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All Journal Semesta Teknika Journal of Electrical Technology UMY JOIV : International Journal on Informatics Visualization Emerging Information Science and Technology
Arrayyan, Ahmad Zaki
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Library & Information Science

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Naive Bayes for Diabetes Prediction: Developing a Classification Model for Risk Identification in Specific Populations Arrayyan, Ahmad Zaki; Setiawan, Hendra; Putra, Karisma Trinanda
Semesta Teknika Vol 27, No 1 (2024): MEI
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/st.v27i1.21008

Abstract

Depending on persuasive statistics, the increasing prevalence of diabetes worldwide is a huge challenge for individuals, families, and nations. According to International Diabetes Federation (IDF) projections, the number of adults with diabetes is expected to rise by an astounding 46% by 2045, to reach 783 million, or one in eight. In response to this growing concern, this research explores the implementation of the Naive Bayes algorithm for predicting diabetes, employing comprehensive data cleansing and randomization techniques. A systematic evaluation of the model's performance is conducted using several training and testing split ratios (65:35, 75:25, 85:15). The outcome showed that the model performed best at the 65:35 split ratio, with accuracy reaching its maximum of 88.16%, precision 0.883, recall 0.881, and f1-score 0.882.
Optimizing the Performance of AI Model for Non-Invasive Continuous Glucose Monitoring: Hyperparameter Tuning and Random Oversampling Approach Putra, Karisma; Prasetyo Kusumo, Mahendro; Prayitno, Prayitno; Wicaksana, Darma; Arrayyan, Ahmad Zaki; Pratama, Sakca Garda; Al-Kamel, Mujib Alrahman; Chen, Hsing-Chung
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.2047

Abstract

Diabetes Mellitus (DM) as a non-communicable disease (NCD) continues to increase every year. Continuous glucose monitoring (CGM) is essential for effective DM management. However, existing disposable glucose monitoring methods still rely on invasive techniques, cause pain, and lack continuous monitoring capabilities. On the other hand, non-invasive techniques are not feasible for CGM due to the biometric data's complexity and the classification system's inadequate performance. This study aims to develop a non-invasive technology to improve the performance of a non-invasive blood glucose classification system using Artificial Intelligence (AI), specifically Convolutional Neural Network (CNN) and an oversampling technique. The oversampling technique could improve data quantity by balancing the amount of data for each class. This study recruited twenty-three participants in the age range of 20 to 22 years comprising seven females and fifteen males. During data recording sessions, blood glucose levels were simultaneously assessed using a gold-standard glucometer and a non-invasive CGM prototype. The proposed CNN model successfully improved the classification accuracy of non-invasive blood glucose monitoring significantly. With the implementation of oversampling for augmenting the data, the accuracy of the proposed model increased to more than 88%. This study concludes that non-invasive approaches combined with AI technology have the potential to provide a convenient and pain-free alternative to traditional monitoring methods, significantly improving diabetes management and enhancing the overall quality of life for those affected by this condition. These findings could revolutionize the field of diabetes management, offering a more comfortable and accurate monitoring solution that could potentially transform the lives of millions of diabetes patients.
Early Detection of Diabetes Mellitus in Women via Machine Learning Arrayyan, Ahmad Zaki; Adinandra, Sisdarmanto
Journal of Electrical Technology UMY Vol. 8 No. 2 (2024): December
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jet.v8i2.24287

Abstract

Diabetes Mellitus (DM) is a major global health concern, responsible for 6.7 million deaths in 2021, equivalent to one death every five seconds. In Indonesia, it was the third leading cause of death in 2019, with a mortality rate of approximately 57.42 per 100,000 people. This study focuses on developing a diabetes prediction model using machine learning, aiming for an accuracy of at least 85%, and incorporates a chatbot-based system to identify potential diabetes in women. The research utilizes primary data, including glucose levels, blood pressure, body mass index, and age, as well as secondary data, such as pregnancy-related metrics, from the UCI Pima Indians Diabetes Database, which contains 768 records with eight attributes.  The study evaluates the performance of three machine learning algorithms: Decision Tree, Logistic Regression, and Random Forest, using metrics such as accuracy, precision, recall, and F1-score. Among these models, the Decision Tree demonstrates excellent performance for Class 0, with precision, recall, and F1-score all at 0.97. However, its performance for Class 1, while decent, leaves room for improvement, achieving a precision of 0.80 and a recall of 0.84, resulting in an F1-score of 0.82. Logistic Regression also performs well for Class 0, with a precision of 0.95 and a recall of 0.99, yielding an F1-score of 0.97. Yet, it struggles with Class 1, where its precision is high at 0.93, but its recall drops significantly to 0.68, producing an F1-score of 0.79. Lastly, Random Forest emerges as the best-performing model overall, achieving an accuracy of 0.96. It excels for Class 0, with a precision of 0.96 and a recall of 0.99, leading to an F1-score of 0.97. For Class 1, it maintains high precision (0.93) but exhibits moderate recall (0.74), resulting in an F1-score of 0.82.
Design a Two-Axis Sensorless Solar Tracker Based on Real Time Clock Using MicroPython Putra, Karisma Trinanda; Arrayyan, Ahmad Zaki; Syahputra, Ramadoni Zaki; Pamungkas, Yoga Agil; Rahaman, Mosiur
Emerging Information Science and Technology Vol. 4 No. 1 (2023): May
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/eist.v4i1.18697

Abstract

Since the majority of the electrical energy required to meet consumer demands is still produced by the typical power plant using fossil fuels, the world's electricity supply will eventually approach a critical point due to the steady depletion of fossil fuels. One solution to replacing energy derived from fossil fuels is solar energy. However, the results of energy generation are significantly influenced by the angle of the solar panels to the direction of sunlight. Therefore, in order to optimize the production of electrical energy, a microcontroller system that can track sunlight is developed using MicroPython. This research calculated the azimuth angle and elevation angle to create a solar tracker that can follow the sun's position based on time. The results of this research reveal that solar trackers at 21.30 volts and rooftop solar panels at 20.44 volts when both produce open circuit voltage. With an solar tracker, solar panels may operate up to at a 20% efficiency in sunny conditions, compared to an average efficiency of 18% for rooftop solar panels.
Co-Authors Al-Kamel, Mujib Alrahman Chen, Hsing-Chung Hendra Setiawan Karisma Trinanda Putra, Karisma Trinanda Pamungkas, Yoga Agil Prasetyo Kusumo, Mahendro Pratama, Sakca Garda Prayitno Prayitno Putra, Karisma Rahaman, Mosiur Sisdarmanto Adinandra, Sisdarmanto Syahputra, Ramadoni Zaki Wicaksana, Darma