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All Journal Technomedia Journal Journal Sensi: Strategic of Education in Information System
iman, Fajar nur
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Languange, Linguistic, Communication & Media

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Alat Pengukur Ketinggian Air Berbasis Microcontroller Sebagai Peringatan Banjir Dengan Notification Handayani, Indri; Setiadi, Ade; iman, Fajar nur
Technomedia Journal Vol 4 No 1 Agustus (2019): Technomedia Journal
Publisher : Pandawan Incorporation, Alphabet Incubator Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1032.93 KB) | DOI: 10.33050/tmj.v4i1.896

Abstract

Ultrasonic sensor is a device that is often used in industrial companies. Ultrasonic sensor has a function as a detector and distance measuring. Ultrasonic sensor work is the sensor will emit ultrasonic waves on the surface and then catch it back, so the results obtained in accordance with the original state. Some car companies use this ultrasonic sensor on the car as a means to measure the distance so as not to collide with other vehicles. This sensor is very suitable for designing a water level gauge, because in addition to effective sensors also provide accurate results. From several experiments conducted, it was generated that ultrasonic sensors were quite effective in measuring at a distance of 1 cm - 300 cm. from the design of ultrasonic sensor-based water meter this results obtained will be displayed on the 16x2cm lcd screen as a giver of information and some led lights to provide restrictions of the height and buzzer as a warning sign that the maximum water level. Keywords: ultrasonic sensor, water level meter, arduino
Data Analysis of Thesis Guidance Students Using Random Forest, Gradient Boosting, and Naïve Bayes Algorithms (Case Study: University of Raharja) Iman, Fajar Nur; Tukiyat, Tukiyat; Taryo, Taswanda
Journal Sensi: Strategic of Education in Information System Vol 11 No 1 (2025): Journal SENSI
Publisher : UNIVERSITAS RAHARJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/sensi.v11i1.3771

Abstract

Thesis guidance is a crucial stage in higher education, as the thesis is one of the primary requirements for earning a bachelor's degree. One of the main challenges in thesis guidance is managing consultation data between students and their supervisors. The application of technology and machine learning approaches offers significant potential in addressing this issue. Machine learning algorithms such as Random Forest, Gradient Boosting, and Naïve Bayes can be utilized to automatically analyze thesis guidance data, thereby assisting supervisors in efficiently monitoring student progress. This research aims not only to provide a solution for supervisors in monitoring the progress of their students but also to offer a valuable tool for university management to evaluate the performance of supervisors in providing guidance. Based on the results and comparisons conducted, it can be concluded that the Gradient Boosting method achieves the highest accuracy, reaching 100%, compared to Random Forest with an accuracy of 98.8% and Naïve Bayes with an accuracy of 97.4%. From the testing data results using the Naïve Bayes, Gradient Boosting, and Random Forest algorithms, different accuracy levels were observed. However, the prediction outcomes were consistent: out of 235 testing data, 25 data points were classified as "Not Eligible," and 210 data points were classified as "Eligible" based on the established criteria.
Co-Authors Indri Handayani, Indri Setiadi, Ade Taswanda Taryo Tukiyat Tukiyat, Tukiyat