Penerapan Algoritma C4.5 Untuk Memprediksi Penjualan Lele Pada Kolam Pancing Galatama

Abstract

Advances in technology and information today can produce a smart innovation in business, which we can call business intelligence. In this technological advancement is the use of previous data which will later be used to obtain a data update to increase sales of a product in this case the technique is called a data mining technique which can extract useful information from the sales data warehouse. The use of data mining techniques is expected to help speed up the decision-making process,enabling decision-makers to manage the information contained in transaction data into new knowledge. Therefore the seller must be more careful in providing the type of product, in this case the researcher conducts research on the sale of catfish which will be in great demand, so that the seller will be able to provide recommendations for the type of catfish that has the most demand, so that the seller can classify types of catfish from the type, price, and weight of the catfish using aclassification method carried out using the C4.5 data mining algorithm. The C4.5 algorithm is a tree data type decision classification algorithm. The decision tree C4.5 algorithm is built in several stages which include selecting the attribute as the root, creating a branch for each value and dividing the case on the branch. This step will be repeated for each branch until all cases in the branch have the same class. From the results of tree testing of decision search results from sales datausing the RapidMiner Studio application, the results that have the highest gain in predicting catfish sales are weight and type so that it will produce the status of selling catfish in demand and those that are not selling by giving a value of >= 25 in katakana in demand. and <25 are said to be not selling well, so the Galatama pond manager can make a reference to pay attention to these two variables in selling catfish to get the best sales.

Penerapan Data Mining Untuk Klasifikasi Data Penjualan Pakan Ternak Terlaris Dengan Algoritma C4.5

“…Dengan mempertimbangkan konteks ini, penelitian ini melakukan penyelidikan dan analisis terhadap tren, faktor-faktor, dan dampak yang berkaitan dengan penjualan pakan ternak (Diansyah & Exprada, 2022). Akan dibahas berbagai aspek dalam hal ini, antara lain:…”

Perbandingan CNN dan Bi-LSTM pada Analisis Sentimen dan Emosi Masyarakat Indonesia Di Media Sosial Twitter Selama Pandemik Covid-19 yang Menggunakan Metode Word2vec

“…Pada penelitian ini, algoritma Convolution Neural Network (CNN) dan Bidirectional Long Short-Term Memory (Bi-LSTM) diusulkan untuk digunakan dalam penelitian analisis sentimen dan emosi menggunakan metode Word2Vec. Algoritma LSTM memiliki keunggulan dimana algoritma ini mampu menyimpan sebuah nilai atau keadaan (cell state), baik untuk periode waktu yang panjang atau singkat [5]. Sedangkan Algortima CNN menggunakan banyak konvolusi kernel dengan ukuran berbeda untuk mengekstrak informasi kunci teks, sehingga menangkap relevansi lokal dengan lebih baik [6].…”