Article Per Year (5 Year)
p-Index From 2021 - 2026
0.562
P-Index
This Author published in this journals
All Journal Golden Ratio of Data in Summary Jurnal Indonesia : Manajemen Informatika dan Komunikasi
Sitepu, Keysa Shifa Adwitia
Unknown Affiliation
Computer Science & IT Economics, Econometrics & Finance Languange, Linguistic, Communication & Media Library & Information Science Social Sciences

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Classification of Pear Varieties Using the K-Nearest Neighbor Algorithm and Extraction of Shape, Color, Texture, and Size Features Putri, Rezkya Nadilla; Kiswanto, Dedy; Sitepu, Keysa Shifa Adwitia
Golden Ratio of Data in Summary Vol. 5 No. 1 (2025): November - January
Publisher : Manunggal Halim Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52970/grdis.v5i1.910

Abstract

This study develops a pear variety classification system based on digital images using the K-Nearest Neighbor (KNN) algorithm. The data used included 195 images from three pear varieties, namely Century, Forel Afrika, and Singo, which were analyzed by utilizing various features such as color (RGB), texture (Local Binary Pattern), shape (area, circumference, length-width ratio), and size (bounding box dimensions). The preprocessing process removes the image's background to increase focus on the main object, thus allowing for more optimal feature extraction. The dataset is divided into 80% for training and 20% for model testing. The evaluation results show that the KNN model can achieve an accuracy of 85%, with an average precision value of 0.85, recall of 0.89, and F1-score of 0.85. These results prove that the KNN algorithm is effective in accurately classifying pear varieties, which can significantly contribute to applying digital image-based technology for automatic classification needs in the agricultural sector.
Peramalan Permintaan Roti Harian dengan Simulasi Monte Carlo di Reza Bakery Sitepu, Keysa Shifa Adwitia; Putri, Rezkya Nadilla; Harliana, Putri
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 6 No. 2 (2025): Mei
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63447/jimik.v6i2.1419

Abstract

The fluctuating daily demand at Reza Bakery leads to an imbalance between production capacity and consumer needs. This study aims to forecast the daily demand of the three best-selling bread products using the Monte Carlo simulation method: Small White Bread, Mocha Bread, and French Chocolate Bread. Sales data from April 2025 covering 30 days were used as the simulation basis. The simulation involves calculating the probability distribution of demand, generating random numbers, and mapping them to demand predictions. Results show that daily demand predictions range from 33–40 units for Small White Bread, 16–22 units for Mocha Bread, and 7–12 units for French Chocolate Bread. This method helps in formulating more efficient production strategies, with good accuracy estimates based on the match between simulation results and historical trends. The simulation offers potential to reduce overstock risks and improve operational efficiency.
Classification of Pear Varieties Using the K-Nearest Neighbor Algorithm and Extraction of Shape, Color, Texture, and Size Features Putri, Rezkya Nadilla; Kiswanto, Dedy; Sitepu, Keysa Shifa Adwitia
Golden Ratio of Data in Summary Vol. 5 No. 1 (2025): November - January
Publisher : Manunggal Halim Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52970/grdis.v5i1.910

Abstract

This study develops a pear variety classification system based on digital images using the K-Nearest Neighbor (KNN) algorithm. The data used included 195 images from three pear varieties, namely Century, Forel Afrika, and Singo, which were analyzed by utilizing various features such as color (RGB), texture (Local Binary Pattern), shape (area, circumference, length-width ratio), and size (bounding box dimensions). The preprocessing process removes the image's background to increase focus on the main object, thus allowing for more optimal feature extraction. The dataset is divided into 80% for training and 20% for model testing. The evaluation results show that the KNN model can achieve an accuracy of 85%, with an average precision value of 0.85, recall of 0.89, and F1-score of 0.85. These results prove that the KNN algorithm is effective in accurately classifying pear varieties, which can significantly contribute to applying digital image-based technology for automatic classification needs in the agricultural sector.
Co-Authors Dedy Kiswanto Putri, Rezkya Nadilla