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All Journal REKAYASA Jurnal Informatika dan Teknik Elektro Terapan ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Jurnal Teknik Elektro dan Komputer TRIAC Jambura Journal of Electrical and Electronics Engineering International Journal of Science, Engineering and Information Technology Among : Jurnal Pengabdian Masyarakat JASEE Journal of Application and Science on Electrical Engineering JEECS (Journal of Electrical Engineering and Computer Sciences) Sains Data Jurnal Studi Matematika dan Teknologi Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Laksono, Deni Tri
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Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences

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Fuzzy Logic Based LoRa and IoT Smart Buoy for Sea Wave Monitoring in Madura SAPUTRO, ADI KURNIAWAN; ALFITA, RIZA; ZUHUDI, MOHAMAD AHSAN; HARDIWANSYAH, MUTTAQIN; LAKSONO, DENI TRI; PURNAMASARI, DIAN NEIPA
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 14, No 1: Published January 2026
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v14i1.26

Abstract

Indonesia is an archipelagic country with most of its territory consisting of water, increasing the risk of water-related natural disasters. Seawater waves, influenced by tidal movements, are one such phenomenon, and wind speed significantly affects their height. Faster sea winds can generate higher seawater waves. To obtain related data, a system capable of detecting seawater wave height and wind speed is required. This study aims to test a LoRa (Long Range)-based seawater wave detection system that is both efficient and reliable. The system uses a BNO055 sensor to measure wave height and an anemometer to detect wind speed. The relationship between wind speed and seawater wave height is analyzed using the fuzzy Mamdani method. Results show the BNO055 sensor has 92% accuracy, the anemometer 98.4%, and the fuzzy Mamdani method yields an error rate of only 0.25%. This system is expected to enhance marine monitoring and safety efforts.
Liveness Detection-Based Home Door Security System for Anti-Spoofing Using Intel RealSense F455 Camera and LBPH saputro, Adi kurniawan; Ubaidillah, Achmad; Diputra, Hamzah Arifianto; Laksono, Deni Tri; Ibadillah, Achmad Fiqhi; Nur, Achmad Zain
Jambura Journal of Electrical and Electronics Engineering Vol 8, No 1 (2026): Januari - Juni 2026
Publisher : Electrical Engineering Department Faculty of Engineering State University of Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjeee.v8i1.34918

Abstract

Spoofing attacks on facial recognition-based security systems are increasing along with the development of smart home technology. To address this issue, this study proposes a home door security system with the implementation of liveness detection-based anti-spoofing technology using an Intel RealSense F455 camera. The system is designed to verify the authenticity of a user's face in real-time by combining facial texture analysis and the user's physiological responses. The facial detection process is carried out using the Haarcascade algorithm to extract a 160×160 pixel facial area, while facial recognition uses the Local Binary Pattern Histogram (LBPH) method which is relatively stable to variations in lighting and viewing angles. The liveness detection mechanism is implemented mechanically by utilizing the Haarcascade Eye to detect the user's eye movements as an indicator of the presence of a live face, so that the system is able to distinguish real faces from fake media in the form of static photos. The system is integrated with a Telegram bot for real-time access monitoring, where automatic notifications are sent every time a door access attempt occurs. Test results show a facial recognition accuracy rate of 98.93%, with the system successfully detecting and verifying 30 registered users and producing an average confidence value consistently above 80%. Furthermore, the liveness detection mechanism proved effective in preventing photo-based spoofing attacks, with a stable detection success rate throughout the testing. These findings suggest that the integration of LBPH and eye-based liveness detection can improve the reliability of facial recognition-based door security systems.
Technical Performance Analysis of a 1 MWp Grid-Connected Photovoltaic System in Pangkalan Kerinci, Indonesia Using PVsyst Simulation Laksono, Dedi Tri; Laksono, Deni Tri; Fahmi, Monika Faswia; Afrianti, Rien; Dodi, Nofri
International Journal of Science, Engineering, and Information Technology Vol 10, No 1 (2025): IJSEIT volume 10 Issue 1 December 2025
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/ijseit.v10i1.32479

