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All Journal International Journal of Electrical and Computer Engineering Jurnal Dedikasi Jurnal Gamma TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Inovasi Teknologi Pendidikan International Journal of Advances in Intelligent Informatics POSITIF CESS (Journal of Computer Engineering, System and Science) ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Proceeding of the Electrical Engineering Computer Science and Informatics Fountain of Informatics Journal JOIV : International Journal on Informatics Visualization Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control KOMPUTIKA - Jurnal Sistem Komputer Jurnal Repositor Jurnal Informatika dan Teknologi Komputer ( J-ICOM) Makara Journal of Technology
Agus Eko Minarno
Universitas Muhammadiyah Malang
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mechanical Engineering Public Health

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Rancang Bangun Website Daur Hidup Batik Menggunakan Metode Waterfall Dan Kerangka Kerja Laravel Aria Maulana Eka Mahendra; Galih Wasis Wicaksono; Agus Eko Minarno
Jurnal Informatika dan Teknologi Komputer (J-ICOM) Vol 5 No 2 (2024): Jurnal Informatika dan Teknologi Komputer ( J-ICOM)
Publisher : E-Jurnal Universitas Samudra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33059/j-icom.v5i2.8936

Abstract

Batik is a traditional art that holds great value in human life. Through a creative process that involves the selection and application of unique motifs on fabric, batik produces alluring works of art. The use of batik is not limited to special moments, but has also penetrated into the world of modern fashion. The lifecycle of batik reflects a rich cultural heritage, boundless creativity and strong human identity. This makes batik a beautiful embodiment of human life, uniqueness, and diversity in maintaining and honoring cultural traditions. The purpose of this research is to create a web-based information platform about the life cycle of batik and provide access to data managers. This system has been built using the PHP programming language, supported by the MySQL database, and using the Laravel framework. The method applied in the development of this research is the waterfall approach. The results of this study produced a web-based information platform about the batik life cycle. Through black-box testing using Selenium Web Driver, this system was proven to operate properly. Thus, it can be concluded that this system meets the needs and is acceptable to users.
The Implementation of Pretrained VGG16 Model for Rice Leaf Disease Classification using Image Segmentation Suseno, Jody Ririt Krido; Azhar, Yufis; Minarno, Agus Eko
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 8, No. 1, February 2023
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v8i1.1592

Abstract

Rice is an agricultural sector that produces rice which is one of the staple foods for the majority of the population in Indonesia. In the cultivation of rice plants there are also factors that affect rice production and are not realized by farmers causing that they are late in handling and diagnosing symptoms and making rice production decline. Therefore, it is necessary to have an early diagnosis of rice plants to identify them correctly, quickly and accurately. Machine learning is one of the classification techniques to detect various plant diseases such as rice plants. There are several studies on machine learning using the Convolutional Neural Network with the VGG16 model to classify rice leaf diseases and using Image Segmentation techniques on rice leaf datasets for make the image becomes a form that is not too complicated to analyze. The data used in this research is Rice Leaf Disease which consists of 3 classes including Bacterial leaf blight, Brown spot, and Leaf smut. Then segmentation is carried out using two techniques, namely threshold and k means. Then data augmentation for make dataset used has a large and varied number and training using VGG16 model with hyperparameter tuning and obtained 91.66% accuracy results for scenarios with the k-means dataset.
Klasifikasi COVID-19 menggunakan Filter Gabor dan CNN dengan Hyperparameter Tuning MINARNO, AGUS EKO; MANDIRI, MOCHAMMAD HAZMI COKRO; ALFARIZY, MUHAMMAD RIFAL
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 9, No 3: Published July 2021
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v9i3.493

