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All Journal Jurnal Ilmiah Matrik Jurnal Elektro Kontrol (ELKON)
Regina Septient Malini
Universitas Islam Indonesia
Computer Science & IT Electrical & Electronics Engineering

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LITERATURE REVIEW: PERAN SISTEM SMART HEALTH SEBAGAI INOVASI DIGITAL DALAM UPAYA PENCEGAHAN STUNTING Regina Septient Malini; Firdaus; Sisdarmanto Adinandra
Jurnal Elektro Kontrol (ELKON) Vol. 5 No. 2 (2025): Jurnal ELKON
Publisher : Teknik Elektro Fakultas Teknik Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/elkon.v5i2.15706

Abstract

Stunting merupakan permasalahan kesehatan masyarakat yang berdampak jangka panjang terhadappertumbuhan fisik dan perkembangan kognitif anak. Seiring dengan perkembangan teknologi digital,pendekatan smart health berbasis Internet of Things (IoT), Artificial Intelligence (AI), dan Machine Learning(ML) mulai diterapkan sebagai solusi inovatif dalam upaya deteksi dan pencegahan stunting. Penelitian inimenggunakan metode systematic literature review terhadap 20 jurnal ilmiah untuk mengidentifikasipenerapan teknologi smart health dalam tiga kategori utama: deteksi, pemantauan, dan pencegahan stunting.Hasil studi menunjukkan bahwa teknologi ini mampu meningkatkan efektivitas deteksi dini, efisiensipemantauan secara real-time, serta memperluas cakupan edukasi gizi kepada masyarakat melalui mediadigital yang interaktif. Namun demikian, implementasinya masih menghadapi sejumlah tantangan, sepertiketerbatasan infrastruktur, rendahnya literasi digital, serta kurangnya integrasi dengan sistem informasikesehatan nasional. Oleh karena itu, keberhasilan penerapan smart health membutuhkan dukungan kebijakan,infrastruktur yang memadai, serta evaluasi berkelanjutan agar dapat diimplementasikan secara optimal danberkelanjutan di berbagai wilayah, khususnya di daerah dengan sumber daya terbatas.
Analisis Tren Historis Dan Prediksi Beban Listrik Pada Tenaga Listrik Menggunakan Artificial Neural Network Dengan Metode Backpropagation: Systematic Literature Review Regina Septient Malini; Alvin Sahroni; Hendra Setiawan
Jurnal Ilmiah Matrik Vol. 27 No. 2 (2025): Jurnal Ilmiah Matrik
Publisher : Direktorat Riset dan Pengabdian Pada Masyarakat (DRPM) Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33557/8kyfgz81

Abstract

Electric load forecasting is a critical step in ensuring the reliability of power systems amid rising energy demand driven by digitalization, industrialization, and urbanization. This article presents a Systematic Literature Review (SLR) on the application of Artificial Neural Networks (ANN) with backpropagation algorithms for load prediction based on historical data, employing the PRISMA framework for study screening and selection. The review analyzes nine relevant national journals to identify trends in accuracy, network configurations, and model effectiveness. Findings indicate that ANN with backpropagation can achieve low prediction error rates, such as a Mean Absolute Percentage Error (MAPE) of 0.05% in industrial sectors and up to 99.88% accuracy in specific cases. ANN also demonstrates strong capability in capturing dynamic changes in energy consumption, making it a reliable method for supporting operational planning and efficient electricity distribution. Despite promising performance, several aspects remain underexplored, including more complex ANN architectures, hyperparameter tuning techniques, limited cross-regional validation, and insufficient comparative analysis with alternative methods such as ensemble learning or deep learning-based algorithms. This review offers comprehensive insights into the integration of artificial intelligence in power systems and lays the groundwork for developing more adaptive, precise, and broadly generalizable load forecasting strategies in the future.
Co-Authors Alvin Sahroni Firdaus Hendra Setiawan Sisdarmanto Adinandra, Sisdarmanto