Article Per Year (5 Year)
p-Index From 2021 - 2026
2.57
P-Index
This Author published in this journals
All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) Jurnal Pengabdian Kepada Masyarakat Sakai Sambayan Journal of Engineering and Scientific Research (JESR) International Journal of Electronics and Communications Systems Prosiding Seminar Nasional Hasil Penelitian Sains, Teknik dan Aplikasi Industri Fakultas Teknik Universitas Lampung Electrician : Jurnal Rekayasa dan Teknologi Elektro Jurnal Teknologi Riset Terapan Nemui Nyimah
Sony Ferbangkara
Universitas Lampung, Bandar Lampung, Indonesia
Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Mechanical Engineering Medicine & Pharmacology Other

Published : 15 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 15 Documents
Search

 1   2 
Penerapan Otomatisasi Penerangan Jalan Umum Tenaga Surya (PJU-TS) Untuk Energi Berkelanjutan di Desa Sri Agung Lampung Tengah Ubaidah; Ahmad Saudi Samosir; Sony Ferbangkara; Martinus; Al Fariziy, M. Naufal
Nemui Nyimah Vol. 5 No. 1 (2025): Nemui Nyimah Vol. 5 No. 1 2025
Publisher : FT Universitas Lampung

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

Abstract

Desa Sri Agung, Kecamatan Padang Ratu, Kabupaten Lampung Tengah, faces issues with several road sections where the street lighting system is not functioning to enhance nighttime visibility. To address this problem, Solar-Powered Public Street Lighting (PJU-TS) was installed. This system utilizes a Light Dependent Resistor (LDR) to detect light intensity and employs an IC 7805 as a voltage regulator. The PJU-TS lights automatically turn on at night in dark environmental conditions and turn off during the day by utilizing solar energy as the primary power source, thereby reducing dependence on conventional electricity. This activity consists of several methodological steps, including field surveys, system design, installation, and performance evaluation of the PJU-TS. The installation was carried out in two strategic locations involving the local community at several points along the roads in Sri Agung Village.. The evaluation results indicate that the PJU-TS system functions optimally to improve road visibility. However, there are still issues with the energy storage battery capacity, which remains small and affects lighting duration. The implementation of public street lighting with Solar Technology (PJU TS) in Sri Agung Village has had a significant impact on improving accessibility, security, and energy efficiency. Additionally, it helps promote energy sustainability and raises public awareness of the use of environmentally friendly technology.
Implementasi Digital Aplikasi GaweTani di Kawasan Food Estate Lampung Barat sebagai Solusi Ketahanan Pangan: Implementasi Digital Aplikasi GaweTani di Kawasan Food Estate Lampung Barat sebagai Solusi Ketahanan Pangan Sony Ferbangkara; Martinus; Rizkima Akbar Setiawan; Resty Annisa; Ubaidah
Nemui Nyimah Vol. 5 No. 2 (2025): Nemui Nyimah Vol. 5 No. 2 2025
Publisher : FT Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/nm.v5i2.210

Abstract

This community service program aims to enhance agricultural efficiency and productivity in the Food Estate area of West Lampung through digital assistance using the GaweTani application. GaweTani is a digital platform designed to help farmers manage their land more efficiently by providing real-time data access related to weather conditions, soil, and market prices. The program was implemented in four stages: preparation of digital infrastructure and area data mapping, development of the GaweTani application, field implementation with training for farmers, and evaluation of the application's impact on productivity and efficiency. Local partners were also involved to assist farmers, ensuring the application is used effectively to maximize harvest yields and improve farmers' welfare. The implementation successfully increased farmers' digital literacy and encouraged the development of sustainable smart agriculture in the area. The application provides integrated management, real-time information access, and technical guidance, which helps farmers make more informed decisions.
Pemodelan AI dengan CNN Untuk Klasifikasi Tanaman Uvaria Grandiflora di Hutan Tropis Indonesia Martinus, Martinus; Ferbangkara, Sony; Annisa, Resty; Hidayatullah, Vezan; Pratama, Rama Wahyu Ajie; Makarim, Alvin Reihansyah
Jurnal Teknologi Riset Terapan Vol 3 No 1 (2025): Januari
Publisher : Penerbit Goodwood

