Article Per Year (5 Year)
p-Index From 2021 - 2026
5.343
P-Index
This Author published in this journals
All Journal JUITA : Jurnal Informatika Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JURNAL INTEGRASI JURNAL REKAYASA TEKNOLOGI INFORMASI Jurnal Teknoinfo MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer ComTech: Computer, Mathematics and Engineering Applications Jurnal Teknologi Komputer dan Sistem Informasi METIK JURNAL Jurnal Ilmiah Ilmu Komputer Fakultas Ilmu Komputer Universitas Al Asyariah Mandar Jurnal Manajemen Informatika dan Sistem Informasi Jurnal Informatika dan Rekayasa Elektronik Journal of Applied Engineering and Technological Science (JAETS) Sains, Aplikasi, Komputasi dan Teknologi Informasi Jurnal Sistem Informasi dan Sistem Komputer JNANALOKA Journal of Applied Data Sciences International Journal of Engineering, Science and Information Technology Jurnal J-PEMAS JAIA - Journal of Artificial Intelligence and Applications JTKSI (Jurnal Teknologi Komputer dan Sistem Informasi)
Muhammad Bambang Firdaus
Fakultas Ilmu Komputer Dan Teknologi Informasi, Universitas Mulawarman, Samarinda
Aerospace Engineering Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

Published : 44 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 44 Documents
Search

 1   2   3   4   5 
Deep Learning Methods for Pneumonia Detection Using ConvNeXt Architecture Akhmad Irsyad; Muhammad Bambang Firdaus; Gubtha Mahendra Putra; Putut Pamilih Widagdo; Hario Jati Setiady; Muhammad Fawaz Saputra; Muhammad Abdillah Rahmat
International Journal of Engineering, Science and Information Technology Vol 6, No 1 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i1.1797

Abstract

Pneumonia remains a major respiratory infection with high mortality rates, especially in regions with limited access to medical specialists. Early and accurate diagnosis plays a critical role in reducing fatal outcomes and improving patient management. Chest X ray imaging is widely used as a primary diagnostic modality, yet interpretation relies heavily on experienced radiologists, whose availability is often insufficient to meet clinical demand. This condition motivates the development of automated pneumonia detection systems based on artificial intelligence. This study investigates the application of deep learning for pneumonia classification using chest X ray images, with a focus on the ConvNeXt architecture. ConvNeXt represents a modern neural network design that integrates structural advantages from Vision Transformers with the efficiency of traditional Convolutional Neural Networks, enabling strong feature extraction while maintaining computational efficiency. The research evaluates multiple ConvNeXt variants, including Tiny, Small, Base, and Large, to analyze the relationship between model complexity and classification performance. ResNet50 is employed as a baseline model to provide a fair comparative assessment against a widely used convolutional architecture. Model evaluation uses accuracy, precision, recall, and F measure to ensure balanced measurement across different classification outcomes and class distributions. Experimental results indicate that ConvNeXt Tiny achieves the highest overall performance, reaching an accuracy of 97.69 percent while using a relatively low number of parameters. This outcome highlights the efficiency of lightweight architectures for medical image analysis tasks. The findings demonstrate that ConvNeXt Tiny delivers strong discriminative capability with reduced computational requirements, making it suitable for deployment in resource constrained clinical environments. This study contributes evidence supporting the effectiveness of modern deep learning architectures for automated pneumonia detection and provides insight into model selection for practical medical imaging applications.
Enhancing the Performance of Machine Learning Algorithm for Intent Sentiment Analysis on Village Fund Topic M. Khairul Anam; Pandu Pratama Putra; Rio Andika Malik; Karfindo Karfindo; Teri Ade Putra; Yesri Elva; Raja Ayu Mahessya; Muhammad Bambang Firdaus; Ikhsan Ikhsan; Chichi Rizka Gunawan
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.v6i2.637

Abstract

This study explores the implementation of Intent Sentiment Analysis on Twitter data related to the Village Fund program, leveraging Multinomial Naïve Bayes (MNB) and enhancing it with Synthetic Minority Over-sampling Technique (SMOTE) and XGBoost (XGB). The analysis categorizes tweets into six labels: Optimistic, Pessimistic, Advice, Satire, Appreciation, and No Intent. Initially, the MNB model achieved an accuracy of 67% on a 90:10 data split. By applying SMOTE, accuracy improved by 12%, reaching 89%. However, adding Chi-Square feature selection did not increase accuracy further. Incorporating XGB into the MNB+SMOTE model led to a 6% improvement, achieving a final accuracy of 95%. Comprehensive model evaluation revealed that the MNB+SMOTE+XGB model achieved 96% accuracy, 96% precision, 96% recall, and a 96% F1-score, with an AUC of 99%, categorizing it as excellent. These findings demonstrate that the combination of SMOTE for addressing class imbalance and XGBoost for boosting performance significantly enhances the MNB model's classification capabilities. The novelty lies in the integration of these techniques to improve intent sentiment classification for public opinion analysis on the Village Fund program. The results indicate that the majority of tweets labeled as "No Intent" reflect a lack of specific sentiment or actionable intent, providing valuable insights into public perception of the program.
Implementation of Naïve Bayes Gaussian Algorithm for Real-Time Classification of Broiler Cage Conditions Rosmasari Rosmasari; Anton Prafanto; Muhammad Bambang Firdaus
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.694

