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All Journal Jurnal Masyarakat Informatika Jurnal Sains dan Teknologi Jurnal Simetris Jurnal Buana Informatika CommIT (Communication & Information Technology) Scientific Journal of Informatics Proceeding SENDI_U Jurnal Terapan Abdimas Sistemasi: Jurnal Sistem Informasi JOURNAL OF APPLIED INFORMATICS AND COMPUTING EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Infotekmesin Indonesian Journal of Electrical Engineering and Computer Science Abdimasku : Jurnal Pengabdian Masyarakat Buletin Poltanesa Advance Sustainable Science, Engineering and Technology (ASSET) Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Jurnal Algoritma Community: Jurnal Pengabdian Pada Masyarakat Jurnal Informatika: Jurnal Pengembangan IT LogicLink: Journal of Artificial Intelligence and Multimedia in Informatics
Egia Rosi Subhiyakto, Egia Rosi
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Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mechanical Engineering Social Sciences Other

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

 1 
Optimization Chatbot Services Based on DNN-Bert for Mental Health of University Students Dzaky, Azmi Abiyyu; Zeniarja, Junta; Supriyanto, Catur; Shidik, Guruh Fajar; Paramita, Cinantya; Subhiyakto, Egia Rosi; Rakasiwi, Sindhu
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.7403

Abstract

Attention to mental health is increasing in Indonesia, especially with the recent increase in the number of cases of stress and suicide among students. Therefore, this research aims to provide a solution to overcome mental health problems by introducing a chatbot system based on Deep Neural Networks (DNN) and BiDirectional Encoder Representation Transformers (BERT). The primary objective is to enhance accessibility and offer a more effective solution concerning the mental health of students. This chatbot utilizes Natural Language Processing (NLP) and Deep Learning to provide appropriate responses to mild mental health issues. The dataset, comprising objectives, tags, patterns, and responses, underwent processing using Indonesian language rules within NLP. Subsequently, the system was trained and tested using the DNN model for classification, integrated with the TokenSimilarity model to identify word similarities. Experimental results indicate that the DNN model yielded the best outcomes, with a training accuracy of 98.97%, validation accuracy of 71.74%, and testing accuracy of 71.73%. Integration with the TokenSimilarity model enhanced the responses provided by the chatbot. TokenSimilarity searches for input similarities from users within the knowledge generated from the training data. If the similarity is high, the input is then processed by the DNN model to provide the chatbot response. This integration of both models has proven to enhance the responsiveness of the chatbot in providing various responses even when the user inputs remain the same. The chatbot also demonstrates the capability to recognize various inputs more effectively with similar intentions or purposes. Additionally, the chatbot exhibits the ability to comprehend user inputs although there are many writing errors.
Implementation of MobileNet Architecture for Skin Cancer Disease Classification Faudyta, Haniifa Aliila; Sinaga, Jesica Trivena; Subhiyakto, Egia Rosi
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8771

Abstract

As the number of occurrences of skin cancer increases year, it becomes more and more crucial to identify the disease accurately and effectively. This study aims to implement and evaluate the MobileNet architecture for classifying nine types of skin lesions using the ISIC 2020 dataset and to compare MobileNet's performance with other CNN architectures, such as VGG-16 and LeNet, in terms of accuracy and computational efficiency. The study also investigates the impact of image preprocessing on model accuracy. The methodology comprises data collection, preprocessing, and model development, leveraging transfer learning from MobileNet pre-trained on ImageNet. Data preprocessing involves resizing images to 224 x 224 pixels and normalizing pixel values. To augment the dataset, techniques such as rotation, zooming, horizontal flipping, and brightness and contrast adjustment are applied. To address class imbalance, oversampling is used to obtain 500 images per class. The model architecture includes Global Average Pooling, a Dense layer with 1024 units and ReLU activation, and a Dropout layer with a 0.2 probability. Various training scenarios with batch sizes (8, 16, 32, 64) and learning rates (0.001, 0.0001) are evaluated, incorporating dropout and ReLU activations. The optimal performance was achieved with oversampling, dropout, and a learning rate of 0.0001, yielding a training accuracy of 99.64% and a validation accuracy of 86.89% after oversampling, resulting in 3,600 training and 900 validation images with an 80:20 data split. The results suggest overfitting due to dataset limitations. Future work should focus on fine-tuning and ensemble methods to improve validation performance.
Comparison of ResNet-50, EfficientNet-B1, and VGG-16 Algorithms for Cataract Eye Image Classification Santoso, Ilham; Manurung, Ayub Michaelangelo; Subhiyakto, Egia Rosi
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.8968

