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All Journal Scan : Jurnal Teknologi Informasi dan Komunikasi Register: Jurnal Ilmiah Teknologi Sistem Informasi International Journal of Technology And Business Journal of Artificial Intelligence and Innovative Applications (JOAIIA) JEECS (Journal of Electrical Engineering and Computer Sciences) Journal of Information Technology and Cyber Security
Whardana, Adithya Kusuma
Universitas Tanri Abeng
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Social Sciences

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Segmentasi pembuluh darah pada citra retina dengan menggunakan Multi-Scale Line Detector (MSLD) dan Adaptive Morphology Whardana, Adithya Kusuma; Sutaji, Deni
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 3, No 1 (2017): Januari-Juni (3/7)
Publisher : Prodi Sistem Informasi - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1207.71 KB) | DOI: 10.26594/register.v3i1.716

Abstract

 Pembuluh darah pada retina merupakan bagian retina yang berfungsi memberikan suplai darah dan oksigen ke dalam retina. Sehingga apabila pembuluh darah tidak tersuplai oksigen, maka dapat ditarik kesimpulan bahwa pembuluh darah retina tersebut bermasalah, banyaknya noise pada daerah pembuluh darah menyebabkan proses dalam segmentasi. Karena permasalahan yang timbul, maka dalam penelitian ini diusulkan metode segmentasi pembuluh darah dengan menggabungkan dua metode, yaitu metode Multi-Scale Line Detector (MSLD) dan Adaptive Morphology. Dari keseluruhan metode memiliki fungsi yang berbeda-beda, MSLD berfungsi dalam proses pemisahan garis yang dibentuk oleh pembuluh darah yang dalam hal ini melalui proses perubahan citra orisinal ke citra green channel, namun dalam proses sebenarnya metode MSLD kurang dalam proses segmentasi, karena timbulnya masalah disaat terjadi garis yang menyilang antara optic disc dan pembuluh darah, sehingga pada saat segmentasi garis yang menyilang tersebut tidak akan ikut disegmentasi, sehingga membutuhkan metode penambahan pada proses segmentasinya, untuk itu diperlukan metode Adaptive Morphology, sehingga saat proses segmentasi sebelumnya yang telah dilakukan dengan menggunakan MSLD bisa disempurnakan dengan menggunakan metode Adaptive Morphology. Penggabungan metode sangat efektif karena bisa menghilangkan area optic disc yang membentuk garis menyilang dengan pembuluh darah secara sempurna dengan tanpa menghilangkan area pembuluh darah, sehingga dalam proses segmentasi dapat menghasilkan tingkat akurasi 97,94%.   The blood vessels of the retina are part of the retina that serves to supply blood and oxygen to the retina. So if the blood vessels are not supplied oxygen, it can be concluded that the retinal blood vessels are problematic, the amount of noise in the blood vessel causes the process in segments.Karena problems arise, then in this study proposed method of blood vessel segmentation by combining two methods, namely Methods of Multi-Scale Line Detector (MSLD) and adaptive morphology. From the whole method has different functions, MSLD function in the process of separation of lines formed by blood vessels in this case through the process of changing the original image to the green channel image, but in the actual process of MSLD method is less In the process of segmentation, due to the emergence of the problem when there is a crossing line between the optic disc and blood vessels, so that when the segmentation of the crossed line will not participate in segmentation, thus requiring additional method in the process of segmentation, for that required adaptive morphology method, Previous segmentation that has been done by using MSLD can be enhanced by using adaptive morphology method. Combination method is very effective because it can eliminate the optic disc area that forms a line crossed with blood vessels perfectly without removing the blood vessel area, so in the process of segmentation can produce an accuracy of 97.94%. 
Humanitarian OpenStreetMap Team Role Towards Mapping in Indonesia Putra, Muhammad Agiet; Whardana, Adithya Kusuma
International Journal of Technology And Business Vol 1 No 1 (2017): IJTB|International Journal of Technology And Business
Publisher : LPPM of STIMIK ESQ

