Penerapan Algoritma Naïve Bayes pada Analisis Sentimen Aplikasi Traveloka pada Platform Playstore


  • Eka Ardiya Putri * Mail Universitas Amikom Purwokerto, Purwokerto, Indonesia
  • Berlilana Berlilana Universitas Amikom Purwokerto, Purwokerto, Indonesia
  • (*) Corresponding Author
Keywords: Sentiment Analysis; Naive Bayes; Playstore; Traveloka; User Reviews

Abstract

The number of internet users in Indonesia is increasing every year, making it the fastest-growing country in the world, next only to China, India and the United States. In 2017, in Indonesia, the digital economy sector had a high impact on GDP, showing a figure of 7.3%, while the total economic development only reached 5.1%. Traveloka appeared in 2012 and has grown rapidly to be classified as the most superior travel application in Southeast Asia. As applied by the Traveloka application, it applies data scraping to collect 5000 review data from the intended platform. With the increase of Traveloka app user reviews on Playstore, the main challenge is to classify the sentiment of the reviews automatically and accurately. The purpose of this research is to find out the extent of user assessment of the Traveloka application. The results show that the model has an Accuracy of 0.91, indicating that 91% of the total data was predicted correctly. The model'sF1 Score of 0.90 reflects the optimal balance between Precision and Recall, indicating that the model is not only correct in predicting positive results, but also able to capture almost all positive examples. Precision of 0.92 indicates a high level of accuracy in positive predictions, while Recall of 0.88 indicates that the model's ability to detect all positive data is very good. In this analysis, out of the 940 data used, 250 True Positive (TP), 18 False Positive (FP), 608 True Negative (TN) and 64 False Negative (FN) were found, with an 80:20 data split. The findings show that the model can predict most of the data accurately, despite some errors in positive and negative classification. These results indicate that the model has high effectiveness in the identification and prediction of positive data, providing a strong basis for further applications in data analysis.

Downloads

Download data is not yet available.

References

H. Hanifah, C. Hayati, and A. Sadiqin, “Mapping out model bisnis sharing economy pada unicorn asal Indonesia,” Journal of Management and Digital Business, vol. 4, no. 2, pp. 216–233, Jul. 2024, doi: 10.53088/jmdb.v4i2.932.

S. G. Gunawan, “PERTANGGUNGJAWABAN HUKUM TRAVELOKA SEBAGAI PELAKU USAHA DALAM FENOMENA PEMBATALAN TIKET SEPIHAK TERHADAP KONSUMEN,” Bureaucracy Journal: Indonesia Journal of Law and Social-Political Governance, vol. 3, no. 1, 2023, doi: 10.53363/bureau.v3i1.207.

N. B. Sidauruk and N. Riza, “SENTIMEN ANALISIS DATA PENGGUNA TERHADAP KAI ACCESS Systematic Literature Review,” Jurnal Mahasiswa Teknik Informatika, vol. 7, no. 2, pp. 1297–1303, 2023, doi: doi.org/10.36040/jati.v7i2.6764.

N. Suarna and W. Prihartono, “PENERAPAN NLP (NATURAL LANGUAGE PROCESSING) DALAM ANALISIS SENTIMEN PENGGUNA TELEGRAM DI PLAYSTORE,” Jurnal Mahasiswa Teknik Informatika, vol. 8, no. 2, p. 1841, 2024, doi: doi.org/10.36040/jati.v8i2.8469.

A. Supian, B. Tri Revaldo, N. Marhadi, and L. Efrizoni, “Acuan Supian Perbandingan Kinerja Naïve Bayes dan SVM pada Analisis Sentimen Twitter Ibukota Nusantara,” Journal of Informatics Science , vol. 12, no. 01, pp. 16–21, 2024, doi: 10.33884/jif.v12i01.8721.

F. Novianti and K. R. N. Wardani, “ANALISIS SENTIMEN MASYARAKAT TERHADAP DATA TWEET TRAVELOKA SELAMA RAPID TEST ANTIGEN MENGGUNAKAN ALGORITMA NAÏVE BAYES,” JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), vol. 8, no. 3, pp. 922–933, Aug. 2023, doi: 10.29100/jipi.v8i3.3973.

