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Journal : Bulletin of Computer Science Research

Analisis Kepuasan Masyarakat Terhadap Proses Pengurusan Sertipikat Analog Ke Elektronik Menggunakan Metode Naïve Bayes Al-Arrafi, Muhammad Ikhsan; Sovia, Rini; Ramadhanu, Agung
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.758

Abstract

The certificate media conversion program from analog to electronic implemented by the Ministry of ATR/BPN in Sejati Village requires evaluation to ensure its effectiveness. The main problem faced is the limited use of quantitative, data-driven analysis in identifying the factors that influence public satisfaction. This study aims to analyze the level of public satisfaction using the Naïve Bayes method to classify and predict the influence of related variables. Data were obtained from 250 respondents through questionnaires based on digital public service indicators, covering demographic variables, perceived benefits, obstacles, support, service speed, and procedural simplicity. The results show that the level of public satisfaction is in the high category, with procedural simplicity and service speed proven to be the most significant variables influencing satisfaction prediction. The Naïve Bayes model achieved an accuracy of 94%, demonstrating its effectiveness in predicting satisfaction levels. These findings serve as a basis for improving policies and strategies to enhance the quality of digital public services, particularly in the implementation of electronic certificate media conversion in the future.
Identifikasi Varietas Kopi Berdasarkan Analisis Warna dan Tekstur Menggunakan Metode Convolutional Neural Network Utama Putra, Kharisma; Ramadhanu, Agung; Arlis, Syafri
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.759

Abstract

Coffee is a plantation commodity with high economic value in Indonesia, with various varieties such as Arabica, Robusta, and Liberica. Differences in coffee varieties can generally be identified through the physical characteristics of the beans, especially color and texture. Based on this, this study aims to develop a digital image-based coffee variety identification system using the Convolutional Neural Network (CNN) method with color and texture analysis as the main features. The research stages include coffee bean image acquisition, pre-processing including color segmentation and image conversion to grayscale, and color and texture feature extraction. This research dataset comes from images of unroasted coffee beans, commonly called green beans, taken using a high-resolution smartphone camera and also using secondary data taken from the Kaggle site. Both types of datasets have the same characteristics and resolution to maintain data consistency. The image dataset is divided into training data and test data, then used to train and test the Convolutional Neural Network (CNN) model. Based on this study, the Convolutional Neural Network (CNN) method can identify coffee varieties based on color and texture analysis. By using 210 training data and 90 test data of coffee bean images, the CNN method can produce an accuracy rate of 94,44%. This research contribution has the potential to be a supporting solution in the process of identifying coffee varieties quickly, accurately, and consistently, so that it can help the coffee industry in the sorting and quality control process.