teknik Informatika, vol. 7, no. 2, 2023, doi: 10.36040/jati.v7i2.6767.

Mulyana D I and AKbar A, “OPTIMASI KLASIFIKASI BATIK BETAWI MENGGUNAKAN DATA AUGMENTASI DENGAN METODE KNN DAN GLCM,” Jurnal Aplikasi Teknologi Informasi dan Manajemen (JATIM), vol. 3 no.2, 2022, doi: 10.31102/jatim.v3i2.1577.

T. Abdillah, U. Khaira, and B. F. Hutabarat, “Komparasi Metode Naive Bayes dan K-Nearest Neighbors Terhadap Analisis Sentimen Pengguna Aplikasi Zenius,” Jurnal PROCESSOR, vol. 19, no. 1, May 2024, doi: 10.33998/processor.2024.19.1.1596.

D. Pratmanto, F. Fandi, D. Imaniawan, and C. Author, “Analisis Sentimen Terhadap Aplikasi Canva Menggunakan Algoritma Naive Bayes Dan K-Nearest Neighbors,” Computer Science (CO-SCIENCE), vol. 3, no. 2, p. 52112, 2023, doi: 10.31294/coscience.v3i2.1917.

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

B. Z. Ramadhan, I. Riza, and I. Maulana, “Analisis Sentimen Ulasan Pada Aplikasi E-Commerce Dengan Menggunakan Algoritma Naïve Bayes,” Journal of Applied Informatics and Computing (JAIC), vol. 6, no. 2, p. 220, 2022, doi: 10.30871/jaic.v6i2.4725.

N. C. Agustina, D. Herlina Citra, W. Purnama, C. Nisa, and A. Rozi Kurnia, “Implementasi Algoritma Naive Bayes untuk Analisis Sentimen Ulasan Shopee pada Google Play Store,” MALCOM, vol. 2, pp. 47–54, 2022, doi: 10.57152/malcom.v2i1.195.

B. Wijaya Rauf, “Sentimen Analisis Pertambangan Di Konawe Utara Dengan Metode Naïve Bayes,” in Prosiding Sempatin, 2023, pp. 1–5. [Online]. Available: https://t.co/fSdh2dCADm

N. Istiqomah and F. Novika, “Sentiment Analysis Penyedia layanan Asuransi dari Media Sosial Twitter,” Jurnal Tekno Kompak, vol. 18, no. 1, pp. 77–89, 2024, doi: 10.33365/jtk.v18i1.3465.

A. V. Sudiantoro and E. Zuliarso, “ANALISIS SENTIMEN TWITTER MENGGUNAKAN TEXT MINING DENGAN ALGORITMA NAÏVE BAYES CLASSIFIER,” in SINTAK SEMINAR NASIONAL, 2018, pp. 69–73.

Z. A. Nurdiyansa and B. Berlilana, “Sentiment Analysis of Reviews on Lazada Apps using Naïve Bayes Algorithm,” JURNAL MEDIA INFORMATIKA BUDIDARMA, vol. 8, no. 1, p. 594, Jan. 2024, doi: 10.30865/mib.v8i1.7255.

I. Habib Kusuma and N. Cahyono, “Analisis Sentimen Masyarakat Terhadap Penggunaan E-Commerce Menggunakan Algoritma K-Nearest Neighbor,” vol. 8, no. 3, 2023.

M. H. Al-Areef and K. Saputra, “Analisis Sentimen Pengguna Twitter Mengenai Calon Presiden Indonesia Tahun 2024 Menggunakan Algoritma LSTM,” SAINTIKOM, vol. 22, pp. 270–279, 2023, doi: 10.53513/jis.v22i2.8680.

A. Guterres, Gunawan, and J. Santoso, “Stemming Bahasa Tetun Menggunakan Pendekatan Rule Based,” Teknika, vol. 8, no. 2, pp. 142–147, Oct. 2019, doi: 10.34148/teknika.v8i2.224.

F. Syahro and N. Fitriani, “PERBANDINGAN PERFORMA MODEL MACHINE LEARNING SUPPORT VECTOR MACHINE, NEURAL NETWORK, DAN K-NEAREST NEIGHBORS DALAM PREDIKSI HARGA SAHAM,” Jar’s, vol. 2, no. 1, p. 13, 2023, doi: 10.24929/jars.v2i1.2983.

M. N. Fahriza and N. Riza, “ANALISIS SENTIMEN PADA ULASAN APLIKASI CHAT GENERATIVE PRE-TRAINED TRANSFORMER GPT MENGGUNAKAN METODE KLASIFIKASI K-NEAREST NEIGHBOR(KNN) Sistematic Literature Review,” Jurnal Mahasiswa Teknik Informatika, vol. 7, no. 2, 2023, doi: 10.36040/jati.v7i2.6767.

D. Darwis, N. Siskawati, and Z. Abidin, “Penerapan Algoritma Naive Bayes untuk Analisis Sentimen Review Data Twitter BMKG Nasional,” Jurnal Tekno Kompak, vol. 15, no. 1, pp. 131–145, 2021, doi: 10.33365/jtk.v15i1.744.

D. Rustiana and N. Rahayu, “ANALISIS SENTIMEN PASAR OTOMOTIF MOBIL: TWEET TWITTER MENGGUNAKAN NAÏVE BAYES,” Jurnal SIMETRIS, vol. 8. no.1, 2017, doi: 10.24176/SIMET.V8I1.841.


Bila bermanfaat silahkan share artikel ini

Berikan Komentar Anda terhadap artikel Penerapan Metode K-Nearest Neighbors dan Naïve Bayes pada Analisis Sentimen Pengguna Aplikasi Bstation melalui Platform Playstore

Dimensions Badge
Article History
Submitted: 2024-08-29
Published: 2024-12-03
Abstract View: 285 times
PDF Download: 339 times
How to Cite
Amrillah, S., Krisbiantoro, D., & Prasetyo, A. (2024). Penerapan Metode K-Nearest Neighbors dan Naïve Bayes pada Analisis Sentimen Pengguna Aplikasi Bstation melalui Platform Playstore. Building of Informatics, Technology and Science (BITS), 6(3), 1281-1292. https://doi.org/10.47065/bits.v6i3.5863
Issue
Section
Articles