Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal Bulletin of Electrical Engineering and Informatics Jurnal Pengabdian Masyarakat Bidang Sains dan Teknologi Jurnal Tiyasadarma: Jurnal Pengabdian Kepada Masyarakat
Muhammad Ansari Adista
Universitas Syiah Kuala
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Transportation Veterinary

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Pemanfaatan Family Folder Untuk Optimalisasi Kegiatan Home Visit Pasien Hipertensi Muhammad Ansari Adista; Zahratul Aini; Syahrizal Syahrizal; Sausan Syadza; Fadhilah Jamal
ABDIKAN: Jurnal Pengabdian Masyarakat Bidang Sains dan Teknologi Vol. 2 No. 2 (2023): Mei 2023
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/abdikan.v2i2.1834

Abstract

Home visit is one of the leading health service programs carried out by the puskesmas in an effort to improve public health. During the home visit, various accurate and up-to-date data will be obtained regarding the health condition of patients, family members, utilization of available facilities at home to support the health of family members, relationships between family members and illnesses suffered by each family member. One of the supporting instruments that can be used to optimize home visit activities as a health service effort is the existence of medical records in the form of a family folder. A home visit service activity for a patient with hypertension in the working area of ​​the Jeulingke Health Center, Syiah Kuala District, Banda Aceh City. Home visit activities to preparation of reports are supervised by supervisors from the Family Medicine Section/Department of the Faculty of Medicine, Syiah Kuala University, Banda Aceh. Home visits were carried out twice in one family. The aim is to evaluate treatment and obtain more information about the patient's health condition, next of kin to the patient's and family's living conditions. Home visit activities are complemented by completing the Family Folder instrument which consists of a genogram, family life cycle, family map, family APGAR, family SCREEM and family life line.
OPTIMALISASI PREVENTIF SEKUNDER PADA PASIEN PASKA STROKE MENGGUNAKAN FAMILY FOLDER Muhammad Ansari Adista; Zahratul Aini; Syahrizal; Nissa Natsir Mahmud; Anisha Putri Arsyad; Riski Arifin
Jurnal Tiyasadarma Vol. 1 No. 1 (2023): JULI 2023 | Jurnal Tiyasadarma
Publisher : LPPM ITEBA

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Salah satu program pelayanan kesehatan unggulan di puskesmas adalah kegiatan kunjungan rumah atau home visit guna meningkatkan upaya kesehatan masyarakat. Pelaksanaan program tersebut dilaksanakan untuk mendapatkan data mengenai kondisi pasien, anggota keluarga pasien, hubungan yang dijalin antar anggota keluarga, serta penyakit yang ada pada anggota keluarga. Data tersebut akan dikumpulkan sebagai rekam medis yang disebut family folder. Kegiatan pengabdian yang dilakukan berupa home visit pada salah satu pasien pasien paska stroke di wilayah kerja puskesmas Lampaseh, Kecamatan Kutaraja, Kota Banda Aceh. Pelaksanaan kegiatan hingga penyusunan laporan didampingi oleh dosen pembimbing dari Bagian/Departemen Family Medicine Fakultas Kedokteran Universitas Syiah Kuala, Banda Aceh. memberi gambaran mengenai dinamika keluarga dan kesehatannya, serta kemampuan keluarga dalam menghadapi permasalahan, terutama terkait kesehatan anggota keluargaKunjungan rumah dilakukan sebanyak dua kali yang bertujuan memberi gambaran mengenai dinamika keluarga dan kesehatannya, serta kemampuan keluarga dalam menghadapi permasalahan, terutama terkait kesehatan anggota keluarga.
Transfer learning-based malnutrition classification using VGG16 and comparative analysis of CNN architectures Ahmad Fauzi; Haerul Yuda Aditiya; Maharina Maharina; Sihabudin Sihabidin; Muhammad Ansari Adista; Iflan Naufal; Natasya Eka Nanda Sonia Puri; Candra Zonyfar
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.11346

Abstract

Childhood malnutrition remains a critical public health challenge in developing countries, with Indonesia ranking fifth globally for stunting burden. Traditional anthropometric assessment methods are time-consuming, resource-intensive, and require trained personnel, necessitating efficient computer-based early detection approaches. This study proposes a deep learning-based method for automated nutritional status classification using facial image analysis. We developed and compared multiple transfer learning architectures including visual geometry group 16 (VGG16), densely connected convolutional network 121 (DenseNet121), mobile network version 2 (MobileNetV2), and residual network 50 (ResNet50) for classifying children into three categories: healthy, malnutrition, and stunting. Results demonstrated that VGG16, a simpler architecture trained for only 10 epochs, achieved optimal performance with 91.8% accuracy, and significantly outperforming more complex modern architectures like ResNet50. This finding challenges the conventional assumption that newer, deeper models invariably perform better, and particularly when working with limited medical datasets. The study revealed that longer training durations led to performance degradation due to overfitting, emphasizing the importance of balancing model complexity with dataset characteristics. These findings support the development of practical artificial intelligence (AI)-based malnutrition screening systems suitable for resource-constrained environments, potentially improving early detection capabilities, and public health outcomes in developing regions.
Co-Authors Ahmad Fauzi Aini, Zahratul Anisha Putri Arsyad Candra Zonyfar Fadhilah Jamal Haerul Yuda Aditiya Iflan Naufal Maharina Maharina Natasya Eka Nanda Sonia Puri Nissa Natsir Mahmud Riski Arifin Sausan Syadza Sihabudin Sihabidin Syahrizal Syahrizal Syahrizal