Abstract

Monitoring large-scale broiler farms poses considerable challenges due to the variable nature of environmental conditions, which have a direct impact on poultry health and productivity. This study proposes a real-time classification system for broiler house conditions, utilizing the Naïve Bayes Gaussian algorithm in conjunction with the Internet of Things (IoT) technology. The system has been developed to address the limitations of manual monitoring by automating the collection of temperature, humidity, and ammonia data through BME-680 and MICS-5524 sensors, which are strategically positioned 30 cm from the floor to optimize ammonia detection. Utilizing a dataset comprising 865 records, meticulously divided into 75% for training (648 records) and 25% for testing (217 records), the model attained an accuracy of 82.03%, a precision of 75.67%, a recall of 82.67%, and an F1-score of 77.67%. A comparative analysis was conducted, which demonstrated significant advantages over alternative classification methods, with Decision Trees achieving 79.5% accuracy and Support Vector Machines reaching 80.8%. The innovation lies in the integration of automated condition classification into an IoT system, enabling rapid responses to environmental changes with processing times of approximately 500 milliseconds from sensing to classification. The system demonstrated an accuracy of 178 data points, with a misclassification rate of 39 out of 217 test samples. The strategic placement of sensors at a height of 30 cm optimizes ammonia detection while ensuring accurate temperature and humidity readings. The system provides historical data, enabling farms to analyze long-term environmental trends, and thereby support data-driven decision-making strategies to enhance broiler welfare and operational efficiency. Usability testing with five poultry farm operators confirmed the dashboard's intuitive design, though recommendations for visual alerts for critical ammonia levels were suggested for future iterations.
Sara Detection on Social Media Using Deep Learning Algorithm Development M. Khairul Anam; Lucky Lhaura Van FC; Hamdani Hamdani; Rahmaddeni Rahmaddeni; Junadhi Junadhi; Muhammad Bambang Firdaus; Irwanda Syahputra; Yuda Irawan
Journal of Applied Engineering and Technological Science (JAETS) Vol. 6 No. 1 (2024): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v6i1.5390

Abstract

Social media has become a key platform for disseminating information and opinions, particularly in Indonesia, where SARA (Ethnicity, Religion, Race, and Intergroup) issues can fuel social tensions. To address this, developing an automated system to detect and classify harmful content is essential. This study develops a deep learning model using Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) to detect SARA-related comments on Twitter. The method involves data collection through web scraping, followed by cleaning, manual labeling, and text preprocessing. To address data imbalance, SMOTE (Synthetic Minority Over-sampling Technique) is applied, while early stopping prevents overfitting. Model performance is evaluated using precision, recall, and F1-score. The results demonstrate that SMOTE significantly improves model performance, particularly in detecting minority-class SARA comments. CNN+SMOTE achieves a accuracy of 93%, and BiLSTM+SMOTE records a recall of 88%, effectively capturing patterns in SARA and non-SARA data. With SMOTE and early stopping, the model successfully manages class imbalance and reduces overfitting. This research supports efforts to curtail hate speech on social media, especially in the Indonesian context, where SARA-related issues often dominate public discourse.
Co-Authors Ade Chrisvitandy Ade Riyanda Putra Agustin Ahmad Zamsuri Alameka, Faza Anam, M Khairul Anjas, Andi Anton Prafanto Anton Prafanto Arda Yunianta Arief Hidayat Awang Harsa Kridalaksana Ayu Mahessya, Raja Basani, Yuniarta Budiman, Edy Bunga Nanti Pikir Chichi Rizka Gunawan Damar Sanggara Habibie Eddy Kurniawan Pradana Efrizoni, Lusiana Erlin Fadli Suandi Fahrul Yamani Fajar Ramadhan Faza Alameka Fernando Elda Pati Gandhi Dwi Laksono Gubtha Mahendra Putra Hadi Asnal, Hadi Hairah, Ummul Hamdani Hamdani - Hamdani Hamdani Hanif Aulia Hario Jati Setiady Hasan J. Alyamani Hasyim Asyari Herman Santoso Pakpahan Ike Yunia Pasa Ikhsan Ikhsan Indah Mukhlis Tamara Irfan Putra Pratama Irsyad, Akhmad Irwanda Syahputra Irwanda Syahputra Junadhi Junadhi Karfindo, Karfindo Kartina Diah K. W. Langgeng Ari Kusuma Lathifah Lathifah Lathifah Lathifah Latifah Lucky Lhaura Van FC, Lucky Lhaura M Syauqi Hafizh Medi Taruk Mohammad Yasin Muh Fathir Fahrezah Muhammad Abdillah Rahmat Muhammad Akbar Muhammad Budi Saputra Muhammad Fawaz Saputra Muhammad Fikri Anshori Muhammad Nopri Fauzi Muhammad Nur Ihwan Muhammad Wisdan Pratama Putra Nariza Wanti Wulan Sari Novi Yona Sidratul Munti Nurjayadi Nurjayadi Pandu Pratama Putra, Pandu Pratama Putut Pamilih Widagdo Rahmaddeni Rahmaddeni Rahmaddeni Rahmaddeni Renol Sulle Rio Andika Malik Rosmasari Rosmasari Rosmasari Rosmasari Rozy Rivaldi Ruri Widi Priatna Sofiansyah Fadli Susi Erlinda Tejawati, Andi Teri Ade Putra Tiopan Herny Manto Gultom Tri Putri Lestari Triyani Arita Fitri Ummul Hairah Wardhana, Reza Wati, Masna Widians, Joan Angelina Wirta Agustin Yaakub, Saleh Yesri Elva Yuda Irawan Yudi Kurniawan Yumami, Eva Zainal Arifin