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INDONESIA
Jurnal Nasional Sains dan Teknik
Published by Universitas Telkom
ISSN : -     EISSN : 30473292     DOI : https://doi.org/10.25124/jnst.v3i1.9447
Core Subject : Engineering,
Electrical & Electronics Engineering
This Jurnal Nasional SAINS dan TEKNIK covers several engineering disciplines. Telecommunications engineering presents innovations in communication systems, networks, and related technologies to improve the efficiency and reliability of communication. Electrical engineering develops and implements electronic solutions, controls, and power systems to support a variety of applications developed by electrical engineering. While in computer engineering, it presents research related to computer architecture, software, information security, and the latest developments in computer technology.
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 42 Documents
 1   2   3   4   5 
Purwarupa Baterai Alumunium Zinc Dengan Elektrolit Hasil Elektrolisis Berbasis Tenaga Surya Dan Sistem Monitoring Berbasis Iot Muhammad Firza Adrien; Ekki Kurniawan; Ir. Uke Kurniawan Usman, M.T
Jurnal Nasional SAINS dan TEKNIK Vol. 4 No. 1 (2026): June 2026
Publisher : Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/jnst.v4i1.11017

Abstract

Kebutuhan akan perealisasian energi terbarukan yang efisien yang diinisiasi oleh pemerintah negara Indonesia sejak terbitnya Undang-Undang Nomor 30 Tahun 2007 mendorong penelitian ini untuk merancang dan menganalisis purwarupa baterai Aluminium-Zinc (AlZn). Tujuannya untuk membangun sistem terintegrasi dengan elektrolisis bertenaga surya 20 Wp dan monitoring IoT, serta membandingkan efektivitas elektrolit NaOH dan KOH. Ruang lingkup penelitian dibatasi pada kinerja purwarupa dalam kondisi paparan cahaya matahari yang fluktuatif. Metodologi penelitian meliputi studi komparatif evolusi pH elektrolit, perakitan tumpukan 12 sel baterai, dan pengujian karakteristik discharge di bawah beban untuk menentukan performa serta kapasitas praktis. Pengukuran baterai ditandai menggunakan implementasi sistem monitoring real-time menggunakan mikrokontroler ESP32 yang terhubung ke platform ThingSpeak untuk visualisasi data. Hasil pengukuran membuktikan superioritas elektrolit KOH yang mencapai pH 12.6. Purwarupa baterai menunjukkan profil tegangan discharge yang stabil, namun dengan kelemahan kritis berupa kapasitas terukur yang sangat rendah, yaitu hanya 0.040 mAh. Keterbatasan performa ini disimpulkan akibat tingginya resistansi internal sel. Tantangan pada kurang cocoknya material housing sebagai kelemahan dalam implementasi.
Design and Implementation of a Cloud-Integrated Desktop ECG System Using a Multi-Layer Perceptron for Arrhythmia Classification Reno Thariqul Akbar; Tito Waluyo
Jurnal Nasional SAINS dan TEKNIK Vol. 4 No. 1 (2026): June 2026
Publisher : Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/jnst.v4i1.11129

Abstract

Cardiovascular diseases (CVDs) remain the foremost cause of mortality globally, necessitating the development of advanced tools for early and accurate cardiac diagnosis. This paper presents the comprehensive design, implementation, and evaluation of a desktop-based Electrocardiogram (ECG) monitoring system. The system architecture integrates a powerful Multi-Layer Perceptron (MLP) deep learning model designed to automatically identify and classify critical heart rhythm abnormalities, including bradycardia, tachycardia, and other forms of arrhythmia. A cornerstone of this system is its seamless and secure integration with a Supabase cloud backend, which facilitates centralized data storage, real-time synchronization, and secure, role-based access for various healthcare professionals, rigorously enforced through PostgreSQL’s Row Level Security (RLS). The MLP model was trained and validated on a diverse and extensive collection of data from the MITBIH Arrhythmia, PTB Diagnostic ECG, and Kaggle databases. Empirical evaluation results demonstrate high model performance, with classification accuracies reaching 92% for both bradycardia and tachycardia, and 89% for general arrhythmia detection. Functional and performance testing further validate the system’s operational reliability, showing an average cloud data synchronization time of approximately 4 seconds and robust, though partially incomplete, RLS policy enforcement. This work contributes a scalable, accurate, and secure solution for advanced cardiac monitoring in desktop environments, effectively bridging the gap between clinical-grade analysis and accessible, userfriendly technology

Page 5 of 5 | Total Record : 42

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