Analisis Sentimen Komentar Resep Masakan Indonesia dengan Machine Learning dan Fitur Ekstraksi Word2Vec
DOI:
https://doi.org/10.53513/jursi.v5i1.12321Keywords:
Sentiment Analysis, Word2Vec, Naïve Bayes, KNN, Random Forest, Indonesian Recipe CommentsAbstract
Perkembangan teknologi digital telah mendorong meningkatnya aktivitas pengguna dalam mengekspresikan opini melalui media sosial, termasuk komentar terhadap resep masakan Indonesia. Analisis sentimen digunakan untuk memahami kecenderungan opini tersebut secara otomatis menggunakan pendekatan machine learning. Penelitian ini bertujuan untuk menganalisis pengaruh penerapan fitur ekstraksi Word2Vec terhadap peningkatan kinerja tiga algoritma klasifikasi teks, yaitu Naïve Bayes, K-Nearest Neighbor (KNN), dan Random Forest, dalam mengklasifikasikan komentar positif dan negatif pada dataset komentar resep masakan Indonesia. Tahapan penelitian meliputi preprocessing data (tokenisasi, stopword removal, dan stemming), pelabelan sentimen, ekstraksi fitur Word2Vec, serta pengujian model menggunakan pembagian data 80:20. Evaluasi dilakukan menggunakan empat metrik, yaitu accuracy, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa penerapan fitur Word2Vec mampu meningkatkan performa seluruh model. Akurasi tertinggi diperoleh pada model Random Forest dengan Word2Vec sebesar 84,07%, disusul Naïve Bayes sebesar 84,07%, dan KNN sebesar 79,70%. Nilai F1-score terbaik juga diperoleh oleh Random Forest sebesar 84,45%. Peningkatan performa ini menunjukkan bahwa representasi semantik Word2Vec mampu memperkaya pemahaman model terhadap makna dan konteks kata dalam teks, sehingga lebih efektif dalam mengidentifikasi sentimen pengguna terhadap resep masakan Indonesia.
References
Z. Li, Y. Fan, B. Jiang, T. Lei, and W. Liu, “A survey on sentiment analysis and opinion mining for social multimedia,” Multimed. Tools Appl., vol. 78, no. 6, pp. 6939–6967, 2019, doi: 10.1007/s11042-018-6445-z.
P. Karthika, R. Murugeswari, and R. Manoranjithem, “Sentiment Analysis of Social Media Network Using Random Forest Algorithm,” IEEE Int. Conf. Intell. Tech. Control. Optim. Signal Process. INCOS 2019, pp. 1–5, 2019, doi: 10.1109/INCOS45849.2019.8951367.
E. S. Alamoudi and N. S. Alghamdi, “Sentiment classification and aspect-based sentiment analysis on yelp reviews using deep learning and word embeddings,” J. Decis. Syst., vol. 30, no. 2–3, pp. 259–281, 2021, doi: 10.1080/12460125.2020.1864106.
I. A. Darmawan, M. F. Randy, I. Yunianto, M. M. Mutoffar, and M. T. P. Salis, “Penerapan Data Mining Menggunakan Algoritma Apriori Untuk Menentukan Pola Golongan Penyandang Masalah Kesejahteraan Sosial,” Sebatik, vol. 26, no. 1, pp. 223–230, 2022, doi: 10.46984/sebatik.v26i1.1622.
O. A. Alcántara Francia, M. Nunez-del-Prado, and H. Alatrista-Salas, “Survey of Text Mining Techniques Applied to Judicial Decisions Prediction,” Appl. Sci., vol. 12, no. 20, 2022, doi: 10.3390/app122010200.
J. Guerreiro and P. Rita, “How to predict explicit recommendations in online reviews using text mining and sentiment analysis,” J. Hosp. Tour. Manag., vol. 43, no. July, pp. 269–272, 2020, doi: 10.1016/j.jhtm.2019.07.001.
K. N. Reddy and D. B. I. Reddy, “Restaurant Review Classification Using Naives Bayes Model,” J. Univ. Shanghai Sci. Technol., vol. 23, no. 08, pp. 646–656, 2021, doi: 10.51201/jusst/21/08443.
M. Azam, T. Ahmed, F. Sabah, and M. I. Hussain, “Feature Extraction based Text Classification using K-Nearest Neighbor Algorithm,” IJCSNS Int. J. Comput. Sci. Netw. Secur., vol. 18, no. 12, pp. 95–101, 2018, doi: https://doi.org/10.29207/resti.v6i4.4186.
