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All Journal International Journal of Electrical and Computer Engineering Jurnal Media Infotama International Journal of Advances in Intelligent Informatics JOIV : International Journal on Informatics Visualization Communications in Science and Technology Conference SENATIK STT Adisutjipto Yogyakarta Jurnal Informatika IJITEE (International Journal of Information Technology and Electrical Engineering) Indonesian Journal of Electrical Engineering and Computer Science Journal of Computer System and Informatics (JoSYC) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal INFOTEL JTKSI (Jurnal Teknologi Komputer dan Sistem Informasi)
Hanung Adi Nugroho
Department Of Electrical And Information Engineering, Universitas Gadjah Mada
Aerospace Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Feature analysis for stage identification of Plasmodium vivax based on digital microscopic image Hanung Adi Nugroho; I Md. Dendi Maysanjaya; Noor Akhmad Setiawan; E. Elsa Herdiana Murhandarwati; Widhia K.Z Oktoeberza
Indonesian Journal of Electrical Engineering and Computer Science Vol 13, No 2: February 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v13.i2.pp721-728

Abstract

Plasmodium parasite is identified to confirm malaria disease.  Paramedics need to observe the presence of this parasite prepared on thick and thin blood films under microscope.  However, false identification still occurs which is caused by human factor during the examination.  Thus, malaria identification based on digital image processing has been widely developed to overcome the error possibility.  This paper proposes a scheme to identify and classify the stages of Plasmodium vivax parasite on digital microscopic image of thin blood films based on feature analysis.  Shape and texture features are extracted from segmented parasite objects.   Feature selection based on wrapper method is then conducted to obtain relevant features which may contribute in improving the classification result.  The classification process is conducted based on Naïve Bayes classifier.  The performance of proposed method is evaluated using 73 digital microscopic images of P.vivax parasite on thin blood films comprising of 29 trophozoites, 10 schizonts and 34 gametocytes stages.  By using six selected features including perimeter, dispersion, mean of intensity, ASM, contrast GLCM and entropy GLCM, the proposed scheme achieves the best classification rate with the accuracy, sensitivity and specificity of 97.29%, 97.30% and 97.30%, respectively.  This indicates that the proposed scheme has a potential to be implemented in the development of a computerised aided malaria diagnosis system for assisting the paramedics.
Comparison of Multiscale Entropy for Lung Sound Classification Achmad Rizal; Risanuri Hidayat; Hanung Adi Nugroho
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 3: December 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i3.pp984-994

Abstract

Lung sound is a biological signal that can be used to determine the health level of the respiratory tract. Various digital signal processing techniques have been developed for automatic classification of lung sounds. Entropy is one of the parameters used to measure the biomedical signal complexity. Multiscale entropy is introduced to measure the entropy of a signal at a particular scale range. Over time, various multiscale entropy techniques have been proposed to measure the complexity of biological signals and other physical signals. In this paper, some multiscale entropy techniques for lung sound classification are compared. The result of the comparison indicates that the Multiscale Permutation Entropy (MPE) produces the highest accuracy of 97.98% for five lung sound datasets. The result was achieved for the scale 1-10 producing ten features for each lung sound data. This result is better than other seven entropies. Multiscale entropy analysis can improve the accuracy of lung sound classification without requiring any features other than entropy.
Shape analysis for classification of breast nodules on digital ultrasound images Hanung Adi Nugroho; Hesti Khuzaimah Nurul Yusufiyah; Teguh Bharata Adji; Widhia K.Z Oktoeberza
Indonesian Journal of Electrical Engineering and Computer Science Vol 13, No 2: February 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v13.i2.pp837-844

