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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Majalah Forum Teknik UGM Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Transmisi: Jurnal Ilmiah Teknik Elektro Jurnal Pendidikan Sains IPTEK Journal of Proceedings Series Proceedings of KNASTIK TELKOMNIKA (Telecommunication Computing Electronics and Control) Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Jurnal Teknik Elektro Prosiding SNATIF Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Advances in Intelligent Informatics Seminar Nasional Informatika (SEMNASIF) ELINVO (Electronics, Informatics, and Vocational Education) Semesta Teknika Jurnal Ilmiah KOMPUTASI Journal of Electrical Technology UMY JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Paradigma Emerging Science Journal Communications in Science and Technology JUTEI (Jurnal Terapan Teknologi Informasi) Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) IJITEE (International Journal of Information Technology and Electrical Engineering) Prosiding Seminar Nasional Program Pengabdian Masyarakat Jurnal Nasional Teknik Elektro dan Teknologi Informasi Journal FORTEI-JEERI Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) Jurnal INFOTEL
Indah Soesanti
Universitas Gadjah Mada
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Library & Information Science Mechanical Engineering Medicine & Pharmacology Social Sciences

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Interference effect during word-task and colour-task in incongruent stroop-task Hotama, Christianus Frederick; Nugroho, Hanung Adi; Soesanti, Indah; Oktoeberza, Widhia KZ
Communications in Science and Technology Vol 2 No 2 (2017)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.2.2.2017.59

Abstract

Stroop-task is one of the most popular studies to check the ability of decision-making and cognitive process during high interference activity in the brain.  In the incongruent Stroop-task, the difference between the colour that we read and the colour that we see produces high interference activities in the brain.  This research aims to analyse the activity differences in each part of the brain during colour-task and word-task.  This study investigates how well the ability of decision-making and cognitive process during high interference activities that occur in the brain.  Electroencephalography (EEG) can record brain activities by recording the brain waves.  The results show that recognising the colour is more difficult than that of the written words in the Stroop-task as indicated by statistical test with t-value greater than threshold value (t>2.0027) and significant level of 0.05.  This study concludes that the colour-task gives more interference effect than the word-task.  The more interference effect is produced, the more wrong decision-making is obtained. 
A systematic review of breast cancer detection on thermal images Aqil Aqthobirrobbany; Dian Nova Kusuma Hardani; Indah Soesanti; Adi Nugroho, Hanung
Communications in Science and Technology Vol 8 No 2 (2023)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.8.2.2023.1270

Abstract

Breast cancer poses a substantial global health concern, primarily regarding its impact on women. Thermal imaging has emerged as a promising tool for early detection with notable technological advancements between 2013 and 2023 in enhancing diagnostic capabilities. However, existing literature reviews often lack adherence to specific scholarly standards and may provide incomplete insights into research trends. This systematic literature review (SLR) addresses these issues by comprehensively analyzing research trends, publication types, contributions, datasets, methodologies, and effective approaches for breast cancer detection using thermal imaging. The review encompasses an examination of 40 articles from reputable digital libraries, revealing a predominant emphasis on deep learning algorithms among 25 applied methods. These algorithms consistently achieve commendable performance, frequently surpassing 90% accuracy rates. Consequently, current research in breast cancer detection via thermal imaging is marked by a strong focus on artificial intelligence, particularly machine and deep learning, recognized as the most promising and effective avenues for investigation.
Enhanced U-Net architecture with CNN backbone for accurate segmentation of skin lesions in dermoscopic images Aqthobirrobbany, Aqil; Al-Fahsi, Resha Dwika Hefni; Soesanti, Indah; Nugroho, Hanung Adi
International Journal of Advances in Intelligent Informatics Vol 10, No 3 (2024): August 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v10i3.1379

Abstract

Addressing the critical public health challenge of skin cancer, particularly melanoma and non-melanoma, this study focuses on enhancing early diagnosis through improved automatic segmentation of skin lesions in dermoscopic images. The researchers propose an optimized U-Net architecture that integrates advanced convolutional neural networks (CNNs) with backbone models such as ResNet50, VGG16, and MobileNetV2, specifically designed to handle the inherent variability and artifacts in dermoscopic imagery. The method's effectiveness was validated using the ISIC-2018 dataset, and our U-Net model incorporating the VGG16 backbone achieved notable improvements in segmentation accuracy, demonstrating an accuracy rate of 0.93. These results signify significant enhancements over existing methods, emphasizing the potential of the proposed approach in aiding precise skin cancer diagnosis and detection. This study makes a valuable contribution to dermatological imaging by presenting an advanced method that substantially boosts the accuracy of skin lesion segmentation, addressing a crucial need in public health.
Pengembangan Deep Learning untuk Sistem Deteksi Dini Komplikasi Kaki Diabetik Menggunakan Citra Termogram Emhandyksa, Medycha; Soesanti, Indah; Susilowati, Rina
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 6: Desember 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2023107382