Abstract

Cataract is a leading cause of blindness worldwide, emphasizing the need for an effective early detection approach. This study evaluates the capabilities of three widely-used deep learning models—ResNet-50, EfficientNet-B1, and VGG-16—in classifying visual data. The analysis was conducted on a dataset of 2,112 images, comprising 1,074 normal cases and 1,038 cataract cases. The findings reveal that ResNet-50 achieved the best accuracy at 98.61%, followed by EfficientNet-B1 at 96.64% and VGG-16 at 93.82%. In comparison, previous research using Convolutional Neural Network (CNN) techniques reported an accuracy of 92.93%. These results highlight ResNet-50's superior potential for image classification tasks in this domain. This study contributes significantly to the selection of robust models for building an automated cataract detection framework.
The Application of Deep Learning for Skin Disease Classification Using the EfficientNet-B1 Model Manurung, Ayub Michaelangelo; Santoso, Ilham; Subhiyakto, Egia Rosi
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.9100

Abstract

The skin, being the largest organ in the human body, plays a vital role in protecting against various external threats. However, cases of skin diseases are steadily rising across countries, making it a significant global health concern. Diagnosis often faces challenges due to symptom variations and low public awareness, highlighting the need for automated technology in skin disease detection. This study developed an automated classification system for skin diseases using EfficientNet-B1, capable of categorizing five skin conditions: Acne and Rosacea, Eczema, Melanoma Skin Cancer Nevi and Moles, Normal, Vitiligo, Psoriasis pictures Lichen Planus and related diseases, Seborrheic Keratoses and other Benign Tumors, Tinea Ringworm Candidiasis and other Fungal Infections. The system utilized 1.571 plus 1641 JPG digital images resized to 224 x 224 pixels, with 80% of the data allocated for training and 20% for testing. The trained model achieved a high accuracy of 99%, demonstrating the system's potential to support faster and more accurate diagnostic processes.
Co-Authors Abiyyi, Ryandhika Bintang Abu Salam Adhitya Nugraha Andreas Wilson Setiawan Ardytha Luthfiarta Astuti, Yani Parti Astuti, Yani Parti Astuti, Yani Parti Bahres, Salim Bin Sulaiman Cahyati, Ade Puput Catur Supriyanto Dzaky, Azmi Abiyyu Erlin Dolphina Erwin Yudi Hidayat Etika Kartikadarma Fahmi Amiq Faradeya, Muhammad Az-Zauqy Faudyta, Haniifa Aliila Fauzi Adi Rafrastara Guruh Fajar Shidik Hafidhoh, Nisa'ul Hapsari, Swita Amalia Hulaifah Al Abrori, Zahra Zul Ika Novita Dewi Iskandar, Marcelino Junta Zeniarja Kurniawan, Defri Kusumasari, Dinita Liya Umaroh, Liya Manurung, Ayub Michaelangelo Miyanthi, Sheryn Aulia Mutia Rahmi Pratiwi Ningrum, Novita Kurnia Ningrum, Novita Kurnia Octaviani, Dhita Aulia Pamungkas, Azriel Sebastian Paramita, Cinantya Prajanto Wahyu Adi, Prajanto Wahyu Prihatmoko, Radig Gedhe Pulung Nurtantio Andono Puspitaningrum, Hapsari Retno Rahadian, Arief Rohman, Muhammad Syaifur Sabilillah, Ferris Tita Safina, Nabila Safina, Nabila Santoso, Ilham Savicevic, Anamarija Jurcev Septiani, Karlina Dwi Simbolon, Calvin Samuel Sinaga, Jesica Trivena Sindhu Rakasiwi Sudibyo, Usman Swanny Trikajanti Widyaatmadja T. Sutojo Triginandri, Rifqi Triono, Justin Matthew Utomo, Danang Wahyu Wardatunizza, Indah Wibowo, Alrico Rizki Widi Utomo, Emanuel Pinesthi Winarsih, Nurul Anisa Sri Yunianto, Amar Haris