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/jatra.v3i1.5012

Abstract

Purpose: This research aims to develop an artificial intelligence (AI) model based on the Convolutional Neural Network (CNN) to classify Uvaria plant species, a tropical genus native to Indonesia. The study addresses the challenge of limited datasets for automatic classification in tropical plant identification. Methodology/approach: Images of Uvaria plants were collected directly from their natural habitat and categorized into four primary classes: leaves, stems, twigs, and trees. The dataset comprises 400 labeled images, split into training (279 images, 70%), validation (40 images, 10%), and testing (81 images, 20%). The CNN model was trained for 200 epochs, using data preprocessing techniques such as normalization and augmentation to improve performance. Results/findings: The CNN model achieved an accuracy of 90% on the test set, indicating strong performance in classifying the four categories of Uvaria plant components. The model showed particularly consistent results in distinguishing between leaves and twigs. Conclusion: Despite the relatively small dataset, the results demonstrate that the CNN algorithm is capable of accurately classifying images of Uvaria species. The dataset is considered sufficient to build an effective classification model. Limitations: The main limitation of this study is the limited number of images, which may restrict the model’s ability to generalize to broader or more varied data in real-world conditions. Contribution: This research contributes to the development of AI-based tools for identifying tropical plant species. It offers a practical model and dataset that can support biodiversity monitoring, environmental research, and conservation efforts in Indonesia and similar tropical regions.
Analisis Akurasi dan Optimalisasi Dataset untuk Klasifikasi Tanaman Aristolochia acuminata dengan Algoritma CNN Ferbangkara, Sony; Mulyani, Yessi; Mardiana, Mardiana; Pratama, Rama Wahyu Ajie; Putri, Renatha Amelia Manggala; Rafi'syaiim, Muhammad Afif
Jurnal Teknologi Riset Terapan Vol 3 No 1 (2025): Januari
Publisher : Penerbit Goodwood

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/jatra.v3i1.5014

Abstract

Purpose: Purpose: Aristolochia acuminata is a rare plant species of significant conservation value. However, the accurate classification of its parts, such as leaves, stems, and twigs, remains a challenge. This study aimed to develop a reliable classification model to support conservation efforts using Convolutional Neural Network (CNN) technology. Methodology/approach: A digital dataset was systematically collected from various parts of Aristolochia acuminata, forming the foundation for training a CNN-based classification model. To evaluate the model performance and determine the optimal training parameters, three experimental scenarios were conducted using 10, 100, and 200 training epochs. The impact of each training duration on the classification accuracy was analyzed. Results: The model trained with 200 epochs achieved the highest accuracy, outperforming those trained with 10 epochs (68.89%) and 100 epochs (86.67%). This suggests that a longer training period enables the model to learn the visual features of each plant part better, leading to improved classification performance. Conclusion: The results confirm the effectiveness of CNN in classifying the components of Aristolochia acuminata. Using 200 training epochs allowed for deeper feature learning without overfitting, proving optimal in this context. Limitations: This study was limited by the dataset size and the number of classes involved. Further expansion of the dataset and class categories could improve the generalizability of the model. Contribution: This study contributes to plant conservation technology by demonstrating how CNN and structured dataset collection can be applied to classify rare plant species, providing a valuable tool for biodiversity preservation.
Design and implementation of a buck converter-based PV emulator using dynamic evolution control Samosir, Ahmad Saudi; Despa, Dikpride; Gusmedi, Herri; Ferbangkara, Sony
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i1.pp809-822

Abstract

This paper presents the design, simulation, and experimental implementation of a photovoltaic (PV) emulator based on a buck converter controlled using the dynamic evolution control (DEC) technique. The proposed system accurately reproduces the nonlinear current-voltage (I-V) and power-voltage (P-V) characteristics of a commercial GREEN CELL SM100-18P (100 Wp) PV module under standard test conditions (1000 W/m2, 25 °C). The electrical characteristics of the reference module are embedded in the controller through a lookup table (LUT), which is integrated with the DEC algorithm to enable adaptive real-time regulation of output voltage and current. System modeling and validation are first conducted in MATLAB/Simulink to analyze steady-state and transient performance. A hardware prototype based on an XL4016 buck converter and Arduino Nano microcontroller is then implemented, with real-time monitoring provided via an ILI9341 TFT display. Experimental results show that the emulator achieves a maximum power deviation of 0.8%, a normalized root mean square error (RMSE) of 0.015, a settling time of approximately 12 ms, overshoot below 1.5%, voltage ripple under 2%, and peak conversion efficiency of 94% near the MPP region. These results confirm that the proposed PV emulator provides accurate static and dynamic reproduction of PV characteristics, offering a low-cost, stable, and repeatable platform for laboratory-scale evaluation of PV-related power electronic converters.
Co-Authors Adhi Nurhartanto, Adhi Afif, M. Ahmad Saudi Samosir Al Fariziy, M. Naufal Andrianto, Rifqi Rhama Annisa, R Annisa, R. Annisa, Resty Deni Achmad Dikpride Despa Djausal, Gita Paramita F.X. Arinto Fahrur Riza Priyana Febrianto, F Herri Gusmedi Hidayatullah, Vezan Khairudin Khairudin M Afif, M Makarim, A. R. Makarim, Alvin Reihansyah MARDIANA Mardiana Mardiana Mardiana Mardiana Mareli Telaumbanua Martinus Martinus Martinus Martinus, Martinus Meizano Ardhi Muhammad Meizano Ardhi Muhammad Muhammad, M A Muhammad, Meizano Ardhi Mulyani, Yessi Pratama, Rama Wahyu Ajie Putri, R A M Putri, Renatha Amelia Manggala Rafi'syaiim, Muhammad Afif Resty Annisa Rifqi Rhama Andrianto Setiawan, Rizkima Akbar Sri Ratna Sulistiyanti Sulistiono, W. E. Sumadi - Ubaidah ubaidah ubaidah Wahyu Eko Sulistiono Wijaya, P Yudi Eka Putra