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Implementasi Algoritma Apriori Pada Aplikasi Penjualan Buah Berbasis Web Indra Irawan; Sunardi; Sitti Harlina
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 9 No 1 (2025): JANUARI-MARET 2025
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v9i1.2971

Abstract

This research aims to implement the Apriori algorithm in a web-based fruit sales application to discover consumer purchasing patterns and provide appropriate product recommendations. The dataset used consists of 10 fruit sales transactions. The fruit sales transaction data is then analyzed using the Apriori algorithm to identify frequently purchased product combinations. The analysis results are then used to provide accurate product recommendations to customers. Implementation is carried out by calculating the support, confidence, and lift ratio values for each product combination based on transaction data. Association rules with a support value of 30% and confidence of 60% found sweet fragrant mango, murcot australian orange, and fuji88 apple, which are then used to provide recommendations to relevant customers. This research aims to improve product recommendation accuracy, customer satisfaction, and overall sales efficiency in the fruit sales industry.
OPTIMIZING TOMATO STORAGE-TIME USING SUPPORT VECTOR MACHINE ALGORITHM TO IMPROVE QUALITY AND REDUCE WASTE Rahmat; Sunardi; Fitriani; Andi Saenong; Muhammad Rusdi Rahman; Herman Heriadi; Hernawati
Jurnal Techno Nusa Mandiri Vol. 23 No. 1 (2026): Techno Nusa Mandiri : Journal of Computing and Information Technology Period o
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/6kt3mn85

Abstract

Tomatoes are an agricultural commodity that is susceptible to spoilage, with a limited shelf life if not stored under optimal conditions. Optimizing tomato storage time is very important for improving product quality and reducing waste in distribution. This study aims to implement the Support Vector Machine (SVM) algorithm in predicting the optimal storage time for tomatoes, taking into account environmental factors such as temperature and humidity, as well as tomato ripeness. The dataset used consists of tomato images taken at various ripeness levels, as well as environmental data during storage. The SVM model was trained to classify tomato ripeness conditions and predict the optimal storage duration before significant quality deterioration occurs. The results of the study show that the SVM model has high accuracy in classifying tomato ripeness and can be used to predict the optimal storage time, which in turn can extend the shelf life of tomatoes and reduce crop waste. This research contributes to more efficient and sustainable tomato post-harvest management.