<![CDATA[Abstrak - Warung Kopi Asiang merupakan salah satu objek dalam penelitian ini, di mana ulasan pengguna yang terdapat di Google Maps dengan menggunakan kombinasi pendekatan berbasis Lexicon, pembobotan TF-IDF, dan algoritma machine learning. Sebanyak 2.358 ulasan dikumpulkan melalui teknik web scraping menggunakan Instant Data Scraper versi 1.4.1.Proses preprocessing diterapkan pada seluruh data, mencakup tahapan case folding, cleaning, normalisasi, tokenization, stopword removal, dan stemming.Pelabelan sentimen dilakukan menggunakan metode berbasis lexicon, menghasilkan tiga kategori positif sebanyak 1. 289 ulasan (58,62%), negatif sebanyak 632 ulasan (28,74%), dan netral sebanyak 278 ulasan (12,64%). Data yang telah dilabeli kemudian dikonversi ke dalam bentuk numerik menggunakan TF-IDF dan diklasifikasikan dengan tiga algoritma yakni Support Vector Machine (SVM), Decision Tree, dan Logistic Regression. Optimasi model dilakukan melalui hyperparameter tuning menggunakan GridSearchCV. Dari hasil evaluasi Support Vector Machine (SVM) dan Logistic Regression sama-sama mencapai akurasi tertinggi sebesar 80%, di mana pada SVM akurasi optimal tersebut sudah tercapai sejak model dasar (default).Decision Tree menghasilkan akurasi sebesar 71%. Temuan ini membuktikan bahwa integrasi metode Lexicon, TF-IDF, dan machine learning bekerja secara efektif dalam menganalisis sentimen ulasan di Google Maps. Kata kunci : Analisis sentimen; Google Maps; Lexicon; Machine Learning; Abstract - Warung Kopi Asiang is one of the subjects of this study, in which user reviews found on Google Maps were analysed using a combination of a lexicon-based approach, TF-IDF weighting, and machine learning algorithms. A total of 2,358 reviews were collected via web scraping using Instant Data Scraper version 1.4.1. Preprocessing was applied to all data, covering the stages of case folding, cleaning, normalisation, tokenisation, stopword removal, and stemming; sentiment labelling was performed using a lexicon-based method, resulting in three categories: 1, 289 reviews (58.62%), 632 negative reviews (28.74%), and 278 neutral reviews (12.64%). The labelled data was then converted into numerical form using TF-IDF and classified using three algorithms: Support Vector Machine (SVM), Decision Tree, and Logistic Regression. Model optimisation was carried out via hyperparameter tuning using GridSearchCV. The evaluation results showed that both Support Vector Machine (SVM) and Logistic Regression achieved the highest accuracy of 80%, with SVM having reached this optimal accuracy from the base (default) model. Decision Tree achieved an accuracy of 71%. These findings demonstrate that the integration of the Lexicon method, TF-IDF, and machine learning works effectively in analysing sentiment in Google Maps reviews Keywords: Sentiment analysis; Google Maps; Lexicon; Machine Learning;]]>
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