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All Journal Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Jurnal Pilar Nusa Mandiri Jurnal Teknik Informatika UNIKA Santo Thomas Compiler Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Jurnal Komputer Antartika Informatics and Software Engineering (ISE)
Azis, Mochammad Abdul
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management

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Journal : Compiler

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Classification and Evaluation of Sleep Disorders Using Random Forest Algorithm in Health and Lifestyle Dataset Widyastuty, Wiwiek; Azis, Mochammad Abdul
Compiler Vol 13, No 1 (2024): May
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/compiler.v13i1.2184

Abstract

Sleep is a fundamental aspect of human life, accounting for approximately one-third of our existence and playing a crucial role in the restoration of physical health and overall quality of life. However, poor sleep quality can interfere with these critical restorative processes, leading to disorders such as apnoea and insomnia. These conditions not only impair daily performance but also have long-term health consequences. Furthermore, the challenges imposed by modern lifestyles have increased the prevalence of these sleep disorders, emphasizing the need for effective diagnostic tools. This research aims to harness the capabilities of Machine Learning (ML), specifically the Random Forest algorithm, to detect and analyse patterns indicative of sleep disorders in collected data sets. Random Forest is particularly suited for this task due to its ability to manage complex data sets by building multiple decision trees, thus creating a comprehensive and robust model for classifying sleep disorders. The findings of the study are promising, showing that the Random Forest algorithm can achieve a high level of accuracy in sleep disorder detection. The model demonstrated a test accuracy rate of 97.33%, with a precision of 96%, and a recall rate of 100%. Additionally, it achieved an F1-Score of 98% and a Kappa Score of 0.945, validating the reliability of this algorithm in producing precise classifications. This research offers significant insights into the patterns of sleep disorders and contributes to the development of targeted interventions aimed at improving sleep quality. Ultimately, this could significantly enhance the quality of life for individuals suffering from sleep disorders.
Combatting Heart Diseases: Advanced Predictions Using Optimized DNN Architecture Azis, Mochammad Abdul; Sumarna, Sumarna
Compiler Vol 12, No 2 (2023): November
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/compiler.v12i2.1915

Abstract

Heart disease has become a global health issue and is recorded as one of the primary causes of death in many countries. In this modern era, with rapid technological advancements and shifting lifestyles, numerous factors contribute to the increasing prevalence of heart diseases. These range from dietary habits, lack of physical activity, stress, to genetic factors. Given the complexity of this ailment, information technology plays a crucial role in providing innovative solutions. One of them is predicting the risk of heart disease, enabling more targeted early prevention and treatment interventions.Correct data analysis is pivotal in making predictions. However, a common challenge often encountered is the imbalance in data classes, which can result in a predictive model being biased. This is certainly detrimental, especially in the context of predicting strokes, where prediction accuracy can mean the difference between life and death.In this research, our focus was on developing a Deep Neural Network (DNN) Architecture model. This model aims to offer more accurate predictions by considering data complexities. By optimizing several key parameters, such as the type of optimizer, learning rate, and the number of epochs, we strived to achieve the model's best performance. Specifically, we selected Adagrad as the optimizer, set the learning rate at 0.01, and employed a total of 100 epochs in its training.The results obtained from this research are quite promising. The optimized DNN model displayed an accuracy score of 0.92, precision of 0.92, recall of 0.95, and an f-measure of 0.93. This indicates that with the right approach and meticulous optimization, technology can be a highly valuable tool in combatting heart diseases.
Co-Authors Agus Junaidi ahmad yani Akbar, Rakasiwi Eko Yulianto Fauzi Ahmad Muda Fazriansyah, Agung Ginabila Hadianto, Nur Hartati, Mega Ratu Imam Nawawi, Imam Miharja, Jaja Muhammad fauzan Pudji Widodo Ramadan, Angga Riski Rifai, Saifulloh Sumarna Sumarna Widyastuty, Wiwiek