The presence of leaf diseases in grapes can reduce the productivity of grapes and result in losses for farmers. Leaf diseases are mainly caused by bacteria, fungi, virus etc. A proper diagnosis of disease in plants is needed in order to take appropriate control measures. This paper aims to assist in the identification and classification of grape leaf diseases Convolutional Neural Network (CNN). CNN is basically an artificial neural network architecture that requires repeated training processes to get good accuracy. CNN consists of 3 stages, namely Data Input, Feature Learning, and Classification. The implementation of CNN in this study uses Keras libraries that use the python programming language. Keras is a framework created to facilitate learning of computers. The CNN training process using 0.0001 learning rate obtained results with an accuracy rate of 91,37%

The rapid development of information technology influences on all fields of the economy. One form of the development of information technology is about how consumers shop through online stores. Researchers need to do this research because nowadays there are many online stores that have developed in Indonesia. Researchers want to know how much influence the quality of information and service quality on customer satisfaction and make a positive contribution and make continuous purchases at the online store. This study uses the Delone and Mclean methods and with AMOS software. The purpose of this study is to analyze a number of hypotheses, which are: H1: System Quality has a positive effect on Use; H2: Use has a positive impact on net benefits; H3: System Quality has a positive impact on User Satisfaction; H4: User Satisfaction has a positive effect on Use; H5: Information Quality has a positive effect on Use; H6: Information Quality has a positive effect on User Satisfaction; H7: User Satisfaction has a positive effect on Net benefits; H8: System Quality has a positive effect on Information Quality; H9: Information Quality has a positive effect on System Quality. The test results can be seen that Quality System has a positive effect on User Satisfaction, this can be seen from the estimated value of 0.529, User Satisfaction has a positive effect on the Net benefits that can be seen from the estimated value of 1,146, and User Satisfaction has a positive effect on Use with an estimated value of 1,352. From the test results with the final research model that supports the hypothesis (H0) said that there is a relationship between variables with the level of satisfaction of the Sorabel Application to the Sorabel Application user as a medium for online purchases

Abstract   Over decades, retail chains and department stores have been selling their products without using the transactional data generated by their sales as a source of knowledge. Abundant data availability, the need for information (or knowledge) as a support for decision making to create business solutions, and infrastructure support in the field of information technology are the embryos of the birth of data mining technology. Association rule mining is a data mining method used to extract useful patterns between data items. In this research, the Apriori algorithm was applied to find frequent itemset in association rule mining. Data processing using Tanagra tools. The dataset used was the Supermarket dataset consisting of 12 attributes and 108.131 transaction. The experimental results obtained by association rules or rules from the combination of item-sets beer wine spirit-frozen foods and snack foods as a Frequent itemset with a support value of 15.489% and a confidence value of 83.719%. Lift ratio value obtained was 2.47766 which means that there were some benefits from the association rule or rules.   Keywords: Apriori, Association Rule Mining.   Abstrak   Selama beberapa dekade rantai ritel dan department store telah menjual produk mereka tanpa menggunakan data transaksional yang dihasilkan oleh penjualan mereka sebagai sumber pengetahuan. Ketersediaan data yang melimpah, kebutuhan akan informasi (atau pengetahuan) sebagai pendukung pengambilan keputusan untuk membuat solusi bisnis, dan dukungan infrastruktur di bidang teknologi informasi merupakan cikal-bakal dari lahirnya teknologi data mining. Data mining menemukan pola yang menarik dari database seperti association rule, correlations, sequences, classifier dan masih banyak lagi yang mana association rule adalah salah satu masalah yang paling popular. Association rule mining merupakan metode data mining yang digunakan untuk mengekstrasi pola yang bermanfaat di antara data barang. Pada penelitian ini diterapkan algoritma Apriori untuk pencarian frequent itemset dalam association rule mining. Pengolahan data menggunakan tools Tanagra. Dataset yang digunakan adalah dataset Supermarket yang terdiri dari 12 atribut dan 108.131 transaksi. Hasil eksperimen diperoleh aturan asosiasi atau rules dari kombinasi itemsets beer wine spirit-frozen foods dan snack foods sebagai Frequent itemset dengan nilai support sebesar 15,489% dan nilai confidence sebesar 83,719%. Nilai Lift ratio yang diperoleh sebesar 2,47766 yang artinya terdapat manfaat dari aturan asosiasi atau rules tersebut.   Kata kunci: Apriori, Association rule mining

PT Gafa Utama Indonesia is one company that provides services in teaching and writing. Until now, PT Gafa Utama Indonesia already has 30 well-known branches in Jabodetabek. In the teaching and learning process, Gafa needs some stationery and teaching aids. The high demand for office stationery, and the mismatch of inventory in the warehouse, affects the fluency in the teaching and learning process. The data used in this study is the report data on the demand for office stationery for the period January-December 2018. This study uses a priori algorithm method and assessment with tanagra tools. The results of manual calculations with Microsoft Excel are the same as those using the tanagra tool. The final result shows the 2 items with the most demand, namely an eraser and a sharpener with at least 50% support, and 50% confidence. These results can be used as a reference for PT Gafa Utama Indonesia in the supply of office stationery