Publication Search

This study aims to analyze the factors that influence students' interest in continuing their education to college using a machine learning approach. Data was collected through an online questionnaire completed by 727 students between July 27 and August 22, 2025, covering 23 variables consisting of respondent identity (gender, grade level, major) as well as internal and external factors such as parental support, learning motivation, and preferred type of college. The data preparation stage was carried out through column cleaning, deletion of empty data, encoding of categorical variables, and division of the dataset into 80% training data and 20% test data. The Naive Bayes algorithm of the CategoricalNB type was used because it was suitable for the categorical nature of the data. The evaluation results showed that the model was able to predict student interest with 96% accuracy. For the class of students interested in continuing their studies, the precision, recall, and F1-score values were above 0.95, while the performance in the class of students who were not interested was slightly lower due to the smaller amount of data. These findings show that Naive Bayes is proven to be effective and reliable in classifying students' interest in continuing their studies and can be the basis for decision-making in designing more targeted educational strategies.

Monitoring realizes efforts to improve the quality of health services. To obtain information on patient condition data during treatment. The monitoring process is done manually. So that it has an impact on the service and condition of the patient during treatment. The design of monitoring the patient's body temperature and heart during treatment with IoT can be controlled through the NodeMCU sensor ESP8266, MLX9014, MAX30100 sensor and Arduino IDE software program. Furthermore, it can detect the patient's temperature and the patient's heart rate during treatment. And processed by the NodeMCU ESP8266, the data from the two sensors is displayed on the SSD1306 OLED LCD and also to the smartphone of the medical officer on duty via Blynk. In order for the tool to work properly and optimally, it is necessary to adjust the pin placement so that it can work optimally. The problem of internet connection interference causes delays, resulting in a mismatch between the measurement of the test equipment and the standard tool. Data values are taken with an accuracy of 70%-93% for the MAX30100 sensor, for the temperature sensor it is close to optimal with a value that is read on a standard tool with an accuracy of 97%-99%.

BLDC motor applications used in various forms in instrumentation, robotics, household, and transportation. One application of transportation equipment used as a propeller of electric bicycle vehicles. The value of the bicycle vehicle adjusted to the speed set, the amount that has determined. The purpose used in this study is to improve the efficiency of the regulation of BLDC motors on electric bicycles. Indicators of increasing performance are increasingly steady-state errors, and transient response required. The method used in this research is to do mathematical modeling in the form of transfer and optimization function equations. The model used is the model with the structure of the transfer function, while the optimization method used in this study is the Ziegler-Nichols method and firefly algorithm. The firefly algorithm is used in this study to obtain optimal Kp, Ki, and Kd values. The results showed that the firefly algorithm achieved better performance compared to the Ziegler-Nichols method.