Abstract

One of the imaging modalities for early detection of breast cancer malignancy is ultrasonography (USG).  The malignancy can be analysed from the characteristic of nodule shape.  This study aims to develop a method for classifying the shape of breast nodule into two classes, namely regular and irregular classes.  The input image is pre-processed by using the combination of adaptive median filter and speckle reduction bilateral filtering (SRBF) to reduce speckle noises and to eliminate the image label.  Afterwards, the filtered image is segmented based on active contour followed by feature extraction process.  Nine extracted features, i.e. roundness, slimness and seven features of invariant moments, are used to classify nodule shape using multi-layer perceptron (MLP).  The performance of the proposed method is evaluated using 105 breast nodule images which comprise of 57 regular and 48 irregular nodule images.  The results of classification process achieve the level of accuracy, sensitivity and specificity at 96.20%, 97.90% and 94.70%, respectively.  These results indicate that the proposed method successfully classifies the breast nodule images based on shape analysis.
Identification of plasmodium falciparum and plasmodium vivax on digital image of thin blood films gf Hanung Adi Nugroho; Made Satria Wibawa; Noor Akhmad Setiawan; E. Elsa Herdiana Murhandarwati; Ratna Lestari Budiani Buana
Indonesian Journal of Electrical Engineering and Computer Science Vol 13, No 3: March 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v13.i3.pp933-944

Abstract

Observing presence of Plasmodium parasite of stained thick or thin blood films through microscopic examination is a gold standard for malaria diagnosis.  Although the microscopic examination has been extensively used, misidentification might occur caused by human factors.  In order to overcome misidentification problem, several studies have been conducted to develop a computer-aided malaria diagnosis (CADx) to assist paramedics in decision-making.  This study proposes an approach to identify species and stage of Plasmodium falciparum and Plasmodium vivax on thin blood films collected from the Laboratory of Parasitology, Faculty of Medicine, Universitas Gadjah Mada.  Adaptive k-means clustering is applied to segment Plasmodium parasites.  A total of 39 features consisting of shape and texture features are extracted and then selected by using wrapper-based forward and backward directions.  Classification is evaluated in two schemes.  The first scheme is to classify the species of parasite into two classes. The second scheme is to classify the species and stage of parasite into six classes.  Three classifiers applied are k-nearest neighbour (KNN), support vector machine (SVM) and multi-layer perceptron (MLP).  Furthermore, to facilitate the multiclass classification, one-versus-one (OVO) and one-versus-all (OVA) methods are implemented.  The first scheme achieves the accuracy of 88.70% based on MLP classifier using three selected features.  While the accuracy gained by the second scheme is 95.16% based on OVO and MLP classifier using 29 selected features.  These results indicate that the proposed approach successfully identifies the species and stage of parasite on thin blood films and has potential to be implemented in the CADx system for assisting paramedics in diagnosing malaria.
Convolutional Neural Network featuring VGG-16 Model for Glioma Classification Agus Eko Minarno; Sasongko Yoni Bagas; Munarko Yuda; Nugroho Adi Hanung; Zaidah Ibrahim
JOIV : International Journal on Informatics Visualization Vol 6, No 3 (2022)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.3.1230

Abstract

Magnetic Resonance Imaging (MRI) is a body sensing technique that can produce detailed images of the condition of organs and tissues. Specifically related to brain tumors, the resulting images can be analyzed using image detection techniques so that tumor stages can be classified automatically. Detection of brain tumors requires a high level of accuracy because it is related to the effectiveness of medical actions and patient safety. So far, the Convolutional Neural Network (CNN) or its combination with GA has given good results. For this reason, in this study, we used a similar method but with a variant of the VGG-16 architecture. VGG-16 variant adds 16 layers by modifying the dropout layer (using softmax activation) to reduce overfitting and avoid using a lot of hyper-parameters. We also experimented with using augmentation techniques to anticipate data limitations. Experiment using data The Cancer Imaging Archive (TCIA) - The Repository of Molecular Brain Neoplasia Data (REMBRANDT) contains MRI images of 130 patients with different ailments, grades, races, and ages with 520 images. The tumor type was Glioma, and the images were divided into grades II, III, and IV, with the composition of 226, 101, and 193 images, respectively. The data is divided by 68% and 32% for training and testing purposes. We found that VGG-16 was more effective for brain tumor image classification, with an accuracy of up to 100%. 
A new approach for sensitivity improvement of retinal blood vessel segmentation in high-resolution fundus images based on phase stretch transform Kartika Firdausy; Oyas Wahyunggoro; Hanung Adi Nugroho; Muhammad Bayu Sasongko
International Journal of Advances in Intelligent Informatics Vol 8, No 3 (2022): November 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v8i3.914