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Abstract

This research aimed to describe the shape or the role of the Humanitarian OpenStreetMap Team in Indonesia. The main role of the Humanitarian OpenStreetMap Team is helping government agencies such as the BNPB, BPBDs, and others to be able to do digital mapping and able to make contingency planning as a scenario to cope with natural disasters. Then also to students with the aim of expanding the mapper community members who can help BPBDs in the vicinity. Noted there are 116 training ever conducted, with 2,777 participants involved, spread across 16 provinces in Indonesia. The method used is descriptive method, based on the literature that comes from the journal related to GIS and OSM, internal reports HOT, HOT and then also through the website. From this research, it was found that the areas that have been getting training from the HOT has successfully implemented the concept of digital mapping and manufacture of contingency planning, as well as more systematic in the handling of natural disasters
Humanitarian OpenStreetMap Team Role Towards Mapping in Indonesia Putra, Muhammad Agiet; Whardana, Adithya Kusuma
IJTB (International Journal of Technology And Business) Vol 1 No 1 (2017): IJTB (International Journal Of Technology And Business)
Publisher : LPPM of STIMIK ESQ

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

Abstract

This research aimed to describe the shape or the role of the Humanitarian OpenStreetMap Team in Indonesia. The main role of the Humanitarian OpenStreetMap Team is helping government agencies such as the BNPB, BPBDs, and others to be able to do digital mapping and able to make contingency planning as a scenario to cope with natural disasters. Then also to students with the aim of expanding the mapper community members who can help BPBDs in the vicinity. Noted there are 116 training ever conducted, with 2,777 participants involved, spread across 16 provinces in Indonesia. The method used is descriptive method, based on the literature that comes from the journal related to GIS and OSM, internal reports HOT, HOT and then also through the website. From this research, it was found that the areas that have been getting training from the HOT has successfully implemented the concept of digital mapping and manufacture of contingency planning, as well as more systematic in the handling of natural disasters
SEGMENTASI MICROANEURYSM PADA CITRA FUNDUS RETINA UNTUK DETEKSI DINI DIABETIC RETINOPATHY Whardana, Adithya Kusuma; Tjandrasa, Handayani
SCAN - Jurnal Teknologi Informasi dan Komunikasi Vol 9, No 3 (2014)
Publisher : Universitas Pembangunan Nasional "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/scan.v9i3.866

Abstract

Abstrak. Diabetik Retinopathy(DR) merupakan kelainan retina akibat dari komplikasi diabetesyang menyebabkan kebutaan. kondisi ini yang dikenal sebagai diabetik retinopati. Salah satucara untuk mengetahui bahwa ada diabetik retinopati dapat dilihat dari adanya kemunculanmicroaneurysm pada retina yang bisa dilihat melalui alat kedokteran kamera fundus. Penelitian inimengajukan suatu langkah untuk segmentasi microaneurysm pada citra retina untuk deteksi secaradini diabetik retinopati. Tahaan dalam penelitian ini adalah me-resize ulang citra, transformasi rgbke red channel, rekonstruksi morfologi, kemudian memperbaiki citra tersebut dengan contrastenhancement. Microaneurysm (MA) dideteksi dengan menggunakan filter Laplacian of gaussian.Area MA diperjelas dengan menggunakan tophat filtering, kemudian tahap terakhir dalamsegmentasi menggunakan thresholding.Kata Kunci: Diabetik Retinopati,citra fundus,deteksi microaneurysm,metode morfologi,Laplacianof gaussian, thresholding.
Segmentasi pembuluh darah pada citra retina dengan menggunakan Multi-Scale Line Detector (MSLD) dan Adaptive Morphology Whardana, Adithya Kusuma; Sutaji, Deni
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 3, No 1 (2017): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v3i1.716

