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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
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Humanities Computer Science & IT Economics, Econometrics & Finance Environmental Science Health Professions Languange, Linguistic, Communication & Media Public Health

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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.
USABILITY TESTING OF SIFINAS DIGITAL APPLICATION DEVELOPMENT AND ITS EFFECT ON USER SATISFACTION AND MANAGERIAL PERFORMANCE Pristiyono, Pristiyono; Suryadi, Sudi; Syahfii, Muhammad; Amelia, Reza Rizki
ECOBISMA (JURNAL EKONOMI, BISNIS DAN MANAJEMEN) Vol 12, No 1 (2025): ECOBISMA
Publisher : Published by the Faculty of Economics and Business, University of Labuhanbatu, North Sumat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/ecobi.v12i1.7134

Abstract

The development of the SIFINAs digital application aims to increase the credibility of MSMEs in the financial management of these MSMEs so that they can control their financial transactions or reports more updated, effective and efficient. The purpose of this study was to test the SIFINAs digital application and to determine its effect on user satisfaction and MSME performance. Overall, there were respondents who stated that they were not willing to respond to the research questionnaire so that the data collected was obtained as many as 37 respondents. The results of the study found that the development of the SIFINAs digital application model is one of the simple and simple application models that greatly supports the performance of MSMEs. The results of testing the research hypothesis show that the development of WEB-based applications affects user satisfaction, user satisfaction affects MSME performance and WEB-based application development affects MSME performance. In the future, the SIFINAs digital application that is being developed is feasible to be used or implemented en masse by medium-level business actors
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.
Implementasi K-Means Dalam Menentukan Tingkat Kepuasan Pelanggan Pada Bengkel Rizal Rantauprapat Rambey, Khiarul Akhyar; Suryadi, Sudi; Harahap, Syaiful Zuhri; Juledi, Angga Putra
Journal of Computer Science and Information System(JCoInS) Vol 6, No 3: JCoInS | 2025
Publisher : Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/jcoins.v6i3.7937

Abstract

The growing automotive industry demands workshops to improve the quality of service for customer satisfaction. However, manual measurement of satisfaction is often inefficient and subjective. This study proposes the application of machine learning algorithms K-Means Clustering to analyze customer satisfaction data in Rizal workshop. This method is used to Group customers into several clusters based on similar satisfaction characteristics. The results of this grouping are expected to provide more objective and in-depth insights to identify patterns of satisfaction, thus enabling the workshop to formulate a more effective and targeted service quality improvement strategy.
Pengembangan Sistem Informasi Akademik Berbasis Web Sebagai Sistem Pengolahan Nilai Siswa di SMK Muhammadiyah 03 Aek Kanopan Menggunakan Metode Research And Development Priyanti, Priyanti; Harahap, Syaiful Zuhri; Nasution, Fitri Aini; Suryadi, Sudi
Journal of Computer Science and Information System(JCoInS) Vol 6, No 3: JCoInS | 2025
Publisher : Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/jcoins.v6i3.7878

Abstract

Web-based academic information system is an effective solution to manage the value of students at SMK Muhammadiyah 03 AEK Kanopan. This study aims to develop and evaluate the feasibility of the system using Research and Development methods. The developed system is designed to address challenges in the current value processing process, such as efficiency, accuracy, and data accessibility. In system development, the methodology used includes needs analysis, system design, implementation, and testing. Needs analysis is conducted to identify important features that must be present in the system, such as value input, final value calculation, report generation, and access for teachers, students, and administrative staff. After that, the system is designed with an intuitive interface and powerful functionality. The results of this study indicate that the web-based academic information system developed is very feasible to be used as a value processing system at SMK Muhammadiyah 03 AEK Kanopan. This feasibility is supported by evaluations from various stakeholders, including teachers and administrative staff, who assess this system can improve efficiency, reduce errors, and facilitate access to value information. Thus, this system is expected to be a reliable tool to support the teaching and learning process in the school.
Analisis Dampak Implementasi Sistem Informasi Manajemen Pada Efisiensi Proses Bisnis Kedai Kopi "Sahoeta Kopi" Wonosari Menggunakan Metode K Means Ardian, Aldi; Suryadi, Sudi; Nasution, Fitri Aini; Bangun, Budianto
Journal of Computer Science and Information System(JCoInS) Vol 6, No 3: JCoInS | 2025
Publisher : Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/jcoins.v6i3.7974

Abstract

This study aims to perform clustering analysis on consumer data coffee shop “Sahoeta coffee” by using the method of K-Means clustering in RapidMiner Studio. The Data used include attributes of Consumer age, number of purchases per day, income per day, and capital per day. The clustering process divides the data into five different clusters, each with different characteristics in terms of purchases and revenue. The clustering results showed that Cluster 0 contained consumers with older age and more frequent shopping, while Cluster 1 contained younger consumers with lower purchases. Clusters 2, 3, and 4 show a pattern of consumers with higher incomes and capital, indicating that they have greater purchasing power. Visualization of clustering results provides a clear picture of consumer segments that can be used to design more specific marketing strategies.
Penerapan Data Mining Untuk Memprediksi Prestasi Akademik Siswa SMKS IT Shah Hamidun Majid Menggunakan Algoritma Decision Tree Sahbana, Ahmad; Nasution, Fitri Aini; Ritonga, Ali Akbar; Suryadi, Sudi
Journal of Computer Science and Information System(JCoInS) Vol 6, No 3: JCoInS | 2025
Publisher : Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/jcoins.v6i3.7939