Abstract

The eye-fundus photograph is widely used for eye examinations. Accurate identification of retinal blood vessels could reveal information that is helpful for clinical diagnoses of many health disorders. Although several methods have been proposed to segment images of retinal blood vessels, the sensitivity of these methods is plausible to be improved. The algorithm’s sensitivity refers to the algorithm’s ability to identify retinal vessel pixels correctly. Furthermore, the resolution and quality of retinal images are improving rapidly. Consequently, new segmentation methods are in demand to overcome issues from high-resolution images. This study presented improved performance of retinal vessel segmentation using a novel edge detection scheme based on the phase stretch transform (PST) function as its kernel. Before applying the edge detection stage, the input retinal images were pre-processed. During the pre-processing step, non-local means filtering on the green channel image, followed by contrast limited adaptive histogram equalization (CLAHE) and median filtering, were applied to enhance the retinal image. After applying the edge detection stage, the post-processing steps, including the CLAHE, median filtering, thresholding, morphological opening, and closing, were implemented to obtain the segmented image. The proposed method was evaluated using images from the high-resolution fundus (HRF) public database and yielded promising results for sensitivity improvement of retinal blood vessel detection. The proposed approach contributes to a better segmentation performance with an average sensitivity of 0.813, representing a clear improvement over several benchmark techniques
Lung sound classification using multiresolution Higuchi fractal dimension measurement Achmad Rizal; Risanuri Hidayat; Hanung Adi Nugroho; Willy Anugrah Cahyadi
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5091-5100

Abstract

Lung sound is one indicator of abnormalities in the lungs and respiratory tract. Research for automatic lung sound classification has become one of the interests for researchers because lung disease is one of the diseases with the most sufferers in the world. The use of lung sounds as a source of information because of the ease in data acquisition and auscultation is a standard method in examining pulmonary function. This study simulated the potential use of Higuchi fractal dimension (HFD) as a feature extraction method for lung sound classification. HFD calculations were run on a series of k values to generate some HFD values as features. According to the simulation results, the proposed method could produce an accuracy of up to 97.98% for five classes of lung sound data. The results also suggested that the shift in HFD values over the selection of a time interval k can be used for lung sound classification.
Deteksi Iris Berdasarkan Metode Black Hole dan Circle Curve Fitting Danny Kurnianto; Indah Soesanti; Hanung Adi Nugroho
JURNAL INFOTEL Vol 5 No 2 (2013): November 2013
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v5i2.3

