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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Economic Journal of Emerging Markets Jurnal Ilmiah Poli Rekayasa Proceedings of KNASTIK Bulletin of Electrical Engineering and Informatics CommIT (Communication & Information Technology) Indonesian Journal of Electrical Engineering and Informatics (IJEEI) SITEKIN: Jurnal Sains, Teknologi dan Industri Jurnal NERS Scientific Journal of Informatics Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) POLYGLOT Emerging Science Journal Syntax Literate: Jurnal Ilmiah Indonesia JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Komtika (Komputasi dan Informatika) International Journal of New Media Technology Jurnal Teknoinfo Jurnal Sisfokom (Sistem Informasi dan Komputer) International Journal of Supply Chain Management Poltekita : Jurnal Ilmu Kesehatan Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Informatika Jurnal Informatika Ekonomi Bisnis Jurnal Informatika dan Rekayasa Perangkat Lunak International Journal of Advances in Data and Information Systems Jurnal Sistem Komputer dan Informatika (JSON) Journal of Applied Data Sciences Walisongo Journal of Information Technology Jurnal Informatika dan Teknologi Komputer ( J-ICOM) Action Research Literate (ARL) Jurnal Indonesia Sosial Teknologi Eduvest - Journal of Universal Studies Jurnal Informatika Ekonomi Bisnis Jurnal Sistem Informasi International Journal of Education, Language, Literature, Arts, Culture, and Social Humanities The Indonesian Journal of Computer Science Malahayati International Journal of Nursing and Health Science Jurnal Komtika (Komputasi dan Informatika)
Achmad Nizar Hidayanto
Fakultas Ilmu Komputer, Universitas Indonesia
Aerospace Engineering Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Neuroscience Nursing Public Health Social Sciences Transportation Other

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Journal : Journal of Applied Data Sciences

 1 
Improved Deep Learning Model for Prediction of Dermatitis in Infants Setiawan, Debi; Noratama Putri, Ramalia; Fitri, Imelda; Nizar Hidayanto, Achmad; Irawan, Yuda; Hohashi, Naohiro
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.542

Abstract

Indonesia's equatorial climate, characterized by summer and rainy seasons, presents environmental conditions that contribute to a high incidence of dermatitis in infants. Dermatitis, an inflammatory skin condition, can lead to significant discomfort in infants, affecting their sleep, growth, and development. Early diagnosis is crucial for effective treatment; however, conventional diagnostic methods in clinics and hospitals—such as physical observation and parental interviews—are often time-consuming, subjective, and may lack precision, creating a need for more efficient diagnostic tools. This study explores the application of deep learning models to enhance the accuracy and speed of dermatitis diagnosis in infants. Four convolutional neural network (CNN) models were evaluated: MobileNet, VGG16, ResNet, and a Custom CNN model specifically designed for this study. Using a dataset of 1,088 skin images collected from three regions in Riau Province, Indonesia, we conducted training and testing to assess each model’s performance in distinguishing between dermatitis-affected and healthy skin. Results show that MobileNet and the Custom CNN outperformed other models, achieving accuracy rates of 97% and 85%, respectively. MobileNet’s high accuracy and efficiency make it a viable option for mobile applications, enabling rapid, on-site diagnosis in resource-limited settings. The Custom CNN model, tailored to the unique features of infant skin, also showed promising results. These findings demonstrate the potential of automated, image-based diagnostic tools for assisting medical professionals in early dermatitis detection, improving patient outcomes. This study contributes a valuable diagnostic solution that leverages deep learning to support healthcare providers, particularly in areas with limited access to specialized medical resources.
CNN-LSTM with Multi-Acoustic Features for Automatic Tajweed Mad Rule Classification Anggraini, Nenny; Rahman, Yusuf; Hidayanto, Achmad Nizar; Sukmana, Husni Teja
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1062