Abstract

Education is the main foundation in the development of superior human resources, especially in the digital era that demands the use of Information Technology. One of the main challenges is how schools are able to effectively manage and analyze academic data. Data mining comes as a solution in extracting hidden information from educational data so that it can support strategic decision making. This study focuses on the application of Decision Tree algorithm in predicting student academic achievement in SMKs It Shah Hamidun Majid. The Decision Tree algorithm was chosen because it is easy to understand and is able to provide accurate classification based on various variables, such as attendance, grades, and student background. By utilizing academic data for the 2023/2024 school year, this study is expected to produce predictive models that help schools identify factors that affect student achievement, provide personalized coaching recommendations, and support data-based policies. The results of this study are expected to be a real contribution in the development of academic information systems that are adaptive, inclusive, and oriented to improving the quality of education at the private vocational school level.
Analisis Clustering Kepuasan Pelanggan Bengkel Mobil Auto Muara Baru Menggunakan Metode K-Means Herdiansyah, Roydido; Suryadi, Sudi; Irmayanti, Irmayanti
Journal of Computer Science and Information System(JCoInS) Vol 6, No 3: JCoInS | 2025
Publisher : Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/jcoins.v6i3.7929

Abstract

This study aims to analyze customer satisfaction of Muara Baru Auto Repair Shop by using K-Means clustering method. Customer satisfaction is a crucial factor in maintaining loyalty and improving service quality in the automotive industry. The Data was collected through surveys involving customers who had used the workshop services, and then analyzed using the k-Means algorithm to identify patterns and clusters in satisfaction levels. The results of the analysis show that there are several clustering that reflect variations in customer satisfaction levels, providing important insights into service aspects that need to be improved as well as areas that have met customer expectations. These findings indicate that the K-Means method is effective in analyzing customer satisfaction and can be used as a basis for workshop management to formulate service improvement strategies to better meet customer expectations.
Analisis Kepuasan Masyarakat Terhadap Kinerja Bupati Labuhanbatu Selatan Periode 2021-2024 Menggunakan Metode Decision Tree dan Naive Bayes Ramadhani, Ramadhani; Harahap, Syaiful Zuhri; Suryadi, Sudi; Masrizal, Masrizal
Journal of Computer Science and Information System(JCoInS) Vol 6, No 3: JCoInS | 2025
Publisher : Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/jcoins.v6i3.7971

Abstract

This study was conducted to analyze the level of customer satisfaction with services by comparing the performance of two classification methods, namely Decision Tree and Naive Bayes, so that an accurate model can be obtained to assist decision making. This problem is important because understanding customer satisfaction patterns can be a strategic basis in improving service quality and maintaining loyalty. The theoretical basis used refers to the concept of machine learning classification, where Decision Tree forms a branching rule-based model based on attributes, while Naive Bayes relies on probability calculations based on Bayes' theorem with the assumption of independence between features. The research methodology includes data collection stages, pre-processing to ensure data quality, model training with both methods, and performance evaluation using Test & Score and Confusion Matrix. Based on the classification results, the Decision Tree method produces fairly good accuracy, precision, and recall, but the Naive Bayes method shows higher performance with an accuracy of 91.67%, a precision of the "Satisfied" class of 98.11%, and a recall of 92.86%, which indicates a very good level of prediction accuracy especially for the majority class. Evaluation of both methods shows that Naive Bayes excels in capturing existing data patterns, although Decision Tree still has good interpretability for classification rule analysis. In conclusion, both methods are capable of classifying customer satisfaction data with adequate performance, but Naive Bayes is recommended as the primary model due to its higher and more consistent evaluation results, while Decision Tree can be used as an alternative when model interpretation is a priority.
Pemanfaatan Facebook Ads Bagi Pelaku UMKM dalam Pemasaran Produk Suryadi, Sudi; Nur Siagian, Taufiqqurahman; Kurniawan Nst, Mhd.Bobbi
Jurnal Mitra Pengabdian Farmasi Vol. 1 No. 3 (2022): Juni 2022
Publisher : Akademi Farmasi YPPM Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

MSME actors in marketing their products conventionally and have not done product marketing through digital marketing. This community service activity aims to: 1) provide insight into the concept of digital marketing and technological products used for product marketing, 2) provide training and simulations on the use of digital media in product marketing, 3) compose effective persuasive messages, and 4 ) provide solutions to realize MSME actors to promote products through digital media. Service activities are carried out using the FGD method, training and mentoring. In general, community service activities went well and could increase public knowledge from an average value of 7 to 9 or an increase of 23.26%. This training and mentoring activity is very good and right on target. From this activity, the community began to 1) understand the concept of digital marketing and technological products that can be used for marketing MSME products, 2) train and simulate the marketing of MSME products using digital media such as Facebook ads.
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