<p style="text-align: justify;">During online learning, students were having difficulties in understanding the concept of the application of integrals to find an area. The provided materials in PowerPoint and learning sources such as books are still insufficient to understand the concept. The students’ feedback showed that a learning video is required to help the students understand the concept of the application of integrals. This research aims to develop a learning video concerning the concept comprehension of integrals’ application and determine its validity and practicality. This research utilized the analysis, design, development, implementation, evaluation (ADDIE) development model, where the subjects of this research were the students of mathematics education at the Ahmad Dahlan University. The data collection was conducted by using questionnaires and interviews. The obtained data was then analyzed for its validity and practicality. The media validity test result shows valid criteria with the assessment of the material expert of 4.629 (very good) and valid criteria with the material validity test of 4.735 (very good). The responses of the students to the learning video show 3.50 with the criteria of Very Good. Based on such results, this concept comprehension learning video is feasible to use.</p>

Household income plays a significant role in determining a country's socioeconomic standing. This measure is often used by the government to formulate the federal budget and policies that are most appropriate for national development. In spite of this, Malaysia's current economic circumstances continue to be characterized by income disparity. Therefore, this shortcoming can be addressed by analyzing the household income survey (HIS) conducted by Department of Statistics Malaysia (DoSM). In this study, the hybrid model is proposed where K-means and multiple linear regression (MLR) for clustering and predicting household income in Malaysia. Based on the experimental results, the K-means clustering analysis in conjunction with the MLR model outperformed the MLR model without clustering with a smaller mean square error. As a result, clustering analysis results in a more accurate estimate of household income because it reduces the variation between households. It is important that household income information reflect the concern of policymakers about the impact of universal and targeted interventions on different socioeconomic groups.