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This paper describes personality classification experiment by applying k-means clustering machine learning algorithms. Several previous studies have been attempted to predict personality types of human beings automatically by using various machine learning algorithms. However, only few of them have obtained good accuracy results. To classify a person into personality types, we used Jungian Type Inventory. Our method consists of three parts: data collection, data preparation, and hyper-parameter tuning. Our testing results showed that the k-means model has 107 inertia value, which is a good number for an unsupervised learning model as an interim result. With the result, we divided the data into 16 clusters, which can be considered as personality types. We continue this research with analysis of large data to be collected in the future.

Students nowadays are hard to be motivated to study lessons with traditional teaching methods. Computers, smartphones, tablets and other smart devices disturb students' attentions. Nevertheless, those smart devices can be used as auxiliary tools of modern teaching methods. In this article, the authors review two popular modern teaching methods: flipped classroom and gamification. Next, they implement flipped classrooms as an element of IoT (Internet of Things) into learning process of computer networks course, by using Cisco networking academy tools, instead of traditional learning. The survey provided to students shows good feedback from students. The authors report the impact of flipped classroom implementation with data obtained from two parallel sections (one flipped classroom and the other traditional classroom). The results show that the flipped classroom approach is better than the traditional classroom approach with a difference of approximately 20% increase in the average of attendance, lab work, quizzes, midterm exams and final exam.

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