F. Diba, M. S. Lydia, and P. Sihombing, “Analisis Random Forest Menggunakan Principal Component Analysis Pada Data Berdimensi Tinggi,” Indones. J. Comput. Sci., vol. 12, no. 4, pp. 2152–2160, 2023, doi: 10.33022/ijcs.v12i4.3329.
R. Wasono, “PERBANDINGAN METODE RANDOM FOREST DAN NAÏVE BAYES UNTUK KLASIFIKASI DEBITUR BERDASARKAN KUALITAS KREDIT,” Semin. Nas. Edusaintek, 2022.
A. W. Baur, M. Breitsprecher, and M. Bick, “Catching Fire : Start-Ups in the Text Analytics Software Industry,” pp. 1–14, 2014.
X. Deng, Y. Li, J. Weng, and J. Zhang, “Feature selection for text classification: A review,” Multimed. Tools Appl., vol. 78, no. 3, pp. 3797–3816, 2019, doi: 10.1007/s11042-018-6083-5.
H. A. R. Harpizon, R. Kurniawan, Iwan Iskandar, R. Salambue, E. Budianita, and F. Syafria, “Analisis Sentimen Komentar Di YouTube Tentang Ceramah Ustadz Abdul Somad Menggunakan Algoritma Naïve Bayes,” … Di YouTube …, vol. 5, no. 1, pp. 131–140, 2022.
E. Indrayuni, “Klasifikasi Text Mining Review Produk Kosmetik Untuk Teks Bahasa Indonesia Menggunakan Algoritma Naive Bayes,” J. Khatulistiwa Inform., vol. 7, no. 1, pp. 29–36, 2019, doi: 10.31294/jki.v7i1.1.
E. H. Muktafin and P. Kusrini, “Sentiments analysis of customer satisfaction in public services using K-nearest neighbors algorithm and natural language processing approach,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 19, no. 1, pp. 146–154, 2021, doi: https://doi.org/10.12928/TELKOMNIKA.V19I1.17417.
H. A. Santoso, E. H. Rachmawanto, A. Nugraha, A. A. Nugroho, D. R. I. M. Setiadi, and R. S. Basuki, “Hoax classification and sentiment analysis of Indonesian news using Naive Bayes optimization,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 18, no. 2, pp. 799–806, 2020, doi: https://doi.org/10.12928/TELKOMNIKA.V18I2.14744.
A. Basuki, “Sentiment Analysis of Service Provider on Twitter Tweet Using Naive Bayes Classifier,” J. Ilm. Tek. Elektro Komput. dan Inform., vol. 5, no. 2, pp. 13–23, 2023, doi: https://doi.org/10.47080/iftech.v5i2.2752.
F. Septianingrum and A. S. Y. Irawan, “Metode Seleksi Fitur Untuk Klasifikasi Sentimen Menggunakan Algoritma Naive Bayes: Sebuah Literature Review,” J. Media Inform. Budidarma, vol. 5, no. 3, p. 799, 2021, doi: 10.30865/mib.v5i3.2983.
O. Irnawati and K. Solecha, “Analisis Sentimen Ulasan Aplikasi Flip Menggunakan Naïve Bayes dengan Seleksi Fitur PSO,” J. Ilm. Intech Inf. Technol. J. UMUS, vol. 4, no. 02, pp. 189–199, 2022, doi: 10.46772/intech.v4i02.868.
B. Guo, C. Zhang, J. Liu, and X. Ma, “Improving text classification with weighted word embeddings via a multi-channel TextCNN model,” Neurocomputing, vol. 363, pp. 366–374, 2019, doi: 10.1016/j.neucom.2019.07.052.
G. Kou, P. Yang, Y. Peng, F. Xiao, Y. Chen, and F. E. Alsaadi, “Evaluation of feature selection methods for text classification with small datasets using multiple criteria decision-making methods,” no. xxxx, 2019.
T. Turki and S. S. Roy, “Novel Hate Speech Detection Using Word Cloud Visualization and Ensemble Learning Coupled with Count Vectorizer,” Appl. Sci., vol. 12, no. 13, 2022, doi: 10.3390/app12136611.
S. Assegaff, E. Rasywir, and Y. Pratama, “Experimental of vectorizer and classifier for scrapped social media data,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 21, no. 4, pp. 815–824, 2023, doi: https://doi.org/10.12928/TELKOMNIKA.v21i4.24180.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Jurnal Sistem Informasi Triguna Dharma (JURSI TGD)
This work is licensed under a Creative Commons Attribution 4.0 International License.
