EVALUASI KUALITAS PELAYANAN SMART CITY MELALUI ANALISIS SENTIMEN BERBASIS NATURAL LANGUAGE PROCESSING (NLP) PADA APLIKASI SIBISA KOTA MEDAN - Ramadha Yanti Parinduri; Mahyudin Situmeang; Roikestina Silaban (Smart Education, 2026)
Abstract: The quality of Smart City services needs continuous evaluation to ensure that digital public services meet citizens' expectations. This study evaluates the quality of Smart City services through sentiment analysis of user reviews on the Sibisa application of Medan City using a Natural Language Processing (NLP) approach. The research employed a quantitative descriptive method by collecting user review data from the Google Play Store. The data were processed through preprocessing stages, including case folding, tokenization, stopword removal, and stemming, followed by sentiment classification into positive, negative, and neutral categories. The analysis results indicate that user opinions provide valuable insights into the strengths and weaknesses of the Sibisa application. Positive sentiments reflect user satisfaction with service accessibility and convenience, while negative sentiments mainly relate to application performance, system stability, and response speed. The findings demonstrate that NLP-based sentiment analysis is effective in supporting the evaluation of Smart City service quality and can serve as a reference for the Medan City Government in improving digital public services. Keywords: natural language processing; sentiment analysis; Sibisa application; smart city; service quality Abstrak: Kualitas pelayanan Smart City perlu dievaluasi secara berkelanjutan untuk memastikan layanan publik digital mampu memenuhi kebutuhan masyarakat. Penelitian ini bertujuan mengevaluasi kualitas pelayanan Smart City melalui analisis sentimen terhadap ulasan pengguna aplikasi Sibisa Kota Medan menggunakan pendekatan Natural Language Processing (NLP). Penelitian menggunakan metode deskriptif kuantitatif dengan mengumpulkan data ulasan pengguna dari Google Play Store. Data diproses melalui tahapan preprocessing yang meliputi case folding, tokenisasi, stopword removal, dan stemming, kemudian dilakukan klasifikasi sentimen ke dalam kategori positif, negatif, dan netral. Hasil analisis menunjukkan bahwa opini pengguna memberikan informasi mengenai kelebihan dan kekurangan aplikasi Sibisa. Sentimen positif menunjukkan kepuasan pengguna terhadap kemudahan akses dan pelayanan, sedangkan sentimen negatif didominasi oleh keluhan mengenai performa aplikasi, stabilitas sistem, dan kecepatan respons. Penelitian ini menyimpulkan bahwa analisis sentimen berbasis NLP efektif digunakan untuk mengevaluasi kualitas pelayanan Smart City serta dapat menjadi dasar bagi Pemerintah Kota Medan dalam meningkatkan kualitas layanan publik berbasis digital. Kata kunci: analisis sentimen; aplikasi Sibisa; natural language processing; kualitas pelayanan; smart city