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All Journal Jurnal Teknik Informatika (JUTIF) Jurnal MediaTIK
Nurfitri, Andi Aisyah
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Aturan Asosiasi Berbasis Algoritma Apriori Pada Penjualan Retail Online Risal, Andi Akram Nur; Adiba, Fhatiah; Nurfitri, Andi Aisyah
Jurnal MediaTIK Volume 6 Issue 2, Mei (2023)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/mediatik.v6i2.1394

Abstract

Penjualan Retail pada data penelitian ini adalah hasil transaksi penjualan tokoh retail non tokoh di Inggris. Untuk meningkatkan penjualan salah satu cara yang harus dilakukan adalah dengan menganalisis arsip dari transaksi penjual untuk melihat produk yang paling sering dibeli oleh pelanggan menggunakan teknik data mining dengan algoritma apriori. Tujuan dari penelitian ini adalah untuk mendapatkan suatu aturan asosiasi produk apa saja yang selalu di beli oleh pelanggang dengan membandingkan aturan min support 10% dengan confidance 70%, min support 10% dengan confidance 50%, dan min support 10 dengan confidance 30%. Hasil min support 10% dengan confidance 70% adalah (Knitted Union Flag Hot Water Bottle) (White Hanging Heart T-Light Holder) min support 11% dengan confidance 100%, hasil dari min support 10% dengan confidance 30% dan 50% (Knitted Union Flag Hot Water Bottle) (White Hanging Heart T-Light Holder) dengan nilai min support 11% dengan confidance 100%. Berdasarkan hasil perbandingan diatas terbentuk sebuah aturan yaitu Jika membeli Knitted Union Flag Hot Water Bottle, maka akan membeli White Hanging Heart T-Light Holder
CLASSIFICATION OF THE LEVEL OF SUGAR CONTENT IN PAPAYA FRUIT BASED ON COLOR FEATURES USING ARTIFICIAL NEURAL NETWORK Nurfitri, Andi Aisyah; Kaparang, Adam Indra; Hidayat, Muh. Taufik; Kaswar, Andi Baso; Andayani, Dyah Darma
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 6 (2023): JUTIF Volume 4, Number 6, Desember 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.6.733

Abstract

Papaya (Carica papaya L) is consumed by many people because it is beneficial for health. Along with increasing consumption or enthusiasts of papaya, the quality of papaya needs to be considered. One of the determining factors of the quality of papaya is its physical characteristics, which can be seen from its color, shape, and texture. Papaya of good quality has a delicious and sweet taste. The sweet taste of papaya is certainly influenced by the sugar content contained in it. However, to determine the sugar content in papaya is only done by human assessment based on its physical characteristics, this assessment is often less accurate. With a system that can determine the sugar content in papaya, it will make it easier for farmers to sort papaya fruit. Therefore, in this study, it is proposed to classify the level of sugar content in papaya based on color features using an Artificial Neural Network. The proposed method consists of 5 stages, namely, image acquisition, preprocessing, segmentation with the Otsu method, morphological operations, and classification with artificial neural networks. The number of papaya datasets used is 300 images which are divided into 3 classes, low class, medium class, and tal class. Based on the results of the tests that have been carried out, an accuracy of 92.85% is obtained for the training data, and for the test data, an accuracy of 100% is obtained. These results indicate that the proposed method can classify the level of sugar content in papaya fruit accurately.
Co-Authors Adiba, Fhatiah Andi Akram Nur Risal Andi Baso Kaswar Andi Baso Kaswar Hidayat, Muh. Taufik Kaparang, Adam Indra