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All Journal Jurnal Ilmiah Didaktika : Media Ilmiah Pendidikan dan Pengajaran JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI PREPOTIF : Jurnal Kesehatan Masyarakat Babali Nursing Research Computer Science (CO-SCIENCE) 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 Darussalam Indonesian Journal of Nursing and Midwifery Journal of Tecnologia Quantica
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 Energy Health Professions Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Veterinary Other

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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.
Analysis of The Implementation of Countermeasure Policies Against Stunting Ardilla, Arista; Zulkarnaini, Zulkarnaini; Utaminingsih, Eka; Irafadillah Effendi, Desy; Vita Sari, Dian; Fatmawati, Fatmawati
Babali Nursing Research Vol. 5 No. 2 (2024): April
Publisher : Babali Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37363/bnr.2024.52321

Abstract

Introduction: Stunting is a major nutrition problem worldwide, especially in poor and developing countries. This problem leads to children's suboptimal brain, mental, and cognitive development. The stunting rate globally was 32.6% in 2000, and by 2017, around 150.8 million people were suffering from malnutrition and stunting. This research aims to determine the implementation of stunting prevention policies in the Puskesmas (Public Health Centre) Blang Cut working area.Methods: The research used a qualitative method with a descriptive approach to analyze the implementation of countermeasure policies to reduce stunting. The Health Belief Model was used as the theoretical framework. The methodological orientation of this research was discourse analysis. The study used an interview guide and a voice recorder to collect information from 9 informants.Results: Puskesmas Blang Cut has implemented several countermeasure policies to reduce stunting. These include increasing awareness about the importance of proper nutrition and hygiene, training healthcare workers on stunting prevention, and monitoring children's growth regularly. Implementing these policies has led to a significant reduction in the prevalence of stunting. However, some challenges still need to be addressed, such as increasing access to healthcare services and improving the quality of healthcare facilities.Conclusion: Communication factors related to implementing Countermeasure Policies in Stunting Reduction have been running well. The puskesmas has carried out all stunting reduction program activities, but the more dominant one is the Supplementary Feeding Program for those affected by stunting.
Analisis Penggunaan Deskriptor Warna Dominan Dan Kolerogram Warna Untuk Temu Kembali Citra Penyakit Kulit Eka Utaminingsih; Muhammad Kahfi Aulia; Fitriani; Fauziah
Indonesia Vol 7 No 1 (2025): April
Publisher : STIKes Darussalam Lhokseumawe

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Abstract

Temu kembali citra berbasis konten (Content-Based Image Retrieval/CBIR) merupakan pendekatan penting dalam sistem diagnosis otomatis, terutama dalam pengenalan dan klasifikasi citra penyakit kulit. Warna adalah fitur visual yang paling dominan dalam citra penyakit kulit, sehingga sangat relevan untuk digunakan dalam ekstraksi fitur. Penelitian ini menganalisis dan membandingkan efektivitas dua deskriptor warna, yaitu warna dominan dan kolerogram warna, dalam sistem CBIR untuk citra penyakit kulit. Dataset yang digunakan terdiri dari 500 citra penyakit kulit dari berbagai jenis seperti psoriasis, dermatitis, dan melanoma. Hasil evaluasi menunjukkan bahwa kombinasi kedua deskriptor meningkatkan akurasi temu kembali hingga 84,6%, dibandingkan penggunaan tunggal yang masing-masing hanya mencapai 75,2% (warna dominan) dan 79,3% (kolerogram warna).
Analisis Komparatif Metode Klasterisasi (AHC-Ward, K-Means, GMM, dan BIRCH) untuk Pengelompokan Sentra Produksi Perkebunan Aceh Utara Sahputra, Ilham; Utaminingsih, Eka; Eviyanti, Cut Yuniza
JATISI Vol 12 No 4 (2025): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v12i4.13330

Abstract

This study compares four clustering techniques AHC-Ward, K-Means, GMM, and BIRCH to group sub-districts in North Aceh Regency based on plantation production data. The goal is to find the most effective clustering method for identifying diverse production patterns. Each algorithm's performance was evaluated using three internal metrics: Silhouette, Davies-Bouldin Index (DBI), and Dunn Index. The analysis showed that AHC-Ward had the most consistent and best performance. This method achieved the highest Silhouette score (0.515), the lowest DBI (0.309), and the highest Dunn Index (0.816). In contrast, K-Means performed the worst, while GMM and BIRCH were in between. Based on the optimal AHC-Ward clustering results, three clusters were found that reflect geographical specialization: a cluster with diverse production, a cluster dominated by oil palm, and a cluster focused on Arabica coffee and tobacco. These findings provide crucial insights for local governments to formulate agricultural policies that are more focused and relevant to the specific characteristics of each cluster.
Quantum Neural Networks: Advantages in Processing High-Dimensional Hilbert Space Data Utaminingsih, Eka; Santi, , Luca; Kakala, Sione
Journal of Tecnologia Quantica Vol. 3 No. 1 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/quantica.v3i1.3388

Abstract

Quantum machine learning has emerged as a promising paradigm for addressing the limitations of classical learning models in handling data with exponentially growing dimensionality. In particular, many problems in physics, chemistry, and quantum information are naturally represented in high-dimensional Hilbert spaces, where classical neural networks face significant challenges related to representation efficiency and scalability. This study aims to analyze the advantages of quantum neural networks in processing data embedded in high-dimensional Hilbert spaces and to clarify the structural sources of their potential superiority over classical architectures. The research adopts a theoretical–computational approach that combines analytical modeling with numerical simulations of variational quantum circuits and comparable classical neural network models across increasing dimensional regimes. Performance is evaluated in terms of learning fidelity, parameter scaling behavior, and stability under dimensional growth. The results show that quantum neural networks consistently maintain higher fidelity with substantially fewer parameters as Hilbert space dimensionality increases, while classical models exhibit rapid performance degradation and escalating complexity. These findings indicate that quantum neural networks benefit from intrinsic alignment with Hilbert space geometry through superposition and entanglement. In conclusion, the study demonstrates that quantum neural networks constitute a distinct and scalable learning framework for high-dimensional data, supporting their relevance for future quantum-enhanced machine learning applications..
Co-Authors AA Sudharmawan, AA Arista Ardilla Aulia, Muhammad Kahfi Ayu Anora Dian Vita Sari Effendi, Desy Iradillah Elfrida Ratnawati Eviyanti, Cut Yuniza Fatmawati Fatmawati, Fatmawati Fauziah Fitriani Ghialti Novilia Irafadillah Effendi, Desy Ismuhadi Kakala, Sione Muhammad Faisal Muhammad Kahfi Aulia Muhammad Multazam Muttaqien Nurwijayanti Prihatin, Nanang Rahmiatul Aula Rico Nur Ilham Rifki Rika Silviani Sahputra, Ilham Santi, , Luca Vita Sari, Dian Zahratul Fitri Zanuar Rizkiansyah