Abstract

Prevalensi komplikasi kaki diabetik secara global mencapai 66% dengan resiko amputasi 20 kali lebih tinggi pada pasien diabetes mellitus. Tindakan pencegahan melalui deteksi dini komplikasi kaki diabetik mutlak dilakukan untuk meminimalisasi resiko amputasi. Penelitian sebelumnya menunjukkan validitas dan akurasi yang tinggi (mencapai 100%) dari sistem deteksi dini komplikasi kaki diabetik menggunakan termografi berbasis kecerdasan buatan. Namun sebagian besar penelitian tersebut terlalu berfokus pada peningkatan performa dan tidak memperhatikan aspek biaya komputasi yang berperan penting pada proses deployment model. Pada penelitian ini dirancang empat model deep convolutional neural network dengan prinsip Occam’s razor melalui pengaturan hyperparameter pada aspek struktur algoritma berupa jumah layer dan aspek optimasi berupa tipe optimizer. Penelitian bertujuan mengembangkan algoritma deep convolutional neural network untuk menghasilkan sistem deteksi dini komplikasi kaki diabetik dengan biaya komputasi terendah (jumlah parameter paling sedikit) dan mempertahankan kemampuan deteksi tetap tinggi (nilai rata-rata parameter evaluasi tertinggi). Data yang digunakan merupakan data primer berupa citra termogram telapak kaki dari RSUP. Dr. Sardjito Yogyakarta yang terdiri dari 20 subjek diabetes mellitus dan 20 subjek kontrol (sehat). Pengambilan data primer dilakukan menggunakan kamera thermal merek HIKMICRO B20 dengan resolusi inframerah 256x192 yang telah memenuhi standar internasional (IACT) untuk menghasilkan citra termogram dua dimensi. Hasil penelitian menunjukkan model 4 dengan Adam optimizer dan pengaturan hyperparameter tertentu merupakan model terbaik dengan jumlah parameter model paling sedikit yaitu 1.570.594 juta dan nilai rata-rata parameter evaluasi tetap tinggi sebesar 96%. Selain arsitektur deep convolutional neural network model 4, kontribusi penelitian yang didapatkan dari penelitian ini adalah penggunaan variasi ukuran filter 3x3, 2x2, dan 1x1 dengan jumlah convolutional layer yang tetap dan pengurangan jumlah hidden layer pada struktur algoritma mampu menurunkan jumlah parameter model dengan tetap mempertahankan kemampuan deteksi yang tinggi. Selain itu penelitian yang dilakukan merupakan penelitian pembuka atau pendahuluan mengenai perancangan sistem deteksi dini komplikasi kaki diabetik menggunakan termografi berbasis kecerdasan buatan deep learning di Indonesia.   Abstract The prevalence of diabetic foot complications globally reaches 66% with a 20 times higher risk of amputation in patients with diabetes mellitus. Preventive measures through early detection of diabetic foot complications are necessary to minimize the risk of amputation. Previous studies have shown high validity and accuracy (up to 100%) of the early detection system of diabetic foot complications using artificial intelligence-based thermography. However, most of these studies focused too much on improving performance and did not pay attention to the computational cost aspect. In this study, four deep convolutional neural network models were designed with Occam's razor principle through hyperparameter settings on the algorithm structure aspect in the form of number of layers and optimization aspect in the form of optimizer type. The research aims to develop a deep convolutional neural network algorithm to produce an early detection system for diabetic foot complications with the lowest computational cost (least number of parameters) and maintain high detection capability (highest average value of evaluation parameters). The data used is primary data in the form of foot thermogram images from the General Hospital. Dr. Sardjito Yogyakarta consisting of 20 diabetes mellitus subjects and 20 control (healthy) subjects. Primary data collection was carried out using a thermal camera brand HIKMICRO B20 with 256x192 infrared resolution that has met international standards (IACT) to produce a two-dimensional color thermogram image. The results show that model 4 with Adam optimizer and certain hyperparameter settings is the best model with the least number of model parameters, namely 1,570,594 million and the average value of evaluation parameters remains high at 96%. In addition to the deep convolutional neural network architecture model 4, the research contribution obtained from this research is the use of filter size variations of 3x3, 2x2, and 1x1 with a fixed number of convolutional layers and a reduction in the number of hidden layers in the algorithm structure can reduce the number of model parameters while maintaining high detection capability. In addition, the research conducted can be an opening or preliminary research on the design of an early detection system for diabetic foot complications using deep learning artificial intelligence-based thermography in Indonesia.
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.
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.
A Brief Study of The Use of Pattern Recognition in Online Learning: Recommendation for Assessing Teaching Skills Automatically Online Based Utami, Pipit; Hartanto, Rudy; Soesanti, Indah
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 7 No. 1 (2022): Mei 2022
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (583.925 KB) | DOI: 10.21831/elinvo.v7i1.51354

