Article Per Year (5 Year)
p-Index From 2020 - 2025
0.702
P-Index
This Author published in this journals
All Journal "Journal of Data Science
Muhammad Iqbal
University Pembangunan Panca Budi, Medan, Indonesia
Automotive Engineering Computer Science & IT

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
Optimization of Kaayana Store Inventory through Transaction Pattern Analysis Using the Apriori Algorithm Suhardiansyah Suhardiansyah; Muhammad Iqbal
Journal Of Data Science Vol. 3 No. 01 (2025): Journal Of Data Science, March 2025
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/jds.v3i01.6398

Abstract

This study aims to optimize inventory management at Kaayana Store by analyzing sales transaction patterns using the Apriori algorithm. The transaction data collected shows that products with the codes ACC (accessories) and BJU (clothing) dominate purchases, accounting for 71.4% of total transactions. The analysis results identify a strong relationship between these products, which are frequently purchased together by consumers. Based on these findings, Kaayana Store needs to ensure the availability of ACC and BJU stocks to meet high demand, avoid stockouts, and improve operational efficiency. Proposed inventory management strategies, such as more precise product placement and bundling promotions, are expected to enhance customer satisfaction and support the sustainability of Kaayana Store's business
Analysis of Product Demand Prediction Using Decision Tree on Sales Data of Ceria Toys Store Anzas Ibezato Zalukhu; Muhammad Iqbal
Journal Of Data Science Vol. 3 No. 01 (2025): Journal Of Data Science, March 2025
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/jds.v3i01.6458

Abstract

Ceria Toys faces challenges in efficiently managing the inventory of electric bicycles, as product demand is influenced by factors such as market trends, seasons, and changing consumer preferences. To address this challenge, this research employs data mining techniques with the decision tree algorithm to predict product demand and assist in inventory management. The evaluation results of the predictive model show varying performance across product categories. The precision for the "Hot" category is 58.36%, while for the "Less Popular" category, it is 64.18%. The recall for the "Hot" category reaches 83.71%, but the recall for the "Less Popular" category is only 32.82%. Although the model performs better in predicting hot products, there is still room to improve the detection of less popular products. To enhance effectiveness, Ceria Toys can balance the dataset or adjust the model. With this information, the store can better prepare stock for hot products and optimize the management of less popular products. These steps are expected to maximize sales, reduce excess stock, and improve overall customer satisfaction.
Analysis of the Most Popular Study Programs at Haji University of North Sumatra Using the Decision Tree Algorithm Dewi Sartika; Muhammad Iqbal
Journal Of Data Science Vol. 3 No. 01 (2025): Journal Of Data Science, March 2025
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/jds.v3i01.6459

Abstract

This study aims to analyze the clustering of the most popular study programs at Universitas Haji Sumatera Utara using the Decision Tree algorithm. This algorithm successfully grouped the study programs based on the applicants' interests, considering gender as the primary variable. The analysis results show that the most popular study programs among women are the Bachelor of Midwifery and the Bachelor of Nursing programs, which each have a very high number of female applicants. On the other hand, programs such as the Regular Bachelor of Law and Management show a more balanced interest between women and men, with Management having almost equal gender proportions. This classification model performed very well in detecting female applicants, with a high recall (95.51%) and good precision (79.84%). However, the model struggles to identify male applicants, with low recall (18.40%) and suboptimal precision (54.76%). This indicates that the model is more sensitive to predicting female applicants. Therefore, it is recommended that Universitas Haji Sumatera Utara enhance more inclusive and balanced marketing strategies, as well as optimize both regular and non-regular registration pathways to attract a more even interest from both genders, in order to achieve gender equality across various study programs and improve the efficiency of student admissions
Prediction of Building Permit Approval in Medan City Using the Naïve Bayes Algorithm for Investment Prospects Nelviony Parhusip; Muhammad Iqbal
Journal Of Data Science Vol. 3 No. 01 (2025): Journal Of Data Science, March 2025
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/jds.v3i01.6461

Abstract

Property investment in Medan City has become increasingly important in line with economic growth and rapid infrastructure development. Over the past decade, property investment in Medan has shown significant growth, as evidenced by the expansion of residential areas, boarding houses, hotels, and integrated apartments such as Manhattan Square, Jati Junction, and Podomoro Deli Park. This study aims to predict and identify patterns of Building Permit Approval (PBG) in Medan City that are significantly related to investment prospects. The algorithm used in this study is Naïve Bayes, implemented using the Orange tool, which enables the prediction of building permit approvals in Medan City. The key findings of this study indicate the existence of significant building permit approval patterns and the identification of potential investment areas. The implications of this research are crucial for investors and developers in formulating more effective investment strategies
Co-Authors Anzas Ibezato Zalukhu Dewi Sartika Nelviony Parhusip Suhardiansyah Suhardiansyah