Abstract

ABSTRAKPenyakit COVID-19 dapat timbul karena berbagai faktor sebab dan akibat, sehingga penyakit ini memiliki efek buruk bagi penderita. Pencitraan CT-Scan memiliki keunggulan dalam memproyeksikan kondisi paru-paru pasien penderita, sehingga dapat membantu dalam mendeteksi tingkat keparahan penyakit. Dalam studi ini, penelitian dilakukan untuk mendeteksi penyakit COVID-19 melalui citra CT-Scan menggunakan metode Filter Gabor dan Convolutional Neural Networks (CNN) dengan Hyperparameter Tuning. Data yang digunakan yaitu citra CT-Scan SARSCoV-2 berjumlah 2481 gambar. Sebelum melatih model, dilakukan preprocessing data, seperti pelabelan, pengubahan ukuran, dan augmentasi gambar. Pengujian Model dilakukan dengan beberapa skenario uji. Hasil terbaik diperoleh pada skenario untuk model Filter Gabor dan CNN dengan Hyperparameter Tuning mendapatkan akurasi sebesar 97,9% dan AUC sebesar 99% dibandingkan dengan model tanpa Hyperparameter Tuning dan Filter Gabor.Kata kunci: COVID-19, CNN, Filter Gabor, Hyperparameter Tuning, COVID-19 Classification ABSTRACTCOVID-19 disease can arise due to various causal and causal factors, so it has an adverse effect on patients. CT-Scan imaging has an advantage in projecting the lung condition of patients with the patient, so it can help in detecting the severity of the disease. In this study, research was conducted to detect COVID-19 disease through CT-Scan imagery using Gabor Filter method and Convolutional Neural Networks (CNN) with Hyperparameter Tuning. The data used is CT-Scan SARSCoV-2 imagery amounting to 2481 images. Before training the model, preprocessing data is performed, such as labeling, resizing, and augmentation of images. Model testing is performed with multiple test scenarios. The best results were obtained in scenarios for The Gabor Filter model and CNN with Hyperparameter Tuning getting 97.9% accuracy and AUC by 99% compared to models without Hyperparameter Tuning and Gabor Filter.Keywords: COVID-19, CNN, Filter Gabor, Hyperparameter Tuning, COVID-19 Classification
Classification of Diabetic Retinopathy Based on Fundus Image Using InceptionV3 Minarno, Agus Eko; Bagaskara, Andhika Dwija; Bimantoro, Fitri; Suharso, Wildan
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2155

Abstract

Diabetic Retinopathy (DR) is a progressive eye condition that can lead to blindness, particularly affecting individuals with diabetes. It is commonly diagnosed through the examination of digital retinal images, with fundus photography being recognized as a reliable method for identifying abnormalities in the retina of diabetic patients. However, manual diagnosis based on these images is time-consuming and labor-intensive, necessitating the development of automated systems to enhance both accuracy and efficiency. Recent advancements in machine learning, particularly image classification systems, provide a promising avenue for streamlining the diagnostic process. This study aims to classify DR using Convolutional Neural Networks (CNN), explicitly employing the InceptionV3 architecture to optimize performance. This research also explores the impact of different preprocessing and data augmentation techniques on classification accuracy, focusing on the APTOS 2019 Blindness Detection dataset. Data preprocessing and augmentation are crucial steps in deep learning to enhance model generalization and mitigate overfitting. The study uses preprocessing and data augmentation to train the InceptionV3 model. Results indicate that the model achieves 86.5% accuracy on training data and 82.73% accuracy on test data, significantly improving performance compared to models trained without data augmentation. Additionally, the findings demonstrate that the absence of data augmentation leads to overfitting, as evidenced by performance graphs that show a marked decline in test accuracy relative to training accuracy. This research highlights the importance of tailored preprocessing and augmentation techniques in improving CNN models' robustness and predictive capability for DR detection. 
Classification of Skin Cancer Images Using Convolutional Neural Network with ResNet50 Pre-trained Model Minarno, Agus Eko; Lusianti, Aaliyah; Azhar, Yufis; Wibowo, Hardianto
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2156

