SENTIMENT ANALYSIS OF IPUSNAS REVIEWS USING NAIVE BAYES  AND K-NEAREST NEIGHBOR ALGORITHMS

Augst Nurandini; Zahra Revadinika Apriliani; Eka Nada Rinjani; Pungkas Subarkah

doi:10.34288/jri.v8i3.528

Authors

Augst Nurandini Universitas Amikom Purwokerto
Zahra Revadinika Apriliani Universitas Amikom Purwokerto
Eka Nada Rinjani Universitas Amikom Purwokerto
Pungkas Subarkah Universitas Amikom Purwokerto

(*) Corresponding Author

DOI:

https://doi.org/10.34288/jri.v8i3.528

Keywords:

sentiment analysis, Naive Bayes, K-Nearest Neighbor, text mining, iPusnas

Abstract

This study aims to analyze the sentiment of iPusnas application user reviews using the classification method with the K-Nearest Neighbor (KNN) and Naive Bayes algorithms. The data used are secondary data in the form of user reviews obtained from the Google Play Store in the period of January to December 2025 totaling 2415 reviews. This study uses a text mining approach with text preprocessing stages, feature extraction using the Bag of Words (BoW) method, and sentiment clas, sification using the Naive Bayes and K-Nearest Neighbor (KNN) algorithms. Model evaluation uses Test and ScoreConfusion Matrix, and Word Cloud. The results show that the Naive Bayes method has better performance with an accuracy value of 0.705 compared to K-Nearest Neighbor (KNN) with an accuracy value of 0.615. Testing the K parameter in the KNN algorithm shows that the best K value is obtained at K = 6 with an accuracy of 0.615. This study shows that Naive Bayes is more effective in classifying sentiment in iPusnas application user reviews.

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References

Albab, M. U., P, Y. K., & Fawaiq, M. N. (2023). Optimization of the Stemming Technique on Text preprocessing President 3 Periods Topic. 20(2), 1–10.

Andzar, A. M. (2023). Penerapan Text Mining Dalam Klasifikasi Judul Skripsi Yang Diusulkan Mahasiswa Menggunakan Metode Naïve Bayes. 6, 184–196.

Ariqoh, S., Sunandar, M. A., Muhyidin, Y., Informatika, T., Tinggi, S., & Wastukancana, T. (2023). ANALISIS SENTIMEN PADA PRODUK CUSHION DI WEBSITE FEMALE DAILY MENGGUNAKAN METODE SUPPORT VECTOR MACHINE ( SVM ). 2(3), 137–142.

Ayuningtyas, N., & Yustanti, W. (2024). Semi-Supervised Learning pada Pelabelan dalam Klasifikasi Multi-Label Data Teks. 06, 240–248.

Cetak, I., & Online, I. (2023). DECODE : Jurnal Pendidikan Teknologi Informasi. 3(2), 161–171.

Chrisinta, D., & Simarmata, J. E. (2024). Eksplorasi Teknik Web Scraping pada Data Mining : Pendekatan Pencarian Data Berbasis Python. 17(1), 58–68. https://doi.org/10.30998/faktorexacta.v17i1.22393

Darmayanti, I., Subarkah, P., Anunggilarso, L. R., & Suhaman, J. (2021). PREDIKSI POTENSI SISWA PUTUS SEKOLAH AKIBAT PANDEMI COVID-19 MENGGUNAKAN ALGORITME K-NEAREST NEIGHBOR. 10(2), 230–238.

Firdaus, A. A., Hadiana, A. I., & Ningsih, A. K. (2024). Klasifikasi Sentimen pada Aplikasi Shopee Menggunakan Fitur Bag of Word dan Algoritma Random Forest. 6(5), 1678–1683.

Fristtikasari, D., Alam, S., & Kurniawan, I. (2024). Analisis Sentimen Pengguna Aplikasi Kitalulus pada Ulasan Google Play Store Menggunakan Metode Naïve Bayes. 10(2), 457–473.

Ikhsan, A. N., Subarkah, P., & Alifian, R. S. (2023). Komparasi Algoritme K-NN , Naïve Bayes , dan Cart untuk Memprediksi Penerima Beasiswa. 12(2), 309–316.

Journal, I., & Information, O. (2025). Identifikasi Topik Dominan pada Hastag Konten Anak di Media Sosial X Menggunakan Word Cloud. 1, 0–5.

Kiasati, A. I., & Zulaikha, S. R. (2023). Analisis Tingkat Kepuasan Mahasiswa Terhadap Aplikasi iPusnas Menggunakan Model End User Computing Satisfaction ( EUCS ). 7(2), 175–188.

Maret, V. N., Tindakan, P., Teknik, K., & Saádi, A. (2025). Al-Amin : Jurnal Ilmu Pendidikan dan Sosial Humaniora. 2.

Megawati, N. A. (2025). Representasi Emosi dan Persepsi Pembaca Digital terhadap Karya Tere Liye di Aplikasi iPusnas Representation of Emotions and Digital Readers ’ Perceptions of Tere Liye ’ s Works on the iPusnas Application. 5, 231–239.

Pebdika, A., Herdiana, R., Solihudin, D., Pintar, P. I., Bayes, N., Penentuan, U., & Bantuan, P. (2023). KLASIFIKASI MENGGUNAKAN METODE NAIVE BAYES UNTUK MENENTUKAN CALON PENERIMA PIP. 7(1), 452–458.

Rahmah, H. N., Modeling, T., & Goreng, M. (n.d.). PENGGUNAAN SOFTWARE ORANGE DATA MINING PADA IMPLEMENTASI TEXT MINING DALAM ANALISIS SENTIMEN NETIZEN DI TWITTER TERHADAP KELANGKAAN MINYAK GORENG IMPLEMENTATION OF TEXT MINING IN NETIZEN SENTIMENT ANALYSIS ON TWITTER ON COOKING OIL SCARCITY USING ORANGE DATA MINING. 1–11.

Siregar, M. Z., Elhanafi, A. M., Irwan, D., Informasi, S., Medan, U. H., Bayes, N., … Media, S. (2025). ANALISIS SENTIMEN TERHADAP ULASAN APLIKASI MEDIA SOSIAL DI GOOGLE PLAY MENGGUNAKAN ALGORITMA NAIVE BAYES. 9(2), 3373–3381.

Subarkah, P., Risma, W., & Aditya, R. (2022). Comparison of correlated algorithm accuracy Naive Bayes Classifier and Naive Bayes Classifier for heart failure classification. 14(2), 120–125.

Tri, A., Septiani, D., Kuncoro, A. P., & Subarkah, P. (2024). Perbandingan Kinerja Metode Naïve Bayes Classifier dan K-Nearest Neighbor pada Analisis Sentimen Ulasan Mobile Banking Jenius Abstrak. 3, 67–77.

Untuk, K. N. N., Seleksi, K., & Beasiswa, P. (2021). Implementasi Algoritma Klasifikasi K-Nearest Neighbor. 6(2), 118–127.

Zuhdi, H. N., & Prasetiyo, B. (2025). Sentiment Analysis On Ipusnas Application Reviews In Google Play Store Using Naive Bayes Classifier Analisis Sentimen pada Ulasan Aplikasi iPusnas di Google Play Store Menggunakan Naive Bayes Classifier. 5(1), 12–19.