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All Journal Jurnal Ilmiah Didaktika : Media Ilmiah Pendidikan dan Pengajaran PREPOTIF : Jurnal Kesehatan Masyarakat Babali Nursing Research Computer Science (CO-SCIENCE) Jurnal Komputer, Informasi dan Teknologi Jurnal Pengabdian kepada Masyarakat Wahana Usada International Journal of Social Science, Educational, Economics, Agriculture Research, and Technology (IJSET) Brilliance: Research of Artificial Intelligence Jurnal MathEducation Nusantara International Review of Practical Innovation, Technology and Green Energy (IRPITAGE) MEDALION JOURNAL: Medical Research, Nursing, Health and Midwife Participation
Utaminingsih, Eka
Universitas Bumi Persada, Aceh, Indonesia
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Health Professions Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Veterinary Other

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Automated Recognition of Batik Aceh Patterns Using Machine Learning Techniques Utaminingsih, Eka; Sahputra, Ilham
Brilliance: Research of Artificial Intelligence Vol. 4 No. 2 (2024): Brilliance: Research of Artificial Intelligence, Article Research November 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i2.4831

Abstract

This research focuses on the automatic recognition of Aceh batik patterns using machine learning techniques. Utilizing a Convolutional Neural Network (CNN) model based on EfficientNet, a dataset consisting of 1,200 Aceh batik images was processed through various stages, from data collection to model training and evaluation. The images are divided into three main classes: Bungong Jeumpa, Ceplok, and Kerawang. The data processing steps include normalization, resizing, and data augmentation to ensure better variation. The model was trained using 75% of the data as a training set and 25% as a testing set. The results indicate that the model performed excellently, achieving an accuracy rate of 98%. According to the classification report, the model achieved an average precision, recall, and F1-score of 0.98. The Kerawang category achieved the highest precision at 100%, while the Bungong Jeumpa and Ceplok categories had F1-scores of 0.98 and 0.97, respectively. These findings demonstrate the potential of machine learning methods in recognizing Aceh batik patterns with high accuracy, supporting the preservation of local culture through technology.
BASIC INVESTMENT TRAINING IN THE CAPITAL MARKET FOR UMKM AND RESIDENTS OF HAGU BARAT LAUT VILLAGE Muhammad Multazam; Rico Nur Ilham; Ayu Anora; Muttaqien; Rahmiatul Aula; Ismuhadi; Utaminingsih, Eka
International Review of Practical Innovation, Technology and Green Energy (IRPITAGE) Vol. 5 No. 2 (2025): July-October 2025
Publisher : RADJA PUBLIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54443/irpitage.v5i2.3484

Abstract

Low financial literacy and access to investment information among MSMEs and rural communities are challenges in realizing financial inclusion in Indonesia. This study aims to evaluate the effectiveness of basic investment training in improving capital market literacy for MSMEs and residents of Gampong Hagu Barat Laut. The method used is a descriptive quantitative approach with a one-group pretest-posttest design, involving 45 participants. The results of the analysis showed a significant increase in investment literacy scores from an average of 42.6 to 73.1 (p <0.001), covering aspects of knowledge, attitudes, and understanding of risk. The conclusion of this study shows that community-based training is effective in improving investment literacy, and needs to be replicated in other areas as a local-based financial inclusion strategy.
DISEASE CLASSIFICATION USING SUPPORT VECTOR MACHINE (SVM) WITH JAVA STANDARD EDITION (JSE) Eka Utaminingsih; Rifki; Zanuar Rizkiansyah; Arista Ardilla; Fitriani
International Journal of Social Science, Educational, Economics, Agriculture Research and Technology (IJSET) Vol. 4 No. 8 (2025): JULY
Publisher : RADJA PUBLIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54443/ijset.v4i8.1064

Abstract

This research focuses on disease clustering, which is a crucial aspect of effective diagnosis and treatment. With the increasing complexity of health data generated from various sources, such as electronic health records and laboratory results, efficient methods are needed to cluster and analyze this data. The use of machine learning algorithms, particularly Support Vector Machine (SVM), offers a promising solution to address this issue. SVM is known for its ability to handle multidimensional data and identify patterns that are not immediately visible. The challenges faced in disease clustering include difficulties in managing large and complex data, as well as the inability of traditional methods to provide accurate and rapid results. Additionally, many healthcare professionals lack access to adequate analytical tools, hindering appropriate clinical decision-making. Therefore, it is essential to develop solutions that can effectively assist in disease clustering. The proposed solution in this study is the development of a Java Standard Edition (JSE) based application that implements the SVM algorithm for disease clustering. This application is designed to provide an intuitive user interface, allowing users to upload data, run the SVM algorithm, and easily obtain clustering results. This research uses clinical data from various diseases, including heart disease, diabetes, hypertension, cancer, asthma, and stroke. Evaluation results show that SVM can cluster diseases with an accuracy of up to 92%. Thus, this study concludes that the application of SVM in a JSE-based application is an effective solution for enhancing disease clustering and supporting better clinical decision-making.
Co-Authors AA Sudharmawan, AA Arista Ardilla Aulia, Muhammad Kahfi Ayu Anora Desy Irafadillah Effendi Dian Vita Sari Effendi, Desy Iradillah Elfrida Ratnawati Fatmawati Fitriani Ghialti Novilia Ismuhadi Muhammad Faisal Muhammad Kahfi Aulia Muhammad Multazam Muttaqien Prihatin, Nanang Rahmiatul Aula Rico Nur Ilham Rifki Rika Silviani Sahputra, Ilham Zahratul Fitri Zanuar Rizkiansyah Zulkarnaini Zulkarnaini