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All Journal Jurnal Pengabdian Mandiri Jurnal Sipakatau
Sri Endang Kornita
Universitas Riau, Indonesia
Religion Arts Humanities Computer Science & IT Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Nursing Physics Social Sciences Veterinary Other

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PENYULUHAN KESEHATAN PENGENALAN DAN PENCEGAHAN STUNTING PADA ANAK Sri Endang Kornita; M. Taufiq; Aidri Iswandi; Azzahra Dinda Gefrisa; Fatimah Zahra Karim; Safna Rahmah Indrayni; Kartika Meynar; Sardiy Sardiy; Kurnia Ningsih; Bella Karmelia; Reski Ramanda
JURNAL PENGABDIAN MANDIRI Vol. 2 No. 9: September 2023
Publisher : Bajang Institute

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Abstract

The problem of stunting is a very important issue and must be addressed together. The main factors that need to be faced in efforts to prevent stunting are poverty and lack of knowledge. Prevention of stunting needs to start when the fetus is in the mother's womb. The importance of a child's First 1000 Days of Life period should not be ignored. To reduce the number of stunting cases, cooperation between the government, health services, social services and the community is very necessary. One of effective way to reduce stunting rates is to increase public awareness through outreach regarding stunting prevention and promotion of healthy eating patterns. In order to make this happen, a community service program has been carried out by the Kukerta team in Ratu Sima Village, South Dumai District, Dumai City, Riau Province. This region has a population of around 10,455 people, with the majority of residents working as entrepreneurs. Based on data from previous reports of Posyandu Tengku Maharatu health workers, there were 3 children who experienced the impact of stunting in the area. Therefore, this program aims to take concrete steps to prevent and reduce stunting rates in Ratu Sima Village. Program activities were carried out at Posyandu Tengku Maharatu on August 12, 2023 using various educational methods, such as outreach, videos, browsers and banners related to stunting prevention and healthy eating patterns. During the implementation of the program, intensive education was carried out regarding preventing stunting and providing healthy food to children in the sub-district. Apart from that, educational materials are also distributed through print media in the form of brochures and banners which are given to the community and local posyandu. Then take part in sweeping and health outreach activities at SDN 002 Ratu Sima by providing education about the importance of eating fish. Overall, this educational program regarding stunting received a positive response from the community, which gives hope that stunting rates can be minimized in the future
Chronic Obstructive Pulmonary Disease Classification from Lung Sounds Using Mel-Frequency Cepstral Coefficients and a Multilayer Perceptron Neural Network Surya Hajar Fitria Dana; Susanti Dwi Ariani; Sri Endang Kornita
Jurnal Sipakatau: Inovasi Pengabdian Masyarakat Vol. 3 No. 4 (2026): Juni
Publisher : PT. Global Research Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66314/sipakatau.v3i4.1019

Abstract

Chronic obstructive pulmonary disease (COPD) is associated with persistent airflow obstruction that may produce measurable changes in respiratory sounds. Automated lung-sound analysis could support objective respiratory assessment, although its performance and generalizability require careful evaluation. This study developed and internally evaluated a binary COPD classification pipeline combining Mel Frequency Cepstral Coefficient (MFCC) features with a Multi-Layer Perceptron (MLP) artificial neural network. Lung-sound recordings were obtained from the publicly available ICBHI 2017 Respiratory Sound Database. After eligibility assessment, signal-quality screening, and segmentation, 1,000 two-second respiratory-sound segments from 90 subjects were retained, comprising 425 COPD and 575 normal segments. Data were partitioned at the subject level into training, validation, and held-out test subsets in an approximate ratio of 64:16:20, ensuring that segments from the same participant did not appear in different subsets. Each segment was represented by a 26-dimensional feature vector derived from the mean and standard deviation of 13 MFCC coefficients. The classifier consisted of 26 input units, two hidden layers with 64 and 32 rectified linear units, a dropout rate of 0.20, and one sigmoid output unit. The model was trained using Adam, inverse-frequency class weighting, L2 regularization, and validation-based early stopping. On the held-out test set of 200 segments, the model achieved an accuracy of 93.50%, precision of 90.91%, sensitivity of 94.12%, specificity of 93.04%, and an F1-score of 92.49%. These findings demonstrate the internal feasibility of an MFCC–MLP pipeline for distinguishing COPD from normal lung-sound segments within the study dataset. However, the results do not establish clinical diagnostic validity or deployment readiness. Independent patient-level, multicenter, prospective, and device-specific validation is required before the method can be considered for clinical decision support or portable health-care applications.
Co-Authors Aidri Iswandi Arsi Mwadhatul Fatiha Azzahra Dinda Gefrisa Baimanah Laelatus Sajad Budian Bella Karmelia Cynthia Dinara Farhan Taufik Nurzadi Fatimah Zahra Karim Feby Angraini Firmansyah Eka Putra Kartika Meynar Kurnia Ningsih M. Taufiq Meylissa Putri Afifah Muhammad Akbar Hendrik Reski Ramanda Ricardo Chen Safna Rahmah Indrayni Salsa Mahadila Laora Sardiy Sardiy Surya Hajar Fitria Dana Susanti Dwi Ariani