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All Journal ComEngApp : Computer Engineering and Applications Journal Techno.Com: Jurnal Teknologi Informasi JSI: Jurnal Sistem Informasi (E-Journal) JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING SEINASI-KESI Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Informasi dan Teknologi Jurnal Digital Teknologi Informasi Jurnal Generic Informanika Jurnal Pengabdian Kepada Masyarakat Inovasi Teknologi The Indonesian Journal of Computer Science
Kesuma, Lucky Indra
Universitas Muhammadiyah
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

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The Combination of Black Hat Transform and U-Net in Image Enhancement and Blood Vessel Segmentation in Retinal Images Cahyo Pambudi Darmo; Lucky Indra Kesuma; Dite Geovani
Computer Engineering and Applications Journal Vol 12 No 3 (2023)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v12i3.452

Abstract

Diabetic Retinopathy (DR) is a disorder of the eye caused by damage to blood vessels in the retina. Damage to the retinal blood vessels can be analyzed by segmenting the blood vessels on the image. This study proposes a combination of image enhancement and blood vessel segmentation in retinal images. Retinal image enhancement is carried out using the black hat transform method to obtain a detailed view of blood vessels in retinal images. Segmentation of blood vessels in retinal images is carried out using the U-Net architecture. The results of image enhancement are measured using MSE and PSNR. This study has an MSE value below 0.05 and a PSNR above 90dB. The MSE and PSNR values obtained show that the black hat transform method is very good at image enhancement. Segmentation has an accuracy value above 0.95 and a sensitivity value above 0.85. In addition, the specificity value and f1-score are above 0.8. This shows that the proposed stages of image enhancement and blood vessel segmentation are able to accurately recognize blood vessel features in retinal images.
Combination of Image Improvement on Segmentation Using a Convolutional Neural Network in Efforts to Detect Liver Disease Umilizah, Nia; Octavia, Pipin; Kesuma, Lucky Indra; Rayani, Ira; Suedarmin, Muhammad
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 7 No. 2 (2024): Vol. 7 No. 2 (2024): Issues January 2024
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v7i2.10221

Abstract

Liver disease is a disease caused by various factors such as the spread of viruses. Liver damage causes the ability to break down red blood cells to be disrupted. Detection of liver disease can be done using the segmentation. Segmentation is useful for separating an area of the liver in an image from other areas. Segmentation carried out manually requires experts and a long time, so automatic segmentation is needed. CNN can be used to perform automatic segmentation. One of the CNN architectures is the U-Net architecture. Segmentation requires quality images to improve recognition of image patterns, so image improvement is needed in the form of contrast enhancement. Contrast improvement was carried out by taking Green Channel images. Contrast enhancement was carried out using the Contrast Stretching and CLAHE methods. The image improvement results show MSE and SSIM values 66.1844 and 0.7088. Evaluation of the image improvements obtained provides significant changes. The improved image is used at the segmentation stage. Segmentation is carried out using the U-Net architecture. The segmentation results obtained performance evaluation values in the form of accuracy 99.6%, sensitivity 98.9%, and specificity 99.7%. This shows that the proposed method can detect liver disease in liver images well
Combination of Image Enhancement and U-Net Architecture for Cervical Cell Semantic Segmentation Rudiansyah, Rudiansyah; Iryani, Lemi; Kesuma, Lucky Indra; Sari, Puspa; Alamsyah, Agung
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 7 No. 2 (2024): Vol. 7 No. 2 (2024): Issues January 2024
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v7i2.10399

Abstract

Cervical cancer is the second leading cause of death in women and ranks fourth as a disease that occurs in women worldwide. Cervical cancer is a disease that is difficult to detect and can be detected when it is in an advanced stage. This requires early prevention by carrying out a pap-smear examination. Pap-smear examination manually requires a relatively long time, so a tool is needed by segmentation. Segmentation is image processing by performing perfection between the intended object and the background. One of the CNN methods commonly used in medical image segmentation is the U-Net architecture. Segmentation in this study was carried out on the nucleus and cytoplasm of the Herlev dataset using the U-Net architecture combined with data augmentation and image enhancement. In the learning process, this research resulted in a fairly high IoU value of 78% and an RMSE close to 20%. The results of this study also yielded an accuracy value of 89%, with an average precision, recall and F1 score of 89%, 89% and 88.67%, respectively. This shows that the combination of the CNN U-Net architecture with image quality improvement and data augmentation is quite good at segmenting cervical cells for the nucleus and cytoplasm
Sistem Informasi Sekolah SPS PAud Balkam Ceria Berbasis Website Airlambang, Dwiki; Krisnanik, Erly; Kesuma, Lucky Indra
PROSIDING SEINASI-KESI Vol 1, No 1 (2022): SEMINAR NASIONAL INFORMATIKA, SISTEM INFORMASI, DAN KEAMANAN SIBER
Publisher : Fakultas Ilmu Komputer UPN Veteran Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Website merupakan hal yang utama dalam memenuhi keperluan untuk sekolah. Paud memiliki fungsi untuk memberikan layanan Pendidikan untuk anak usia dini yang memerlukan sistem informasi website untuk menyebarkan informasi dan untuk tahapan dalam pendaftaran karena sistem yang saat ini digunakan masih secara manual. Solusi atas permasalahan maka dibutuhkan adanya sistem informasi berbasis website. Dalam mencegah terjadinya hilang berkas atau dokumen pendaftaran dan dalam proses menyebarkan informasi yang belum memadai, menjadi solusi dalam pengelolaan hal tersebut. Proses pengembangan sistem dengan menggunakan metode waterfall dalam menyesuaikan keperluan dari sistem yang di proses melalui hasil observasi dan wawancara ke objek penelitian. Sistem informasi menggunakan Visual Studio Code sebagai aplikasi untuk koding dan framework laravel untuk bahasa pemrograman. Harapan dengan adanya sistem ini menjadi solusoi atas permasalahan yang dialami oleh sekolah.
Application of The User Centered Design Method To Evaluate The Relationship Between User Experience, User Interface and Customer Satisfaction on Banking Mobile Application Sudirjo, Frans; Ratna Tungga Dewa, Dominica Maria; Indra Kesuma, Lucky; Suryaningsih, Lilik; Yuniarti Utami, Eva
Jurnal Informasi dan Teknologi 2024, Vol. 6, No. 1
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60083/jidt.v6i1.465