D. Wijaya, R. A. Saputra, and F. Irwiensyah, “KLIK: Kajian Ilmiah Informatika dan Komputer Analisis Sentimen Ulasan Aplikasi Samsat Digital Nasional Pada Google Playstore Menggunakan Algoritma Naïve Bayes,” Media Online, vol. 4, no. 4, 2024, doi: 10.30865/klik.v4i4.1738.

I. Darmawan and O. Nurul Pratiwi, “ANALISIS SENTIMEN ULASAN PRODUK TOKO ONLINE RUBYLICIOUS UNTUK PENINGKATAN LAYANAN MENGGUNAKAN ALGORITMA NAIVE BAYES,” 2020.

N. R. Siahaan et al., “ANALISIS SENTIMEN ULASAN APLIKASI MEDIA SOSIAL WHATSAPP MENGGUNAKAN METODE NAIVE BAYES CLASSIFIER,” JURNAL ILMIAH BETRIK, vol. 14, no. 02, 2023, doi: doi.org/10.36050/betrik.v14i02%20AGUSTUS.104.

A. A. Kurniawan and M. Mustikasari, “Implementasi Deep Learning Menggunakan Metode CNN dan LSTM untuk Menentukan Berita Palsu dalam Bahasa Indonesia,” vol. 5, no. 4, pp. 2622–4615, 2020, doi: 10.32493/informatika.v5i4.7760.

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. K. Fauziyyah, “ANALISIS SENTIMEN PANDEMI COVID19 PADA STREAMING TWITTER DENGAN TEXT MINING PYTHON,” Jurnal Ilmiah SINUS, vol. 18, no. 2, p. 31, Jul. 2020, doi: 10.30646/sinus.v18i2.491.

Yuyun, Nurul Hidayah, and Supriadi Sahibu, “Algoritma Multinomial Naïve Bayes Untuk Klasifikasi Sentimen Pemerintah Terhadap Penanganan Covid-19 Menggunakan Data Twitter,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 5, no. 4, pp. 820–826, Aug. 2021, doi: 10.29207/resti.v5i4.3146.

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.

A. Ariansyah and U. Indahyanti, “Fitur Ekstraksi pada Pemodelan Topik Menggunakan Metode Latent Dirichlet Allocation pada Peristiwa Kebocoran Data,” Indonesian Journal of Applied Technology, vol. 1, no. 2, 2024, doi: 10.47134/ijat.v1i2.3041.

P. Gede Surya Cipta Nugraha and N. Wayan Wardani, “Stemming Dokumen Teks Bahasa Bali Dengan Metode Rule Base Approach,” JATISI, vol. 7, no. 3, 2020, doi: 10.35957/jatisi.v7i3.538.

M. Hafizh Mahendra, D. Triantoro Murdiansyah, and K. Muslim Lhaksmana, “Analisis Sentimen Tweet COVID-19 Menggunakan Metode K-Nearest Neighbors dengan Ekstraksi Fitur TF-IDF dan CountVectorizer,” Jurnal Ilmu Multidisiplin, vol. 1(2), pp. 37–43, 2023, doi: 10.69688/dike.v1i2.35.

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.

A. Nurian, M. S. Ma’arif, I. N. Amalia, and C. Rozikin, “ANALISIS SENTIMEN PENGGUNA APLIKASI SHOPEE PADA SITUS GOOGLE PLAY MENGGUNAKAN NAIVE BAYES CLASSIFIER,” Jurnal Informatika dan Teknik Elektro Terapan, vol. 12, no. 1, Jan. 2024, doi: 10.23960/jitet.v12i1.3631.


Bila bermanfaat silahkan share artikel ini

Berikan Komentar Anda terhadap artikel Penerapan Algoritma Naïve Bayes pada Analisis Sentimen Aplikasi Traveloka pada Platform Playstore

Dimensions Badge
Article History
Submitted: 2024-10-25
Published: 2024-12-03
Abstract View: 64 times
PDF Download: 34 times
How to Cite
Putri, E., & Berlilana, B. (2024). Penerapan Algoritma Naïve Bayes pada Analisis Sentimen Aplikasi Traveloka pada Platform Playstore. Building of Informatics, Technology and Science (BITS), 6(3), 1467−1476. https://doi.org/10.47065/bits.v6i3.6130
Issue
Section
Articles