Jawa Tengah memiliki beragam tempat wisata yang menarik untuk dijelajahi, namun keterbatasan waktu dan terbatasnya sumber informasi membuat sulit untuk memberikan informasi yang tepat. Selain itu, pengelola objek wisata kesulitan memberikan informasi yang tepat kepada pengunjung. Tujuan dari penelitian ini adalah memasukkan neural network ke dalam aplikasi chatbot objek wisata Jawa Tengah untuk meningkatkan kemampuannya dalam memahami dan bereaksi secara tepat terhadap pertanyaan pengguna. Teknik AI Project Cycle yang dipadukan dengan pendekatan Natural Language Processing (NLP) berfungsi sebagai tahap preprocessing pada pengolahan kata. Penelitian ini menghasilkan akurasi sebesar 99,92% dan loss sebesar 0%. Program chatbot ini bertujuan untuk memberikan kemudahan akses informasi tempat wisata di Jawa Tengah kepada pengguna.

Employment is the primary activity that humans engage in to generate income. With the advancement of technology and research, there are many new job opportunities leading to confusion in choosing a job path. This leads to individual confusion in making job choices. Ignorance of one's own talents and personality, as well as ignorance of the various options available, can be the source of this ignorance. This research aims to develop a Decision Tree model to assist users in determining the appropriate IT field. The system uses AI Project Cycle and data processing tools such as Google Collaboratory, which is based on Python programming language. The results show that the Decision Tree algorithm can be applied to recommend jobs in the IT field to help users find suitable fields in the IT field.

With the development of communication technology today, the smartphone is one of the choices on communication tools that many users have now. Even now, smartphones have various types and prices that reach user needs, so that with limited funds, they can adjust it to their needs. By creating a smartphone selection recommendation systems, it can make it easier for people in determining smartphone purchases based on available funds, with features, brands, and advantages that are already sophisticated today. The recommendation system is made using a combined method called Modified Simple Additive Weighting (M-SAW), where Simple Additive Weighting (SAW) is used for weighting and normalization process, meanwhile Weighted Product (WP) is used for fixing the weight of criteria, determinating vector S, and ranking process. The result for best recommendation using this method is Oppo F5 Youth with score 0,444. With this recommendation results, makes the user make their decision for smartphone recommendation using this system.