Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal Technologia: Jurnal Ilmiah JAREE (Journal on Advanced Research in Electrical Engineering) Journal of Soft Computing Exploration
Muhammad Daffa Abiyyu Rahman
Electrical Engineering, Universitas Lambung Mangkurat, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Facial Movement Recognition Using CNN-BiLSTM in Vowel for Bahasa Indonesia Rahman, Muhammad Daffa Abiyyu; Wicaksono, Alif Aditya; Yuniarno, Eko Mulyanto; Nugroho, Supeno Mardi Susiki
JAREE (Journal on Advanced Research in Electrical Engineering) Vol 8, No 1 (2024): January
Publisher : Department of Electrical Engineering ITS and FORTEI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/jaree.v8i1.372

Abstract

Speaking is a multimodal phenomenon that has both verbal and non-verbal cues. One of the non-verbal cues in speaking is the facial movement of the subject, which can be used to find the letter being spoken by the subject. Previous research has been done to prove that lip movement can translate to vowels for Bahasa Indonesia, but detecting the whole facial movement is yet to be covered. This research aimed to establish a CNN-BiLSTM model that can learn spoken vowels by reading the subject's facial movements. The CNN-BiLSTM model yielded a 98.66% validation accuracy, with over 94% accuracy for all five vowels. The model is also capable of recognizing whether the subject is currently silent or speaking a vowel with 98.07% accuracy.
Benchmarking deep transfer learning for imbalanced skin cancer classification: Integrating focal loss, explainable AI, and web deployment Yazid Aufar; Muhammad Daffa Abiyyu Rahman; M. Fadli Ridhani
Journal of Soft Computing Exploration Vol. 7 No. 1 (2026): March 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v7i1.20

Abstract

Non-melanoma skin cancer (NMSC) classification faces challenges like severe data imbalance and the "black-box" nature of AI, limiting clinical trust. This study benchmarks four pre-trained convolutional models (ConvNeXt-Tiny, EfficientNetV2-S, DenseNet121, MobileNetV3-Large) for the imbalanced multi-class classification of Squamous Cell Carcinoma, Actinic Keratosis, and benign Nevus. Images were preprocessed using morphological hair removal and inpainting. The methodology integrated a 5-fold Stratified Group-KFold cross-validation, Focal Loss to address class imbalance, and Grad-CAM for Explainable AI (XAI) transparency. Results showed ConvNeXt-Tiny achieved the highest and most stable performance with a Balanced Accuracy of 76.98% (± 0.31 standard deviation) and a Macro F1-Score of 0.7513, significantly outperforming the other architectures. Grad-CAM confirmed the model's precise focus on pathological lesion borders. Ultimately, the optimal model was deployed as a real-time Streamlit web application, establishing a robust and practical clinical decision-support system.
IMPLEMENTASI SISTEM MONITORING ENERGI LISTRIK GEDUNG AKADEMIK DALAM MENDUKUNG GREEN CAMPUS Ditza Pasca Irwangsa; Ahmad Zaki; Muhammad Daffa Abiyyu Rahman
Technologia : Jurnal Ilmiah Vol 17 No 3 (2026): Technologia (Juli)
Publisher : Universitas Islam Kalimantan Muhammad Arsyad Al Banjari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31602/jit.v17i3.23755

Abstract

Konsumsi energi listrik pada gedung akademik perguruan tinggi terus meningkat akibat penggunaan sistem pendingin ruangan, pencahayaan, komputer, dan berbagai perangkat elektronik pendukung pembelajaran. Tingginya konsumsi energi tidak hanya meningkatkan biaya operasional, tetapi juga menjadi tantangan dalam penerapan konsep green campus. Penelitian ini bertujuan mengevaluasi implementasi sistem monitoring energi listrik dan strategi konservasi energi pada gedung akademik Universitas Islam Kalimantan Muhammad Arsyad Al Banjari. Metode penelitian menggunakan pendekatan deskriptif kuantitatif melalui observasi lapangan, pengumpulan data konsumsi energi, monitoring penggunaan energi listrik, evaluasi sistem pendingin dan pencahayaan, serta analisis perilaku pengguna energi. Data energi divisualisasikan dan dianalisis menggunakan indikator Intensitas Konsumsi Energi (IKE). Hasil penelitian menunjukkan bahwa sistem pendingin ruangan merupakan penyumbang konsumsi energi terbesar sebesar 48%, diikuti sistem pencahayaan sebesar 27% dan perangkat elektronik sebesar 18%. Implementasi monitoring energi dan strategi konservasi berupa optimalisasi penggunaan AC, penggantian lampu LED, pengendalian operasional peralatan listrik, serta pengurangan beban standby mampu meningkatkan efisiensi energi hingga 27,11%. Penelitian ini menunjukkan bahwa sistem monitoring energi berbasis pengelolaan data dapat mendukung pengambilan keputusan konservasi energi dan implementasi green campus secara berkelanjutan. Kata kunci: monitoring energi, sistem informasi energi, konservasi energi, green campus, gedung akademik.
Co-Authors Ahmad Zaki Ditza Pasca Irwangsa Eko Mulyanto Yuniarno M. Fadli Ridhani Supeno Mardi Susiki Nugroho, Supeno Mardi Wicaksono, Alif Aditya Yazid Aufar