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p-Index From 2021 - 2026
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All Journal Bulletin of Computer Science Research International Journal of Social Science, Educational, Economics, Agriculture Research, and Technology (IJSET)
Muhammad Syahputra Novelan
Universitas Pembangunan Panca Budi, Medan
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Economics, Econometrics & Finance Education

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APPLICATION OF INTELLIGENT SYSTEMS (DEEP LEARNING) TOWARDS THE USE OF AI APPLICATIONS IN DAILY LIFE (PANCABUDI UNIVERSITY MEDAN) Ardiansyah; Aldy Agustian; Perianus lombu; Kiki Wulandari; Muhammad Syahputra Novelan
International Journal of Social Science, Educational, Economics, Agriculture Research and Technology (IJSET) Vol. 4 No. 11 (2025): OCTOBER
Publisher : RADJA PUBLIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54443/ijset.v5i1.1566

Abstract

As technology advances in this digital era, technology has become a very important thing for human life and has a dependency, marked by the use of digital machines that cause very rapid, significant changes to all sectors of human life, making it easier for humans to carry out activities and have dependencies. Artificial intelligence or better known as AI (Artificial Intelligence) is a major supporter in the development of intelligent systems (Intelligence Systems) that increase efficiency and innovation in various sectors of life. Therefore, the latest advances in AI's predictive capabilities can create a productive work environment. Although AI offers great potential to encourage innovation and better decision-making, there are also challenges in the use of AI such as ethical issues, data security, and infrastructure limitations that must be overcome to ensure responsible use.
Analysis of Food Distribution Efficiency Clustering Based on Cost and Delivery Time Using the K-Means Algorithm Muhammad Syahputra Novelan; Solly Aryza; Puspita Wanny
Bulletin of Computer Science Research Vol. 6 No. 6 (2026): October 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i6.1345

Abstract

Food distribution efficiency is an important factor in maintaining supply chain performance, particularly in controlling transportation costs and ensuring timely delivery. Inefficiencies in distribution may lead to increased operational expenses and delays in delivering food products to their destinations. This study aims to analyze food distribution efficiency by grouping distribution activities based on transportation costs and delivery time using the K-Means clustering algorithm. The research stages consist of data collection, data preprocessing, feature selection, data standardization, determination of the optimal number of clusters, implementation of the K-Means algorithm, and cluster evaluation. Transportation cost and delivery time are used as the main variables to identify distribution patterns and classify distribution activities into efficiency groups. The resulting clusters are intended to distinguish distribution activities with relatively efficient, moderate, and less efficient characteristics, providing a basis for identifying operational patterns that require improvement. Data visualization is also used to support the interpretation of clustering results and facilitate comparisons among groups. The experimental results, based on 330 simulated food distribution records, produced three clusters. Cluster 1 consisted of 110 records, with an average distribution cost of IDR 176,363 and an average delivery time of 2.83 hours. Cluster 2 contained 111 records, with an average distribution cost of IDR 338,472 and an average delivery time of 5.83 hours. Cluster 3 comprised 109 records, with an average distribution cost of IDR 535,296 and an average delivery time of 9.49 hours. The clustering evaluation yielded a Silhouette Score of 0.679 and a Davies–Bouldin Index of 0.444. This study is expected to provide an analytical framework for supporting data-driven decisions in food distribution management. The proposed approach can assist distribution managers in evaluating cost and delivery-time patterns and identifying opportunities to improve operational efficiency. The validity of the findings depends on the quality of the operational data and the evaluation of the resulting clusters.
Co-Authors Aldy Agustian Ardiansyah Kiki Wulandari Perianus lombu Puspita Wanny Solly Aryza