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All Journal International Journal of Electrical and Computer Engineering Jurnal Media Infotama TELKOMNIKA (Telecommunication Computing Electronics and Control) 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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Classification of Brain Tumors on MRI Images Using DenseNet and Support Vector Machine Eko Minarno, Agus; Setiyo Kantomo, Ilham; Setiawan Sumadi, Fauzi Dwi; Adi Nugroho, Hanung; Ibrahim, Zaidah
JOIV : International Journal on Informatics Visualization Vol 6, No 2 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.2.991

Abstract

The brain is a vital organ in the human body, performing various functions. The brain has always played a major role in the processing of sensory information, the production of muscular activity, and the performance of high-level cognitive functions. Among the most prevalent diseases of the brain is the development of aberrant tissue in brain cells, which results in the formation of brain tumors. According to data from the International Agency for Research on Cancer (IARC), more than 124,000 people worldwide were diagnosed with brain tumors in 2014, and more than 97,000 people died due to the condition. Current research indicates that magnetic resonance imaging (MRI) is the most effective means of detecting brain cancers. Because brain tumors are associated with significant mortality risk, a large number of brain tumor MRI imaging datasets were used in this research to detect brain cancers using deep learning techniques. To classify three forms of brain tumors, including glioma, meningioma, and pituitary, a deep learning model called DenseNet 201 paired with Support Vector Machines (SVM) was employed in this work included three types of brain tumors. Based on the results of the tests that were conducted, the best accuracy results obtained in this study were 99.65 percent, with a comparison ratio of 80 percent for training data and 20 percent for testing data, oversampled with the SMOTE method, with the best accuracy results obtained in this study being 99.65 percent.
Classification of Diabetic Retinopathy Disease Using Convolutional Neural Network Minarno, Agus Eko; Cokro Mandiri, Mochammad Hazmi; Azhar, Yufis; Bimantoro, Fitri; Nugroho, Hanung Adi; Ibrahim, Zaidah
JOIV : International Journal on Informatics Visualization Vol 6, No 1 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.1.857

Abstract

Diabetic Retinopathy (DR) is a disease that causes visual impairment and blindness in patients with it. Diabetic Retinopathy disease appears characterized by a condition of swelling and leakage in the blood vessels located at the back of the retina of the eye. Early detection through the retinal fundus image of the eye could take time and requires an experienced ophthalmologist. This study proposed a deep learning method, the Efficientnet-b7 model to identify diabetic retinopathy disease automatically. This study applies three preprocessing techniques that could be implemented in the dataset "APTOS 2019 Blindness Detection". In preprocessing technique trial scenarios, Usuyama preprocessing technique obtained the best results with accuracy of 89% of train data and 84% in test data compared to Harikrishnan preprocessing technique which has 82% accuracy in test data, and Ben Graham preprocessing has 81% accuracy in test data. In this study, Hyperparameter tuning was conducted to find the best parameters for use on the EfficientNet-B7 Model. In this study, we tested the Efficientnet-B7 model with an augmentation process that can reduce the occurrence of overfitting compared to models without augmentation. Preprocessing techniques and augmentation techniques can influence the proposed EfficientNet-B7 model in terms of performance results and reduce the overfitting of models.
Decision-layer interpretability for CNN-based glaucoma classification via sparse feature selection and ANFIS Etik Irijanti; Igi Ardiyanto; Hanung Adi Nugroho
Communications in Science and Technology Vol 11 No 1 (2026)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.11.1.2026.1968

Abstract

Glaucoma is a leading cause of irreversible vision loss, and its early detection remains challenging due to the presence of subtle structural varitions in retinal fundus images. Convolutional neural networks (CNNs) have demonstrated strong performance for automated classification of glaucoma; however, the relationship between extracted features and prediction outcomes frequently proves challenging to interpret. Most existing explainable artificial intelligence (XAI) approaches rely on post hoc visualizations, which provide limited insight into the decisions-making process. To address this limitation, this present study proposes a hybrid CNN–feature selection–ANFIS framework (CNN–FS–ANFIS) that integrates interpretability directly within the decision layer. In this framework, the first stage of the process involves adapting a CNN backbone to the glaucoma classification task through the use of transfer learning. This is then used as a fixed feature extractor to obtain retinal representations for decision-layer modeling. Subsequently, a feature selection stage is applied driven by sparsity to construct a compact and structured subset of informative features. These features are then fed into an Adaptive Neuro-Fuzzy Inference System (ANFIS), enabling predictions to be expressed through explicit fuzzy rule-based reasoning. The impact of feature compactness is examined in a controlled experimental setting, where the feature subset size is varied from three to nine. The findings demonstrate that compact feature subsets can achieve consistent and competitive performance. By means of LASSO-selected features, the ANFIS decision layer achieved an AUC of 0.84±0.01, sensitivity of 0.82±0.13, specificity of 0.74±0.10, and an F1-score of 0.79±0.04. Rule-base analysis further exhibited that two-to three-rule ANFIS configurations-maintained AUC values of approximately 0.84 while preserving a transparent and manageable decision structure. The proposed framework, therefore, enables direct analysis of the relationship between selected CNN features, fuzzy rules, and model outputs. This traceable decision pathway has the potential to support more transparent and auditable glaucoma screening systems.
Reversible data hiding with selective bits difference expansion and modulus function Aulia Arham; Hanung Adi Nugroho; Domi Sepri
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 3: June 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i3.26310

