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This paper presents a study of correlation between subjects of Diploma in Electrical Engineering (Electronics/Power) at Universiti Teknologi MARA(UiTM) Cawangan Terengganu using Artificial Neural Network (ANN). The analysis was done to see the effect of mathematical subjects (Pre-calculus and Calculus 1) and core subject (Electric Circuit 1) on Electronics 1. Electronics 1 is found to be a core subject with the history of high failure rate percentage (more than 25%) in previous semesters. This research has been conducted on current final semester students (Semester 5). Seven (7) models of ANN are developed to observe the correlation between the subjects. In order to develop an ANN model, ANN design and parameters need to be chosen to find the best model. In this study, historical data from students’ database were used for training and testing purpose. Total number of datasets used are 58 sets. 70% of the datasets are used for training process and 30% of the datasets are used for testing process. The Regression Coefficient, (R) values from the developed models was observed and analyzed to see the effect of the subject on the performance of students. It can be proven that Electric Circuit 1 has significant correlation with the Electronics 1 subject respected to the highest R value obtained (0.8100). The result obtained proves that student’s understanding on Electric Circuit 1 subject (taken during semester 2) has direct impact on the performance of students on Electronics 1 subject (taken during semester 3). Hence, early preventive measures could be taken by the respective parties.    Keywords: Artificial neural network, Diploma in Electrical Engineering, Graduate on time, Correlation.

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Effective teaching and learning, not only taking into account the good teaching methodology and facilities but the acoustic level in the classroom should also be addressed. RT-60 is the time required for sound decay in a classroom. The time taken for the sound to decay depends on the absorption coefficients (α) of surface material and the volume of the room. This paper aims to identify potential treatment materials for improving the value of RT-60 of the selected unoccupied teaching hall/classroom at one of the higher learning institution in Malaysia. The value of RT-60 is calculated using Sabine formula and used the same parameter like the surface material, surface area, and room’s volume identified in previous research. The best RT-60 value is 0.6s for the classroom with 10k-20k cubit feet in volume and it coincides with the volume of the selected classroom which is about 14320 cubic feet. To ensure that room has the optimum RT-60 values, the materials used on the ceiling, walls, floors, windows, and doors are assessed for sound absorption rate and then materials with appropriate absorption coefficient are proposed for improvement purposes. As a result, it found that gypsum board is suitable to use for wall and plywood 12mm thick is proposed for ceiling material. By applying this proposed material will then enhance the acoustical performance of the unoccupied teaching hall especially in RT-60 value.