<![CDATA[Abstrak - Aplikasi Access by KAI merupakan platform digital utama yang diluncurkan oleh PT Kereta Api Indonesia (Persero) untuk mendukung layanan pemesanan tiket, pembatalan, serta layanan e-boarding secara mandiri bagi pengguna jasa transportasi kereta api. Meskipun demikian, digitalisasi layanan ini memicu beragam respons dan ulasan dari masyarakat di Google Play Store. Penelitian ini bertujuan untuk menganalisis sentimen pengguna Aplikasi Access by KAI dengan membandingkan performa empat algoritma machine learning, yaitu Support Vector Machine (SVM), Random Forest, Naive Bayes, dan Stochastic Gradient Descent (SGD) Classifier. Data ulasan dikumpulkan melalui teknik web scraping menggunakan pustaka Python google-play-scraper, menghasilkan 2.891 ulasan bersih setelah melalui tahap preprocessing teks dan pendekatan klasifikasi biner dengan mengesampingkan rating netral (bintang 3). Dataset tersebut kemudian dibagi menggunakan rasio 80% untuk data training (2.312 ulasan) dan 20% untuk data testing (579 ulasan) dengan pembobotan fitur menggunakan TF-IDF. Hasil eksperimen menunjukkan bahwa keempat algoritma memberikan performa yang sangat kompetitif dengan akurasi di atas 86%. Algoritma Random Forest berhasil meraih performa keseluruhan terbaik dengan nilai Akurasi sebesar 87,56% dan F1-Score sebesar 87,48%. Di sisi lain, Naive Bayes mencatatkan nilai Precision tertinggi sebesar 88,74%. Temuan ini membuktikan bahwa pendekatan klasifikasi biner dikombinasikan dengan ekstraksi TF-IDF mampu menghasilkan model analisis sentimen yang andal dan robust untuk evaluasi layanan aplikasi publik sektor transportasi. Kata kunci : Analisis Sentimen; Access by KAI; Komparasi Algoritma; Random Forest; TF-IDF; Abstract - The Access by KAI application is the primary digital platform launched by PT Kereta Api Indonesia (Persero) to support ticket booking, cancellation, and self-service e-boarding for railway passengers. This digitalization has triggered diverse public responses and reviews on the Google Play Store. This study aims to analyze user sentiment toward the Access by KAI application by comparing the performance of four machine learning algorithms: Support Vector Machine (SVM), Random Forest, Naive Bayes, and Stochastic Gradient Descent (SGD) Classifier. Review data were collected through a web scraping technique using the Python google-play-scraper library, yielding 2,891 cleaned reviews after text preprocessing and a binary classification approach that excludes neutral ratings (3 stars). The dataset was split using an 80:20 ratio for training data (2,312 reviews) and testing data (579 reviews), with feature weighting performed using TF-IDF. The experimental results show that all four algorithms achieved highly competitive performance with accuracy above 86%. The Random Forest algorithm achieved the best overall performance with an Accuracy of 87.56% and an F1-Score of 87.48%, while Naive Bayes recorded the highest Precision value of 88.74%. These findings demonstrate that the binary classification approach combined with TF-IDF feature extraction can produce a reliable and robust sentiment analysis model for evaluating public transportation application services. Keywords: Sentiment Analysis, Access by KAI; Algorithm Comparison; Random Forest; TF-IDF;]]>
