Kegiatan workshop pengabdian masyarakat yang bertajuk “Workshop Penggunaan Sistem Informasi Prediksi Penjualan Bahan Bangunan pada UD Berkat Jaya Kecamatan Talawi” dilatarbelakangi karna belum adanya sistem komputerisasi dalam penentuan stok barang yang kemudian berhasil dikerjakan dan tentu belum mengerti cara penggunaan aplikasinya maka kegiatan ini bertujuan untuk memberikan panduan dalam penggunaan dan pemanfaatan fitur-fitur aplikasi Prediksi Penjualan Bahan Bangunan. Dengan workshop ini, pemilik dan admin UD Berkat Jaya Kecamatan Talawi dapat memahami cara memprediksi stok barang secara objektif, sehingga memudahkan dalam pengambilan keputusan yang lebih efektif. Metode yang digunakan dalam kegiatan ini meliputi penjelasan deskriptif dan praktik langsung. Pemateri memberikan penjelasan tentang fitur-fitur dalam Sistem Informasi Prediksi Penjualan Bahan Bangunan dengan praktik langsung, sehingga pemilik dan admin dapat lebih memahami materi dan konsep yang disampaikan. Luaran yang menjadi target dalam pengabdian ini adalah publikasi artikel pada jurnal nasional belum terakreditasi.

Handling of dental disease problems requires that it be handled quickly and correctly, but not all teams of dental experts can carry out treatment quickly due to the lack of a team of dental experts who are in the workplace or hospital 24 hours a day.  Apart from that, the public also has very little knowledge of information about dental disease, so that to treat dental disease, people have to consult a dentist. To classify images of dental disease, feature extraction is needed. Feature extraction is taking characteristics of an object that can describe the image. One example of image feature extraction used is Red, Green, Blue (RGB). This feature extraction is often used to identify or classify an image. Dental image data that will be used in the classification process are tooth abrasion, anterior crosbite, cavities and gingivitis. K-Nears Neigbor is the simplest data mining algorithm.  The aim of this algorithm is to find the results of the closest distance classification for each object.  In determining the distance, the data is initially divided into two parts, namely training data and testing data. After receiving the training data and testing data, the distance from each testing data (Equilidence Distance) to the training data is calculated. The K-Nearest Neighbors method can be applied to classify dental disease based on images of types of dental disease using Matlab software. As a result of the image data training process, 40 image data were input, training results obtained were 100%.