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.

Customer loyalty is an important factor to the survival of a company. The problem facting today is related to the descreasing number of customer loyalty that happened to PT MNC Play. If this is allowed   to continue it is not impossible this will endanger the continuity of the company s business.Keep in mind the factors that cause customers to have loyal and disloyal status, data mining classification techniques can be used to classify loyal and disloyal customers. Many data mining classification algorithms can be used, so it needs to be comparative to know the accuracy of each algorithm, the algorithem used is C4.5 and Na ve Bayes. The data used were 28,899MNC Play customer of Semarang City.The results of the classification   process were evaluated using cross validation, confusion matrix, ROC Curve to find the more accurate data mining classification algorithm to determine loyal and disloyal customers.Keywords: Classification, Customers Loyalty, Na ve Bayes, C4.5.