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All Journal International Journal of Electrical and Computer Engineering International Journal of Reconfigurable and Embedded Systems (IJRES) Tekno : Jurnal Teknologi Elektro dan Kejuruan Bulletin of Electrical Engineering and Informatics Register: Jurnal Ilmiah Teknologi Sistem Informasi
Setumin, Samsul
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Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering

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Automated DeepLabV3+ based model for left ventricle segmentation on short-axis late gadolinium enhancement-magnetic cardiac resonance imaging images Awang Damit, Dayang Suhaida; Sulaiman, Siti Noraini; Osman, Muhammad Khusairi; A. Karim, Noor Khairiah; Setumin, Samsul
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3362-3371

Abstract

Accurate segmentation of myocardial scar tissue on late gadolinium enhancement-magnetic cardiac resonance imaging (LGE-CMR) is exceptionally vital for clinical applications, enabling precise diagnosis and effective treatment of various cardiac diseases, such as myocardial infarction and cardiomyopathies. However, the ventricle (LV) variations in the size and shape, artifacts, and image resolution of LGE-CMR has made automatic segmentation of myocardial scar tissue more challenging. While many existing approaches delineate the LV myocardium region using multi-modal segmentation, these models may be computationally complex and suffer from misalignment. Therefore, this study proposed an automatic dual-stage DeepLabV3+ based approach tailored for myocardial scar segmentation on short-axis LGE-MRI exclusively. To segment myocardial scar tissue, the second stage employs the segmented LV chamber from the previous stage. The encoder part of the framework utilizes a MobileNetV2 and ResNet50 backbone for the first and second segmentation, respectively, aiming for optimal resolution of feature maps. Both stages tailor an improved Atrous Spatial Pyramid Pooling module in the DeepLabV3+ model with fine-tuned dilated atrous rates to effectively extract the LV chamber and myocardial scar from the complex LGE-MRI background. Based on the results, the proposed dual-stage network recorded an outstanding segmentation performance, with mean Dice score of 96.02% for LV chamber segmentation and 68.01% for scar tissue extraction.
Classification of pediatric pneumonia using ensemble transfer learning convolutional neural network Cahyani, Denis Eka; Hariadi, Anjar Dwi; Setyawan, Faisal Farris; Gumilar, Langlang; Setumin, Samsul
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7825

Abstract

Pneumonia is a condition characterised by the sudden inflammation of lung tissue, which is triggered by microorganisms such as fungi, viruses, and bacteria. Chest X-ray imaging (CXR) can detect pneumonia, but it requires considerable time and medical expertise. Consequently, the objective of this study is to diagnose pneumonia using CXR imaging in order to effectively detect early cases of pneumonitis in children. The study employs the ensemble transfer learning convolutional neural network (ETL-CNN) transfer learning ensemble, which combines multiple CNN transfer learning models. Resnet50-VGG19 and VGG19-Xception are the ETL-CNN models used in this investigation. Comparing ETL-CNN models to CNN transfer learning models such as Resnet50, VGG19, and Xception. Pediatric CXR pneumonia, which consists of a normal and pneumonia image, is the source of these study results. The results of this analysis indicate that Resnet50-VGG19 achieved the highest level of accuracy, 99.14%. Additionally, the Resnet50-VGG19 obtained the highest levels of precision and recall when comparing to other models. Consequently, the conclusion of this study is that the Resnet50-VGG19 model can generate acceptable classification performance for pediatric pneumonia based on CXR. This study improves classification results for performance when compared to earlier studies.
Performance evaluation of generative adversarial networks for generating mugshot images from text description Bahrum, Nur Nabilah; Setumin, Samsul; Othman, Nor Azlan; Fitri Maruzuki, Mohd Ikmal; Abdullah, Mohd Firdaus; Che Ani, Adi Izhar
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.5895

Abstract

The process of identifying photos from a sketch has been explored by many researchers, and the performance of the identification process is almost perfect, particularly for viewed sketches. Suspect identification based on sketches is one of the applications in forensic science. To identify the suspect using these kinds of methods, a face sketch is required. Hence, the methods require skilled artists to sketch the suspect based on descriptions provided by eyewitnesses. However, the skills of these artists are different from one another, which results in different rendered sketches. Therefore, this work attempts to propose a new identification method based only on forensic face-written descriptions. To investigate the feasibility of the proposed method, this study has evaluated the performance of some text-to-photo generators on both viewed and forensic datasets using three different models of GAN which are SAGAN, DFGAN, and DCGAN. Then, the generated images are compared to the real photo contained within those datasets to evaluate how well the proposed method recognizes the faces. The results demonstrated that the recognition rate for the generated photos by the DCGAN models is better than the other two models which achieve a 38.3% recognition rate at rank-10 for mugshot identification.
Design and implementation of smart traffic light controller with emergency vehicle detection on FPGA Mohamad Hadis, Nor Shahanim; Abdullah, Samihah; Abdul Sukor, Muhammad Ameerul Syafiqie; Hamzah, Irni Hamiza; Setumin, Samsul; Ibrahim, Mohammad Nizam; Azmin, Azwati
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 14, No 1: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v14.i1.pp48-59

