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All Journal JAIS (Journal of Applied Intelligent System) JAREE (Journal on Advanced Research in Electrical Engineering) Journal of Soft Computing Exploration Industrika : Jurnal Ilmiah Teknik Industri Andalasian International Journal of Applied Science, Engineering, and Technology
Setiawan, Fachruddin Ari
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Safety Risk Analysis Optimization Using Fuzzy: A Literature Review Rahma, Naufa Aulia; Setiawan, Fachruddin Ari; Pradana, Dio Alif
Industrika : Jurnal Ilmiah Teknik Industri Vol. 8 No. 1 (2024): Industrika: Jurnal Ilmiah Teknik Industri
Publisher : Fakultas Teknik Universitas Tulang Bawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37090/indstrk.v8i1.1341

Abstract

Risk analysis is the procedure to identify risks to the surrounding environment, especially in the workplace. There are several methods for analyzing risk. Nowadays, many risk analysis methods are integrated with optimization methods, such as fuzzy. This approach is used to optimize the results of risk analysis. This study aims to investigate the development of collaboration between risk analysis methods and fuzzy in the industrial sector and research that can be carried out in the future. The authors collected 500 articles from Dimensions.ai between 2018 and 2023 and evaluated the references obtained with the bibliometric analysis tool. The authors analyzed the dataset obtained based on the countries that conducted the most research on safety risk analysis optimization and collaboration, themes that were widely used, the themes distribution, and the relationship between existing themes. The Results show that China has published many studies in collaboration with other countries. Research themes that use risk analysis methods are widely used in human assessment where it can be seen in the keywords. Currently, there has been a lot of research on risk assessment integrated with fuzzy and humans. For future research, fuzzy approaches and risk analysis can be integrated with other accident analysis methods. Keywords: Bibliometric, Fuzzy, Literature Review, Risk Analysis, Risk Assessment
Analysis of Inter-Subject and Session Variability using Brain Topographic Map Setiawan, Fachruddin Ari; Pradana, Dio Alif; Nandang, Iim
(JAIS) Journal of Applied Intelligent System Vol. 9 No. 1 (2024): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jais.v9i1.10051

Abstract

The study described investigates the application of Brain-Computer Interface (BCI) technology, focusing on Motor Imagery (MI) signals which enable individuals to control movements through mental visualization. A major challenge in this field is accurately distinguishing between different movements, particularly when dealing with data from multiple subjects and recording sessions, known as inter-subject and inter-session variability. To address this, the authors employ the Wavelet Packet Transform-Common Spatial Patterns (WPT-CSP) method to enhance the resolution of MI signals. They visualize the results using Brain Topographic Maps (Topomaps) to depict brain activity during MI tasks, facilitating the analysis of variability across subjects and sessions. Utilizing dataset 2a from the Brain-Computer Interface Competition (BCIC) IV, the study demonstrates the efficacy of this approach in identifying variability patterns. This research holds promise for improving BCI technology applications in various domains, and future work could explore refining signal processing techniques and validation on larger datasets. Topomap.
Breast tumor classification using adam and optuna model optimization based on CNN architecture Sari, Christy Atika; Rachmawanto, Eko Hari; Daniati, Erna; Setiawan, Fachruddin Ari; Hyperastuty, Agoes Santika; Mintorini, Ery
Journal of Soft Computing Exploration Vol. 5 No. 2 (2024): June 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i2.373

Abstract

Breast cancer presents a significant challenge due to its complexity and the urgency of the intervention required to prevent metastasis and potential fatality. This article highlights the innovative application of Convolutional Neural Networks (CNN) in breast tumor classification, marking substantial progress in the field. The key to this advancement is the collaboration among medical professionals, scientists, and artificial intelligence experts, which maximizes the potential of technology. The research involved three phases of training with varying proportions of training data. The first training phase achieved the highest accuracy rate of 99.72%, with an average accuracy of 99.05% in all three phases. Metrics such as precision, recall, and F1 score were also highly satisfactory, underscoring the model's efficacy in accurately classifying breast tumors. Future research aims to develop more complex and precise predictive models by incorporating larger and more representative datasets. This progression promises to improve understanding, prevention, and management of breast cancer, offering hope for significant advances in 2024 and beyond.
Design of smart baby incubator for low-birth-weight newborns Pradana, Dio Alif; Mukhammad, Yanuar; Suharto, Idola Perdana Sulistyoning; Setiawan, Fachruddin Ari
Journal of Soft Computing Exploration Vol. 5 No. 4 (2024): December 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i4.494

