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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING J-Intech (Journal of Information and Technology)
Putri Listio, Syntia Widyayuningtias
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Computer Science & IT Decision Sciences, Operations Research & Management Engineering Library & Information Science

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Visit Recommendation Model: Recursive K-Means Clustering Analysis of Retail Sales Data Kristanto, Bagus Kristomoyo; Putri Listio, Syntia Widyayuningtias; Amien, Mukhlis; Baskoro, Panji Iman
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.8138

Abstract

In the context of retail distribution, this study employs recursive K-means clustering on retail sales data to optimize clusters of nearest-distance stores for salesperson route recommendations. This approach addresses the stochastic salesperson problem by generating effective routes, enhancing cost reduction, and improving service efficiency. The recursive K-means algorithm dynamically adjusts to continuous changes in store numbers, locations, and transaction data. Consequently, this research successfully developed a model that automatically re-clusters the data with each change, providing continuously updated and effective store recommendations.
Penerapan Metode Convolutional Neural Network (CNN) Dalam Mengklasifikasikan Penyakit Daun Tanaman Padi Christiawan, Gracia Yoel; Putra, Roy Andani; Sulaiman, Azis; Poerbaningtyas, Evy; Putri Listio, Syntia Widyayuningtias
J-INTECH (Journal of Information and Technology) Vol 11 No 2 (2023): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v11i2.1006

Abstract

Rice is a staple crop in Indonesia. Most farmers choose rice as the main crop for agricultural land. Starting from the land to the tropical climate that occurs in Indonesia, it is very suitable for rice plants. Among these supports arise obstacles faced by farmers. Rice leaf diseases include Brownspot, Blas, Bacterial Leaf Blight (HDB). Classification of these diseases can be done using the CNN (Convolutional Neural Network) method. So far, the detection process for rice plant leaf diseases has been done manually. The CNN method can detect images from pixel to pixel so it is considered effective for detecting disease from images alone. This research uses a dataset of 1630 data which is divided into 3 disease classes. This research compares the number of epochs and uses the CNN InceptionV3 architecture. The results of this research show very good results with a lift of 98% with data that is not overfitting.
Co-Authors Bagus Kristomoyo Kristanto Baskoro, Panji Iman Christiawan, Gracia Yoel Evy Poerbaningtyas Mukhlis Amien Putra, Roy Andani Sulaiman, Azis