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Poverty is one of the main issues in Indonesia although many policies have been implemented by the government to overcome this problem. With this problem, a study was conducted which aims to identify factors that affect poverty in East Java in 2023 using the principal component analysis (PCA) method. PCA is a multivariate analysis technique used to extract information from correlated data, so as to summarize several variables into principal components. In this study, the variables used include the number of poor people, percentage of poor people, poverty severity index, open unemployment rate, labor force participation rate, and life expectancy from 38 districts/cities in East Java. It was found that the data characteristics had low variance with the exception of one variable, and met the assumptions of multivariate normal distribution, interrelationship between variables, data sufficiency, and correlation between variables suitable for PCA. Factor analysis with PCA produces two main components, namely community living conditions and labor conditions, which can represent the original variables in their influence on poverty in East Java. Suggestions from this study are expected to be a reference for policy makers in improving community welfare and labor conditions in East Java. Future research is expected to add related variables to obtain more detailed results.

Indonesia has significant fisheries potential due to its vast waters. Its abundant fishery resources have strong export potential. However, export activities often face challenges that cause export volumes to fluctuate. This fluctuation is influenced by various factors. These factors can be minimized using statistical methods such as Principal Component Analysis (PCA) and Factor Analysis. This study includes data characterization for each variable and testing PCA and factor analysis assumptions, including multivariate normality testing, independence testing (Bartlett's test), sampling adequacy (KMO test), anti-image correlation testing, PCA testing, and factor analysis. The results indicate that the percentage contribution of fisheries to GDP, the number of coastal villages with disaster mitigation facilities, and the average daily per capita calorie consumption from fish are relatively less dispersed and not highly variable around the mean. Additionally, all data meet the assumptions, and the sample size is adequate. Factors such as aquaculture pond production and the percentage contribution of fisheries to GDP sufficiently explain the data variations.

Factor analysis is a statistical method aimed at exploring correlations or relationships among variables studied, which are then grouped into fewer new factors. Principal Component Analysis (PCA) is a statistical technique used to reduce the dimensions of data with the goal of identifying hidden patterns or significant structures within the data. The research results indicate the presence of 5 factors influencing the decision-making process in online shopping among students of the University of North Sumatra via Shopee, namely the Funding Application Factor (28.141%), Information Reputation Factor (13.983%), Communication Satisfaction Factor (7.452%), Types and Compensation Factor (6.683%), and Guarantees and Prices Factor (5.794%). These five factors obtained a cumulative variance of 62.051%, indicating that they influence online shopping decisions through Shopee among University of North Sumatra students by 62.051%.

This paper will employ a novel approach that builds upon the lasso method, utilizing it in two stages.   The first stage applies to the principal components to select the important principal component and exclude the unimportant ones. This technique is effective in identifying significant principal components while attempting to eliminate bias in selecting these components over others. Additionally, it removes the ranking in determining the principal components compared to classical methods.  Moreover, the second stage involves determining the effective importance within each component by zeroing out the scores loading values within each component. To compare the performance of the proposed method in principal component analysis, a simulation approach can be used. Subsequently, the performance of the proposed method is tested using real data.

In the agricultural sector, the automatic identification of chili pepper varieties is crucial for improving production efficiency and quality. This study developed a chili pepper variety detection system based on characteristics using the Principal Component Analysis (PCA) method. The PCA method was used to reduce the dimensionality of chili pepper image data, thereby facilitating the classification process while retaining the key features necessary for chili pepper variety identification. The recognition system for chili pepper identification involves inputting chili pepper image data into a computer. The computer then interprets and identifies the chili pepper variety, and the test data utilizes a dataset of chili pepper images from various varieties. The research results indicate that the proposed system achieves a high level of accuracy in detecting and classifying chili pepper varieties. Consequently, this system can assist farmers and agricultural industry stakeholders in the chili pepper sorting and selection process, thereby improving operational efficiency and the quality of the harvest.