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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Jurnal Teknologi Informasi dan Ilmu Komputer JISKa (Jurnal Informatika Sunan Kalijaga) Indonesian Journal of Electrical Engineering and Computer Science Journal of Internet and Software Engineering Jurnal Informatika: Jurnal Pengembangan IT
Alfian, Ganjar
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Computer Science & IT Electrical & Electronics Engineering Engineering Library & Information Science

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Journal : Bulletin of Electrical Engineering and Informatics

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Application of the outlier detection method for web-based blood glucose level monitoring system Nurhaliza, Rachma Aurya; Octava, Muhammad Qois Huzyan; Hilmy, Farhan Mufti; Farooq, Umar; Alfian, Ganjar
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7717

Abstract

Recent advancements in biosensors have empowered individuals with diabetes to autonomously monitor their blood glucose levels through continuous glucose monitoring (CGM) sensors. Nevertheless, the data collected from these sensors may occasionally include outliers due to the inherent imperfections of the sensor devices. Consequently, the identification of these outliers is critical to determine whether blood glucose levels deviate significantly from the norm, necessitating further action. This study employs an outlier detection approach based on the 3-sigma method and the interquartile range (IQR), along with the application of the Winsorizing technique to correct the identified outliers. Additionally, a web-based system for visualizing blood glucose levels is developed, utilizing both outlier detection methods. In order to assess the system's performance, two types of testing are conducted: black box testing and load testing. The results of black box testing indicate that all test scenarios operate as anticipated. As for the load testing response times, it is observed that the 3-sigma visualization page loads an average of 606.75 milliseconds faster compared to the IQR visualization page. This study's outcomes are expected to enhance data quality, enhance the precision of analyses, and facilitate more informed decision-making by identifying and addressing extreme data points.
Forecasting graduate student enrollment in university using regression analysis Dwiansyah, Anggraini; Fahrurrozi, Imam; Fakhrurrifqi, Muhammad; Farooq, Umar; Alfian, Ganjar
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.9713

Abstract

The government ensures educational quality in universities through a quality assurance (QA) system implemented via accreditation, which evaluates both study programs and institutions. A key concern in accreditation is the decline in new student enrollment, making accurate predictions of enrollment numbers essential for quality assessment. This study proposes a linear regression (LR) model to forecast future university student enrollments based on enrollment figures from the previous year as input feature. Using a dataset from one of Indonesia’s leading university spanning 2013 to 2023, the experimental results demonstrate that the LR model outperforms other regression techniques, including multi-layer perceptron (MLP), K-nearest neighbors (KNN), decision tree (DT), and random forest (RF). The LR model achieves R² values between 0.87 and 0.95, reflecting a strong linear relationship between current and future student numbers. It also delivers high accuracy, with root mean square error (RMSE) values ranging from 11.72 to 41.21 per year. The trained LR model has been integrated into a web-based system, offering data visualization and enrollment predictions to support university management in monitoring quality, addressing enrollment challenges, and facilitating informed decision-making.
Co-Authors Benes, Filip Dharmawan, Edeline Felicia Divi Galih Prasetyo Putri Dwiansyah, Anggraini Fahrurrozi, Imam Farooq, Umar Fauzan, Reyhan Muhammad Fauziyyah, Anni Karimatul Fitriyani, Norma Latif Hanafi, Hanan Hardiyanti, Margareta Hilmy, Farhan Mufti Imanuel, Dennis Alfa Irfan Saputra Kusnady, Rasyid Muhammad Fakhrurrifqi Muhammad Syafrudin Muhtadlor, Rosyihan Nurhaliza, Rachma Aurya Octava, Muhammad Qois Huzyan Oktiawati, Unan Yusmaniar Pinandhita, Pudyasta Satria Pratomo, Dinar Nugroho Rosyid, Nur Rohman Saputra, Yuris Mulya Setiawan, Lalu Hendra Permana Stasa, Pavel Taufiq, Umar Teja Nursasongka, Raden Mas Tri Widodo Yusuf, Muhammad Abyan Farras Zhafarizza, Ghifari Nafhan Muhammad Zuhri, Arief