Application of Simple Additive Weighting Method in Web-Based Student Learning Interest Detection Using Digital Questionnaires - Kukuh Daruningsih; Widi Hastomo (Journal of Information Systems and Technology Research, 2026)
Learning interest plays a vital role in shaping students' motivation and academic achievement, particularly at the junior high school level, where students are required to determine their educational pathways. However, the major selection process in schools is often based on subjective judgments rather than systematic evaluation of students' interests, which may lead to inappropriate recommendations. This study develops a web-based decision support system to identify student learning interests and support major selection using a structured approach. Data were collected from 18 Grade IX students through a digital questionnaire designed based on predefined learning interest criteria. The Simple Additive Weighting (SAW) method is applied to calculate preference scores and generate major recommendations. The research methodology includes requirements analysis, system design, implementation, and testing. System functionality was evaluated using Black Box Testing to ensure that all features operate correctly. The results show that the system successfully processes questionnaire responses and produces consistent recommendations based on SAW calculations. Black Box Testing confirms that all functions operate as expected without errors. The proposed system demonstrates that integrating digital questionnaires with SAW can provide a structured, transparent, and efficient decision support tool for junior high school major selection. Although this system is currently limited to the junior high school level, it has the potential to be further developed for broader educational levels