Predicting academic performance of the student is crucial task as it depends on various factors. To perform such predictions the machine learning and data mining algorithms are useful. This paper presents investigation of application of C5.0, J48, CART, Naïve Bayes (NB), K-Nearest Neighbour (KNN), Random Forest and Support Vector Machine for prediction of students’ performance. Three datasets from school level, college level and e-learning platform with varying input parameters are considered for comparison between C5.0, NB, J48, Multilayer Perceptron (MLP), PART, Random Forest, BayesNet, and Artificial Neural Network (ANN). Paper presents comparative results of C5.0, J48, CART, NB, KNN, Random forest and SVM on changing tuning parameters. The performance of these techniques is tested on three different datasets. Results show that the performances of Random forest and C5.0 are better than J48, CART, NB, KNN, and SVM.

This research aimed to create a decision support system for admission selection of PMDK scholarship pathway using the SMART (Simple Multi-Attribute Rating Technique) method with web-based and if this research was successful, it could help STMIK Widya Cipta Dharma to select PMDK scholarship should be given to a student of STMIK Widya Cipta Dharma. PMDK is the tittle of scholarship for interest and talent scouting. This research was conducted at STMIK Widya Cipta Dharma Samarinda, a method of data collection used interviews, that asked questions related to the selection of PMDK scholarship admissions by observation directly to STMIK Widya Cipta Dharma Samarinda. This research development system method used the development of a decision support system method. The software model used PHP programming language and database using MYSQL. The final result of this research is a decision support system for admission selection of the PMDK scholarship pathway using the SMART (Simple Multi-Attribute Rating Technique) method with web-based, can help selection of PMDK scholarship faster by admin, and provide information to students. So contribution of this paper is gives a best recommendation for selection of PMDK scholarship using decision support systems.

The paper projected to study the field of m-learning focusing on investigating the facilities required to initiate an m-learning environment. Facilities and regular practices of conventional learning and e-learning was considered to find the potential facilities for m-learning environment. We used Integrated Tertiary Educational Supply Chain Model framework that stands on conventional education and illustrates the combined form of education supply chain and research supply chain model. Two surveys were conducted to collect data from students and teachers of higher education. The responses from both of the surveys have been presented and later compared with the findings from our studies of the existing learning environments. The significance of this research is in identifying the facilities for a learner and educator centric m-learning environment.

The game is one of the big industries today and can be an alternative entertainment to spend holidays and free time and become a very fun activity for children and adults. The game can also be used as an interactive and attractive promotional media with the theme of traveling. One of the development methods that can support the game platform is Finite State Machine. This method is used to regulate the behavior of Non-Player Character (NPC) in order to guide players to complete the game. The results of the study showed that "Explores The Tenggarong City" game is based on Android, has an interesting gameplay and makes the process of knowledge about destination very enjoyable so that players are expected to know information about the destination in Tenggarong City. This research contributes to the development of NPC and FSM in promotional games.

A binary distillation process with desired composition rate is considered. The aim is to find a control (Top and bottom compositions) which is optimal with respect to energy consumption and which is robust at the same time with respect to the response speed(less time) and minimum overshot. The solution approach is based on the formulation of two optimization techniques, Invasive Wood (IWO) and Differential Evolution (DE) with respect to Integral Square Error (ISE) and Integral Absolute Error (IAE) fitness function with using Proportinal_Integral-Derivative (PID) controller. An overall model including the dynamics of the distillation process is assumed with model reduction methods. This optimal control is compared with classical approach. The numerical results are presented and showed the effectiveness of the proposed control. MATLAB package is used for simulation and analysis.