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All Journal International Journal of Electrical and Computer Engineering ComEngApp : Computer Engineering and Applications Journal Jurnal Teliska TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Sinergi Annual Research Seminar EMITTER International Journal of Engineering Technology Proceeding of the Electrical Engineering Computer Science and Informatics VOLT : Jurnal Ilmiah Pendidikan Teknik Elektro Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Elektronika dan Telekomunikasi Journal of Applied Smart Electrical Network and System (JASENS) Jurnal Techno Bahari Jurnal Rekayasa Elektro Sriwijaya International Journal of Research in Vocational Studies (IJRVOCAS) Aptekmas : Jurnal Pengabdian Kepada Masyarakat International Journal of Advanced Science Computing and Engineering International Journal of Mechanics, Energy Engineering and Applied Science (IJMEAS)
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Analisis Klasifikasi Kematangan Buah Tomat dengan Pendekatan Transfer Learning Model EfficientNet Mardianto, Yudhi; Dewi, Tresna; Risma, Pola
Techno Bahari Vol 11 No 1 (2024)
Publisher : Politeknik Negeri Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52234/tb.v11i1.306

Abstract

Ketahanan pangan harus terus dipertahankan seiring dengan peningkatan populasi penduduk suatu negara. Namun pertanian mengalami berbagai masalah diantaranya pergeseran fungsi lahan pertanian dan semakin menurunnya jumlah petani. Masalah pertanian ini dapat diatasi dengan integrasi teknologi pada bidang pertanian atau disebut dengan pertanian pintar. Integrasi pertanian ini dapat berupa sistem otomatisasi robotika yang sangat mengandalkan pengolahan data dan citra untuk dapat berfungsi dengan baik. Pengolahan data dan citra membutuhkan komputasi yang besar, sehingga perlu diambil langkah untuk mengurangi biaya komputasi diantaranya dengan kecerdasan buatan Convolution Neural Network yang dapat disederhanakan dengan metode transfer learning. Metode transfer learning menyediakan model yang telah ditraining dengan data besar sehingga pengguna transfer learning dapat melakukan training dengan data sedikit untuk mendapatkan hasil akurasi yang baik. Salah satu model yang membutuhkan komputasi singkat adalah EfficientNet yang merupakan pengembangan dari MobileNet dan RestNet. Penelitian ini menggunakan pendekatan transfer learning Model EfficientNet untuk klasifikasi kematangan buah tomat. Buah tomat adalah buah yang hampir selalu digunakan dalam masakan masyarakat Indonesia dan cepat sekali berubah warna dari orange menuju merah sehingga diperlukan metode yang tepat dalam pengemasan.
Fuzzy logic-based control for robot-guided strawberry harvesting: visual servoing and image segmentation approach Dewi, Tresna; Bambang, Muhammad Refo; Kusumanto, RD; Risma, Pola; Oktarina, Yurni; Sakuraba, Takahiro; Fudholi, Ahmad; Rusdianasari, Rusdianasari
SINERGI Vol 28, No 3 (2024)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2024.3.021

Abstract

The concept of digital farming can help farmers increase their agricultural production yield. One of the technologies to support digital farming is robotics, which can be utilized to complete a redundant task efficiently for 24 hours. This paper presents a simple and effective harvesting robot that is applied to harvest a ripe strawberry. The mechanical and electrical design is kept simple to ensure it is reproducible. The input from a proximity sensor and image detection by a Pi camera is utilized by FLC (Fuzzy Logic Controller) to improve the effectiveness of the harvesting task. The image processing method in this study is image segmentation, which fits with the limited source of the microcontroller available in the market. The experiment included 60 times (20 times center, left, and right position) harvesting using the FLC algorithm and 60 times without FLC to show the effectiveness of the proposed method. From 60 experiments without an FLC experiment, there is an 80% hit rate for strawberries positioned in the middle of an image plane and 55% for left and right strawberries. From 60 times of FLC experiment, 95% hit rate for strawberries positioned in the middle of an image plane, 80% for left and right strawberries. The average time required to finish the task without FLC for strawberries in the middle is 13.51 s, the left is 11.04 s, and the right is 17.28 s. While the average time required to finish the task with FLC for strawberry in the middle is 12.90 s, the left side is 11.71 s, and the right side is 10.93 s. This study is intended to show that simple designs can be helpful and affordable when applied to greenhouse farming in Indonesia. 
From Waste to Power: Fly Ash-Based Silicone Anode Lithium-Ion Batteries Enhancing PV Systems Amalia, Kania Yusriani; Dewi, Tresna; Rusdianasari, Rusdianasari
EMITTER International Journal of Engineering Technology Vol 12 No 2 (2024)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v12i2.885