Abstract

The integration of the internet of things (IoT) has significantly enhanced human life but also raises concerns about information security and privacy. Information security can be achieved through cryptography, which encrypts data to make it unreadable, or steganography, which hides data within other media. For sensitive media, such as military, medical, and forensic imaging, specialized techniques like reversible data hiding (RDH) are necessary to ensure the media can be fully restored after data extraction. Many researchers have proposed improvements to the RDH method in recent years. In this study, we propose an improved RDH method utilizing difference expansion and a modulus function. The method embeds data into the 4-bit, 3-bit, and 2-bit least significant bits (LSB) of the difference value of pixels, with a range of -2 and 2. The experimental findings demonstrate that our approach achieves a embedding capacity of 0.2507 bpp with 55.445 dB of peak signal-to-noise ratio (PSNR) for common images and 0.3849 bpp with 54.6810 dB of PSNR for medical images, using 2-bit difference values. The results demonstrate that our approach surpasses previous methods and holds promise for practical applications in IoT systems and the medical field, where secure and reversible data embedding is essential.
Similarity measurement on digital mammogram classification Erna Alimudin; Hanung Adi Nugroho; Teguh Bharata Adji
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 4: August 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i4.10698

Abstract

Breast cancer is one of the dominant causes of death in the world. Mammography is the standard for early detection of breast cancer. In examining mammograms, the overall parenchyma pattern of the left and right breast was placed side by side for symmetry assessed of left and right breast tissue by radiologist. Thus, in building computer-aided diagnosis (CAD) system for screening mammography, it is necessary to adapt the working procedure of the radiologist. In this study, 30 training images and 30 testing images from Kotabaru Oncology Clinic in Yogyakarta were used. The first step was to enhance the image quality with median filter and contrast limited adaptive histogram equalization (CLAHE). Then, feature extraction was processed by histogram-based and by gray level co-occurrence matrix (GLCM) based. Furthermore, the similarity measurement process was used to measure the difference value between selected features, i.e. angular second moment (ASM), inverse difference moment (IDM), contrast, entropy based GLCM, and energy, on the left and right mammograms. This process was intended to assess the symmetry of left and right mammograms as radiologists do in mammography screening. The obtained results of the classification between normal and abnormal images with backpropagation algorithm were accuracy of 0.933, sensitivity of 0.833, and specificity of 1.000.
A High-Capacity Reversible Watermarking Technique Using Bit-Level Expansion and Pixel Shifting Aulia Arham; Syukron Abu Ishaq Alfarozi; Hanung Adi Nugroho
Communications in Science and Technology Vol 10 No 2 (2025)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.10.2.2025.1856

Abstract

This paper proposes a high-capacity reversible watermarking method using adaptive bit-level expansion and pixel-class-guided shifting. Pixels are classified into expandable (P0) and non-expandable (P1) according to their 2-bit LSB patterns, and a lightweight reversible transformation converts P1 into P0 with minimal distortion. A shifting map enables exact recovery and avoids overflow/underflow. Secret data are embedded through a 2-bit LSB expansion rule that ensures full reversibility. Experiments on common and medical images demonstrate a consistent embedding capacity of 1.0 bpp, achieving PSNR values above 46 dB and SSIM above 0.97. In addition, the scheme exhibits low computational overhead (<0.7s per image, >380 kbps) while preserving the original histogram distribution. These results demonstrate that the proposed scheme provides an effective balance between embedding capacity, visual quality, and computational efficiency for secure medical imaging and authenticated reversible data embedding.
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 Aulia Arham Aulia Arham 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 Domi Sepri Dwi Haryono E. Elsa Herdiana Murhandarwati Elisabeth Deta Lustiyati Erna Alimudin Erwin Setyo Nugroho Etik Irijanti 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 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 Syukron Abu Ishaq Alfarozi 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 Yusufiyah, Hesti Khuzaimah Nurul Zaidah Ibrahim Zubri, Aldino