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All Journal Andalas Journal of Electrical and Electronic Engineering Technology Electrician : Jurnal Rekayasa dan Teknologi Elektro
Titik Nurhayati
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Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering

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Optimized High-Gain DC-DC Converter for PV Applications Harmini, Harmini; Titik Nurhayati; Supari; Priyo Adi Sesotyo; Satria Pinandita; Ery Sadewa
Andalas Journal of Electrical and Electronic Engineering Technology Vol. 5 No. 2 (2025): November 2025
Publisher : Electrical Engineering Dept, Engineering Faculty, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/ajeeet.v5i2.179

Abstract

Photovoltaic (PV) systems frequently encounter low and fluctuating output voltages, which can significantly impede efficient energy utilization and necessitate more advanced power conversion solutions. Addressing this challenge, the study aims to develop a high-gain DC-DC converter topology that offers stable voltage regulation, making it suitable for PV applications. The proposed solution targets the essential need for substantial voltage boosting while maintaining reliable performance even under varying solar irradiance conditions. The core of the design is based on a Quadratic Boost Converter (QBC) integrated with Voltage Multiplier Cells (VMC), collectively referred to as QBC-VMC. This innovative configuration enhances the voltage gain capability compared to traditional converters. To ensure precise control of the output voltage, a Proportional-Integral (PI) controller is implemented. The system undergoes thorough analysis, including detailed modeling, simulation, and the design of its control structure, to optimize performance. The results demonstrate that the proposed converter can achieve a voltage gain of up to 12 times the input voltage. The PI controller effectively maintains a stable output voltage at approximately 600 V with a tolerable variation of ±0.7%. Additionally, the system exhibits an energy conversion efficiency approaching 81%, even under fluctuating irradiance conditions. This indicates a strong dynamic response and steady-state performance, essential for reliable PV operation. By integrating QBC-VMC with PI control, the proposed approach significantly enhances voltage stability and energy conversion efficiency. Overall, this system provides a promising solution for high-performance PV power systems, capable of delivering reliable power output under varying environmental conditions.
Klasifikasi Jenis Penyakit Tumor Otak Menggunakan Convolutional Neural Network Arsitektur Inception-V3 Hendra Ady Juliartadi; Andi Kurniawan Nugroho; Sri Heranurweni; Titik Nurhayati
Electrician : Jurnal Rekayasa dan Teknologi Elektro Vol. 19 No. 3 (2025)
Publisher : Department of Electrical Engineering, Faculty of Engineering, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/elc.v19n3.2868

Abstract

Tumor otak merupakan salah satu penyakit yang paling mengancam nyawa dan memerlukan diagnosis yang akurat. Salah satu tantangan utama dalam mendiagnosis tumor otak terletak pada kesamaan visual antara jenis tumor pada gambar MRI, yang seringkali menyulitkan klasifikasi manual. Untuk mengatasi hal ini, sistem klasifikasi berbasis deep learning dikembangkan menggunakan metode Convolutional Neural Network (CNN) dengan arsitektur Inception-V3. Data yang digunakan diperoleh dari Kaggle dan terdiri dari empat kelas glioma, meningioma, pituitari, dan notumor. Penelitian ini menerapkan teknik pre-procesing seperti Contrast Limited Adaptive Histogram Equalization (CLAHE) dan augmentasi data untuk meningkatkan kinerja model. Berbagai kombinasi hyperparameters diuji, dan kinerja terbaik dicapai dengan batch size 32 dan learning rate 0,0001. Model mencapai accuracy 97,92%, precision 98%, recall 98%, dan F1-score 98%. Selain itu, arsitektur Inception-V3 dibandingkan dengan arsitektur lain yaitu MobileNetV2 dan ResNet50, yang mencapai accuracy 83,67% dan 71,79%. Hasil menunjukkan bahwa arsitektur Inception-V3 unggul dalam mengklasifikasikan jenis tumor otak dari gambar MRI. Penelitian ini diharapkan dapat berkontribusi dalam meningkatkan akurasi dan kecepatan diagnosis tumor otak bagi medis.
Co-Authors Andi Kurniawan Ery Sadewa Harmini, Harmini Hendra Ady Juliartadi Priyo Adi Sesotyo Satria Pinandita Sri Heranurweni Supari