Lucky Indra Kesuma

@um-palembang.ac.id

Information Technology/ Faculty Of Engineering
Universitas Muhammadiyah Palembang

Lucky Indra Kesuma


               Download Compact PDF  
https://researchid.co/luckyindra25

RESEARCH, TEACHING, or OTHER INTERESTS

Management Information Systems, Information Systems and Management, Information Systems, Computer Science
7

Scopus Publications

283

Scholar Citations

11

Scholar h-index

13

Scholar i10-index

Scopus Publications

  • Combination of Gamma Correction and Vision Transformer in Lung Infection Classification on CT-Scan Images
    Lucky Indra Kesuma, Pipin Octavia, Purwita Sari, Gracia Mianda Caroline Batubara, Karina Karina
    Journal of Electronics Electromedical Engineering and Medical Informatics, 2025
    Lung infection is an inflammatory condition of the lungs with a high mortality rate. Lung infections can be identified using CT-Scan images, where the affected areas are analyzed to determine the infection type. However, manual interpretation of CT-Scan results by medical specialists is often time-consuming, subjective, and requires a high level of accuracy. To address these challenges, this study proposes an automated classification method for lung infections using deep learning techniques. Convolutional Neural Networks (CNNs) are widely used for image classification tasks. However, CNN operates locally with limited receptive fields, making capturing global patterns in complex lung CT images challenging. CNN also struggles to model long-range pixel dependencies, which is crucial for analyzing visually similar regions in lung CT-Scans. This study uses a Vision Transformer (ViT) to overcome CNN limitations. ViT employs self-attention mechanisms to capture global dependencies across the entire image. The main contribution of this study is the implementation of ViT to enhance classification performance in lung CT-Scan images by capturing complex and global image patterns that CNN fails to model. However, ViT requires a large dataset to perform optimally. To overcome these challenges, augmentation techniques such as flipping, rotation, and gamma correction are applied to increase the amount of data without altering the important features. The dataset comprises lung CT-scan images sourced from Kaggle and is divided into Covid and Non-Covid classes. The proposed method demonstrated excellent classification performance, achieving accuracy, sensitivity, specificity, precision, and F1-Score above 90%. Additionally, the Cohen’s kappa coefficient reached 89%. These results show that the proposed method effectively classifies lung infections using CT-Scan images and has strong potential as a clinical decision-support tool, particularly in reducing diagnostic time and improving consistency in medical evaluations.
  • DDUSeg-Net as a Design of Convolutional Neural Network Architecture for Semantic Segmentation in Cervical, Cancer
    International Journal of Intelligent Engineering and Systems, 2025
    Cervical cancer is a significant health issue for women and ranks fourth in the world among the most dangerous cancers.An automatic diagnostic system is needed for pap smears to assist medical experts in diagnosing cervical cancer.One of the automatic diagnosis systems in detecting cervical cancer is semantic segmentation.Convolutional Neural Networks (CNN), particularly the U-Net architecture, have been widely used for segmentation tasks in medical imaging.Although U-Net has demonstrated effectiveness, its performance on low-quality images is often suboptimal, with issues such as loss of fine details during the down-sampling process.This study combines image enhancement and Double Dropout USeg-Net (DDUSeg-Net).Image enhancement techniques are applied to pap-smear images to improve image quality such as Gamma Correction for enhanced contrast, and Median Filtering for reduced noise.The proposed DDUSeg-Net architecture builds on the U-Net model by incorporating two U-Net blocks for more detailed feature extraction.SegNet's pooling indices are added to preserve spatial information during the segmentation process.Additionally, dropout layers are introduced to prevent overfitting and reduce the model's overall complexity.The image enhancement results indicate that the Mean Squared Error (MSE), Peak Signal to Ratio (PNSR), and Structural Similarity Image Index (SSIM) are above 85%.The performance metrics for the DDUSeg-Net model obtained accuracy, precision, recall, and F1-score above 90%.This analysis used 2D pap-smear images from the Herlev dataset.Overall, the combination of image enhancement and DDUSegNet demonstrates strong robustness in the segmentation of pap-smear images, effectively balancing the detection of the intersection areas between the nucleus, cytoplasm, and background.
  • Combination of Image Enhancement and Double U-Net Architecture for Liver Segmentation in CT-Scan Images
    Dwi Fitri Brianna, Lucky Indra Kesuma, Dite Geovani, Puspa Sari
    Journal of Electronics Electromedical Engineering and Medical Informatics, 2025
    Liver cancer can be identified using CT-Scan liver image segmentation. Liver segmentation can be performed using CNN architecture like U-Net. However, the segmentation results using U-Net architecture are affected by image quality. Low image quality can affect the accuracy of segmentation results. This study proposes a combination of image enhancement and segmentation stages on CT-Scan liver images. Image enhancement is achieved by using a combination of CLAHE to enhance contrast and Bilateral Filter to reduce noise. The segmentation architecture proposed in this study is Double U-Net which is a development of U-Net architecture by adding a second U-Net block with the same structure as a single U-Net. The first U-Net is used to extract simple features, while the second U-Net is used to extract more complex features and enhance the segmentation results of the first U-Net. PSNR and SSIM measure the results of image enhancement. The PSNR is more than 40dB and the SSIM result is close to 1. These results show that the proposed image enhancement method can enhance the quality of original images. The segmentation results were measured by calculating accuracy, sensitivity, specificity, dice score, and IoU. The result of liver segmentation obtained 99% for accuracy, 98% for sensitivity, 99% for specificity, 98% for dice score, and 90% for IoU. This shows that liver segmentation using Double U-Net obtained good segmentation. Results of image enhancement and image segmentation show that the proposed method is very good for enhancing image quality and performing liver segmentation accurately.
