Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Enrichment: Journal of Multidisciplinary Research and Development
Agus Komarudin
Universitas Jenderal Achmad Yani, Indonesia
Humanities Economics, Econometrics & Finance Engineering Health Professions Social Sciences

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Grouping Education Students at Pusdikjas Institutions of The TNI-AD's Disjasad Using the K-Means Clustering Method Imam Wibowo Pramudjianto; Ade Kania Ningsih; Agus Komarudin
Enrichment: Journal of Multidisciplinary Research and Development Vol. 1 No. 7 (2023): Enrichment: Journal of Multidisciplinary Research and Development
Publisher : International Journal Labs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55324/enrichment.v1i7.64

Abstract

The Military Physical Education Center (Pusdikjas) is responsible for providing military physical education to support the duties of the Indonesian Army. Each year, the student admission and education process generates substantial and recurring performance-score data, creating a need for systematic grouping that can support the interpretation of student performance. This research aims to apply the K-Means clustering algorithm to group Pusdikjas education students and to determine the optimal number of clusters using the Silhouette Coefficient. The dataset covers student data from 2018–2022 and contains 13 attributes, with NRP, attitude and behavior, knowledge and skills, and physical scores selected for the clustering process. Data were processed through cleaning, numerical normalization, K-Means clustering, and validation by comparing cluster solutions from K=2 to K=10. The clustering output used 1,427 student records. The results show that K=2 provides the highest Silhouette Coefficient, reaching 0.6543347543126016. Cluster 1 contains 751 students and is interpreted in the manuscript as the very satisfactory category, whereas Cluster 2 contains 676 students and is interpreted as the satisfactory category. The two-cluster solution therefore provides the strongest separation among the tested alternatives. Practically, the resulting grouping can support Pusdikjas in organizing and reviewing student performance data more systematically. Methodologically, the comparison of several K values demonstrates the importance of cluster validation before selecting the final grouping solution.
Decision Support System For Determining Food Menu Using Analytical Hierarchy Process (Ahp) Method Ninda Maulina; Wina Witanti; Agus Komarudin
Enrichment: Journal of Multidisciplinary Research and Development Vol. 1 No. 7 (2023): Enrichment: Journal of Multidisciplinary Research and Development
Publisher : International Journal Labs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55324/enrichment.v1i7.65

Abstract

Food menus in the culinary and restaurant industries often involve various complex features, such as food origin, food category, diet category, ingredients, and time. This study aims to develop a Decision Support System (DSS) based on the AHP method to assist chefs, restaurant managers and food business owners in compiling a menu, meeting nutritional needs, taking into account preferences and limitations, and creating a pleasant dining experience. This research uses a system-development and quantitative decision-modeling approach in which AHP is implemented as the decision calculation method. The analysis procedure includes hierarchy identification, pairwise comparison, matrix normalization, priority-weight calculation, consistency testing using CI and CR, global-priority ranking, and functional verification using black-box and white-box testing. The results show preferences in the process of determining food menus by producing Cold Coffee with a value of 0.30 (29%), Biscuit Dough Donuts with a value of 0.25 (25%), Chicken Dimsum with a value of 0.21 (21%), Succotash with a value of 0.15 (15%), and Toffee Banana with a value of 0.10 (10%). Practically, the system is intended to support managers or chefs by making the comparison of menu alternatives more structured and by showing the priority of each alternative according to the established criteria.
Co-Authors Ade Kania Ningsih Imam Wibowo Pramudjianto Ninda Maulina Wina Witanti