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Journal : International Journal of Engineering, Science and Information Technology

Grouping Sales Levels Smartphone Of Offline Store Using BIRCH Clustering Algorithm Rahmadani Sari, Putri Dwi; Qamal, Mukti; Rosnita, Lidya
International Journal of Engineering, Science and Information Technology Vol 4, No 4 (2024)
Publisher : Department of Information Technology, Universitas Malikussaleh, Aceh Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v4i4.558

Abstract

From 2020 to 2024, TM_Store and Jaya Com exhibited different sales patterns based on cluster analysis using the BIRCH algorithm. The background of this research is to provide strategic insights to both stores for improving their sales performance through data analysis. The sales data used includes brand, type, month, year, stock quantity, quantity sold, unit price, and total sales. The BIRCH method was chosen for its effectiveness in handling large datasets and providing accurate clustering results. The clustering results indicate a significant increase in the "Moderate" category, from 12 sales in 2020 to 354 sales in 2023. Meanwhile, the "Very High" category also saw an increase from 5 sales in 2020 to 97 sales in 2023, with sales in the "Very Low" category remaining high at 70 sales in 2023. On the other hand, Jaya Com was dominated by the "Very High" category, with a sharp increase from 25 sales in 2020 to 597 sales in 2023. The "High" category also showed significant growth, from 6 sales in 2020 to 98 sales in 2023. This data indicates that Jaya Com focuses on high-performance products, while TM_Store shows a more balanced distribution across various sales categories. Based on the analysis, Jaya Com had 1988 data points with 1984 cluster points, whereas TM_Store had 2012 data points with 1811 cluster points. Overall, the study concludes that the BIRCH algorithm can identify significant sales patterns in both stores, aiding in the development of more effective and efficient promotional strategies tailored to each sales category's performance.
Identification of Papaya Ripeness Using the Support Vector Machine Algorithm Maito, Rizki Minta; Qamal, Mukti; Fajriana, Fajriana
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.710

Abstract

Papaya is a tropical fruit that is commonly consumed and found in Indonesia. The ripeness level of papaya is typically assessed based on its colour. However, farmers and consumers often make mistakes identifying the fruit's ripeness. This research aims to design an application capable of determining the ripeness level of papaya based on colour images using Red, Green, Blue (RGB) and Hue, Saturation, Value (HSV) features and applying the Support Vector Machine (SVM) algorithm for ripeness classification. The dataset consists of images of California papayas, with 150 samples. The outcome of this study is a digital image application that can classify papaya ripeness into three categories: raw, half-ripe, and fully ripe. The evaluation used 80% of the data for training and 20% for testing. The results show an accuracy of 80%. With this relatively high level of accuracy, it can be concluded that the SVM algorithm is reliable for classifying papaya ripeness levels of Papayas.