One of the markets which is the largest wholesaler of clothing in Central Java is the Klewer market area and its surroundings. The Simple Inc Store is one of the clothing stores in the area. Simple Inc Store is a large kiosk that sells clothing products in the form of various types of t-shirts, shirts, jackets, sweaters and pants. Sales of products in these stores are still done conventionally, namely customers come directly to the store to choose and buy products. The number of clothing products that are sold makes customers experience difficulties in the process of selecting clothing products. Therefore, it is necessary to develop a recommendation system that can assist customers in choosing clothing products. The purpose of this research is to create a Knowledge Based Recommendation model for the Recommendation System for Selection of Clothing Products. The system development method used in this research is Rapid Application Development (RAD), which consists of stages, namely business modeling, data modeling and process modeling. The recommendation system method used in this research is knowledge based recommendation. Knowledge based recommendation has the advantage of being able to set the level of user priority based on the user's needs for the product by calculating the similarity value between customer needs and clothing product attributes. Knowledge based recommendation modeling for this clothing product selection recommendation system can provide 5 choices of clothing product search attributes, namely brand, price, material, color and size. Based on the results of modeling the knowledge based recommendation method with 20 sample data, it can provide recommendations for clothing products based on the criteria needed by customers by calculating the similarity value between customer needs and the attributes possessed by each clothing product. Clothing products with the highest similarity value will be displayed as a result of clothing product recommendations, namely the highest similarity value of 0.6 is obtained for Maternal t-shirts. The results of this knowledge-based recommendation modeling can be used as a reference in developing a recommendation system for the selection of clothing products.

In times of a pandemic like this, all learning activities are carried out online via the internet with the help of learning applications that can support one's education. In the current era, there is a great need for interactive distance learning applications. An interactive English learning application in web form will be easily accessed and used by anyone. Therefore, on this occasion we created a website-based learning application, especially for English subjects because that language is an international language. In this research, an English learning application will be built that can be run on a web-based basis. The material displayed in this application is tenses, listening, speaking, idioms, expressions, regular and irregular verbs, and slang. This application is designed with UML modeling, developed using HTML programming language. We also use the waterfall software process model in designing the BeBI (Learning English) application system. The purpose of making this application is to make it easier to learn English with material that is easy to understand and this application has an easily accessible interface. The waterfall model that we use is the Sommerville waterfall with the consideration that the system design steps will be organized, focused, and easy to follow. Where the result of designing this application is an educational application system for users which is expected to help the community in learning English and make it easier for someone to prepare themselves to take English tests such as TOEFL and TOEIC. For further development, it is hoped that this application can be accessed on various platforms.

Bisnis mulai berkembang pesat pada masa sekarang. Salah satunya adalah bisnis dalam bidang bakery. Mega Saputera Bakery merupakan salah satu usaha dibidang penjulaan roti. Banyak jenis roti yang dijual dengan rasa yang berbeda-beda antara lain roti pisang coklat, roti mocca, roti konde, roti donat, roti pisang coklat goreng, roti kelapa, roti strawberry dan roti nanas. Dalam satu hari banyak transaksi yang terjadi pada pembelian roti. Tujuan dari penelitian yang dilakukan dengan pemodelan association rule untuk mengetahui pola pembelian roti oleh konsumen. Berdasarkan pola pembelian maka dapat di rekomendasikan produksi roti. Rekomendasi ini akan bermanfaat dalam prosuksi roti sehingga dalam produksi roti akan lebih optimal dan tidak merugikan pemilik karena banyak roti yang tidak terjual. Pada penelitian yang dilakukan nilai support dan nilai confident yang digunakan niali support minimal 25% dan nilai confident minimal 70%. Hasil penelitian didapatkan hasil untuk kombinasi 2 itemset didapatkan 11 kombnasi 2 itemset, 10 kombinasi 3 itemset dan 5 Kombinasi 4 itemset. Nilai lift ratio paling tinggi 2,073 dan paling rendah 1.285

UD. Borimin merupakan salah satu usaha yang bergerak dalam bidang penjualan beras. Banyak jenis merk beras yang dijual oleh UD. Borimin. Transaksi penjulan dalam sehari cukup banyak yang dapat dilihat pada nota atau kwitansi penjualan beras. Data transksi penjulaan dapat dilihat beras yang sering dibeli konsumen. Tujuan penelitian ini adalah pemodelan Association rule untuk mengetahui pola pembelian merk beras. Algoritma Apriori di gunakan untuk pembuktian pemodelan Association Rule. Metode pengembangan sistem Extreme Programming (XP) dengan tahapan perencanaan dan perancangan. Penelitian ini menggunakan data sebanayak 563 transaksi dengan minimun support 30% dan confidence 70%. Hasil penelitian didapatkan kombinasi 2 itemset didapatkan 5 kombinasi itemset dan kombinasi 3 itemset sebanyak 3 kombinasi itemset. Hasil final asosiasi didapatkan 17 kombinasi merk beras berdasarkan pola pembelian beras.

Hotel adalah tempat peristirahatan sementara yang menyediakan fasilitas akomodasi dan layanan lainnya seperti kamar tidur, kamar mandi, restoran, kolam renang, spa, dan pusat kebugaran yang dikelola oleh perusahaan dengan melakukan pembayaran sesua ketentuan. Machine learning merupakan cabang dari kecerdaan buatan yang cara bekerjanya belajar dari pemikiran manusia dengan menggunakan algoritma matematika. Penelitian ini menggunakan Decission Tree, SVM, dan ANN. Tujuan penelitian membandingkan akurasi dari Decission Tree, SVM, dan ANN dalam identifikasi Reservasi Hotel. Hasil penelitian didapatkan Algortima Decision Tree didapatkan nilai akurasi sebesar 85.7%, Support Vector Machine (SVM), sebesar 82.71%, dan Artificial Neural Network (ANN) 80.48%.