Abstract

Sistem pengenalan identitas personal berdasarkan ciri biometrika adalah suatu sistem pengenalan seseorang berdasarkan pada ciri biometrika yang melekat pada orang tersebut. Iris mata merupakan salah satu ciri biometrik yang handal untuk sistem pengenalan identitas personal. Bagian sistem pengenalan identitas personal berdasarkan biometrik iris yang dianggap paling krusial adalah deteksi lokasi iris, karena akurasi deteksi iris berpengaruh pada tingkat akurasi sistem secara keseluruhan. Lokasi iris pada citra mata dibatasi oleh dua buah lingkaran yang memisahkan antara bagian iris dengan pupil dan sklera. Telah banyak metodemetode yang diusulkan oleh para peneliti untuk menghasilkan deteksi lokasi iris dengan akurat dan cepat. Masalah akurasi, kecepatan waktu eksekusi dan ketahanan terhadap noise merupakan bidang penelitian yang menantang pada deteksi iris. Makalah ini menyajikan metode deteksi iris menggunakan metode black hole dan circle curve fitting. Langkah pertama, mencari batas dalam lingkaran iris yang memisahkan antara daerah iris dan pupil. Dengan metode black hole yang bekerja berdasarkan fakta bahwa lokasi pupil merupakan daerah lingkaran yang paling hitam dan memiliki distribusi nilai intensitas yang seragam, maka lokasi pupil dapat ditentukan dengan teknik pengambangan. Batas lingkaran pupil dapat ditentukan dengan circle curve fitting dari parameter lingkaran daerah pupil. Langkah kedua, mencari batas luar lingkaran iris yang memisahkan antara iris dan sklera. Peta tepi citra iris dicari dengan menggunakan deteksi tepi Canny, kemudian diambil satu komponen tepi arah vertikal yang dapat mewakili batas lingkaran luar iris. Dari komponen tepi tersebut, dihitung jari-jari iris yang berpusat di pusat pupil. Dengan jari-jari iris dan pusat iris maka dapat ditentukan batas luar iris menggunakan circle curve fitting
Systematic literature review of dermoscopic pigmented skin lesions classification using convolutional neural network (CNN) Erwin Setyo Nugroho; Igi Ardiyanto; Hanung Adi Nugroho
International Journal of Advances in Intelligent Informatics Vol 9, No 3 (2023): November 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v9i3.961

Abstract

The occurrence of pigmented skin lesions (PSL), including melanoma, are rising, and early detection is crucial for reducing mortality. To assist Pigmented skin lesions, including melanoma, are rising, and early detection is crucial in reducing mortality. To aid dermatologists in early detection, computational techniques have been developed. This research conducted a systematic literature review (SLR) to identify research goals, datasets, methodologies, and performance evaluation methods used in categorizing dermoscopic lesions. This review focuses on using convolutional neural networks (CNNs) in analyzing PSL. Based on specific inclusion and exclusion criteria, the review included 54 primary studies published on Scopus and PubMed between 2018 and 2022. The results showed that ResNet and self-developed CNN were used in 22% of the studies, followed by Ensemble at 20% and DenseNet at 9%. Public datasets such as ISIC 2019 were predominantly used, and 85% of the classifiers used were softmax. The findings suggest that the input, architecture, and output/feature modifications can enhance the model's performance, although improving sensitivity in multiclass classification remains a challenge. While there is no specific model approach to solve the problem in this area, we recommend simultaneously modifying the three clusters to improve the model's performance.
Segmentation of retinal blood vessels for detection of diabetic retinopathy: A review Aras, Rezty Amalia; Lestari, Tri; Nugroho, Hanung Adi; Ardiyanto, Igi
Communications in Science and Technology Vol 1 No 1 (2016)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.1.1.2016.13

