Desa Batu Mekar merupakan salah satu desa di Kabupaten Lombok Barat di Kecamatan Lingsar. Desa Batu Mekar memiliki banyak produk baik berupa kerajian tangan seperti tas, hiasan lampu yang dibuat dari bahan ketak atau produk pertanian dan perkebunan seperti kopi, gula aren dan lain sebagainya. Permasalahan yang ada pada Desa Batu Mekar adalah harga penjualan produk hasil kerajinan yang rendah karena produk hanya dititipkan di toko oleh-oleh. Menurunnya jumlah penjualan ketak karena penurunan jumlah wisatawan yang berkujung di pulau Lombok akibat gempa. Selain itu, terbatasnya media promosi. Penelitian ini dikembangkan menggunakan metode Rapid Application Development. Penelitian ini menghasilkan sistem informasi penjualan online sebagai media penjualan hasil kerajinan, sehingga masyarakat dapat langsung menjual produknya tanpa melalui perantara sekaligus sebagai media promosi sehingga produk kerajinan dapat dijual secara langsung dan dapat dikenal oleh masyarakat luas. Aplikasi yang dikembangkan dapat digunakan sebagai media promosi serta sarana penjualan produk kerajinan desa Batu Mekar.

In this research the author aims to apply the K-NN and Naive Bayes algorithms for predicting student graduation rates at Sekolah Tinggi Pariwisata (STP) Mataram, The comparison of these two methods was carried out because based on several previous studies it was found that K-NN and Naive Bayes are well-known classification methods with a good level of accuracy. But which one has a better accuracy rate than the two algorithms, that's what researchers are trying to do. The output of this application is in the form of information on the prediction of student graduation, whether to graduate on time or not on time. The selection of STP as the research location was carried out because of the imbalance between the entry and exit of students who had completed their studies. Students who enter have a large number, but students who graduate on time according to the provisions are far very small, resulting in accumulation of the high number of students in each period of graduation, so it takes the initial predictions to quickly overcome these problems. Based on the results of designing, implementing, testing, and testing the Student Graduation Prediction Application program using the K-NN and Naive Bayes Methods with the Cross Validation method, the result is an accuracy for the K-NN method of 96.18% and for the Naive Bayes method an accuracy of 91.94% with using the RapideMiner accuracy test. So based on the results of the two tests between the K-NN and Naive Bayes methods which produce the highest accuracy, namely the K-NN method with an accuracy of 96.18%. So it can be concluded that the K-NN method is more feasible to use to predict student graduation

Currently school teachers, especially in Mataram City, West Lombok Regency, Central Lombok and East Lombok are not familiar with learning with the concept of computational thinking (Computational Thinking) so they cannot teach their students how to think computationally as an approach to solving existing problems. Considering one of the demands of the industrial revolution 4.0, where problem solving skills are one of the abilities that students must have. In this case, these abilities need to be taught by teachers at school. Therefore, this problem must be solved immediately by increasing the ability of teachers in learning computational thinking so that teachers can apply computational thinking learning methods to their students. From the problems listed, it is necessary to approach how to train teachers to teach computationally thinking to their students. In Lombok, West Nusa Tenggara, to apply Computational Thinking (CT) in formulating problems and revealing solutions, namely through socialization and training and mentoring of free computational thinking materials to teachers in schools in Lombok, NTB which was held in the form of CT Bebras socialization activities, which is expected to help introduce and apply Computational Thinking (CT) material as a creative learning method in schools in NTB.

This service activity aims to contribute knowledge to teachers to be able to understand and implement computational thinking in the subjects they are taught. The lack of trained personnel and the lack of understanding in implementing computational thinking gives the Bebras Bureau the opportunity to contribute. This is in line with Mendikbud's desire to implement computational thinking in the children's education curriculum as a provision for more innovative learning to answer the needs of the industrial era 4.0. Computational thinking is the process of thinking in formulating a problem and its solution so that the solution can be represented in a form that can be executed by an information-processing agent. The implementation of the service was carried out on the Mathematics subject teacher at Nurul Islam Mataram Elementary School. The implementation stages consist of planning, preparation, socialization, training, and evaluation. The results of the evaluation showed that most of the teacher participants agreed to apply the results of the training to students and most participants agreed to join the follow-up programs from Bebras. It is hoped that this activity can run continuously and be supported positively by the parties involved.

Clustering is a useful technique that organizes a large number of non-sequential text documents into a small number of clusters that are meaningful and coherent. Effective and efficient organization of documents is needed, making it easy for intuitive and informative tracking mechanisms. In this paper, we proposed clustering documents using cosine similarity and k-main. The experimental results show that based on the experimental results the accuracy of our method is 84.3%.