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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Pengabdian kepada Masyarakat Nusantara Prosiding SeNTIK STI&K
Himawan, Salamet Nur
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Social Sciences

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Hyperparameter Tuning on Graph Neural Network for the Classification of SARS-CoV-2 Inhibitors Himawan, Salamet Nur; Sohiburoyyan, Robieth; Iryanto, Iryanto
Journal of Applied Informatics and Computing Vol. 7 No. 2 (2023): December 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i2.6735

Abstract

COVID-19 is caused by the SARS-CoV-2 virus, which results in a range of symptoms, from mild to severe, and can lead to fatalities. As of October 2023, WHO has recorded 771 cases of COVID-19 globally. Various efforts have been made to control the spread of the virus, including vaccination, isolation measures, and intensive medical care. The emergence of new SARS-CoV-2 variants has led to the ongoing evolution of virus transmission. Continued research is essential to understand this virus and develop strategies to address the pandemic. Inhibitors of SARS-CoV-2 play a crucial role in the vaccine development process. Inhibitors can impede the virus's development, helping reduce disease severity and control the pandemic. The classification of inhibitors is expected to serve as a foundation for selecting compounds that can be developed into vaccines. This research develops a Graph Neural Network model for inhibitor classification and uses the random search method for hyperparameter tuning. Graph Neural Networks are chosen due to their excellent performance in modelling graph data. This study demonstrates the success of hyperparameter tuning in improving the performance of the Graph Neural Network for accurate classification of SARS-CoV-2 inhibitors.
Comparative Analysis of Feature Extraction Techniques for Facial Paralysis Classification Himawan, Salamet Nur; Suheryadi, Adi; Cahyanto, Kurnia Adi; Sitanggang, Filemon; Pamungkas, Kiki Adi
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 2 (2025): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i2.645

Abstract

Facial paralysis significantly affects a person's ability to communicate and perform essential functions. Facial paralysis classification plays a vital role in the diagnosis and monitoring of facial disorders. Traditional diagnostic methods often rely on subjective evaluations, leading to inconsistent outcomes. The aim of this study is to evaluate and compare various feature extraction techniques to enhance the accuracy and efficiency of facial paralysis classification. The primary contribution of this research lies in its comprehensive analysis of texture-based (Local Binary Patterns, Histogram of Oriented Gradients, Gabor filters) and geometric feature extraction methods, providing insights into their respective strengths and limitations for facial paralysis detection. This study utilizes the YouTube Facial Palsy (YFP) dataset, comprising annotated images of paralyzed and non-paralyzed faces. Preprocessing included resizing images to 128x128 pixels to standardize inputs. Feature extraction methods were applied to the dataset, and the extracted features were classified using machine learning algorithms, including Support Vector Machines (SVM), Random Forest (RF), and k-Nearest Neighbors (KNN). Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The best-performing method achieved an accuracy of 85% using HOG features combined with KNN. The findings highlight that texture-based methods, particularly HOG, excel in capturing subtle asymmetries, while geometric features offer computational efficiency and interpretability with fewer extracted features. This study underscores the importance of selecting suitable feature extraction methods based on task requirements, and emphasizes the potential of hybrid approaches to leverage the strengths of different methods. Future research should explore advanced geometric descriptors and integrate hybrid models to enhance clinical applicability
DETEKSI KELUMPUHAN WAJAH MENGGUNAKAN YOLO DENGAN IMPLEMENTASI WEB Pamungkas, Kiki Adi; Himawan, Salamet Nur; Suheryadi, Adi; Cahyanto, Kurnia Adi; Sitanggang, Filemon
Prosiding Seminar SeNTIK Vol. 8 No. 1 (2024): Prosiding SeNTIK 2024
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat

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Abstract

Kelumpuhan wajah merupakan ketidakmampuan seseorang untuk menggerakkan otot-otot pada wajah.. Deteksi awal kelumpuhan wajah sangat penting untuk memberikan intervensi medis yang cepat dan mencegah perburukan kondisi pasien. Dalam penelitian ini, kami mengembangkan sebuah sistem berbasis deep learning yang menggunakan model YOLO (You Only Look Once) untuk mendeteksi paralisis secara otomatis. Sistem ini diintegrasikan dengan sebuah aplikasi web, yang memungkinkan pengguna untuk mengunggah citra untuk dilakukan deteksi secara real-time. Pengujian terhadap sistem ini menunjukkan akurasi yang tinggi dalam mendeteksi kelumpuhan wajah pada citra. Hasil penelitian menunjukkan bahwa model YOLO dapat membedakan wajah yang lumpuh dengan baik, terlihat pada precision dan recall yang mencapai nilai 0.91 dan 0.97
Optimalisasi Perpustakaan Desa Dalam Meningkatkan Literasi Masyarakat di Rambatan Kulon Rendi, Rendi; Iryanto, Iryanto; Himawan, Salamet Nur
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 6 No. 1.1 (2024): Jurnal Pengabdian kepada Masyarakat Nusantara (JPkMN) SPECIAL ISSUE
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jpkmn.v6i1.1.5128

Abstract

Kualitas sumber daya manusia dapat dilihat dari tingkat literasi masyarakat. Pemanfaatan perpustakaan desa memiliki peran penting dalam peningkatan literasi masyarakat. Pada Desa Rambataan Kulon terdapat perpustakaan yang dapat dimanfaatkan dan di gunakan sebagai pojok baca bagi masyarakat Desa. Namun sayangnya pemanfaatan perpustakaan belum cukup baik dan kegiatan-kegiatan pada perpustakaan belum terlalu banyak untuk meningkatkan literasi. Dalam mengoptimalkan perpustakaan dilakukan kegiatan “KACANG” yang merupakan singkatan dari KenaAli, CintAi liNGkungan dengan berlokasi di Perpustakaan Buku HarapanKu di Balai Desa Rambatan Kulon. Kegiatan yang dilakukan terdiri dari Senam Bersama, Story Telling dan Belajar sambil Bermain. Kegiatan pengabdian masyarakat yang dilakukan ini diharapkan memiliki potensi untuk meningkatkan minat baca dan minat berkunjung ke perpustakaan Buku HarapanKu Desa Rambatan Kulon guna meningkatkan budaya sadar literasi bagi masyarakat sekitar Desa
Klasifikasi Stroke Pada Citra Ct Otak Menggunakan Transfer Learning Efficientnet-B0 Rendi, Rendi; Himawan, Salamet Nur; Sohiburoyyan, Robieth; Wati, Vera
Prosiding Seminar SeNTIK Vol. 9 No. 1 (2025): Prosiding SeNTIK 2025
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat

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Abstract

Penelitian ini mengusulkan model klasifikasi stroke berbasis citra CT otak menggunakan metode transfer learning dengan arsitektur EfficientNet-B0. Hasil menunjukkan akurasi 96,83%, menandakan performa klasifikasi yang sangat baik dan berpotensi membantu radiolog dalam diagnosis cepat dan akurat.
Co-Authors Iryanto Iryanto Kurnia Adi Cahyanto Pamungkas, Kiki Adi Rendi Rendi, Rendi Sitanggang, Filemon Sohiburoyyan, Robieth Suheryadi, Adi Wati, Vera