  • Enhanced Palm Oil Image Quality Using RGF-ESRGAN Architecture
    Arif Fadillah, Anita Desiani, Dian Palupi Rini, Lucky Indra Kesuma, Nuni Gofar, Faishal Fitra Ramadhan, Rifki Kurniawan
    Proceedings 7th International Conference on Informatics Multimedia Cyber and Information System Icimcis 2025, 2025
  • Median Filter and U-Net Architecture for Robust Segmentation Nucleus and Cytoplasm on Pap Smear
    Rudiansyah, Anita Desiani, Dian Palupi Rini, Lucky Indra Kesuma, Fitri Salamah, Silfani Cahaya Putri
    Proceedings 6th International Conference on Informatics Multimedia Cyber and Information System Icimcis 2024, 2024
    Cervical cancer is a condition caused by a layer of malignant cells that grows and develops rapidly on the cervix due to infection with the human papillomavirus virus (HPV). Cancer detection of the cervix can be done by a pap smear examination. This study aims to build an automatic segmentation model by combining augmentation and segmentation. The augmentation techniques used in this study are flip and median filters. Augmentation techniques aim to increase the quantity and variation of data to improve the quality of segmentation results. U-Net architecture is often used for image segmentation. By combining data augmentation and U-NET architecture, it is expected to meet the needs of this model which requires a lot of data. A combination of augmentation with U-NET is anticipated to improve the performance of the model significantly. The parameters used to measure the performance of the proposed method include accuracy, precision, recall, and Intersection over Union (IoU). The results of this method show an accuracy of 92%, precision of 91%, recall of 90%, and IoU of 84%. The results of the performance evaluation show that the method proposed for pap smear segmentation has excellent and powerful capabilities. However, the IoU performance in this study needs to be improved for further study. The proposed method can be used as a model for the development of automatic cervical cancer detection applications in the medical field.
  • ELREI: Ensemble Learning of ResNet, EfficientNet, and Inception-v3 for Lung Disease Classification based on Chest X-Ray Image
    International Journal of Intelligent Engineering and Systems, 2023
    A Chest X-ray (CXR) image can diagnose lung diseases.However, a diagnosis requires time and high accuracy, so an automatic system is needed.Convolutional neural network (CNN) is a reliable method for image classification and has many architectures.ResNet is a CNN architecture that can overcome gradient vanishing, but it has a deep network structure to detect errors.The EfficientNet CNN architecture can proportionally uniformize all depth, width, and resolution dimensions in each layer as needed, but it takes a long time in training.The Inception-v3 CNN architecture uses Inception blocks by reducing dimensions to small convolutions, but it has larger parameters than other architectures.ELREI is an acronym for ensemble learning of ResNet, EfficientNet, and Inception-v3 with weighted voting.ELREI combines the classification results on the ResNet, EfficientNet, and Inception-v3 architectures to overcome the limitations and combine the advantages of each architecture.ELREI works on the training stage at each epoch rather than the final results of each architecture.In addition to voting, ELREI uses a fully convolutional Network (FCN) at the final stage for the best weight determination and to prevent overfitting during training.The results of the accuracy, precision, recall, and F1-score of the ELREI method are excellent, above 98%.The training graph of the ELREI ensemble method proves that ELREI can overcome overfitting that occurs on the architectures.The results show the Ensemble ELREI method is excellent and robust for lung disease classification based on CXR images, which are carried out in 4 classes: normal, COVID-19, lung opacity, and pneumonia.
  • Improved Chest X-Ray Image Quality Using Median and Gaussian Filter Methods
    Lucky Indra Kesuma, Ermatita Ermatita, Erwin Erwin, Purwita Sari, Rudhy Ho Purabaya
    Proceedings 4th International Conference on Informatics Multimedia Cyber and Information System Icimcis 2022, 2022
    The lungs are one of the organs of the body that are responsible for the human respiratory process and are very susceptible to dangerous diseases. For this reason, early detection and diagnosis of lung organs is needed, one of which is through examination of Chest X-Ray (CXR) images. Examination of the results of CXR images is still done manually by doctors and radiologists, this requires time and high accuracy. To facilitate the examination, it is necessary to image quality enhancement in order to get better image quality results so as to produce an accurate diagnosis. The initial stage in this research is to apply the contour improvement technique to the image using Morphology Opening, and followed by noise reduction using Median Filter and Gaussian filter. The results of the two methods of noise reduction are compared with the results of image quality in order to find out the best method that can be applied. The implementation of image quality enhancement results was measured quantitatively using Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), and Structural Similarity Index Metrics (SSIM). In the Morphology Opening and Median Filter methods, the values obtained are 39.187, 22.252, and 0.952, respectively. Meanwhile, the Morphology Opening and Gaussian Filter methods obtained values of 38.717, 23.917 and 0.956. Based on these results, it can be concluded that both methods are able to improve image quality well.