Abstract

The increasing demand for clean energy in Indonesia has accelerated the deployment of grid-connected photovoltaic (PV) systems. This study presents a technical performance analysis of a 1 MWp grid-connected PV system located in Pangkalan Kerinci, Indonesia, using the PVsyst v7.4.6 simulation software. The system comprises 1818 Trina Solar TSM-DE19-550Wp modules mounted on a fixed-tilt structure (2.2° tilt, 180° azimuth) with two SMA Sunny Central 400 MV-11 inverters (DC/AC ratio = 1.25). Meteorological data from Meteonorm 8.1 (1996–2015) was used to simulate annual irradiation and system output. The results show an annual energy production of 1,404,124 kWh, corresponding to a specific yield of 1,404 kWh/kWp/year. The Performance Ratio (PR) reached 82.75%, indicating high system efficiency under tropical climatic conditions. Loss analysis revealed that thermal losses (9.36%) and IAM losses (2.58%) were the dominant factors, while inverter losses accounted for 2.86%. Module mismatch and wiring losses were minimal at 2.15% and 1.5%, respectively. The high PR and low degradation assumptions confirm the suitability of the selected configuration for equatorial regions. This study provides a robust technical benchmark for similar PV installations in Sumatra and supports optimal design decisions for future utility-scale solar projects in Indonesia.
Performance Evaluation of a 250 Wp Solar Photovoltaic Water Pumping System in Tropical Climate: A Case Study in Pangkalan Kerinci, Indonesia Afrianti, Rien; Laksono, Dedi Tri; Laksono, Deni Tri; Fahmi, Monika Faswia
International Journal of Science, Engineering, and Information Technology Vol 10, No 1 (2025): IJSEIT volume 10 Issue 1 December 2025
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/ijseit.v10i1.32508

Abstract

Access to clean water in remote tropical regions remains a critical challenge, particularly where grid electricity is unavailable. Solar photovoltaic (PV) water pumping systems offer a sustainable, off-grid solution. This study evaluates the technical performance of a 250 Wp PV-powered deep well water pumping system installed in Pangkalan Kerinci, Indonesia (0.41°N, 101.85°E). The system employs a single Trina Solar TSM-310PD14 module connected to a Sun Pumps SDS-D-128 DC membrane pump via an MPPT controller. Using PVSyst v7.4.6, a one-year simulation was conducted under realistic meteorological and hydraulic conditions, including a static water table at 4 m depth and a daily water demand of 0.30 m³. Results indicate that the system reliably meets annual water needs (109 m³/year) with negligible water deficit (0.163 m³, or 0.15%). The annual specific water yield is 522 m³/kWp/bar, while the system operates at an overall efficiency of 4.3% and a pump efficiency of 16.7%. Energy analysis shows 15.5 kWh/year delivered to the pump, with 42.6 kWh/year of excess solar energy unused due to tank capacity limits. The performance ratio (PR) is approximately 87%, confirming high system reliability in equatorial conditions. This study demonstrates the technical viability of small-scale solar pumping for rural water supply in Indonesia.
Design of IoT-Based Smart Hydroponic Farming with Solar Energy for Sustainable and Precision Crop Production Fahmi, Monika Faswia; Laksono, Deni Tri; Laksono, Dedi Tri
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 10 No. 2 (2025): JEECS (Journal of Electrical Engineering and Computer Sciences)
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v10i2.10

