Pringgo Widyo Laksono

Accessible public transportation is the foundation of inclusive mobility, especially for people with disabilities. Unfortunately, executive train toilets in Indonesia have not fully met the physical and functional accessibility standards. Therefore, toilets that are easily accessible to all users are needed, ones that are not only functionally adequate, but also preserve the local cultural values. This study aims to design an accessible executive train toilet by combining universal design principles with local cultural values for all passengers. This study uses qualitative-quantitative methods and participatory design elements based on the universal design and local culture. The study was conducted in four main stages: collecting data on identifying the needs of the user through an online questionnaire with 84 respondents and a group discussion with seven stakeholders; studying the local culture using ethnographic interviews with 42 Javanese residents and in-depth interviews with cultural experts; quantitative and qualitative data analysis; and the design process, which includes deciding the size of the toilet components, visualization, and identifying alternative designs. The design evaluation stage is based on weighted scoring. The study resulted in a universally accessible executive train toilet design based on the local culture by increasing the width of the door, expanding the size of the toilet room, automating facilities, Braille signs, ergonomic design, emergency calls, and the local Javanese philosophy (color, ornaments, shape, materials, availability of water, bar soap). This study has practical implications for policymakers regarding the transportation policies and infrastructure development toward more inclusive railway services in developing countries.

This study implements machine learning using the Naive Bayes algorithm to create a text classification in an engineering professional program information system. The methods used include text data collection, preprocessing, feature extraction, Naive Bayes model training, and evaluation using data testing. This study made a classification model to predict text categories with a test accuracy rate of 0.975 and a training accuracy of 0.967. This research contributes to the development of text classification in information systems and can be used as a basis for further study.