Abstract

The aim of this research is to evaluate user satisfaction through UI/UX design in mobile banking and find out what kind of UI/UX appearance users want using the user-centered design method. This research uses a qualitative approach. In addition to the qualitative approach, this research also incorporates a quantitative approach. Data collection in this research used usability testing, interviews, and questionnaires. We calculated the data obtained from the questionnaire statistically using Microsoft Excel. The results of questionnaire data analysis can be in the form of graphs or numbers. Researchers create a list of required elements or features to maintain, add, improve, or remove after collecting and analyzing data. Researchers will implement the requirements list by providing prototype design recommendations. The research results showed that 90% of participants successfully completed the 10 task scenarios given during the evaluation of ongoing mobile banking usability testing. Furthermore, during the evaluation of mobile banking prototype recommendations, 99% of participants successfully completed the 10 task scenarios that had been given. The appearance that users want is a more attractive appearance by adding more icons and illustrations, as well as making the appearance more modern. Additional features that users want, namely adding an e-wallet top-up feature, not limiting the account mutations that can be seen, adding a share feature after transfer, adding fingerprint and face ID features in the login section, and adding a copy feature to be able to copy account numbers.
Combination of Image Enhancement and Double U-Net Architecture for Liver Segmentation in CT-Scan Images Fitri Brianna, Dwi; Indra Kesuma, Lucky; Geovani, Dite; Sari, Puspa
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 1 (2025): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i1.582

Abstract

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.
Simulasi Algoritma Apriori dan FP-Growth Dalam Menentukan Rekomendasi Kodefikasi Barang Pada Transaksi Persediaan Sari, Purwita; Kesuma, Lucky Indra; Oklilas, Ahmad Fali; Buchari, M. Ali
The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i1.3632

Abstract

Keberhasilan proses pembangunan memerlukan dukungan optimal dalam pertukaran data dan informasi antar instansi guna mencapai integrasi sistem yang seimbang antara pemerintah dan para pengguna. SAKTI, sebuah aplikasi keuangan tingkat instansi, telah dirancang untuk mengelola segala aspek keuangan, mulai dari perencanaan hingga pertanggungjawaban anggaran. Aplikasi SAKTI ini mengintegrasikan semua aplikasi satuan kerja yang ada, bertujuan untuk meningkatkan efektivitas, efisiensi, transparansi, dan akuntabilitas dalam pengelolaan keuangan. Meskipun telah diimplementasikan sejak awal tahun 2022, operator komitmen masih menghadapi kendala dalam penentuan kodefikasi barang, terutama karena kurangnya familiaritas dengan tugas tersebut dan jumlah barang yang banyak sebagai referensi. Kesalahan yang dilakukan oleh operator komitmen dapat berdampak pada proses pendetailan aset pada modul persediaan dan aset. Dalam penelitian ini, peneliti menggunakan metode Algoritma Apriori dan frequent pattern growth (FP-growth) sebagai alat untuk menemukan sejumlah aturan asosiasi dari data transaksi barang yang disimpan dalam basis data aplikasi SAKTI. Hasil simulasi menunjukkan bahwa aturan yang memenuhi minimum support dan minimum confidence, dengan pemilihan terbanyak adalah Ballpoint Standar Tecno, refill tisu plastik, Lak Ban Hitam 2 Inchi Merk Daimaru, dan Ballpoint Kenko K1 (0,5) sebesar 100%.
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 Vol. 13 No. 5 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i5.4424