Abstract

 Pembuluh darah pada retina merupakan bagian retina yang berfungsi memberikan suplai darah dan oksigen ke dalam retina. Sehingga apabila pembuluh darah tidak tersuplai oksigen, maka dapat ditarik kesimpulan bahwa pembuluh darah retina tersebut bermasalah, banyaknya noise pada daerah pembuluh darah menyebabkan proses dalam segmentasi. Karena permasalahan yang timbul, maka dalam penelitian ini diusulkan metode segmentasi pembuluh darah dengan menggabungkan dua metode, yaitu metode Multi-Scale Line Detector (MSLD) dan Adaptive Morphology. Dari keseluruhan metode memiliki fungsi yang berbeda-beda, MSLD berfungsi dalam proses pemisahan garis yang dibentuk oleh pembuluh darah yang dalam hal ini melalui proses perubahan citra orisinal ke citra green channel, namun dalam proses sebenarnya metode MSLD kurang dalam proses segmentasi, karena timbulnya masalah disaat terjadi garis yang menyilang antara optic disc dan pembuluh darah, sehingga pada saat segmentasi garis yang menyilang tersebut tidak akan ikut disegmentasi, sehingga membutuhkan metode penambahan pada proses segmentasinya, untuk itu diperlukan metode Adaptive Morphology, sehingga saat proses segmentasi sebelumnya yang telah dilakukan dengan menggunakan MSLD bisa disempurnakan dengan menggunakan metode Adaptive Morphology. Penggabungan metode sangat efektif karena bisa menghilangkan area optic disc yang membentuk garis menyilang dengan pembuluh darah secara sempurna dengan tanpa menghilangkan area pembuluh darah, sehingga dalam proses segmentasi dapat menghasilkan tingkat akurasi 97,94%.   The blood vessels of the retina are part of the retina that serves to supply blood and oxygen to the retina. So if the blood vessels are not supplied oxygen, it can be concluded that the retinal blood vessels are problematic, the amount of noise in the blood vessel causes the process in segments.Karena problems arise, then in this study proposed method of blood vessel segmentation by combining two methods, namely Methods of Multi-Scale Line Detector (MSLD) and adaptive morphology. From the whole method has different functions, MSLD function in the process of separation of lines formed by blood vessels in this case through the process of changing the original image to the green channel image, but in the actual process of MSLD method is less In the process of segmentation, due to the emergence of the problem when there is a crossing line between the optic disc and blood vessels, so that when the segmentation of the crossed line will not participate in segmentation, thus requiring additional method in the process of segmentation, for that required adaptive morphology method, Previous segmentation that has been done by using MSLD can be enhanced by using adaptive morphology method. Combination method is very effective because it can eliminate the optic disc area that forms a line crossed with blood vessels perfectly without removing the blood vessel area, so in the process of segmentation can produce an accuracy of 97.94%. 
Face Recognition Menggunakan Opencv Dengan Bahasa Pemograman Python OOP Untuk Sistem Presesi Rumah Sakit Mirza Fuadi; Ucuk Darusalam; Adithya Kusuma Whardana
Journal of Artificial Intelligence and Innovative Applications (JOAIIA) Vol 2, No 3 (2021): Agustus
Publisher : Journal of Artificial Intelligence and Innovative Applications (JOAIIA)

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Abstract

Sistem pengenalan wajah ini menggunakan OpenCV sebagai library deteksi objek. Hal ini sistem   dikarenakan library OpenCV terdapat metode Haar Cascade Classifier kedalam sistem deteksinya, sehingga memudahkan dalam sistem deteksi wajah yang dapat mengidentifikasi sebuah identitas dengan memanfaatkan library yang ada pada OpenCV. Bahasa pemrograman Python digunakan untuk membangun sistem ini. Metodelogi penelitian ini dimulai dari studi literatur, pengumpulan data, perancangan sistem, kemudian menganalisis data. Setelah sistem selesai dibuat, dilakukan pengujian sistem terhadap karakteristik wajah yang dapat mengidentifikasikan identitas seseorang.
Diabetic Retinopathy Blood Vessel Detection Using CNN and RNN Techniques Whardana, Adithya Kusuma; Rentelinggi, Parma Hadi; Timothy, Hezkiel Dokta
Journal of Information Technology and Cyber Security Vol. 1 No. 2 (2023): July
Publisher : Department of Information Systems and Technology, Faculty of Intelligent Electrical and Informatics Technology, Universitas 17 Agustus 1945 Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30996/jitcs.8716

Abstract

This research aims to detect diabetic retinopathy using Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). The main objective is to compare these two methods in detecting the condition. Based on the study’s result after training 10 times on each method, the accuracy results were 92% for the CNN method and 50% for the RNN method. These results show, this study with the dataset used, the CNN method is much more effective in detecting diabetic retinopathy than the RNN method. The CNN method is better due to its ability to extract spatial features from images, which is important in image classification tasks.
Classification Techniques in Finding Malignant Breast Cancer Detection Whardana, Adithya Kusuma; Mufti, Abdul Latief; Hermawan, Hendar; Aziz, Umar Alfaruq Abdul
Journal of Information Technology and Cyber Security Vol. 2 No. 1 (2024): January
Publisher : Department of Information Systems and Technology, Faculty of Intelligent Electrical and Informatics Technology, Universitas 17 Agustus 1945 Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30996/jitcs.8829

