PT. Dewangga Travindo is a company that operates in the field of travel services which includes tours, travel, and Hajj and Umrah pilgrimages which is based in the city of Semarang and has received permission from the Ministry of Religion No. D/606 of 2013. Every year there is always an increase in sales of services. Hajj and Umrah. The higher transaction activity every day results in a large buildup of data in the database which will only become data waste. The ability to process data is increasingly sophisticated using data mining, which is an activity of looking for relationships between items to obtain patterns as information to assist in decision making. However, considering the large number of competitors offering the same services, it is necessary to increase competitiveness to overcome market segmentation at PT Dewangga Travindo. For this reason, this research was carried out which aims to overcome customer segmentation using the Clustering method with the K-Means algorithm which produces a visual cluster model with RStudio tools using RFM attributes applied to carry out segmentation. The data used in this research is data on Hajj and Umrah pilgrims in the 2018-2020 period.

Abstrak Permasalahan sampah sudah menjadi permasalahan dunia, terutama sampah anorganik dan B3 yang tidak dapat diurai secara alami, sementara jumlah produksi sampah terus bertambah seiringdengan pertumbuhan penduduk. Dari data statistik Kabupaten Semarang jumlah sampah yang terangkut mulai tahun 2019 sebanyak : 220 487 M3, tahun 2020 : 247 095 M3 dan tahun 2021 : 280 859 M3, hal ini menunjukkan peningkatan jumlah sampah naik secara liner. Desa KebumenKecamatan Banyubiru Kabupaten Semarang menghadapi permasalahan yang serupa dengan meningkatnya volume sampah rumah tangga berdampak pada lingkungan yang kurang sehat. Meskipun sudah ada bank sampah pada wilayah tersebut namun ada beberapa kendala yangdihadapi yaitu manajemen sampah, reduce, reuse dan recycle atau 3 R belum optimal. Program PKM (Program Kemitraan Masyarakan) Universitas Dian Nuswantor dengan penerapan manajemen sampah yang efektif dan efisien dengan metode FDG (Focus Group Discussion) dan Edukasi dan Pelatihan diharapkan sampah yang terdapat diwilyah tersebut diolah baik sehingga dapat meniminalkan dampak negatif sampah terhadap lingkungan hidup desa Kebumen dan dapatmenciptakan circular ekonomi, sehingga dapat meningkatkan taraf ekonomi masyarakat setempat Kata kunci: Pengelolaan, Sampah, Manajemen, Taraf Hidup, Ekonomi

ANN or artificial neural network is a way to solve various kinds of problems to make decisions based on training. One of the methods of JSt which contains competitive and supervised learning. Where this layer will automatically learn the classification of the closest input distances and will be distributed to the same class. there are 2 types of coffee beans that are famous in the world, namely arabica and robusta, for some people or the layman it will be very difficult to distinguish these 2 types of coffee beans apart from the fact that the shape is almost the same the color looks almost the same but there are a number of differences in the two coffee beans which we can see from the shape of the seed. Robusta has a shape that tends to be round and smaller in size, and has a rougher texture. Arabica, on the other hand, is slightly flatter and longer in shape. The size is slightly bigger than Robusta but the texture of Arabica is smoother than Robusta. This is the basis of this study where the images of the two coffee beans will be extracted using the first-order texture feature extraction method based on MU parameters, standard deviation, skewness, energy, entropy, and smoothness. The method for collecting data was in the form of a quantitative method using images from each coffee bean, both Arabica and Robusta, with a total of 130 images. The comparison between training_data and test_data is 80:20. Through research conducted in the form of performance parameters with the best accuracy, including: Learning rate 0.01, max epoch or maximum iteration of 10 and 30%, the amount of training data used is 39 training images and 26 test images resulting in an accuracy presentation of 71% for the training process and error with a percentage of 96% for the test process.