Abstract

Strategic transformation and technological innovation in the realm of governments require officials to adapt to new perspectives. Efforts to increase the efficiency of the bureaucratic system and improve public services are directed at realizing ideal government governance. This research aims to build a data integration model for Higher Education State Property (BMN) using the ETL (Extract, Transform, Load) method using Pentaho. Researchers used the ETL method because the function of the asset module in the SAKTI Application, such as the room goods list (DBR), was not optimal, which made it difficult for the BMN team in the inventory process. Usually, an inventory is carried out using the SITARI application to identify recorded items and their location. The steps taken are to reconcile the BMN data in the SIMAN application with the procurement documents, then import the appropriate data into the SITARI application. Considering the large amount of BMN data, it is still possible for operator errors to occur in matching the data. This research is expected to produce an integration model that can reduce the level of data synchronization errors and make it easier for the BMN team to present more accurate reports.
Classification of Covid-19 Diseases Through Lung CT-Scan Image Using the ResNet-50 Architecture Kesuma, Lucky Indra; Rudiansyah
Computer Engineering and Applications Journal (ComEngApp) Vol. 12 No. 1 (2023)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Covid-19 is a disease of the respiratory tract caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) virus. One way to diagnose Covid-19 can be done by examining lung abnormalities on the results of a Computed Tomography Scan (CT-Scan) of the lungs. However, the determination of the diagnostic results obtained requires high accuracy and quite a long time. For this reason, an automatic system is needed to make it easier for medical personnel to diagnose Covid-19 disease quickly and accurately. One way to do this with the help of a computer is pattern recognition. In this study, pattern recognition techniques were used which were divided into three stages, namely pre-processing, feature extraction and classification. The methods used in the pre-processing stage are grayscale and Contrast Limited Adaptive Histogram Equalization (CLAHE) to improve image quality and contrast. The extraction stage uses the Principal Component Analysis (PCA) method, because it can reduce data dimensions without eliminating important features in the data. For the classification stage, a deep learning-based method is used, namely the Convolutional Neural Network (CNN). The CNN architecture used in this study is Resnet-50. The method proposed in this research is evaluated by measuring the performance values of accuracy, recall, precision, F1-score, and Cohen Kappa. The results of the study indicate that the PCA method has worked optimally in dimension reduction, without losing important features on CT-scan images of the lungs. Besides that, the proposed method has succeeded in classifying Covid-19 very well, as seen from the accuracy, Recall, Precision, F1-Score and Cohen Kappa values above 90%.
Implementation of Image Quality Improvement Methods and Lung Segmentation on Chest X-Ray Images Using U-Net Architectural Modifications Rudiansyah; Kesuma, Lucky Indra; Anggara, M Ikhsan
Computer Engineering and Applications Journal (ComEngApp) Vol. 12 No. 2 (2023)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

COVID-19 is an infectious disease that causes acute respiratory distress syndrome due to the SARS-CoV-2 virus. Rapid and accurate screening and early diagnosis of patients play an essential role in controlling outbreaks and reducing the spread of this disease. This disease can be diagnosed by manually reading CXR images, but it is time-consuming and prone to errors. For this reason, this research proposes an automatic medical image segmentation system using a combination of U-Net architecture with Batch Normalization to obtain more accurate and fast results. The method used in this study consists of pre-processing using the CLAHE method and morphology opening, CXR image segmentation using a combination of U-Net-4 Convolution Block architecture with Batch Normalization, then evaluated using performance measures such as accuracy, sensitivity, specificity, F1-score, and IoU. The results showed that the U-Net architecture modified with Batch Normalization had successfully segmented CXR images, as seen from all performance measurement values above 94%.
Co-Authors Ade Sukmawati Ade Sukmawati Ahmad Fali Oklilas Ahmad Rifai Airlambang, Dwiki Alamsyah, Agung Anggara, M Ikhsan Azhar Andika Putra BAKHTIAR K Batubara, Gracia Mianda Caroline Bayu Wijaya Putra Buchari, M. Ali Darmo, Cahyo Pambudi Dite Geovani Dwi Fitri Brianna Erly Krisnanik Fitri Brianna, Dwi Frans Sudirjo Geovani, Dite Intan Hesti Indriana Iryani, Lemi Karina Karina Lemi Iryana M. Ali Buchari Marissa Cristianti Mira Afrina Muhammad Ikhsan Anggara Naraloka, Therezia Nia Umilizah Octavia , Pipin Octavia, Pipin PAISAL PAISAL Pandi Adriansyah Purwita Sari Purwita Sari Purwita Sari, Purwita Puspa Sari, Puspa Putri Maharani Ratna Tungga Dewa, Dominica Maria Rayani, Ira Rudiansyah Rudiansyah Rudiansyah Rudiansyah Rudiansyah Rudiansyah Rudiansyah Rudiansyah, Rudiansyah Sari , Purwita Suedarmin, Muhammad Suryaningsih, Lilik Therezia Naraloka Tuti Ayu Lestari Umilizah, Nia Wiwik Handayani Yuniarti Utami, Eva