RECENT SCHOLAR PUBLICATIONS

  • Aplikasi Bicara Pintar untuk Meningkatkan Kemampuan Komunikasi Siswa Tunarungu di Slb-B Ypac Palembang
    A Desiani, LI Kesuma, DD Sartika, AM Padhil, TH Putri, P Azzahra, ...
    Jurnal Kreativitas Pengabdian Kepada Masyarakat (PKM) 9 (4), 270-287 , 2026
    2026
  • Analysis of Critical Success Factors in Information Technology Project Management: A Literature Review
    Z Alfharizi, LI Kesuma, D Haryanto
    Journal of Artificial Intelligence and Engineering Applications (JAIEA) 5 (2 … , 2026
    2026
  • Implementasi Ensemble Weighted Voting Pada Arsitektur Densenet Mobilenet Xception Untuk Klasifikasi Penyakit Diabetic Retinopathy
    LI Kesuma, A Desiani, P Sari, ZR Saputra, M Ihsan, FN Muzayyadah
    IDEALIS: InDonEsiA journaL Information System 9 (1), 133-143 , 2026
    2026
  • Implementasi Sistem Informasi Manajemen Aset Inventaris Alat Kerja Karyawan Pustekinfo DPR RI Berbasis Web
    DA Rapel, ZR Saputra, LI Kusuma
    Infotek: Jurnal Informatika dan Teknologi 9 (1), 251-260 , 2026
    2026
  • Enhanced Palm Oil Image Quality Using RGF-ESRGAN Architecture
    A Fadillah, A Desiani, DP Rini, LI Kesuma, N Gofar, FF Ramadhan, ...
    2025 International Conference on Informatics, Multimedia, Cyber and … , 2025
    2025
  • Combination of Gamma Correction and Vision Transformer in Lung Infection Classification on CT-Scan Images
    LI Kesuma, P Octavia, P Sari, GMC Batubara, K Karina
    Journal of Electronics, Electromedical Engineering, and Medical Informatics … , 2025
    2025
    Citations: 1
  • Pengembangan Perangkat Lunak: Proses, Metode, dan Praktik Terbaik
    P Sari, LI Kesuma, A Impron, SF Nisrina, R Mayefis
    GET PRESS INDONESIA , 2025
    2025
  • Combination of Image Enhancement and Double U-Net Architecture for Liver Segmentation in CT-Scan Images
    DF Brianna, LI Kesuma, D Geovani, P Sari
    Journal of Electronics, Electromedical Engineering, and Medical Informatics … , 2025
    2025
    Citations: 6
  • DDUSeg-Net as a Design of Convolutional Neural Network Architecture for Semantic Segmentation in Cervical Cancer.
    A Desiani, DP Rini, LI Kesuma
    International Journal of Intelligent Engineering & Systems 18 (1) , 2025
    2025
    Citations: 2
  • Median Filter and U-Net Architecture for Robust Segmentation Nucleus and Cytoplasm on Pap Smear
    A Desiani, DP Rini, LI Kesuma, F Salamah, SC Putri
    2024 International Conference on Informatics, Multimedia, Cyber and … , 2024
    2024
    Citations: 1
