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All Journal Jurnal Otomasi Kontrol dan Instrumentasi JPEMAS: Jurnal Pengabdian Kepada Masyarakat Jurnal Fisika Edutik : Jurnal Pendidikan Teknologi Informasi dan Komunikasi
Rafli Filano
Jurusan Fisika, Fakultas Sains dan Matematika, Universitas Diponegoro, Semarang
Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Physics Other

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Advancements and Challenges of Deep Learning in Diagnostic Radiology: A Systematic Literature Review Affan Alfarabi; Filano, Rafli; Dirgayussa, I Gde Eka; Akbar, Ridho Lailatul; Zakiah, Hafizah
Jurnal Fisika Vol. 15 No. 2 (2025): Jurnal Fisika 15 (2) 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jf.v15i2.27967

Abstract

The rapid integration of Deep Learning (DL) in medical imaging is revolutionizing radiology and addressing critical challenges in diagnostic accuracy and healthcare delivery. In Indonesia and other developing countries, the shortage of radiologists and uneven distribution of healthcare services underline the urgency of exploring DL applications as potential solutions. This study aims to systematically review recent trends, effectiveness, and challenges of DL in diagnostic radiology, as well as to provide insights into its potential adaptation in the Indonesian healthcare system. Using a systematic literature review of peer-reviewed articles (2020–2025) from PubMed, IEEE Xplore, ScienceDirect, and Google Scholar, we identified and synthesized evidence on DL applications across multiple imaging modalities, including CT, MRI, X-ray, and ultrasound. Results show that DL achieves radiologist-level accuracy in tasks such as disease detection, segmentation, and automated report generation, while also improving workflow efficiency and clinical decision-making. However, challenges remain in terms of data availability, model interpretability, ethical issues, and clinical integration. This study provides recommendations for advancing DL adoption in radiology, emphasizing the need for standardized validation, clinician training, and context-specific implementation strategies in Indonesia. The findings highlight both the global and local significance of DL in enhancing healthcare access and equity.
Perbandingan Teknik Prapemrosesan Citra terhadap Akurasi CNN dalam Deteksi Diabetic Retinopathy Maulidiyah, Nurul; Dirgayussa, I Gde Eka; Filano, Rafli; Maulana, Yusuf
Edutik : Jurnal Pendidikan Teknologi Informasi dan Komunikasi Vol. 6 No. 2 (2026): EduTIK : April 2026
Publisher : Jurusan PTIK Universitas Negeri Manado

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Diabetic Retinopathy (DR) is a diabetes complication and the leading cause of blindness in the working-age population, affecting more than 93 million people worldwide. The quality of retinal fundus images is significantly affected by lighting conditions and contrast, making image preprocessing a critical factor that influences the accuracy of deep learning-based detection models. This study aims to compare the effect of four preprocessing techniques—original images, color Histogram Equalization (HE), grayscale HE, and Color Constancy—on the performance of a Convolutional Neural Network (CNN) based on the AlexNet architecture for DR detection. The APTOS 2019 Kaggle dataset was used, comprising 3,722 color retinal fundus images: 1,830 non-DR and 1,892 DR images. Model validation was performed using 10-Fold Cross Validation, and performance was evaluated using confusion matrix, ROC curve, accuracy, sensitivity, and specificity. Results show that original images yielded the best overall performance with accuracy of 96.10%, sensitivity of 97.98%, specificity of 94.29%, and AUC of 0.994. Grayscale HE produced the highest AUC (0.996), while Color Constancy had the lowest AUC (0.989). These findings indicate that color information in fundus images contains important discriminative features, and preprocessing does not always improve overall accuracy. The AlexNet model shows potential for implementation as a DR screening system based on Computer-Aided Diagnosis (CAD) with relatively low computational complexity
Peningkatan Kesehatan Mental Dini untuk Pencegahan Bullying di Kuttab Anas Bin Malik Maharsi, Retno; Herbanu, Aldi; Labibah, Asy Syifa; Filano, Rafli; Tresnaningtyas, Sekar Asri; Susanti, Dwi; Resfita, Nova; Maulidiyah, Nurul; Sonefisal, Seiswondyshu Hannoudzaky; Suci, Rilensya Rahma
JPEMAS: Jurnal Pengabdian Kepada Masyarakat Vol. 4 No. 2 (2026): JPEMAS: Jurnal Pengabdian Kepada Masyarakat
Publisher : Yayasan Pendidikan Tanggui Baimbaian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71456/adc.v4i2.1782