Abstract

Conventional hydroponic farming systems frequently encounter limitations related to unstable environmental control, suboptimal nutrient management, and strong dependence on grid-based electricity, which collectively hinder their sustainability and scalability, particularly in remote or energy-constrained regions. Recent studies have explored smart hydroponic technologies. However, many remain reliant on external power sources or lack integrated, autonomous control of multiple critical growth parameters. Therefore, this problem reveals a research gap in the development of fully self-powered and intelligent hydroponic systems. This study proposes the design and implementation of a solar-powered, IoT-based smart hydroponic farming system that enables real-time monitoring and closed-loop environmental control. The system integrates multi-sensor measurements, including pH, DS18B20 temperature, total dissolved solids (TDS), and light-dependent resistor (LDR) sensors, coupled with an on–off control strategy to regulate light intensity (115 ADC), water temperature (28 °C), pH (5.5-6.5), and nutrient concentration (840 ppm). A standalone photovoltaic energy subsystem, consisting of a 100 Wp solar panel and a 65 Ah battery, was designed based on a daily energy demand of 378.85 Wh to ensure continuous autonomous operation. Experimental results demonstrate high sensor accuracy, with measurement errors of 0.75% for pH, 0.095% for TDS, and 0.24% for temperature. Moreover, the proposed system effectively stabilizes environmental parameters within predefined setpoints, outperforming uncontrolled conditions. These findings confirm the system’s reliability and potential as a sustainable precision agriculture solution for off-grid hydroponic applications.
Detection of Rice Diseases: Leaf Blast, Bacterial Leaf Light, and Brown Spot Using Image Enhancement and Faster Region-Based Convolutional Neural Network Fahmi, Monika Faswia; Laksono, Deni Tri; Ibadillah, Achmad Fiqhi; Laksono, Dedi Tri
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 2 (2026): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i2.287

Abstract

Rice diseases such as leaf blight, blast, and brown spot remain major constraints on food security and rural livelihoods across Southeast Asia, causing significant yield losses each year. In Indonesia, particularly in Lamongan, East Java, these pathogens threaten smallholder productivity and disrupt national rice supply chains. This study aims to enhance automated rice disease detection under real agricultural conditions by integrating image preprocessing techniques with a deep learning-based detection framework. The main contribution lies in developing a hybrid pipeline that combines RGB-to-grayscale conversion and contrast stretching prior to model training, effectively mitigating low-contrast conditions and noise commonly found in field-acquired image datasets. The enhanced images are subsequently processed using the Faster Region-Based Convolutional Neural Network (Faster R-CNN) with a ResNet-50 backbone to localize and classify disease symptoms. Experiments conducted on a dataset of 1,500 annotated rice leaf images achieved high detection performance, with accuracies of 97.37% for leaf blight, 94.12% for blast, and 95.24% for brown spot. Compared with the baseline Faster R-CNN model, the proposed approach improved classification accuracy from 0.8906 to 0.9297, reduced false negatives from 0.439 to 0.1998, increased foreground classification accuracy from 0.55 to 0.78, and descreased total loss from 0.839 to 0.6493. These results demonstrate that integrating RGB-to-grayscale conversion and contrast stretching significantly enhances feature representation, leading to improved detection accuracy, reduced error rates, and more stable training behavior. Overall, the proposed framework provides a robust and reliable approach for rice disease identification and offers strong potential for practical deployment in precision agriculture systems.
Co-Authors Achmad Fiqhi Ibadillah Achmad Zain Nur Adi Kurniawan Adi Kurniawan Saputro Aditya Prayoga Afrianti, Rien Agus Rifai, Abdul Aziz Agustino, M. Rizky Daniyal, Moh. Dedi Tri Laksono, Dedi Tri Dian Neipa Purnamasari Diputra, Hamzah Arifianto Efendi, Akhmad Fahmi, Monika Faswia Farkhan, M. Hardiwansyah, Muttaqin Ibadillah, Achmad Fiqih Jannan, Ahmad Muthoharul Lukman Hakim Miftachul Ulum, Miftachul Muhammad Fuad Nofri Dodi, Nofri Riza Alfita Sri Wahyuni Sya’diyah, Lailatus Ubaidillah, Achmad Umam, Khanif Khoirul Waskita Wicaksono, Muhammad Arya Rangga ZUHUDI, MOHAMAD AHSAN