No abstract

Data mining is a process of looking for patterns or pulling large and selected data information using certain techniques or methods. The K-Means and Decision Tree methods are part of the Data Mining technique. This study will combine the K-Means method to cluster data into three clusters then the results of the clustering will be classified using the Decision Tree Method with a comparison of the Gain Ratio, Information Gain and Gini Index criteria. The data is processed into two, namely training data and testing data with a percentage of 70:30, 80:20 and 90:10. The results of the research are to find out which criteria produce the best decision tree and performance based on the highest accuracy value from each data group. The data is taken from the UCI Repository with a total of 811 records and 52 attributes. From the data processing carried out, it is known that for the 70:30 data split, the accuracy value with the

The tourism sector is one of the country's biggest foreign exchange earners. Foreign tourist visits to Indonesia reached 16.1 million during 2019. Therefore foreign tourist visits become a very important thing. In this study clustering will be carried out or grouping data on foreign tourist visits into 5 groups for the category of countries with very high, high, high enough, low and very low visits. Data processing was performed using the K-Means clustering method and the Principle Component Analysis (PCA) dimension reduction method. From the data processing, K-Means modeling results combined with the PCA method resulted in a smaller or better Davies Bouldin Index (DBI) evaluation value of 0.310 compared to K-Means modeling alone which obtained a DBI value of 0.382. The tools used in data processing are RapidMiner. The results of clustering are expected to be a reference for related parties to maximize the promotion of overseas tourism.

Berkembangnya ilmu pengetahuan dan teknologi informasi membuat manusia mencari cara yang cepat untuk memudahkan kegiatan usaha. Salah satu caranya adalah menggunakan teknologi informasi berupa website. Kelompok Tani Bibit Durian Maju Makmur adalah sebuah kelompok tani yang bergerak dibidang tanaman dan berada di desa Alasmalang Kabupaten Banyumas. Namun penjualan dan pemasaran bibit durian pada Kelompok Tani Maju Makmur belum memanfaatkan adanya website ataupun penggunaan e-commerce karena keterbatasan pengetahuan tentang internet. Para petani hanya menggunakan handphone untuk sarana penjualan maupun pemasaran. Dengan pembuatan website untuk Kelompok Tani Maju Makmur dapat mengatasi masalah transaksi penjualan dan pemasaran bibit durian. Perancangan sistem informasi berbasis website ini menggunakan Dreamweaver CS6, PHP, HTML, dan XAMPP. Metode yang digunakan dalam pengembangan sistem ini yaitu dengan model waterfall. Untuk pengujian unit menggunakan Black Box Testing. Dengan keberadaan website dapat membantu konsumen atau pelanggan dalam mengakses informasi kapan dan dimana saja, serta bisa menjadi sarana promosi untuk memperluas jangkauan pemasaran produk dan menambah penghasilan.

Clustering is a method of dividing datasets into several groups that have similarity or the same characteristics. High-dimensional Datasets will influence the effectiveness of the grouping process. This study compares two dimension reduction algorithms, namely Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) using K-Means clustering method to find out the best algorithm with the smallest Bouldin Davies Index evaluation. The dataset of this study involved public data from UCIMachine Learning which contains the number of weekly sales of a product. Data processing is performed by comparing the number of clusters from 3 to 10 and the dimension reduction from 2 to 10. From the data processing the RapidMiner tools, application with dimension reduction can provide better results than without dimension reduction. In particular, the PCA algorithm shows better results than the SVD, with which the best number of clusters is 5, and the number of dimensional reductions is 3 with a Bouldin Index of 0.376.