Klasifikasi Tingkat Kepuasan Pelayanan Pembuatan Paspor Menggunakan Algoritma Naïve Bayes - Dini Oktaviani; Syarifah Putri Agustini Alkadri; Sucipto Sucipto (Pusat riset dan Inovasi Nasional, 2025)
This research is motivated by the importance of improving the quality of passport making services at the Pontianak City Immigration Office which still faces obstacles such as complicated procedures, limited quotas, lack of officer direction, and mismatches in passport collection schedules that cause public dissatisfaction. This research aims to classify the level of satisfaction of passport making services using the Naïve Bayes algorithm, measure classification accuracy, and develop a website-based system that helps evaluate and improve service quality effectively and efficiently. The method used is a quantitative approach with data collection through questionnaires, interviews, and direct observation of 205 respondents, then the data is processed using the Naïve Bayes algorithm which assumes independence between variables to classify satisfaction levels based on variables such as officer friendliness, officer ability, ease of procedure, and timeliness of service. The main findings show that the Naïve Bayes algorithm is able to classify satisfaction levels with 73% accuracy, 76% precision, 70% recall, and 73% F1-score, signaling the effectiveness of this method in identifying community satisfaction patterns. However, the results also indicate the need for improvement in user interface aspects and system responsiveness so that the system can be widely accepted and provide optimal benefits. The implication of this research is that the application of Naïve Bayes-based data mining methods can be an effective tool in evaluation and decision-making to improve the quality of public services, especially in the field of passport making, and encourage the development of interactive and empirical data-based public service information systems.