Abstract

The skin, an astonishingly expansive organ within the human body, plays a pivotal role in safeguarding us against the environment's harsh elements. It acts as a formidable barrier, shielding our delicate internal systems from the scorching heat of the sun and the harmful effects of relentless exposure to light. Nevertheless, it is not impervious to damage, especially when subjected to excessive sunlight and the potentially hazardous ultraviolet (UV) radiation that accompanies it. Prolonged UV exposure can wreak havoc on our skin cells, potentially setting the stage for the development of skin cancer. This condition demands prompt and accurate diagnosis for effective treatment. To address the pressing need for swift and precise skin cancer diagnosis, cutting-edge technology has come to the fore in the form of deep learning systems. These sophisticated systems have been meticulously designed and trained to classify skin lesions autonomously with remarkable accuracy. The Convolutional Neural Network (CNN) architecture is a formidable choice for handling image classification tasks among the array of deep learning techniques. In a recent breakthrough study, a CNN-based model was meticulously constructed to explicitly classify skin lesions, leveraging the power of a pre-trained ResNet50 architectural model to augment its capabilities. This groundbreaking ResNet50 architecture was meticulously trained to classify seven distinct skin lesions, surpassing the performance of its predecessor, MobileNet. The results achieved in this endeavor are nothing short of impressive. The overall accuracy of the ResNet50 model stands at a commendable 87.42% when tasked with classifying the seven diverse classes within the dataset. Delving further into its proficiency, we find that the Top2 and Top3 accuracy rates soar to an astounding 95.52% and 97.86%, respectively, illustrating the model's exceptional precision and reliability.
Leveraging ESRGAN for High-Quality Retrieval of Low-Resolution Batik Pattern Datasets Azhar, Yufis; Marthasari, Gita Indah; Regata Akbi, Denar; Minarno, Agus Eko; Haqim, Gilang Nuril
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.3202

Abstract

As one of the world's cultural heritages in Indonesia, batik is one of the quite interesting research subjects, including in the realm of image retrieval. One of the inhibiting factors in searching for batik images relevant to the query image input by the user is the low resolution of the batik images in the dataset. This can affect the dataset's quality, which automatically also impacts the model's performance in recognizing batik motifs with complex details and textures. To address this problem, this study proposes using the Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) method to increase the resolution of batik images. By increasing the resolution, it is hoped that ESRGAN can clarify the details and textures of the initial low-resolution image so that these features can be extracted better. This study proves that ESRGAN can produce high-resolution batik images while maintaining the details of the batik motif itself. The resulting image's high PSNR and low MSE values confirm this. The implementation of ESRGAN has also been proven to improve the performance of the image retrieval system with an increase in precision and average precision values between 1-5% compared to other methods that do not implement it.
UMM metaverse batik as a learning media to introduce nitik batik motifs in the Sonobudoyo Museum Minarno, Agus Eko; Faiz, Ahmad; Wibowo, Hardianto; Akbi, Denar Regata; Munarko, Yuda
Jurnal Inovasi Teknologi Pendidikan Vol. 12 No. 1 (2025): March
Publisher : Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jitp.v12i1.81821

Abstract

The exposure of Yogyakarta's Nitik Batik motifs is one of the important efforts to maintain and introduce Indonesia's cultural heritage to the younger generation. In this context, metaverse-based learning media is used as an innovative solution. This research discusses the implementation of metaverse-based learning media with an Extended Reality (XR) approach to introduce the Yogyakarta Nitik Batik motif. This research uses the Game Development Life Cycle (GDLC) development method to design a VR-based Batik museum virtual space, with black box testing and refinement testing to assess functionality and fun aspects. Involving 33 participants from visitors to the Sono Budoyo Batik exhibition in Yogyakarta, this study analyzed the data descriptive quantitative to develop recommendations for improving user experience and introducing Yogyakarta Nitik Batik culture through the metaverse. The test results showed that the virtual space of the Batik Museum passed the functional test without failure and had a feasibility rate of 86.1% in the category of "Excellent." These findings indicate that VR technology effectively introduces and preserves Batik culture, especially as an educational material in virtual media. This metaverse based learning media is anticipated to be an innovative step in introducing Yogyakarta's dotted Batik while offering a valuable immersive experience for users. Future research can be done by adding gamification to increase visitor involvement and optimizing multimedia aspects that have not been the main focus.
Batik Classification using Microstructure Co-occurrence Histogram Minarno, Agus Eko; Soesanti, Indah; Nugroho, Hanung Adi
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2152

