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All Journal Bulletin of Electrical Engineering and Informatics Makara Journal of Technology International Journal of Electrical, Computer, and Biomedical Engineering (IJECBE)
Mia Rizkinia
Department Of Electrical Engineering, Faculty Of Engineering, Universitas Indonesia, Depok 16424, Indonesia
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology

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Journal : Bulletin of Electrical Engineering and Informatics

 1 
Real-time stress detection and monitoring system using IoT-based physiological signals Atika Hendryani; Dadang Gunawan; Mia Rizkinia; Rinda Nur Hidayati; Frisa Yugi Hermawan
Bulletin of Electrical Engineering and Informatics Vol 12, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i5.5132

Abstract

Currently, medical experts use psychophysiological questionnaires to evaluate human stress levels during counseling or interviews. Typically, biochemical samples use urine, saliva, and blood samples to identify the effects of stress on the human body. This research explains that stress detection can be done by analyzing psychological signals and the importance of monitoring stress levels. The authors develop research on stress detection based on psychological signals. The system then processes the recorded data; the android application displays the calculation results. The database can also be accessed as a spreadsheet via a web application. The design of real-time stress detection and monitoring using internet of things (IoT) can work well.
Benchmarking machine learning algorithm for stunting risk prediction in Indonesia Novalina, Nadya; Aksar Tarigan, Ibrahim Amyas; Kayla Kameela, Fatimah; Rizkinia, Mia
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8997

Abstract

Stunting is a condition caused by poor nutrition that results in below-average height development, potentially leading to long-term effects such as intellectual disability, low learning abilities, and an increased risk of developing chronic diseases. One effort to reduce stunting is to apply a machine learning algorithm with a data science approach to develop risk prediction models based on factors in stunting. The study used the current cross industry standard process for data mining (CRISP-DM) framework to gain insight and analyzed 1561 records of data collected from the Indonesia family life survey (IFLS) for the prediction models. Two sampling methods, random undersampling, and oversampling synthetic minority oversampling technique (SMOTE), were employed and compared to overcome the data imbalance problem. Four machine learning classifier algorithms were trained and tested to determine the best-performing model. The experiment results showed that the algorithms yielded an average accuracy of more than 75%. Using the undersampling technique, the accuracy obtained by logistic regression, k-nearest neighbor (KNN), support vector classifier (SVC), and decision tree classifier were 95.21%, 78.91%, 92.97%, and 86.26% respectively. Meanwhile, the oversampling technique reached 96.17%, 88.50%, 93.29%, and 95.21%, respectively. Logistic regression emerges as the best classification, with oversampling yielding superior performance.
Development of frequency modulated continuous wave radar antenna to detect palm fruit ripeness Rahmawati, Yosy; Rizkinia, Mia; Zulkifli, Fitri Yuli
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8749

Abstract

Oil palm fruits farmers in Indonesia have determined the ripeness of oil palm fruits in the traditional way, namely using human eye visuals, which have the weakness of inconsistent levels of accuracy and are prone to errors. The development of increasingly sophisticated technology will help oil palm fruits farmers recognize the characteristics of fruit maturity. Advanced technology, such as frequency modulated continuous wave (FMCW) radar, can assist farmers in accurately identifying fruit maturity. To ensure high accuracy and sensitivity, an antenna with low side lobe level (SLL), high gain, and wide bandwidth in the 23-26 GHz range is required. Using CST Microwave Studio 2023, a designed and simulated antenna achieved an SLL of 24 dB, a gain of 15 dBi, and a bandwidth of 2.5 GHz. These results indicate that higher gain enhances energy directionality and overall antenna performance. Additionally, a smaller angular value improves the antenna’s radiation focus, making it more effective for precision sensing in oil palm fruit ripeness detection.
Co-Authors Aksar Tarigan, Ibrahim Amyas Ardisurya Atika Hendryani Dadang Gunawan Dodi Sudiana Fitri Yuli Zulkifli Frisa Yugi Hermawan Isbat, Faiz Khairul Kayla Kameela, Fatimah Novalina, Nadya Okuda, Masahiro Rinda Nur Hidayati Yosy Rahmawati