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All Journal International Journal of Electrical and Computer Engineering Jurnal Dedikasi Bulletin of Electrical Engineering and Informatics Jurnal Inovasi Teknologi Pendidikan International Journal of Advances in Intelligent Informatics Proceeding of the Electrical Engineering Computer Science and Informatics JOIV : International Journal on Informatics Visualization Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control SINTECH (Science and Information Technology) Journal Jurnal Repositor Indonesian Journal of Electrical Engineering and Computer Science JHCLS
Yuda Munarko
Universitas Muhammadiyah Malang
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health

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A taxonomy of Malay social media text Ruhaila Maskat; Yuda Munarko
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 1: October 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i1.pp465-472

Abstract

In this paper, we proposed a preliminary taxonomy of Malay social media text. Performing text analytics on Malay social media text is a challenge. The formal Malay language follows specific spelling and sentence construction rules. However, the Malay language used in social media differs in both aspects. This impedes the accuracy of text analytics. Due to the complexity of Malay social media text, many researches has chosen to focus on classifying the formal Malay language. To the best of our knowledge, we are the first to propose a formal taxonomy for Malay text in social media. Narrow and informal categorisations of Malay social media text can be found amidst efforts to pre-process social media text, yet cherry-picked only some categories to be handled. We have differentiated Malay social media text from the formal Malay language by identifying them as Social Media Malay Language or SMML. They consists of spelling variations, Malay-English mix sentence, Malay-spelling English words, slang-based words, vowel-les words, number suffixes and manner of expression.This taxonomy is expected to serve as a guideline in research and commercial products.
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%. 
Automatic plant recognition using convolutional neural network on malaysian medicinal herbs: the value of data augmentation Noor Aini Mohd Roslan; Norizan Mat Diah; Zaidah Ibrahim; Yuda Munarko; Agus Eko Minarno
International Journal of Advances in Intelligent Informatics Vol 9, No 1 (2023): March 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v9i1.1076

Abstract

Herbs are an important nutritional source for humans since they provide a variety of nutrients. Indigenous people have employed herbs, in particular, as traditional medicines since ancient times. Malaysia has hundreds of plant species; herb detection may be difficult due to the variety of herb species and their shape and color similarities. Furthermore, there is a scarcity of support datasets for detecting these plants. The main objective of this paper is to investigate the performance of convolutional neural network (CNN) on Malaysian medicinal herbs datasets, real data and augmented data. Malaysian medical herbs data were obtained from Taman Herba Pulau Pinang, Malaysia, and ten kinds of native herbs were chosen. Both datasets were evaluated using the CNN model developed throughout the research. Overall, herbs real data obtained an average accuracy of 75%, whereas herbs augmented data achieved an average accuracy of 88%. Based on these findings, herbs augmented data surpassed herbs actual data in terms of accuracy after undergoing the augmentation technique.
Artificial Intelligence and Quality of Composition Verdicts in Indonesia: Lessons from New Zealand Hidayah, Nur Putri; Wicaksono, Galih Wasis; Aditya, Christian Sri Kusuma; Munarko, Yuda
Journal of Human Rights, Culture and Legal System Vol. 4 No. 1 (2024): Journal of Human Rights, Culture and Legal System
Publisher : Lembaga Contrarius Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53955/jhcls.v4i1.175

Abstract

The quality of the decision is not only related to the judge's considerations but also its suitability to the composition of the decision so that the resulting decision is not easily overturned at the level of legal action and increases public confidence in the judicial institution. This research aims to analyze the quality of judges' decisions in Indonesia in terms of the composition of the decision texts that have been made. This research uses normative legal research methods, a statutory approach, and a comparative approach. The study results show that decisions are not based on the structure of decisions determined by the Supreme Court. One of the reasons is the minimal use of AI, even though AI can help judges identify which parts of the decision structure are not yet in the decision prepared by the judge and improve them so that it is hoped that it will produce uniformity and decisions that are certain and not easily overturned. Indonesia needs to learn from New Zealand guidelines for using AI at the court and tribunal level. Judges can apply AI, some related to summarizing information and administration.
Classification of Malaria Using Convolutional Neural Network Method on Microscopic Image of Blood Smear Minarno, Agus Eko; Izzah, Tsabita Nurul; Munarko, Yuda; Basuki, Setio
JOIV : International Journal on Informatics Visualization Vol 8, No 3 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3.2154