Abstract

Batik Nitik is a distinctive form of batik originating from the culturally rich region of Yogyakarta, Indonesia. What sets it apart from other batik styles is its remarkable motif similarity, a characteristic that often poses a considerable challenge when attempting to distinguish one design from another. To address this challenge, extensive research has been conducted with the primary objective of classifying Batik Nitik, and this research leverages an innovative approach combining the microstructure histogram and gray level co-occurrence matrix (GLCM) techniques, collectively referred to as the Microstructure Co-occurrence Histogram (MCH).The MCH method offers a multi-faceted approach to feature extraction, simultaneously capturing color, texture, and shape attributes, thereby generating a set of local features that faithfully represent the intricate details found in Batik Nitik imagery. In parallel, the GLCM method excels at extracting robust texture features by employing statistical measures to portray the subtle nuances within these batik patterns. Nevertheless, the mere fusion of microstructure and GLCM features doesn't inherently guarantee superior classification performance. This research paper has meticulously examined many feature fusion scenarios between microstructure and GLCM to pinpoint the optimal configuration that would yield the most accurate results. The dataset used consists of 960 Batik Nitik samples, comprising 60 categories. The classifiers employed in this study are K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), Naïve Bayes (NB), and Linear Discriminant Analysis (LDA). Based on the experimental results, the fusion of microstructure and GLCM features with the (LDA) classifier yields the best performance compared to other scenarios and classifiers.
Enhanced BatikGAN SL Model for High-Quality Batik Pattern Generation Minarno, Agus Eko; Akbi, Denar Regata; Munarko, Yuda
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.3096

Abstract

Batik represents one of the most prominent traditional cultural forms in Indonesia, serving not only as an art form but also as a symbol of cultural identity and heritage. The creation of intricate and unique Batik patterns is a highly skilled craft that has been passed down through generations. Still, modern efforts to innovate and enhance Batik designs face significant challenges. Specifically, there is a growing demand for high-quality Batik patterns that maintain the aesthetic and cultural value of traditional motifs while incorporating modern design elements. This study aims to address these challenges by introducing an enhanced BatikGAN SL model that leverages local features. The model's performance was rigorously evaluated using the Batik Nitik dataset, which consists of 126 Batik motifs collected from artisans in Yogyakarta, a region renowned for its rich Batik traditions. This dataset allowed for a robust testing ground, representing a diverse array of motifs and styles. In comparative evaluations, the enhanced BatikGAN SL model outperformed not only its predecessor but also models utilizing histogram-equalized datasets, which are often employed to improve image contrast. Key metrics, including the Fréchet Inception Distance (FID) score of 20.087, Peak Signal-to-Noise Ratio (PSNR) of 25.665, and Structural Similarity Index Measure (SSIM) of 0.918, demonstrated significant improvements in both the visual and technical quality of the generated Batik patterns. These metrics indicate that the proposed model excels in producing patterns with more precise details, better contrast, and higher overall image fidelity when compared to previous approaches.
Batik Image Representation using Multi Texton Co-occurrence Histogram Minarno, Agus Eko; Soesanti, Indah; Nugroho, Hanung Adi
JOIV : International Journal on Informatics Visualization Vol 8, No 3-2 (2024): IT for Global Goals: Building a Sustainable Tomorrow
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3-2.3095