Abstract

The rules of mad recitation in the Qur’an are a crucial aspect of tajwīd, governing the lengthening of vowel sounds that affect both meaning and recitational accuracy. Despite its importance, there is currently no reliable automatic system capable of classifying mad rules based on voice input. This study proposes a deep learning-based approach using a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model to automatically classify mad rules from Qur’anic recitations. The research follows the CRISP-DM methodology, covering data understanding, preparation, modeling, and evaluation stages. Acoustic features were extracted from 3,816 annotated audio segments of Surah Al-Fātiḥah, combining Mel-Frequency Cepstral Coefficients (MFCC), Chroma, Spectral Contrast, and Root Mean Square (RMS) to represent phonetic and prosodic attributes. The CNN layers captured spatial characteristics of the spectrum, while LSTM layers modeled temporal dependencies of the audio. Experimental results show that the combination of all four features achieved an accuracy of 97.21%, precision of 95.28%, recall of 95.22%, and F1-score of 95.25%. These findings indicate that multi-feature integration enhances model robustness and interpretability. The proposed CNN-LSTM framework demonstrates potential for practical deployment in voice-based tajwīd learning tools and contributes to the broader field of Qur’anic speech recognition by offering a systematic, ethically grounded, and data-driven approach to mad classification.
A Hybrid YOLO–CNN Model for Automatic Detection and Severity Assessment of Atopic Dermatitis in Infant Images Setiawan, Debi; Putri, Ramalia Noratama; Herlina, Sara; Hidayanto, Achmad Nizar; Irawan, Yuda; Hohashi, Naohiro
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1212