Abstract

Increased traffic volumes resulting from urbanization, industrialization, and population growth have given rise to complex issues, including congestion, accidents, and traffic violations at intersections. In the absence of a functional smart traffic light system, traffic congestion occurs due to imbalanced traffic flow at intersections. Current traffic management lacks provisions for ensuring the unobstructed movement of emergency vehicles, even a small delay for which can have significant consequences. This paper presents a smart traffic light controller developed using Verilog hardware description language (HDL) in Quartus Prime 21.1 and Questa Intel field programmable gate array (FPGA) Starter Edition 2021.2, and implemented on an Altera DE2-115 FPGA. The controller is designed specifically to detect emergency vehicle at four-way intersections for inputs radio frequency identification (RFID) readers and infrared (IR) sensors. The RFID readers and IR sensors are managed through slide switches on the FPGA board. The smart traffic light controller contains three sub-modules: clock division, counter, and finite state machine (FSM) operation, enabling it to manage traffic in scenarios with emergency vehicles, high traffic density, and low traffic density. This proposed system can alleviate intersection congestion by controlling access and allocating time effectively. In conclusion, the project ensures the smooth passage of emergency vehicles by continuously monitoring their presence and giving them priority in traffic flow.
Sistem Inkubator Penyemaian Bibit Tanaman Hidroponik menggunakan Logika Fuzzy berbasis Internet of Things Hadi, Mokh. Sholihul; Arfiyansyah, Rizky; Mustika, Soraya Norma; Mizar, Muhammad Alfian; Lestari, Dyah; Setumin, Samsul
TEKNO: Jurnal Teknologi Elektro dan Kejuruan Vol 33, No 2 (2023)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um034v33i2p%p

Abstract

Hidroponik adalah suatu budidaya tanaman yang populer di Indonesia yang disebabkan oleh kurangnya lahan untuk bercocok tanam. Lahan yang sedikit serta tidak menggunakan tanah dalam budidayanya, membuat hidroponik cocok dikembangkan di mana saja terutama di area perkotaan bahkan di dalam ruangan. Terdapat beberapa tahapan dalam budidaya hidroponik salah satunya adalah pembibitan. Pada tahapan ini, bibit akan disemai hingga siap dipindah ke instalasi hidroponik, pada umumnya ketika sudah mulai muncul daun kedua hingga keempat. Pada tahap pembibitan, masalah yang sering dihadapi terutama pada indoor farming adalah etiolasi yaitu batang tidak kokoh yang umumnya disebabkan kurangnya sinar yang dibutuhkan oleh tanaman. Atau masalah lain ialah daun yang berwarna kuning yang disebabkan oleh lingkungan yang kurang memadai. Dari permasalahan ini, dirancang alat yang mampu mengondisikan lingkungan yang baik untuk pembibitan tanaman hidroponik berupa inkubator. Inkubator dirancang dengan sumber cahaya LED growlight dan penyiraman otomatis di dalamnya, serta sensor yang digunakan memantau kondisi di area inkubator seperti suhu dan kelembapan udara, water level, intensitas cahaya, dan kamera. Selain itu, terdapat pengendalian suhu dan udara di dalamnya berupa kipas dan mist maker yang menggunakan Fuzzy Inferensi System Sugeno, sehingga pengoptimalan suhu dan kelembapan di area inkubator terjaga sesuai pembacaan sensor. Untuk pemantauan inkubator terhubung ke platform website untuk memantau kondisi aktuator dan sensor secara realtime. Hasil pengujian menunjukkan sistem yang dibuat dapat memonitor beberapa variable yang berpengaruh pada tanaman serta mengendalikan suhu dan kelembaban udara dengan baik.
Systematic review of a lightweight convolutional neural network architectures on edge devices Abu Talib, Muhammad Abbas; Setumin, Samsul; Abu Bakar, Siti Juliana; Che Ani, Adi Izhar; Cahyani, Denis Eka
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 14, No 2: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v14.i2.pp339-352

