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All Journal Journal of Biomedicine and Translational Research Sinkron : Jurnal dan Penelitian Teknik Informatika PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat BERNAS: Jurnal Pengabdian Kepada Masyarakat JOURNAL LA MEDIHEALTICO Al Khidma: Jurnal Pengabdian Masyarakat Asian Journal of Community Services (AJCS) Homeostasis: Jurnal Mahasiswa Pendidikan Dokter Journal of Global Pharma Technology CSRID
Fujiati ., Fujiati
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Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Dentistry Economics, Econometrics & Finance Education Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Neuroscience Nursing Public Health Social Sciences Veterinary Other

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Analisis Perbandingan Algoritma Klasifikasi Terhadap Data Problem Mesin ATM Dengan Rapidminer Tanjung, Dahriani Hakim; Dewi, Rofiqoh; Fujiati, Fujiati; Salim, Rinrin Meilani
CSRID (Computer Science Research and Its Development Journal) Vol. 16 No. 2 (2024): June 2024
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid.16.2.2024.188-200

Abstract

The aim of the proposed research is to compare and test the accuracy of data mining classification algorithms. Comparing algorithms that depend on different parameters of a given data set. There are learning and classification algorithms that are used to analyze, study and classify the available data. However, the problem is finding the best algorithm and the desired results with the highest level of accuracy in predicting future values ​​or events from a data set. Where the classification models used are the C4.5 and Naïve Bayes algorithms. Testing and validation using k-fold Cross Validation as well as evaluating the performance of the prediction model using the ROC-AUC graph with graphic visualization. The data used as samples were taken from ATM machine problem data with a total of approximately 250 samples. Testing was carried out with the help of the Rapidminer tool with operators and parameters used in creating models of the algorithms being compared. The tests that have been carried out prove that the C4.5 algorithm has the best performance with an average accuracy value of 96.00%, a recall value of 97.78% and a precision value of 92.14%, while the naïve Bayes algorithm produces an accuracy value of 83. 00%, the recall value is 76.40% and the precision value is 84.82%. Apart from that, evaluation and validation in this test is also seen based on the ROC curve called AUC (Area Under the ROC Curve) where for the C4.5 algorithm the value is 0.931 while naïve Bayes is 0.894 so the C4.5 algorithm is categorized as Very Good Classification because it has a value between 0.90-1.00. These results show that the C4.5 algorithm is proven to be a potentially effective and efficient classification algorithm.
Membangun Hidup Sehat Tanpa Hipertensi (Manajemen Pengendalian dan Pencegahan Hipertensi Mandiri, Mengenal Tanaman Herbal Antihipertensi, dan Membuat Suplemen Herbal Sendiri): Building a Healthy Life Without Hypertension (Self-Management of Hypertension Control and Prevention, Getting to Know Antihypertensive Herbal Plants, and Making Your Own Herbal Supplements) Fujiati, Fujiati; Isnaini, Isnaini; Joharman, Joharman; Asnawati, Asnawati; Al Audhah, Nelly; Silapurna, Endah Labaty; Hayatie, Lisda; Setyohadi, Dwi
PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Vol. 10 No. 4 (2025): PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat
Publisher : Institute for Research and Community Services Universitas Muhammadiyah Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33084/pengabdianmu.v10i4.8509

Abstract

Hypertension is a major risk factor for cardiovascular disease, posing significant public health challenges globally. The prevalence of hypertension with uncontrolled blood pressure and its increasing number is not only caused by individual negligence but can be caused by community ignorance as a result of a lack of information on a disease. Measures to control and prevent hypertension in the community can be carried out including hypertension self-management education, introduction of antihypertensive plants, and training in making health supplements from herbs. To improve the degree of public health and knowledge of the community in the area of West Martapura Subdistrict, counseling, and training were conducted with the PKK mobilizing team of West Martapura Subdistrict using visual (electronic) media, leaflets in the form of education and demonstrations of making health supplements (fermented garlic). In this activity, the average post-test results of knowledge about self-management of hypertension prevention and control were 90.5, the average knowledge of herbal plants that have the potential as antihypertensives was 89.0 and the average post-test value of knowledge and skills in making health supplements from herbal plants in the form of fermented garlic was 84.5.
Designing a Stunting Prediction Model Using Machine Learning to Support SDGs Achievement in Indonesia Sinaga, Mikha; Fujiati, Fujiati; Halawa, Darma
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15296

Abstract

Stunting remains a major public health challenge in Indonesia, with national prevalence among children under five reaching 21.6% in 2022, according to the Ministry of Health. This condition, defined by the World Health Organization as a height-for-age less than -2 SD, is associated with long-term consequences including impaired cognitive development, reduced educational attainment, and diminished economic productivity. Addressing stunting is therefore critical to achieving Sustainable Development Goals (SDGs) related to hunger, health, and education. Despite multiple national initiatives, early identification of stunting risk is still limited by reliance on conventional, reactive surveillance methods. Recent advances in machine learning (ML) provide promising alternatives for proactive stunting prediction, with several studies reporting high predictive accuracy using ensemble methods, hybrid frameworks, and geographically weighted models. Building upon this evidence, the present study develops and evaluates ML models for stunting risk prediction using a large dataset of 10,000 records from North Sumatra, Indonesia. The dataset included three predictor variables—age, height, and weight—and a target variable, nutritional status (Normal, Stunted, Severely Stunted, Tall). Four algorithms were compared: K-Nearest Neighbors (KNN), Naïve Bayes, Decision Tree, and Random Forest. Performance was assessed using accuracy, precision, recall, F1-score, and ROC area, with 10-fold cross-validation ensuring robust estimation. Results demonstrated that Decision Tree (88.6% accuracy) and Random Forest (88.3% accuracy) outperformed KNN (84.7%) and Naïve Bayes (72%). ROC areas further confirmed the superiority of ensemble-based approaches, particularly Random Forest (0.979). Statistical significance was tested using McNemar’s test, revealing that Decision Tree and Random Forest achieved comparable performance (p = 0.651), both significantly outperforming KNN and Naïve Bayes (p < 0.05). This study contributes a context-specific evaluation of ML methods for stunting prediction in North Sumatra, emphasizing not only predictive accuracy but also interpretability to support health policy and program implementation. By bridging data-driven insights with actionable decision support, the proposed framework advances progress toward SDG-aligned strategies and provides a foundation for more targeted and preventive interventions in child nutrition and growth monitoring.
Co-Authors Andi Sanjaya Aranda, Viren Lolita Asnawati Asnawati Bambang Setiawan Dahriani Hakim Tanjung, Dahriani Hakim Dwi Setyohadi, Dwi Eko Suhartono Halawa, Darma Haryati Haryati Husin, Gt Muhammad Irhamna Irawanto Isnaini Isnaini Joharman Joharman Joharman Joharman Lisda Hayatie, Lisda Mashuri Mashuri Meitria Syahadatina Noor Multiansyah, Multiansyah Musafaah Musafaah Najiha, Aisya Nelly Al Audhah Nelly Al Audhah Noorsifa, Noorsifa Rahma, Rizkia Alifa Ridho, Anisa Rifka Rofiqoh Dewi Salim, Rinrin Meilani Sari, Annida Permata Sembiring, Hilda Elsera Br Silapurna, Endah Labati Silapurna, Endah Labaty Sinaga, Mikha Siti Khairiah, Siti Wiwi Verina, Wiwi