Abstract

This paper introduces a novel approach to batik image representation using the texton-based and statistical Multi Texton Co-occurrence Histogram (MTCH). The MTCH framework is leveraged as a robust batik image descriptor, capable of encapsulating a comprehensive range of visual features, including the intricate interplay of color, texture, shape, and statistical attributes. The research extensively evaluates the effectiveness of MTCH through its application on two well-established public batik datasets, namely Batik 300 and Batik Nitik 960. These datasets serve as benchmarks for assessing the performance of MTCH in both classification and image retrieval tasks. In the classification domain, four distinct scenarios were explored, employing various classifiers: the K-Nearest Neighbors (K-NN), Support Vector Machine (SVM), Decision Tree (DT), and Naïve Bayes (NB). Each classifier was rigorously tested to determine its efficacy in correctly identifying batik patterns based on the MTCH descriptors. On the other hand, the image retrieval tasks were conducted using several distance metrics, including the Euclidean distance, City Block, Bray Curtis, and Canberra, to gauge the retrieval accuracy and the robustness of the MTCH framework in matching similar batik images. The empirical results derived from this study underscore the superior performance of the MTCH descriptor across all tested scenarios. The evaluation metrics, including accuracy, precision, and recall, indicate that MTCH not only achieves high classification performance but also excels in retrieving images with high similarity to the query. These findings suggest that MTCH is a highly effective tool for batik image analysis, offering significant potential for applications in cultural heritage preservation, textile pattern recognition, and automated batik classification systems.
Co-Authors Abu Abbas Mansyur Achmad Fauzi Saksenata Ahmad Annas Al Hakim Ahmad Faiz, Ahmad Ahmad Heryanto, Ahmad Akbi, Denar Regata Alfarizy, Muhammad Rifal Alfian Yuniarto Anbiya, Dhika Rizki Andhika Pranadipa Andrian Rakhmatsyah Aria Maulana Eka Mahendra Arif Bagus Nugroho Aripa, Laofin Arrie Kurniawardhani arrie kurniawardhany, arrie AULIA ARIF WARDANA Ayu Septya Maulani Bagaskara, Andhika Dwija Basuki, Setio Bayu Yudha Purnomo Bella Dwi Mardiana Chandranegara, Didih Rizki Cokro Mandiri, Mochammad Hazmi Deris Stiawan Dwi Rahayu Dyah Ayu Irianti Eko Budi Cahyono Fachry Abda El Rahman Feny Aries Tanti Firdhansyah Abubekar Fitri Bimantoro Galang Aji Mahesa Gita Indah Marthasari Hanung Adi Nugroho Haqim, Gilang Nuril Hardianto Wibowo Hariyady Hariyady Harmanto, Dani Hasanuddin, Muhammad Yusril Hazmi Cokro Mandiri, Mochammad Ibrahim, Zaidah Indah Soesanti Iqbal Fairus Zamani Irfan, Muhammad irma fitriani Izzah, Tsabita Nurul Lailis Syafa'ah Lailis Syafa’ah Linggar Bagas Saputro Lusianti, Aaliyah Mandiri, Mochammad Hazmi Cokro Moch Ilham Ramadhani Moch. Chamdani Mustaqim Muhammad Afif Muhammad Azhar Ridani Muhammad Hussein Muhammad Nafi Maula Hakim Muhammad Nasrul Tsalatsa Putra Muhammad Nuchfi Fadlurrahman Nanik Suciati Naser Jawas, Naser Nia Dwi Nurul Safitri Noor Aini Mohd Roslan Norizan Mat Diah Prabowo, Christian Ramadhani, Moch Ilham Rangga Kurnia Putra Wiratama Ratna Sari Riksa Adenia Rizalwan Ardi Ramandita Rizka Nurlizah Sabrila, Trifebi Shina Sari, Veronica Retno Sari, Zamah Sasongko Yoni Bagas Setiyo Kantomo, Ilham Sumadi, Fauzi Dwi Setiawan Suryani Rachmawati Suseno, Jody Ririt Krido Toton Dwi Antoko Trifebi Shina Sabrila Tsabitah Ayu Ulfah Nur Oktaviana Veronica Retno Sari Vizza Dwi Wahyu Andhyka Kusuma Wahyu Budi Utomo Wicaksono, Galih Wasis Wicaksono, Galih Wasis Widya Rizka Ulul Fadilah Wildan Suharso Yesicha Amilia Putri Yoga Anggi Kurniawan Yuda Munarko Yudhono Witanto Yufis Azhar Yundari, Yundari Zaidah Ibrahim Zaidah Ibrahim Zamah Sari Zamani, Iqbal Fairus