Abstract

Online learning has become a trend for the current generation of students who have been exposed to advanced information and communication technology. Smart education can use pattern recognition. Manual assessments are subjective and inconsistent. To overcome these problems, pattern recognition can be used in the non-verbal aspect assessment system. This study describes pattern recognition in online learning about the functions, modalities, and algorithms and specifically related to the recognition system of non-verbal aspects of teaching skills. The literature study was carried out through the stages of planning, selection, extraction, and selection. There are 86 articles reviewed. The first result is the functions of implementing pattern recognition in online learning are engagement recognition, attention detection, emotion recognition, learning behavior, learning activity recognition, authentication, teaching training, etc. using four classifications of modality: visual, audio, biosignal, behavioral, and CNN as the most widely used learning algorithm. Secondly, all modalities (except behavioral) and CNN algorithm can be used for assessing teaching skills. Early development of the non-verbal aspect assessment system can use Facial Expression Recognition (FER) and Hand Gesture Recognition (HGR). The future analysis needs to focus on technology characteristics, the meaningfulness of the content, and the proper teaching mode. In the end, hopefully, prospective teachers will acquire technology that can make it easier for them to practice teaching and get objective assessments.
Co-Authors Adha Imam Cahyadi Adhi Soesanto, Adhi Adhi Susanto Adhistya Erna Permanasari Afrisal, Hadha Agus Eko Minarno Agus Jamal Al-Fahsi, Resha Dwika Hefni Andrey Nino Kurniawan Andrey Nino Kurniawan Nino Kurniawan Andrey Nino Kurniawan, Andrey Nino Anna Nur Nazilah Chamim Aqil Aqthobirrobbany Aqthobirrobbany, Aqil Arief Rachma Wibowo Bambang Sutopo Bana Handaga Beta Estri Adiana Cepi Ramdani Chamim, Anna Nur Nazilah Danny Kurnianto Desyandri Desyandri Dewi Purnamasar Diah Priyawati Dian Nova Kusuma Hardani Domy Kristomo Dwi Rochmayanti Dwi Rochmayanti Dwi Rochmayanti Eka Firmansyah Elfrida Ratnawati Emhandyksa, Medycha Faaris Mujaahid Fathania Firwan Firdaus Fikri Zaini Baridwan Hanifah Rahmi Fajrin Hanung Adi Nugroho Hedi Purwanto Hendriyawan A., M. S. Henry Sulistyo Hidayatul Fitri Hotama, Christianus Frederick Husnul Rahmawati Sakinnah I Made Agus Wirahadi Putra Ikhwan Mustiadi Indriana Hidayah Isbadi Urifan Karisma Trinanda Putra, Karisma Trinanda Krisna Nuresa Qodri Litasari Litasari Litasari M.S. Hendriyawan Achmad Maesadji Tjokronagoro Maesadji Tjokronagoro Maesadji Tjokronegoro Meirista Wulandari Muhammad Arzanul Manhar Muhammad Rausan Fikri Mustar, Muhamad Yusvin Noor Akhmad Setiawan Nurokhim Nurokhim Oki Iwan Pambudi Oktoeberza, Widhia KZ Oyas Wahyunggoro Paulus Tofan Rapiyanta Pipit Utami Ramadoni Syahputra Ratnasari Nur Rohmah Rina Susilowati Risanuri Hidayat Rudy Hartanto Sekar Sari Siti Helmyati Soesanto, Adhi Sulistyo, Henry Sunu Wibirama Syahfitra, Febrian Dhimas Thomas Sri Widodo Thomas Sri Widodo Thomas Sri Widodo Thomas Sri Widodo Tole Sutikno Warsun Najib Widyawan Widyawati Prima, Widyawati Wijaya, Nur Hudha Wijaya, Nur Hudha Wiyagi, Rama Okta Yudhi Agussationo Yundari, Yundari