Abstract

The most fundamental aspect of cancer is that it is marked by abnormal and uncontrolled cell growth, allowing it to spread to the surrounding areas of existing tissues. One of the most common cancers experienced by people in Indonesia, according to the Indonesian Ministry of Health, is breast cancer. The diagnosis of diseases, especially cancer, also requires a visual form that is later used as an image to determine the condition within the patient's organs. The use of mammography images is one implementation of X-rays aimed at revealing the structure of human bones and tissues. The use of images is also recognized in information technology in the field of digital image processing, which is useful for analyzing, enhancing, compressing, and reconstructing images using a collection of computational techniques. One application of digital image processing techniques for breast mammography images is recognizing the possibility of breast cancer through computer automation using classification methods supported by googlepredict.net architectures. The results obtained in this study use a dataset sourced from King Abdul Aziz University, totaling 2378 images. The method used in this research is Convolutional Neural Network (CNN), with the addition of the GoogleNet architecture. The convolution extraction method runs with the GoogleNet architecture, enhancing deep learning for optimal breast cancer recognition. The overall results of this study found an average precision value of 90%, recall of 92%, F-1 Score of 91.49%, and accuracy of 91.49%.
Alzheimer Disease Prediction Through Guided Predictive Modeling With Machine Learning Alfayat, Mahesa Pramudya; Whardana, Adithya Kusuma
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 10 No. 1 (2025): JEECS (Journal of Electrical Engineering and Computer Sciences)
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v10i1.5

Abstract

Alzheimer's disease is a progressive neurodegenerative disorder characterized by the accumulation of misfolded brain proteins, especially beta-amyloid plaques, resulting in cognitive deterioration and memory impairment. However, there has been no effort of early detection to facilitate prompt intervention and preventive strategies. This research fulfills this essential need by employing the Open Access Series of Imaging Studies (OASIS) dataset supplied by the Alzheimer's Disease Neuroimaging Initiative (ADNI). The research employs the Cross-industry Standard Process for Data Mining (CRISP-DM) methodology to create and assess a classification model utilizing Artificial Neural Networks (ANN). The model attains a remarkable accuracy rate of 96%, exhibiting elevated precision, recall, and F1-scores across all categories. A 10-fold cross-validation technique was utilized to assess the model's robustness, resulting in an average accuracy of 90.7%. These findings underscore the efficacy of artificial neural networks in identifying Alzheimer's disease in its initial phases. This research utilizes advanced data mining approaches to improve predictive capacities and highlights the promise of machine learning in tackling intricate healthcare issues.
Early Breast Cancer Detection Using Gabor Filter and Convolutional Neural Network for Microcalcification Identification Mufti, Abdul Latief; Whardana, Adithya Kusuma
Journal of Information Technology and Cyber Security Vol. 3 No. 2 (2025): July
Publisher : Department of Information Systems and Technology, Faculty of Intelligent Electrical and Informatics Technology, Universitas 17 Agustus 1945 Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30996/jitcs.132037

Abstract

Breast cancer poses a considerable challenge in Indonesia, resulting in numerous fatalities. This study aims to improve the accuracy and efficiency of early breast cancer diagnosis by leveraging modern image processing and artificial intelligence. The dataset used is the Mini-DDSM (Mini Digital Database for Screening Mammography), taken from Kaggle and vetted by radiologists into a Region of Interest (ROI) consisting of three categories: Benign, Cancer, and Normal. The methodology encompasses comprehensive image preprocessing, which includes resizing, cropping, RGB-to-grayscale conversion, Laplacian of Gaussian (LoG) filtering, Gabor filtering, global threshold segmentation, and image enhancement. A Convolutional Neural Network (CNN) is employed for classification purposes. Ninety percent of the images are allocated for training, while 10% are designated for testing, with critical parameters such as learning rate, batch size, and epochs being tuned throughout the training process. The CNN architecture was assessed based on recognition rate, error rate, epoch count, and training duration. The results provide a flawless validation accuracy of 100% over 32 trials. The findings demonstrate that the suggested method markedly enhances early breast cancer identification using microcalcification analysis in mammography images, assisting medical professionals in early diagnosis and potentially elevating patient recovery rates through prompt detection and treatment.
Co-Authors Alfayat, Mahesa Pramudya Aziz, Umar Alfaruq Abdul Deni Sutaji Handayani Tjandrasa Hermawan, Hendar Mirza Fuadi Mufti, Abdul Latief Putra, Muhammad Agiet Rentelinggi, Parma Hadi Timothy, Hezkiel Dokta Ucuk Darusalam