Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Jurnal PkM MIFTEK Journal of Intelligent Systems Technology and Informatics
Fadilah, Azki
Unknown Affiliation
Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Engineering Languange, Linguistic, Communication & Media Social Sciences Other

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
PENINGKATAN PENYADARAN MASYARAKAT DI DESA PASANGGRAHAN TERKAIT LINGKUNGAN SEHAT Rahayu, Sri; Nurlatifah, Hilda; Maharani, Windy Putri; Prasetiowati, Lulu; Fatimah, Rahma Siti; Subagja, Moh Yahman; Annurulloh, Anugrah Dwi; At-Thoriq, Muhammad; Nashrulloh, Muhammad Hallaj; Mardiana, Dindin; Saumi, Lea Siti; Fadilah, Azki; Torik, Givari Zabal; Khoerudin, Muhammad; Faisal, Moch Rizky; Iskandar, Rio Januar; Hamzah, Doni; Ridwan, Ridwan; Fawaz, Azriel Al; Nurjaman, Muhammad Ilyas; Ramadhan, Asep Shantika
Jurnal PkM MIFTEK Vol 5 No 2 (2024): Jurnal PkM MIFTEK
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/miftek/v.5-2.1959

Abstract

Penelitian ini bertujuan untuk meningkatkan kesadaran masyarakat Desa Pasanggrahan terkait lingkungan sehat melalui serangkaian kegiatan Kuliah Kerja Nyata (KKN). Metode yang digunakan adalah pendekatan kuantitatif, melibatkan 20 tokoh masyarakat formal sebagai responden. Kegiatan yang dilaksanakan meliputi seminar di sekolah dasar, kerja bakti, sosialisasi pemilahan sampah, pemberian tong sampah, dan loka karya. Hasil penelitian menunjukkan peningkatan signifikan dalam pemahaman dan partisipasi masyarakat terhadap lingkungan sehat, terutama dalam kegiatan posyandu dan pengelolaan sampah. Meskipun demikian, tantangan implementasi dan keberlanjutan program masih ada. Program ini berhasil meningkatkan kesadaran masyarakat, namun diperlukan langkah-langkah lanjutan untuk memastikan dampak jangka panjang, termasuk penyediaan fasilitas penunjang, edukasi berkelanjutan, dan penguatan kolaborasi antar pemangku kepentingan. Penelitian ini memberikan kontribusi penting dalam upaya peningkatan kualitas hidup masyarakat desa melalui pengelolaan lingkungan sehat yang berkelanjutan.
Brain Tumor Classification using Convolutional Neural Network with ResNet Architecture Fadilah, Azki; Azkia, Azka
Journal of Intelligent Systems Technology and Informatics Vol 1 No 1 (2025): JISTICS, March 2025
Publisher : Aliansi Peneliti Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64878/jistics.v1i1.8

Abstract

Brain tumors are dangerous, sometimes fatal illnesses that require prompt, accurate diagnosis to enhance patient outcomes. Given the intricacy and diversity of tumor characteristics, manual interpretation of brain MRI data is frequently laborious and prone to human error. This research aims to create an automated system for classifying brain tumors by integrating the Convolutional Neural Network (CNN) algorithm with the ResNet architecture. The suggested approach makes use of 7,023 MRI pictures that have been divided into four categories: non-tumor, pituitary tumor, meningioma, and glioma. Image normalization, grayscale conversion, scaling, and data augmentation methods, including rotation and flipping, were among the preprocessing processes used to enhance model performance. The ResNet design was chosen because it effectively trains deeper networks by utilizing residual connections to prevent vanishing gradient problems. Metrics such as F1-score, accuracy, precision, and recall were used to train and assess the system. According to the testing data, the model performed consistently across all classes and attained an outstanding accuracy of 94.14%. These results validate the promise of deep learning methods, especially CNNs with ResNet enhancements, for classification tasks involving medical images. The system created in this work is a promising tool for assisting clinical decision-making, cutting down on diagnostic time, and improving the accuracy of brain tumor identification and classification.
Co-Authors Annurulloh, Anugrah Dwi At-Thoriq, Muhammad Azkia, Azka Faisal, Moch Rizky Fatimah, Rahma Siti Fawaz, Azriel Al Hamzah, Doni Iskandar, Rio Januar Khoerudin, Muhammad Maharani, Windy Putri Mardiana, Dindin Nashrulloh, Muhammad Hallaj Nurjaman, Muhammad Ilyas Nurlatifah, Hilda Prasetiowati, Lulu Ramadhan, Asep Shantika Ridwan, Ridwan Saumi, Lea Siti SRI RAHAYU Subagja, Moh Yahman Torik, Givari Zabal