Abstract

A lightweight convolutional neural network (CNN) has become one of the major studies in machine learning field to optimize its potential for employing it on the resource-constrained devices. However, a benchmark for fair comparison is still missing and thus, this paper aims to identify the recent studies regarding the lightweight CNN architectures including the types of CNN, its applications, edge devices usage, evaluation types and matrices, and performance comparison. The preferred reporting items for systematic reviews and meta-analysis (PRISMA) framework was used as the main approach to collect and interpret the literature. In the process, 37 papers were identified as meeting the criteria for lightweight CNNs aimed at image classification or regression tasks. Of these, only 20 studies explored the use of these models on edge devices. To conclude, MobileNet appeared as the most used architecture, while the types of CNN focused on image classification for the general-purpose application. Following that, the NVIDIA Jetson Nano was the most utilized edge device in recent research. Additionally, performance evaluation commonly included measures like accuracy and time, along with metrics such as recall, precision, F1-Score, and other similar indicators. Finally, the average accuracy for performance comparison can serve as threshold value for future research in this scope of study.
A Bibliometric Analysis of Metaheuristic Research and Its Applications Hendy, Hendy; Irawan, Mohammad Isa; Mukhlash, Imam; Setumin, Samsul
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 9 No 1 (2023): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v9i1.2675

Abstract

Metaheuristic algorithms are generic optimization tools to solve complex problems with extensive search spaces. This algorithm minimizes the size of the search space by using effective search strategies. Research on metaheuristic algorithms continues to grow and is widely applied to solve big data problems. This study aims to provide an analysis of the performance of metaheuristic research and to map a description of the themes of the metaheuristic research method. Using bibliometric analysis, we examined the performance of scientific articles and described the available opportunities for metaheuristic research methods. This study presents the performance analysis and bibliometric review of metaheuristic research documents indexed in the Scopus database between the period of 2016-2021. The overall number of papers published at the global level was 3846. At global optimization, heuristic methods, scheduling, genetic algorithms, evolutionary algorithms, and benchmarking dominate metaheuristic research. Meanwhile, the discussion on adaptive neuro-fuzzy inference, forecasting, feature selection, biomimetics, exploration, and exploitation, are growing hot issues for research in this field. The current research reveals a unique overview of metaheuristic research at the global level from 2016-2021, and this could be valuable for conducting future research.
Lung diseases identification using hybrid transfer learning and bidirectional long short-term memory Eka Cahyani, Denis; Tri Oktoviana, Lucky; Yasin, Mohamad; Wahyuningsih, Sapti; Dionixius, Dionixius; Maulidaningsih, Ranti; Setumin, Samsul
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.9114

Abstract

Lung diseases rank as the third most prevalent cause of mortality globally. Accurate identification of lung disease is essential to provide appropriate medical intervention for patients. This research devised a categorization system for lung diseases using chest X-Rays (CXR). The system can identify bacterial pneumonia, viral pneumonia, COVID-19, tuberculosis, and normal CXR. The approach for detecting lung diseases utilize a combination of hybrid transfer learning and bidirectional long short-term memory. The research included convolutional neural network (CNN) models including Resnet50-BiLSTM, VGG19-BiLSTM, InceptionV3-BiLSTM, Resnet50, VGG19, and InceptionV3. The Resnet50-BiLSTM model outperforms other models in terms of accuracy and overall performance. The Resnet50-BiLSTM model achieved an accuracy of 99.87%. The models that achieve the second greatest accuracy are Resnet50, VGG19-BiLSTM, VGG19, InceptionV3-BiLSTM, and InceptionV3. The research utilizes precision, recall, and F1-Measure to demonstrate that Resnet50-BiLSTM outperforms other methods by achieving the greatest value. This research improves the performance outcomes when compared to earlier studies.
Co-Authors A. Karim, Noor Khairiah Abdul Sukor, Muhammad Ameerul Syafiqie Abdullah, Mohd Firdaus Abdullah, Samihah Abu Bakar, Siti Juliana Abu Talib, Muhammad Abbas Arfiyansyah, Rizky Awang Damit, Dayang Suhaida Azmin, Azwati Bahrum, Nur Nabilah Che Ani, Adi Izhar Denis Eka Cahyani Dionixius, Dionixius Dyah Lestari Eka Cahyani, Denis Fitri Maruzuki, Mohd Ikmal Hamzah, Irni Hamiza Hariadi, Anjar Dwi Hendy Hendy Ibrahim, Mohammad Nizam Imam Mukhlash Langlang Gumilar M. Alfian Mizar Maulidaningsih, Ranti Mohamad Hadis, Nor Shahanim Mohammad Isa Irawan Mokh Sholihul Hadi Osman, Muhammad Khusairi Othman, Nor Azlan Sapti Wahyuningsih, Sapti Setyawan, Faisal Farris Siti Sendari Soraya Norma Mustika Sulaiman, Siti Noraini Syafaat, Mokhammad Tri Oktoviana, Lucky Yasin Mohamad Zaeni, Ilham Ari Elbaith