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All Journal Jurnal Jurusan Ilmu Keolahragaan Journal of Software Engineering Journal of Intelligent Systems JAIS (Journal of Applied Intelligent System) JOIV : International Journal on Informatics Visualization JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING ILKOM Jurnal Ilmiah Musamus Journal of Primary Education Abdimasku : Jurnal Pengabdian Masyarakat Asean Journal of Sport for Development and Peace Indonesian Research Journal on Education Jurnal Informatika: Jurnal Pengembangan IT
Catur Supriyanto
Universitas Dian Nuswantoro
Religion Arts Humanities Chemistry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Physics Public Health 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.
Detection and Localization of Brain Tumors on MRI Images Using the YOLO Algorithm Bayu Satria, Zaky Indra; Supriyanto, Catur
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.10047

Abstract

This study addresses the critical need for early and accurate brain tumor diagnosis on MRI images by comparing five versions of the YOLO algorithm (YOLOv5, YOLOv7, YOLOv8, YOLOv9, and YOLOv12) with consistent parameters. Utilizing a pre-annotated Kaggle MRI brain dataset, the research meticulously verified annotations and employed data augmentation (flipping, rotation, blurring, noise) to expand the dataset from 801 to approximately 1362 images, enhancing model generalization and robustness. Models were trained and evaluated on metrics including precision, recall, mAP@0.5, mAP@0.5:0.95, and inference time. YOLOv12 demonstrated superior overall performance, achieving the highest recall (97.32%), mAP@0.5 (92.2%), and mAP@0.5:0.95 (76.57%), establishing its robustness for accurate detection and object localization. While YOLOv7 achieved the highest precision (96.89%) and excellent inference speed, its overall mAP and recall were surpassed by other iterations. YOLOv9 and YOLOv8 also showed strong competitive performance, indicating significant advancements in the newer YOLO generations. The findings confirm the efficacy of the YOLO algorithm for brain tumor detection and localization in MRI images, with YOLOv12 proving to be the most effective variant in this comparative analysis.
Co-Authors . Safuan, . Abdollah, Mohd. Faizal Abdul Rachman Syam Tuasikal Abu Salam Ahmed, Foez Al Fahreza, Muhammad Daffa Alamsyah, Sayyidul Aulia Amalia Amalia Amalia, Syafira Rosa Amiral, Afinzaki Andreas Wilson Setiawan Antony Eka Aditya, Antony Eka Astuti, Yani Parti Bayu Satria, Zaky Indra Dyan Yuliana Dzaky, Azmi Abiyyu Egia Rosi Subhiyakto, Egia Rosi Erna Sri Rahayu, Erna Sri Fauzi Adi Rafrastara Gede Doddy Tisna MS Guruh Fajar Shidik Hapsari Peni Heru Agus Santoso HIMAWAN WISMANADI Junta Zeniarja Lin, wei Jhe Majid, Nur Kholis Marjuni, Aris Mohammad Reza Maulana, Mohammad Reza Muchamad Arif Al Ardha Muljono Muljono Mulyanto, Edy Nining Widyah Kusnanik Nurhasan Nurhasan, Nurhasan Octaviani, Dhita Aulia Oman Somantri Paramita, Cinantya Pitaloka, Tia Amika Purwanto Purwanto Purwanto, Purwanto Rahadian, Arief Rizka Safriyani Rizki, Ainun Zulfikar Romi Satria Wahono Rusdiawan, Afif Rustam, Suhardi Safar, Noor Zuraidin Mohd Sindhu Rakasiwi Sudibyo, Usman Swanny Trikajanti Widyaatmadja Wahyu Adi Nugroho Wakhidah, Elfa Wahyu Yang, Chung Bing YUSUF FUAD