Decision Support System Method for Selecting Formal Education Levels in a violation student based Microcontroller and IOT - Sumantri; Ratri Enggar Pawening; Moh. Jasri; Ilmirrizki Imaduddin; Bambang; Tijaniyah; M. Fadhilur Rahman (Jurnal JEETech, 2026)
This study develops a decision support system to determine appropriate formal education levels based on student violations using the Multi Attribute Decision Making (MADM) method with the Simple Additive Weighting (SAW) technique. The system integrates a microcontroller and Internet of Things (IoT) technology for real-time data collection and monitoring. Several criteria, including violation frequency, type, behavior, and academic performance, are evaluated and weighted to produce ranking results. The result of the SAW method calculation shows that the third alternative (Senior High School or SLTA) obtained a score of 17.15. This value is the highest compared to the other alternatives. This means that the SLTA level becomes the primary category where smoking violations by students are strictly prohibited. This is because students at the SLTA level tend to exhibit more rebellious behavior and higher ego, causing rules to be sometimes ignored. The microcontroller functions as the control system for cigarette smoke. The sensor used for detection is the MQ-2 gas sensor, which is also highly sensitive to air quality, including CO₂, ammonia, benzene, and cigarette smoke. The Internet of Things (IoT) is used for remote monitoring of cigarette smoke detection in a room. This system utilizes notifications from the Telegram application connected to the principal’s mobile phone.