A brain tumor is a very common and devastating malignant tumor that leads to a shorter lifespan if not detected early enough. Brain tumor classification is a critical step after the tumor has been identified to create an effective treatment plan. This study aims to investigate the three deep learning tools, VGG-16 ResNet50 and AlexNet in order to detect brain tumor using MRI images. The results performance are then evaluated and compared using accuracy, precision and recall criteria. The dataset used contained 155 MRI images which are images with tumors, and 98 of them are non-tumors. The AlexNet model perform extremely well on the dataset with 96.10% accuracy, while VGG-16 achieved 94.16% and ResNet-50 achieved 91.56%. These accuracies positively impact the early detection of tumors before the tumor causes physical side effects such as paralysis and other disabilities.

Education is a basic strategy in developing potential human resources and empowering unprivileged individuals to get life skills and knowledge which are recognized engines of economic growth and social development in any nation. TVET holds the master key for achieving this growth because it is driven by wheels of training skilled and entrepreneurial workforce needed in workplace. Also the strength and success of TVET lie in nature of their cooperation with industries. The purpose of this study was to ascertain the linkage between vocational high school (SMK) and industries in the field of information technology in Malang City. Quantitative design with descriptive approach in four (4) public vocational high schools selected with simple random sampling method with 32 participants. School management and Information Technology teachers are as the subject. The study findings showed that cooperation between vocational high schools with industries has great impact on the quality of vocational education as seen in the strong positive relationship. The study also revealed that various challenges of attaining quality vocational education in Malang. Still, the study revealed that common cooperation type that exist between SMK with industry is mainly Internship placement and job recruitment, on-job training with no significant cooperation in infrastructure.

Heavy duty diesel (HDD) construction equipment plays an important role in building and rebuilding Indonesia’s infrastructure and has a significant impact on national energy consumption in the form of diesel fuel. This paper demonstrates an ‘energy-productivity’ (ENPROD) model, a practice-ready methodology in developing an energy estimating model for HDD equipment that establishes a baseline of current fuel use based on the equipment’s productivity rates. The model was formulated by using the productivity model to obtain the total duration of work, and combined with the NONROAD’s brake specific fuel consumption (BSFC) to gain the fuel consumption rate in term of liter per horsepower-hour (liter/hp-hr). ENPROD model is good to be used as a benchmark for estimating fuel use from some certain types of construction equipment performing earthwork activities. Based on the methodology presented in this paper, the results revealed several trends related to total fuel use of the equipment.

This study aims to determine the contribution of entrepreneurship knowledge, skills competence, and self-efficacy to entrepreneurship readiness. The design used in this research using a quantitative approach and knowledge test techniques using regression analysis. The number of samples in this study as many as 181 students. The results showed that there was a significant contribution between entrepreneurship knowledge to entrepreneurship readiness. There is also a significant contribution between the skills competence to entrepreneurship readiness and there is a significant contribution between self-efficacy to entrepreneurship readiness. Futhermore, there is a significant contribution simultaneously between entrepreneurship knowledge, skill competence; self-efficacy to entrepreneurship readiness.