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Journal : Informatics Management, Engineering and Information System Journal

Using Supervised Machine Learning to Predict Sales in Marketplaces: Case study Predicting Sales of Padimas Bread in Marketplaces in Indonesia Raharjo, Adi; Utama, Nur Ichsan; Lubis, Muharman
Informatics Management, Engineering and Information System Journal Vol. 1 No. 2 (2023): Informatics Management, Engineering and Information System Journal
Publisher : LPPM STMIK Mardira Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56447/imeisj.v1i2.264

Abstract

This project intends to apply supervised machine learning to anticipate sales of Padimas bread in marketplaces in Indonesia, with an emphasis on evaluating sales data to gain more profits and predict future income. Data from the Shopee, Tokopedia, and TikTok markets in 2023 was analyzed, employing techniques like exploratory sales data analysis and machine learning. The analysis findings encompass the top-selling products, the highest sales figures, regions with the most substantial sales, overall market sales, sales patterns, and revenue forecasts. The primary discoveries encompass the widespread appeal of chocolate toast, the most substantial sales of banana chocolate toast in West Java, and Shopee as the marketplace with the most significant sales and revenue. Sales trends exhibit a pattern of oscillation around an average value, but income from sales demonstrates a downward tendency until the 30th day. The strategic ramifications of this analysis encompass augmenting the production of sought-after goods, amplifying sales in specific regions, and delving into prospective marketplaces.
Grouping Students Based On Academic Values Using The K-Means Method At A Vocational High School In Bandung Firmansyah, Robi; Risanti, Rini; Oktavia, Oktavia; Fahmi, Miftah; Raharjo, Adi
Informatics Management, Engineering and Information System Journal Vol. 3 No. 2 (2025): Informatics Management, Engineering and Information System Journal
Publisher : LPPM STMIK Mardira Indonesia

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

Abstract

A vocational high school in Bandung is dedicated to cultivating competitive and employable graduates.  Nonetheless, the institution faces challenges in conducting comprehensive assessments of pupils' academic data to identify their strengths and weaknesses.  Currently, data analysis relies on basic descriptive methodologies, which often fail to yield adequate insights for informed strategic decision-making.  Furthermore, there is a lack of interactive visualization tools to enhance the presentation of student grouping data. This study aims to address these concerns by utilizing the K-Means algorithm to categorize pupils based on their academic performance.  This classification yields three clusters that delineate students' attributes in high, medium, and low score categories.  The evaluation results indicate that the model comprising three clusters has the highest Silhouette Score of 0.3364.  This research generates an interactive website as a visualisation tool to display the outcomes of student grouping correctly. The implementation of this method is anticipated to enhance the school's management of academic data and deliver tailored learning recommendations that more effectively address the needs of students within each cluster.  Therefore, the educational quality of this vocational high school in Bandung can be markedly enhanced.