Abstract

The newborns mortality rate in Indonesia is still quite high, indicated by the neonatal mortality rate (AKN) of 15 per 1000 Live Births, where the target is only below 10 per 1000 Live Births. This mortality rate can be caused by Low-Birth-Weight (BBLR) cases that leads to death. One form of handling for these cases is using a Baby Incubator for intensive cares, which requires monitoring manually and requires the presence of a nurse around the baby incubator so that the condition of the baby incubator room remains stable. Several studies have been conducted and produced a smart incubator system to address these shortcomings. However, most of the smart incubators only focused on monitoring the condition of the incubator room without observing the condition of the baby inside. Based on this, a study was conducted that focused to producing a smart baby incubator that is capable of real-time monitoring of of room conditions (temperature, humidity, and oxygen levels) and baby conditions (temperature, heart rate, oxygen saturation, baby crying, and baby visuals) by applying the Internet of Things (IoT). The results of this study have the largest number of parameters monitored compared to previous studies.
Modified Extremum Seeking Control for Target Tracking and Formation Control in Pursuit-Evasion Game Setiawan, Fachruddin Ari; Agustinah, Trihastuti; Fuad, Muhammad
JAREE (Journal on Advanced Research in Electrical Engineering) Vol 6, No 2 (2022): October
Publisher : Department of Electrical Engineering ITS and FORTEI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/jaree.v6i2.320

Abstract

In a pursuit-evasion game, the mobile robot pursuer's ability to navigate from its initial position to the evader while maintaining a safe distance from other objects requires a good obstacle avoidance system. This study aims to perform target tracking in evader sieges and obstacle avoidance against other pursuer robots and static obstacles by proposing a modified extreme seeking controller (ESC). A modified backstepping control (BC) was used as an autopilot control for a nonholonomic mobile robot to execute the modified ESC command. The modified BC based on the modified ESC requires the positions of the targeted evader, pursuers, and obstacles. The pursuer uses this information to capture an evader by arranging the desired formation without colliding with static obstacles or other robots. The results of the simulations show that the pursuers successfully surround the evader and construct the formation without colliding with obstacles. The proposed method resulted in the closest distance of 2.071 m between the pursuers, 1.954 m between each pursuer and the evader, and 2.425 m between the pursuers and static obstacles.
A Histopathology Grading of Breast Cancer Using Visual Geometry Group Method Hyperastuty, A. Santika; Setiawan, Fachruddin Ari; Pradana, Dio Alif; Puspitasari, Rahma Ajeng; Inayah, Lailatul; Winarti, Eko
Andalasian International Journal of Applied Science, Engineering and Technology Vol. 5 No. 2 (2025): July 2025
Publisher : LPPM Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/aijaset.v5i02.255

Abstract

Breast cancer continues to rank among the world's leading causes of death for women. Developing successful treatment plans requires a timely and accurate diagnosis. Although histopathological image analysis is still the gold standard for evaluating malignancy, it is prone to inconsistencies and human error. The objective of this research is to use the Visual Geometry Group's (VGG16) deep learning technique to automate the evaluation of breast cancer histology. A collection of breast cancer histopathology images spanning 85 epochs was used to train the VGG16 model, which is well-known for its excellent performance in image classification tasks. For training and testing, the model uses batch sizes of 33 and 64, respectively, and a Stochastic Gradient Descent (SGD) optimizer with a learning rate of 0.01. With an F1 score of 0.98, 89.3% training accuracy, and 98% validation accuracy, the experimental findings show excellent performance. These results indicate that VGG16 is highly effective in distinguishing between different tissue grades of breast cancer. Despite its high performance, challenges remain regarding computational efficiency and interpretability for clinical use. Future research should focus on exploring lightweight architectures, improving model explanations, and validating more diverse and larger datasets to enhance real-world applicability in digital pathology.
Co-Authors Christy Atika Sari Eko Hari Rachmawanto Erna Daniati Ery Mintorini Hyperastuty, A. Santika Hyperastuty, Agoes Santika Inayah, Lailatul Muhammad Fuad Nandang, Iim Pradana, Dio Alif Puspitasari, Rahma Ajeng Rahma, Naufa Aulia Sulistyoning Suharto, Idola Perdana Trihastuti Agustinah winarti, eko Yanuar Mukhammad