  • Pemodelan Integrasi Data Barang Milik Negara di Perguruan Tinggi Menggunakan Metode ETL (Extract, Transform, Load) dengan Pentaho
    Purwita Sari, Lucky Indra Kesuma, Mira Afrina, Dedy Kurniawan
    The Indonesian Journal of Computer Science 13 (5), 8410-8425 , 2024
    2024
    Citations: 5
  • Simulasi Algoritma Apriori dan FP-Growth Dalam Menentukan Rekomendasi Kodefikasi Barang Pada Transaksi Persediaan
    P Sari, LI Kesuma, AF Oklilas, MA Buchari
    The Indonesian Journal of Computer Science 13 (1) , 2024
    2024
    Citations: 13
  • Combination of image enhancement and u-net architecture for cervical cell semantic segmentation
    R Rudiansyah, L Iryani, LI Kesuma, P Sari, A Alamsyah
    Journal of Informatics and Telecommunication Engineering 7 (2), 575-586 , 2024
    2024
    Citations: 14
  • Combination of Image Improvement on Segmentation Using a Convolutional Neural Network in Efforts to Detect Liver Disease
    N Umilizah, P Octavia, LI Kesuma, I Rayani, M Suedarmin
    Journal of Informatics and Telecommunication Engineering 7 (2), 375-384 , 2024
    2024
    Citations: 10
  • Application of the user centered design method to evaluate the relationship between user experience, user interface and customer satisfaction on banking mobile application
    S Frans, MRTD Dominica, IK Lucky, S Lilik, YU Eva
    Jurnal Informasi Dan Teknologi 6 (1) , 2024
    2024
    Citations: 50
  • The combination of black hat transform and U-Net in image enhancement and blood vessel segmentation in retinal images
    CP Darmo, LI Kesuma, D Geovani
    Computer Engineering and Applications Journal 12 (3), 129-145 , 2023
    2023
    Citations: 14
  • ELREI: Ensemble Learning of ResNet, EfficientNet, and Inception-v3 for Lung Disease Classification based on Chest X-Ray Image.
    LI Kesuma
    International Journal of Intelligent Engineering & Systems 16 (5) , 2023
    2023
    Citations: 27
  • Analisis Kualitas Pelayanan Website Tokopedia. co. id dan Shopee. co. id Menggunakan Metode Webqual 4.0 Dan TAM
    LI Kesuma, N Umilizah, M Cristianti
    Jurnal Informatika dan Sistem Informasi 3 (1), 64-76 , 2023
    2023
    Citations: 6
  • Perancangan Basis Data
    D Anggoro, P Sari, LI Kesuma, Y Sonatha, MF Rustan, L Faizal
    Get Press , 2023
    2023
  • Implementation of Image Quality Improvement Methods and Lung Segmentation on Chest X-Ray Images Using U-Net Architectural Modifications
    R Rudiansyah, L Indra Kesuma
    Computer Engineering and Applications Journal 12 (2), 71-78 , 2023
    2023
    Citations: 12