Abstract

Malaria, a critical global health issue, can lead to severe complications and mortality if not treated promptly. The conventional diagnostic method, involving a microscopic examination of blood smears, is time-consuming and requires extensive expertise. To address these challenges, computer-assisted diagnostic methods have been explored. Among these, Convolutional Neural Networks (CNN), a deep learning technique, has shown considerable promise for image classification tasks, including the analysis of microscopic blood smear images. In this study, we employ the NIH Malaria dataset, which consists of 27,558 images, to train a CNN model. The dataset is divided into parasitized (malaria-infected) and uninfected. The CNN architecture designed for this study includes three convolutional layers and two fully connected layers. We compare the performance of this model with that of a pre-trained VGG-16 model to determine the most effective approach for malaria diagnosis. The proposed CNN model demonstrates high accuracy, achieving a value of 96.81%. Furthermore, it records a recall of 0.97, a precision of 0.97, and an F1-score of 0.97. These metrics indicate a robust performance, outperforming previous studies and highlighting the model's potential for accurate malaria diagnosis. This study underscores the potential of CNN in medical image classification and supports its implementation in clinical settings to enhance diagnostic accuracy and efficiency. The findings suggest that with further refinement and validation, such models could significantly improve the speed and reliability of malaria diagnostics, ultimately aiding in better disease management and patient outcomes.
Classification of Dermoscopic Images Using CNN-SVM Minarno, Agus Eko; Fadhlan, Muhammad; Munarko, Yuda; Chandranegara, Didih Rizki
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.2153

Abstract

Traditional machine learning methods like GLCM and ABCD rules have long been employed for image classification tasks. However, they come with inherent limitations, primarily the need for manual feature extraction. This manual feature extraction process is time-consuming and relies on expert domain knowledge, making it challenging for non-experts to use effectively. Deep learning methods, specifically Convolutional Neural Networks (CNN), have revolutionized image classification by automating the feature extraction. CNNs can learn hierarchical features directly from the raw pixel values, eliminating the need for manual feature engineering. Despite their powerful capabilities, CNNs have limitations, mainly when working with small image datasets. They may overfit the data or struggle to generalize effectively. In light of these considerations, this study adopts a hybrid approach that leverages the strengths of both deep learning and traditional machine learning. CNNs are automatic feature extractors, allowing the model to capture meaningful image patterns. These extracted features are then fed into a Support Vector Machine (SVM) classifier, known for its efficiency and effectiveness in handling small datasets. The results of this study are encouraging, with an accuracy of 0.94 and an AUC score of 0.94. Notably, these metrics outperform Abbas' previous research by a significant margin, underscoring the effectiveness of the hybrid CNN-SVM approach. This research reinforces that SVM classifiers are well-suited for tasks involving limited image data, yielding improved classification accuracy and highlighting the potential for broader applications in image analysis.
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.
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.
Classification of Malaria Cell Image using Inception-V3 Architecture Minarno, Agus Eko; Aripa, Laofin; Azhar, Yufis; Munarko, Yuda
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1301

Abstract

Malaria is a severe global public health problem caused by the bite of infected mosquitoes. It can be cured, but only with early detection and effective, quick treatment. It can cause severe conditions if not properly diagnosed and treated at an early stage. In the worst scenario, it can cause death. This study aims at focusing on classifying malaria cell images. Malaria is classified as a dangerous disease caused by the bite of the female Anophles mosquito. As such, it leads to mortality when immediate action and treatment fails to be administered. In particular, this study aims to classify malaria cell images by utilizing the Inception-V3 architecture. In this study, training was conducted on 27,558 malaria cell image data through Inception-V3 architecture by proposing 3 scenarios. The proposed scenario 1 model applies the SGD optimizer to generate a loss value of 0.13 and an accuracy value of 0.95; scenario 2 model applies the Adam optimizer to generate a loss value of 0.09 and an accuracy value of 0.96; and lastly scenario 3 implements the RMSprop optimizer to generate a loss value of 0.08 and an accuracy value of 0.97. Applying the three scenarios, the results of the study apparently indicate that the Inception-V3 model using the RMSprop optimizer is capable of providing the best accuracy results with an accuracy of 97% with the lowest loss value, compared to scenario 1 and scenario 2. Further, the test results confirms that the proposed model in this study is capable of classifying malaria cells effectively.
Co-Authors A.A. Ketut Agung Cahyawan W Abims Fardiansa Aditya Dwi Maryanto Agus Eko Minarno Ahmad Faiz, Ahmad Akbi, Denar Regata Aminuddin Aminuddin Anisah Rahmatul Laily Annisa Annisa Aripa, Laofin Arrie Kurniawardhani Basuki, Setio Chandranegara, Didih Rizki Christian Sri kusuma Aditya, Christian Sri kusuma Dyah Anitia Dyah Hestiningtyas Fachry Abda El Rahman Hanung Adi Nugroho Hardianto Wibowo Hariyady Hariyady Hazmi Cokro Mandiri, Mochammad Ibrahim, Zaidah Izzah, Tsabita Nurul Lailatul Husniah Maskur Maskur Maulina Balqis Muhammad Athaillah Noor Aini Mohd Roslan Norizan Mat Diah Nur Hayatin Nur Putri Hidayah Rahma Ningsih Rendiyono Wahyu Saputro Ruhaila Maskat Sasongko Yoni Bagas Sumadi, Fauzi Dwi Setiawan Susi Ekawati Vicky Novreza Wahyu Andhyka Kusuma Wicaksono, Galih Wasis Widya Rizka Ulul Fadilah Wildan Suharso Yufis Azhar Zaidah Ibrahim Zaidah Ibrahim