Abstract

Kegiatan Pengabdian kepada Masyarakat (PkM) yang dilaksanakan oleh Program Studi Teknik Biomedis ITERA di Kuttab Anas bin Malik bertujuan untuk meningkatkan kesadaran dan pengetahuan siswa mengenai pentingnya kesehatan mental sebagai faktor yang memengaruhi cara berpikir, berperilaku, dan berinteraksi sosial. Latar belakang kegiatan ini didasarkan pada meningkatnya permasalahan kesehatan mental pada anak dan remaja serta adanya perilaku bullying di lingkungan pendidikan yang berdampak negatif terhadap kondisi emosional, kepercayaan diri, dan perkembangan sosial akademik siswa. Selain itu, keterbatasan pemahaman siswa terkait pengelolaan emosi dan interaksi sosial positif, serta belum adanya program edukasi kesehatan mental yang terstruktur, menjadi dasar pelaksanaan kegiatan ini. Metode yang digunakan berupa pendekatan edukatif dan interaktif melalui penyampaian materi mengenai empati, bullying dan dampaknya, serta pembiasaan perilaku sosial positif dengan teknik role play, drama, presentasi interaktif, dan lagu edukatif yang dilaksanakan dalam tiga tahap, yaitu pra-sosialisasi, sosialisasi, dan pasca-sosialisasi. Hasil kegiatan menunjukkan adanya peningkatan pemahaman siswa terhadap kesehatan mental serta terbentuknya perilaku sosial yang lebih positif. Kegiatan ini juga berkontribusi dalam menciptakan lingkungan sekolah yang lebih aman, nyaman, dan kondusif serta mendukung upaya pencegahan bullying secara efektif.
Perancangan dan Implementasi Sistem IoT Pemantauan Kualitas Air Berbasis Salinitas dan Suhu untuk Identifikasi Risiko Habitat Larva Nyamuk Malaria Filano, Rafli; Arzi, Yudha Hamdi; Wati, Rosita; Setiawan, Rudi; Alfarabi, Affan; Oktrina, Salma Anindya; Putri, Maulina Adelia; Santoso, Budi; Rahman, Yusuf Aulia
Jurnal Otomasi Kontrol dan Instrumentasi Vol 18 No 1 (2026): Jurnal Otomasi Kontrol dan Instrumentasi
Publisher : Pusat Teknologi Instrumentasi dan Otomasi (PTIO) - Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/joki.2026.18.1.8

Abstract

Malaria remains a public health problem in Indonesia, particularly in endemic coastal regions where aquatic habitats serve as breeding sites for Anopheles larvae. This study develops a multi-point Internet of Things (IoT)-based aquatic environmental monitoring system to detect conditions that support larval development through real-time measurement of water salinity and temperature. The system employs a WQ7706D digital salinity sensor, an ESP32 microcontroller, and a low-power NRF24L01 wireless communication module. Laboratory testing indicates that the sensor achieves stable readings after a 20-second stabilization period, with salinity variation of ±0,05 ppt under steady conditions. Field implementation at two coastal water sites in Hanura Village recorded salinity ranges of 1,35–2,3 ppt and temperature ranges of 28,8–30,5°C, which potentially support larval development. The wireless communication system successfully transmitted data up to 150 m with minimal packet loss. Power consumption analysis shows a daily energy requirement of 1,794 Ah, enabling autonomous operation for 10 ± 1  days using a 12 V 20 Ah battery without recharging. The main contribution of this research is the design of an IoT-based aquatic monitoring system that integrates energy optimization, stable wireless communication, and quantitative identification of malaria larval habitat risk.
Co-Authors Affan Alfarabi Akbar, Ridho Lailatul Alfarabi, Affan Arzi, Yudha Hamdi Dirgayussa, I Gde Eka Dwi Budi Santoso Dwi Susanti Herbanu, Aldi Labibah, Asy Syifa Maharsi, Retno Maulidiyah, Nurul Oktrina, Salma Anindya Putri, Maulina Adelia Rahman, Yusuf Aulia Resfita, Nova Rosita Wati Rudi Setiawan Sonefisal, Seiswondyshu Hannoudzaky Suci, Rilensya Rahma Tresnaningtyas, Sekar Asri Yusuf Maulana, Yusuf Zakiah, Hafizah