MOST CITED SCHOLAR PUBLICATIONS

  • Application of the user centered design method to evaluate the relationship between user experience, user interface and customer satisfaction on banking mobile application
    S Frans, MRTD Dominica, IK Lucky, S Lilik, YU Eva
    Jurnal Informasi Dan Teknologi 6 (1) , 2024
    2024
    Citations: 50
  • ELREI: Ensemble Learning of ResNet, EfficientNet, and Inception-v3 for Lung Disease Classification based on Chest X-Ray Image.
    LI Kesuma
    International Journal of Intelligent Engineering & Systems 16 (5) , 2023
    2023
    Citations: 27
  • Classification of COVID-19 diseases through lung CT-scan image using the ResNet-50 architecture
    LI Kesuma
    Computer Engineering and Applications Journal 12 (1), 11-30 , 2023
    2023
    Citations: 17
  • Arsitektur U-Net pada Segmentasi Citra Hati sebagai Deteksi Dini Kanker Liver.
    T Naraloka, LI Kesuma, A Sukmawati, M Cristianti
    Techno. com 21 (4) , 2022
    2022
    Citations: 16
  • Combination of image enhancement and u-net architecture for cervical cell semantic segmentation
    R Rudiansyah, L Iryani, LI Kesuma, P Sari, A Alamsyah
    Journal of Informatics and Telecommunication Engineering 7 (2), 575-586 , 2024
    2024
    Citations: 14
  • The combination of black hat transform and U-Net in image enhancement and blood vessel segmentation in retinal images
    CP Darmo, LI Kesuma, D Geovani
    Computer Engineering and Applications Journal 12 (3), 129-145 , 2023
    2023
    Citations: 14
  • Simulasi Algoritma Apriori dan FP-Growth Dalam Menentukan Rekomendasi Kodefikasi Barang Pada Transaksi Persediaan
    P Sari, LI Kesuma, AF Oklilas, MA Buchari
    The Indonesian Journal of Computer Science 13 (1) , 2024
    2024
    Citations: 13
  • Implementation of Image Quality Improvement Methods and Lung Segmentation on Chest X-Ray Images Using U-Net Architectural Modifications
    R Rudiansyah, L Indra Kesuma
    Computer Engineering and Applications Journal 12 (2), 71-78 , 2023
    2023
    Citations: 12
  • Identification of Floods in Palembang Area Using Fuzzy Logic Method of Mamdani and Sugeno
    A Sukmawati, L Iryana, P Adriansyah, LI Kesuma
    Journal Of Informatics And Telecommunication Engineering 6 (2), 434-444 , 2023
    2023
    Citations: 12
  • Improved chest X-ray image quality using median and gaussian filter methods
    LI Kesuma, E Ermatita, E Erwin, P Sari, RH Purabaya
    2022 International Conference on Informatics, Multimedia, Cyber and … , 2022
    2022
    Citations: 12
  • Implementasi Metode Multistage Random Sampling untuk Aplikasi Quick Count pada Pilkada Kota Palembang Berbasis Java Mobile
    P Sari, LI Kesuma, A Rifai
    J. Ilmu Komput. dan Teknol. Inf 1 (1), 10-15 , 2021
    2021
    Citations: 12
  • Biometric fingerprint implementation for presence checking and room access control system
    S Muslimin, Y Wijanarko, LI Kesuma, R Maulidda, Y Hasan, H Basri
    4th Forum in Research, Science, and Technology (FIRST-T1-T2-2020), 490-494 , 2021
    2021
    Citations: 11
  • Combination of Image Improvement on Segmentation Using a Convolutional Neural Network in Efforts to Detect Liver Disease
    N Umilizah, P Octavia, LI Kesuma, I Rayani, M Suedarmin
    Journal of Informatics and Telecommunication Engineering 7 (2), 375-384 , 2024
    2024
    Citations: 10
  • Reliabilitas Instrumen Kualitas E-Learning Menggunakan Teori Whyte & Bytheway Dan Webqul 4.0
    LI Kesuma
    Jurnal Digital Teknologi Informasi 2 (2), 94-98 , 2019
    2019
    Citations: 8
  • Combination of Image Enhancement and Double U-Net Architecture for Liver Segmentation in CT-Scan Images
    DF Brianna, LI Kesuma, D Geovani, P Sari
    Journal of Electronics, Electromedical Engineering, and Medical Informatics … , 2025
    2025
    Citations: 6
  • Analisis Kualitas Pelayanan Website Tokopedia. co. id dan Shopee. co. id Menggunakan Metode Webqual 4.0 Dan TAM
    LI Kesuma, N Umilizah, M Cristianti
    Jurnal Informatika dan Sistem Informasi 3 (1), 64-76 , 2023
    2023
    Citations: 6
  • Decision Support System for Determining COVID-19 Aid Recipients using the Simple Additive Weighting (SAW) Method
    LI Kesuma, I Indra, I Irmawati
    Journal Of Engineering And Technology Innovation (JETI) 2 (01), 18-25 , 2023
    2023
    Citations: 6
  • LITERASI DIGITAL: CERDAS MENGGUNAKAN MEDIA SOSIAL DALAM MENANGGULANGI BERITA PALSU (HOAX) DILINGKUNGKAN UNIVERSITAS SJAKHYAKIRTI
    AA Putra, P Maharani, LI Kesuma
    Jurnal Pengabdian Kepada Masyarakat Inovasi Teknologi 1 (01), 13-17 , 2023
    2023
    Citations: 6
  • Model Integrasi Aplikasi X dan Y dengan pendekatan Enterprise Application Integration
    P Sari, LI Kesuma, MA Buchari, BW Putra
    JSI: Jurnal Sistem Informasi (E-Journal) 15 (1) , 2023
    2023
    Citations: 6
  • Sistem Informasi Sekolah SPS PAud Balkam Ceria Berbasis Website
    D Airlambang
    Universitas Pembangunan Nasional Veteran Jakarta , 2022
    2022
    Citations: 6