Article Per Year (5 Year)
p-Index From 2021 - 2026
1.628
P-Index
This Author published in this journals
All Journal Building of Informatics, Technology and Science Ecobisma (Jurnal Ekonomi, Bisnis dan Manajemen) U-NET Jurnal Teknik Informatika Journal of Computer Science and Information Systems (JCoInS) Jurnal Mitra Pengabdian Farmasi
Suryadi, Sudi
Unknown Affiliation
Humanities Computer Science & IT Economics, Econometrics & Finance Environmental Science Health Professions Languange, Linguistic, Communication & Media Public Health

Published : 11 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Building of Informatics, Technology and Science

 1 
Analisis Perbandingan Naïve Bayes dan Neural Network dalam Klasifikasi Minat Masyarakat pada Kursus Komputer Fitria, Nabila Syah; Suryadi, Sudi; Nasution, Fitri Aini
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6999

Abstract

In the digital era, the use of technology in education is growing, especially in improving people's digital literacy through computer courses. To analyze people's interest in courses, a data mining-based approach is needed that can process large amounts of data and identify certain patterns. Naïve Bayes and Neural Network are two widely used classification methods, where Naïve Bayes works based on independent probabilities between features, while Neural Network uses artificial neural networks to capture more complex patterns. This study aims to compare the two methods in classifying people's interest in LKP Ibay Komputer and evaluate the accuracy of each model. The classification results show that both methods produce the same predictions, namely 53 data are categorized as interested and 20 data as not interested. The model accuracy reaches 100%, indicating very high classification performance. Although these results seem ideal, perfect accuracy like this often raises questions regarding the validity and robustness of the model in real-world scenarios. Factors such as relatively small dataset sizes, overly structured data patterns, or lack of variation in training data can cause results that appear too good. Therefore, it is important to conduct additional evaluations such as cross-validation or testing on different datasets to ensure that the model does not experience overfitting and remains reliable in broader predictions. With these results, it can be concluded that both Naïve Bayes and Neural Networks have optimal performance in classifying people's interest in computer courses, but the choice of method can be adjusted according to needs, where Naïve Bayes excels in computational efficiency, while Neural Networks are more adaptive to more complex data.
Implementasi Metode MAUT dalam Analisis Penentuan Tenaga Pengajar Non ASN Terbaik Maulana, Imam; Irmayani, Deci; Suryadi, Sudi
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7460

Abstract

The need for quality teaching staff is becoming increasingly important along with the development of technology and globalization, including in educational institutions such as SDN 115467 Kanopan Ulu. In addition to teaching staff from ASN, this school also relies on non-ASN staff who play a significant role in supporting the quality of education. However, the process of determining the best non-ASN teaching staff is often faced with the challenges of subjectivity and differences in assessment standards. To overcome this, this study proposes the implementation of a Decision Support System (DSS) based on the Multi Attribute Utility Theory (MAUT) method. The MAUT method allows for more objective, transparent, and fair decision-making by considering various assessment criteria, such as competence, experience, and contribution of teaching staff. In this study, non-ASN teaching staff data were analyzed using the Microsoft Excel application and DSS software during the research period in October 2024. Based on the application of this method, Tuti Alawiyah (A15) was ranked first with the highest score, namely 0.731. These results indicate that Tuti Alawiyah has the best performance according to the criteria used in the MAUT method, reflecting her superiority over other candidates. The results of the study indicate that the MAUT method is able to provide accurate and consistent evaluation results, thus supporting a more rational and in-depth decision-making process. This study not only provides theoretical contributions to the development of the DSS system, but also provides practical benefits for educational institutions to improve the motivation of non-ASN teaching staff and, overall, the quality of education. This topic is relevant to the needs of modern education in Indonesia, especially in efforts to improve the transparency and accuracy of teaching staff assessments.
Co-Authors Amelia, Reza Rizki Ardian, Aldi Bangun, Budianto Fitria, Nabila Syah Herdiansyah, Roydido imam maulana, imam Irmayani, Deci Irmayanti Irmayanti, Irmayanti Juledi, Angga Putra Kurniawan Nst, Mhd.Bobbi Nasution, Fitri Aini Nur Siagian, Taufiqqurahman Pristiyono, Pristiyono Priyanti Priyanti Ramadhani Ramadhani Rambey, Khiarul Akhyar Ritonga, Ali Akbar Sahbana, Ahmad Syahfii, Muhammad Syaiful Zuhri Harahap Tanjung, Riska Wulandari Wahyu Azhar Ritonga