Abstract

Diabetic detinopathy (DR) is effect of diabetes mellitus to the human vision that is the major cause of blindness. Early diagnosis of DR is an important requirement in diabetes treatment. Retinal fundus image is commonly used to observe the diabetic retinopathy symptoms. It can present retinal features such as blood vessel and also capture the pathologies which may lead to DR. Blood vessel is one of retinal features which can show the retina pathologies. It can be extracted from retinal image by image processing with following stages: pre-processing, segmentation, and post-processing. This paper contains a review of public retinal image dataset and several methods from various conducted researches. All discussed methods are applicable to each researcher cases. There is no further analysis to conclude the best method which can be used for general cases. However, we suggest morphological and multiscale method that gives the best accuracy in segmentation.
Co-Authors - Nurfadilah, - A.A. Ketut Agung Cahyawan W Achmad Rizal Ade Sofa Adhistya Erna Permanasari Agus Eko Minarno Ahmad Nasikun Al-Fahsi, Resha Dwika Hefni Albert Ch. Soewongsono, Albert Ch. Alfarisi, Ikhsan Anondho Wijanarko Aqil Aqthobirrobbany Aqthobirrobbany, Aqil Aras, Rezty Amalia Arham, Aulia Arif Masthori Atmaja Perdana, Chandra Ramadhan Azof Ghazali Sujono Bhisma Murti Cahyani Windarto Chitra Octavina Cindy Claudia Febiola, Cindy Claudia Citra Prasetyawati Cokro Mandiri, Mochammad Hazmi Danny Kurnianto Dewanta, Wika Dewi Kartika Sari Dian Nova Kusuma Hardani Dianursanti Dimas, Dimas Dindin Hidayat Dwi Haryono E. Elsa Herdiana Murhandarwati Elisabeth Deta Lustiyati Erwin Setyo Nugroho Eva Yuliana Fitri Faisal Najamuddin Fathania Firwan Firdaus Faza Maula Azif Fitri Bimantoro Ganesha L Putra Guyub Nuryanto Handani, Deni Hasdani, Hasdani Hasnely, Hasnely Hastuti, Uki Retno Budi Heri Hermansyah Heru Supriyono Hesti Khuzaimah Nurul Yusufiyah Hotama, Christianus Frederick Hutami, Augustine Herini Tita I Md. Dendi Maysanjaya Ibnu Taufan, Ibnu Ibrahim, Zaidah Ichsan Setiawan Igi Ardiyanto Ignatia Dhian Estu Karisma Ratri Imelda Imelda Indah Soesanti Indriana Hidayah Ismail Setiawan Jafaruddin Jafaruddin, Jafaruddin Kartika Firdausy Kirana, Thea Koko Ondara Krisna Nuresa Qodri KZ Widhia Oktoeberza Lina Choridah Listyalina, Latifah M. Khairun Iffat Made Satria Wibawa Maemonah, Maemonah Mahdi Abdullah Syihab Marshell Tendean Momoji Kubo Muhammad Bayu Sasongko Muhammad Rausan Fikri Naomi Shibasaki-Kitakawa Nasikun, Ahmad Ndii, Meksianis Z Nenden Siti Aminah Noor Abdul Haris Noor Akhmad Setiawan Nora Anisa Br. Sinulingga Novianti Puspitasari Nugroho, Anan Nur Fadhilah Nurcahyani Wulandari Nurfauzi, Rizki Oktoeberza, Widhia KZ Oyas Wahyunggoro Perdana, Adli Waliul Persada, Anugerah Galang Pranowo, Vicko Prasojo, Sasmito Praswasti P. D.K Wulan Puspitasari, Novianti Putri Bungsu Rachman, Anung Ratna Lestari Budiani Buana Rima Fitria Adiati Rina Sri Widayati Riri Ferdiana Risanuri Hidayat Rita Arbianti Rizky Naufal Perdana Robert Silas Kabanga Rochim, Febry Putra Roekmijati W. Soemantojo Saftirta Gatra Dewantara Sandy Anwar Mursito Sarjana Sarjana Sasongko Yoni Bagas Septian Rico Hernawan Setiyo Kantomo, Ilham Sudaryanto . Sukiyo Sukiyo Sumadi, Fauzi Dwi Setiawan Sunu Wibirama Suzanna Ndraha Syahrul Purnawan Syahwami, Syahwami Tania Surya Utami TATI NURHAYATI Teguh Bharata Adji Toshiy Yonemoto Tri Lestari Ulung Jantama Widhia K.Z Oktoeberza Widhia K.Z Oktoeberza Widya Sari Wika Dewanta Willy Anugrah Cahyadi Windarta, Budi Woraratpanya, Kuntpong Yenny Rahmawati Yuda Munarko Yufis Azhar Yulaikha Istiqomah Yulyanti, Vesi Zaidah Ibrahim Zubri, Aldino