Abstract

Atopic dermatitis is one of the most common skin diseases affecting infants and children worldwide and has a particularly high prevalence in tropical countries. Traditional diagnosis methods, which still rely on physical examinations and laboratory tests often face challenges such as delays, high costs, and limited facilities, thereby necessitating an artificial intelligence–based system that is more efficient and accurate. This study aims to develop a hybrid YOLO–CNN model for the automatic detection and severity classification of atopic dermatitis in infants. The dataset comprises 2,000 infant skin images, including lesions categorized as mild, moderate, and severe, obtained from an online repository and field observations conducted in three villages. The labeling process was performed by a specialist doctor to ensure clinical validity. In the first step, YOLO was used to detect the lesion area in real time by generating a bounding box. This produced a region of interest (ROI), which was subsequently analyzed by a CNN model employing transfer learning in the second step to determine the severity level. Experimental results indicate that YOLO achieved high detection performance, with an mAP@0.5 of 91.2% and an F1-score of 90.2%, while the CNN model attained an average accuracy of 85% and a macro-F1 score of 85% in classification. The visualization of predictions indicates that most lesions were detected with confidence levels ≥0.9, confirming the model’s consistency. These findings highlight the potential of the hybrid YOLO–CNN framework as a supportive system for digital clinical diagnosis, applicable to both mobile applications and teledermatology services, particularly in regions with limited medical personnel. Future research should employ larger, multi-center datasets and integrate explainable AI approaches to promote broader clinical adoption.
Co-Authors . Herianto . Herianto . Herianto . Herianto Ade Irma Suryani Adhiawan Soegiharto Agri Fina Agung Terminanto Agus Harjoko Ahmadin, Yudhiansyah Ajie Tri Hutama Alfatianisa, Kirana Alfiany, Noverina Aniati Murni Arymurthy Anita Muliawati Antia Antia Ardiati Utami Sarjono Arthamevia, Aisha Asymala, Asymala Permata Sari Atalya Yoseba S. Ayuning Budi, Nur Fitriah Azainil Azainil Barus, Okky Beny Maulana Achsan Bob Hardian Syahbuddin Cahyaningtyas, Astri Canrakerta Canrakerta Canrakerta, Canrakerta Chris Solontio Debi Setiawan, Debi Devi Fitrianah Dewi Puspa Dewi Puspasari Diah Kumalasari Dian Setia Hartana Dian Setia Hartana Dian Setia Hartana Diane Fitria Dwiza Riana Dyna Marissa Khairina Ejo Imandeka Ermawan, Geri Yesa Ernawati Pasaribu, Ernawati Fahmi, Rizki Ali Fajar Budi Utomo Fakhri Mubarak, Muhammad Fatimah Azzahro Febiani, Dyah Ayu Fitri Kartiasih Fitri, Imelda Friendly Nur Shakti Geri Yesa Ermawan Gusmao, Mazarino Neil Araujo Pires Leite Handayani, Putu Wuri Handini Mekkawati Hanny Handiyani Hapsari, Ika Chandra Hari Prasetyo Tri Wicaksono Hartana, Dian Setia Henki Bayu Seta Herlina, Sara Hisyam Fahmi Hohashi, Naohiro Husni Teja Sukmana I Gusti Ngurah Adi Wicaksana Ihsan Lutfi Ika Chandra Hapsari Ika Chandra Hapsari Ika Chandra Hapsari Ika Chandra Hapsari Ika Chandra Hapsari Ika Chandra Hapsari Imairi Eitiveni Indra Budi Irfandi, Zikri Isal, Yugo Kartono J.W. Saputro J.W. Saputro J.W. Saputro Jwalita Galuh Garini Kamrozi Kemas Khaidar Ali Indrakusuma Kenedi Binowo Kongkiti Phusavat Kongkiti Phusavat Kongkiti Phusavat Krishna Yudhakusuma P.M. Lasiyanto Putro, Pamuji M. Aulia Hafidh Maemonah, Maemonah Mahdi, Askarul Mahmud, Mufti Maulida, Mutia Mediati, Ati Surya Mediawati, Ati Surya Meganingrum Arista Jiwanggi Meganingrum Arista Jiwanggi Meganingrum Arista Jiwanggi Meganingrum Arista Jiwanggi Mohammed Al Kwarizmi Dwi Anggara Muh. Alviazra Virgananda Muhamad Ikbal Muhamad Raihan Fikriansyah Muhammad Daril Nofriansyah Muhammad Imam Santosa Muhammad Labib Jundillah Muhammad Rizky Anditama Muhammad Rizky Anditama Mutia Maulida Nafiiyah, Nur Nazar, Rizal Mochamad Nenny Anggraini, Nenny Ni Wayan Trisnawaty Nilamsari Putri Utami Ninda Lutfiani Noratama Putri, Ramalia Noverina Alfiany Nugroho, Widijanto Satyo Nugroho, Widijanto Satyo Nur Fitriah Ayuning Budi Nur Fitriah Ayuning Budi Pamuji Lasiyanto Putro Panca O. Hadi Putra Pang Ning-Tan Pangesti, Dyah Pertiwi, Ratih Putri Phusavat, Kongkiti Prasetya, Roliand Prastiti, Rizdiani Tri Purwandari, Betty Putro, Prasetyo Adi Wibowo Qorib Munajat Qurotul Aini R. Yugo Kartono Isal Rahmad Mulyadi Ramadiani - Ramalia Noratama Putri Rania Azzahra Rayhan Anandya Reihan Putra Oktavio Rifqi Firdaus Al Jauhari Rizha Febriyanti Rizki Tri Prasetio Robby Hermansyah Rosa Nur Rizky FT Rr Tutik Sri Hariyati Ryan Randy Suryono Samik-Ibrahim, Rahmat Mustafa Saputro, J.W. Sartika Djamaluddin, Sartika Septian Bagus Wibisono Septian Bagus Wibisono Setiawan, Suryana Setiawati, Deni Setyowati , Setyowati Setyowati Setyowati Setyowati Setyowati Sherah Kurnia Sihotang, Jhon Rafles Simanjuntak, Almon Junior Sita Wardhani Solontio, Chris Suriazdin, Syahrul Alam Suryana Setiawan Suryana Setiawan Syafiq Abdillah U. Syafira, Adinda Rizkita Syahrul Alam Suriazdin Syahrul Tuba Syanandi, Muhammad Destara Theresiawati Tri Wicaksono, Hari Prasetyo Trimanadi, Raden Untung Rahardja Wachid Yoga Afrida Wahyu Catur Wibowo Widijanto Satyo Nugroho Wilantika, Nori Wisnubroto, Agus Sigit Yova Ruldeviyani Yuda Irawan Yudhiansyah Ahmadin Yudhiansyah Ahmadin Yudhiansyah Ahmadin Yudhianto, Riswan Haryo Yudi Ramdhani Yunarso Anang Yusuf Rahman Zikri Irfandi Zulkarnaen, M. Ari