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All Journal Jurnal Buana Informatika Telematika : Jurnal Informatika dan Teknologi Informasi Wikrama Parahita : Jurnal Pengabdian Masyarakat Indonesian Journal of Artificial Intelligence and Data Mining Applied Technology and Computing Science Journal International Journal of Informatics and Computation Prosiding Seminar Nasional Multidisiplin Ilmu Respati Jurnal Computer Science and Information Technology (CoSciTech) Journal of Computer, Electronic, and Telecommunication (COMPLETE) Jurnal Ilmu Komputer dan Teknologi (IKOMTI)
Wijaya, Nurhadi
Universitas Respati Yogyakarta
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Social Sciences Other

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Journal : Applied Technology and Computing Science Journal

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Performance Comparison of Convolutional Neural Network with Traditional Machine Learning Methods in Adult Autism Detection Wijaya, Nurhadi; Muliani, Sri Hasta; Nurain, Maisarah
TEKNOLOGI DITERAPKAN DAN JURNAL SAINS KOMPUTER Vol 7 No 1 (2024): June
Publisher : Universitas Nahdlatul Ulama Surabaya

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Diagnosing Autism Spectrum Disorder (ASD) in adults is a challenging task, requiring precise and efficient early detection methods. However, there is limited research in this area. Hence, this study seeks to address this gap by evaluating the effectiveness of Convolutional Neural Networks (CNNs) compared to traditional machine learning techniques for detecting autism in adults. The study introduces a CNN-based model and conducts a performance comparison with conventional algorithms such as Support Vector Machine (SVM), Decision Tree, K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), and Gradient Boosting (GBoost). The objectives are to evaluate the efficacy of CNNs in adult autism detection, identify algorithm strengths and weaknesses, and explore healthcare implications. The research utilizes the Autism Screening on Adults dataset, with 704 records and 21 features, employing preprocessing steps to optimize data quality. The proposed CNN model encompasses convolutional layers, max-pooling, dropout, and dense layers, while baseline algorithms serve as benchmarks. Evaluation metrics include the Confusion Matrix and Classification Report. The CNN model achieved remarkable accuracy (99%) and precision in adult autism detection, outperforming traditional algorithms. SVM emerged as the closest competitor but fell short. This study underscores CNN's potential for precise autism detection in adults, with implications for early intervention and telehealth applications. The research highlights CNNs' effectiveness and superiority over traditional machine learning algorithms, suggesting their promise for accurate diagnosis. Future research opportunities include expanding datasets, optimizing model parameters, and addressing ethical considerations for practical healthcare implementation
Optimizing Breast Cancer Detection: A Comparative Study of SVM and Naive Bayes Performance Diqi, Mohammad; Hiswati, Marselina Endah; Hamzah, Hamzah; Ordiyasa, I Wayan; Mulyani, Sri Hasta; Wijaya, Nurhadi; Wanda, Putra
TEKNOLOGI DITERAPKAN DAN JURNAL SAINS KOMPUTER Vol 7 No 1 (2024): June
Publisher : Universitas Nahdlatul Ulama Surabaya

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This study evaluates the performance of Support Vector Machine (SVM) and Naive Bayes algorithms in classifying breast cancer using the Breast Cancer Wisconsin dataset. Both models exhibited high accuracy, with Naive Bayes achieving a slightly higher overall accuracy of 97% and demonstrating a balanced performance between precision and recall. The SVM model showed strong proficiency in detecting positive cases, with an overall accuracy of 95%, though it faced minor challenges in recall for negative cases. These results highlight the effectiveness of both algorithms in breast cancer detection, emphasizing the significance of model selection based on specific diagnostic requirements. Although there are limitations, such as the small sample size and assumptions made in the model, the findings provide useful insights into the use of machine learning in medical diagnostics. This supports the idea that these models have the potential to enhance early detection and treatment results. Future research should focus on utilizing larger, more diverse datasets, exploring advanced feature processing techniques, and integrating additional algorithms to enhance further the accuracy and reliability of breast cancer detection systems.
Co-Authors Agus Qomaruddin Munir Ahmad Wakhid Anang Faktchur Rachman Evrita Lusiana Utari, Evrita Lusiana Hamzah Hamzah Hamzah I Wayan Ordiyasa Ikhwan Mustiadi Imam Mahfudz I'tisyam Irawadi Buyung, Irawadi Joko Aryanto kasmawaru kasmawaru Listiawan, Indra Listyalina, Latifah Marselina Endah Hiswati Martin, Benedicto Reynaka Filio Mohammad Diqi Muliani, Sri Hasta Nurain, Maisarah Ordiyasa, I Wayan Putra Wanda, Putra Rike Pradila Sri Hasta Mulyani Trinidya, Fike