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All presidential and legislative candidates want to be the winner. However, they do not know the determinants of voters' reasons for making choices. This study aims to investigate the role of education level, political party brand reputation, religiosity brand personality and e-WOM on voters' decisions with implications for voters' loyalty.

The survey method was used to collect data from 1206 respondents who have the right to vote through offline and online using Google forms shared on social media platforms—data analysis using Structural Equation Modeling using the SmartPLS 4.0 program.

The results showed that Brand Reputation of Politics can encourage brand Religious Personality and e-WOM. Brand Religious Personality is a factor that causes the decision to vote. However, the level of education is not a determining factor for Brand Religious Personality and e-WOM. In addition, brand religious personality, e-WOM and Decision to vote can mediate Brand Reputation of Political loyalty.

The findings from this study can help political parties and candidates develop strategies tailored to voters' needs and increase their chances of winning elections.

The novelty in this study is the development of a model that has never been tested before that uses factor sources from marketing science literacy such as Brand, e-WOM and Loyalty. This study also used moderation variables namely choosing decisions, e-WOM, and religious brand personality. The object of this research was conducted in Indonesia, which is included in the list of developing countries but has never been done in any country. The analysis tool uses the new SEM-PLS version 4.0, so it has a level of novelty and implications that are important for political marketing.

To enhance the fit, comfort and overall satisfaction of lower body attire for online shoppers, this study introduces a reclassification method of the lower body profiles of young females in complex environments, which is used in the framework of remote clothing mass customization.

Frontal and lateral photographs were collected from 170 females prior, marked as size M. Employing a salient object detection algorithm suitable for complex backgrounds, precise segmentation of body profiles was achieved while refining the performance through transfer learning techniques. Subsequently, a skeletal detection algorithm was employed to delineate distinct human regions, from which 21 pivotal dimensional metrics were derived. These metrics underwent clustering procedures, thus establishing a systematic framework for categorizing the lower body shapes of young females. Building upon this foundation, a methodology for the body type combination across different body parts was proposed. This approach incorporated a frequency-based filtering mechanism to regulate the enumeration of body type combinations. The automated identification of body types was executed through a support vector machine (SVM) model, achieving an average accuracy exceeding 95% for each defined type.

Young females prior to being marked as the same lower garment size can be further subdivided based on their lower body types. Participants' torso types were classified into barrel-shaped, hip-convex and fat-accumulation types. Leg profile shapes were categorized into slender-elongated and short-stocky types. The frontal straightness of participants’ legs was classified as X-shaped, I-shaped and O-shaped types, while the leg side straightness was categorized based on the knee hyperextended degree. The number of combinations can be controlled based on the frequency of occurrence of combinations of different body types.

This methodological advancement serves as a robust cornerstone for optimizing clothing sizing and enabling remote clothing mass customization in E-commerce, providing assistance for body type database and clothing size database management as well as strategies for establishing a comprehensive remote customization supply chain and on-demand production model.