Abstract

Indonesia's high solar irradiance, averaging 4.8 kWh/m²/day, presents a significant opportunity to harness solar power to meet growing energy demands. Fly ash, abundant in Indonesia and rich in silicon dioxide (40-60% SiO2), can be repurposed into high-value silicon anodes. The successful extraction of silicon from fly ash, increasing SiO2 content from 49.21% to 93.52%, demonstrates the potential for converting industrial waste into valuable battery components. Combining these advanced batteries with PV systems improves overall efficiency and reliability. Energy charge and discharge experiments reveal high energy efficiency for silicon-anode batteries, peaking at 80.53% and declining to 67.67% after ten cycles. Impedance spectroscopy tests indicate that the S120 sample, with the lowest impedance values, is most suitable for high-efficiency applications. Photovoltaic (PV) system integration experiments show that while increased irradiance generally boosts power output, other factors like PV cell characteristics and load conditions also play crucial roles. In summary, leveraging Indonesia's solar potential with fly ash-based silicon anode batteries and advanced predictive analytics addresses energy and environmental challenges. This innovative approach enhances battery performance and promotes the circular economy by converting waste into high-value products, paving the way for a sustainable and efficient energy future.
Innovative bio-inspired solar cells using fly ash-based dye-sensitized cells with fruit extract enhancements and Averrhoa bilimbi electrolyte Utami, Retyo Wizi Nafa; Dewi, Tresna; Indrayani, Indrayani
SINERGI Vol 29, No 1 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2025.1.020

Abstract

This study responds to the urgent need for renewable energy in Indonesia, driven by climate change and the energy crisis, by developing dye-sensitized solar cells (DSSCs) using locally sourced, eco-friendly materials. Traditional silicon-based photovoltaic cells, which have plateaued at 27% efficiency, are costly and environmentally unfriendly, leading to the demand for alternatives like DSSCs, which offer lower production costs, flexibility, and effective performance in diffuse light. The research focuses on designing DSSCs with Fe and Mg extracted from fly ash as counter electrodes, dragon fruit peel as a natural dye sensitizer, and Averrhoa bilimbi as an electrolyte booster. UV-Vis spectroscopy demonstrated that dragon fruit dye absorbs light effectively in the 360-700 nm range, peaking at 550 nm, making it an ideal sensitizer for wide-band gap semiconductors. Voltage output tests showed that Fe-doped DSSCs consistently outperformed Mg-doped ones, with Fe-based cells generating a maximum voltage of 413 mV compared to 163 mV for Mg-based cells. Long-term testing over three months further demonstrated Fe-doped cells' superior performance, peaking at 454.6 mV, while Mg-doped cells reached 261.96 mV. These results highlight Fe's effectiveness as a doping material, improving DSSC efficiency and supporting the use of natural dyes and sustainable materials. The study aligns with prior research on the critical role of material properties and solar irradiance in DSSC performance, demonstrating the potential of using fly ash and natural dyes for efficient solar energy solutions in South Sumatra. Future research will focus on optimizing material composition for enhanced performance.
Design and Performance of Solar-Powered Surveillance Robot for Agriculture Application Dewi, Tresna; Sukwadi, Ronald; Wahju, Marsellinus Bachtiar
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 8, No. 3, August 2023
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v8i3.1722

Abstract

Agriculture can benefit from robotics technology to overcome the drawback of limited human labor working in this sector. One of the robot applications in agriculture is a surveillance robot to monitor the condition. This paper describes a surveillance robot that is powered by a capacitor bank charged by a mini solar panel. The solar-powered robot is well-suited for deployment in open agricultural areas in Indonesia, where the irradiance is high. This potential is excellent for generating electricity and charging electric vehicles, such as those used in agriculture. The surveillance robot developed and tested in this study has been successfully deployed in an agriculture-like setting with all-terrain contours and the capacity to avoid obstacles. During high irradiance sunny weather, the shortest charging time was 2 hours. Hence, the proposed technology is effective for designing a surveillance robot for agricultural applications.
Object Detection Approach Using YOLOv5 For Plant Species Identification Clinton, Billi; Amperawan, Amperawan; Dewi, Tresna
Jurnal Elektronika dan Telekomunikasi Vol 24, No 2 (2024)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.643

Abstract

In the modern era of agriculture and horticulture, biodiversity conservation requires plant species identification skills, and automatic detection is a challenging and interesting task. However, many factors often make some people mistaken in recognizing plant species that have unique and varied visual characteristics, making manual identification difficult. This problem requires an effective and accurate model for identifying plant species. So this research aims to produce a model to identify plant species that are effective and have a high level of accuracy. This research offers the use of the YOLOv5 algorithm method. The training process with epoch 200 and 53 minutes with a total of 1,220 images. Based on the results of the model performance test, the mAP value was 85.73%, precision 98.27%, and recall 94.36%. During testing, the model can identify plant species accurately on single objects and multiple objects. The results of this research show that the proposed method is successful in identifying plant species accurately.
Design and Implementation of Solar Energy in ATG, CCDS and Pantry Maintenance Monitoring Systems Kamil, Muhammad Insan; Arifin, Fatahul; Dewi, Tresna
International Journal of Research in Vocational Studies (IJRVOCAS) Vol. 4 No. 3 (2024): IJRVOCAS - December
Publisher : Yayasan Ghalih Pelopor Pendidikan (Ghalih Foundation)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53893/ijrvocas.v4i3.295

Abstract

This study aims to design, implement, analyze and evaluate the PV system used in the ATG, CCDS and Maintenance Pantry Monitoring Systems. This study was conducted at PT. Pertamina Patra Niaga, with data collection carried out from June 2024. As for the experimental design carried out to determine the reliability of the equipment made which will be used for ATG motoring, CCDS and Pantry Maintenance, this was carried out to reduce the consumption of fossil energy which has been used through Electricity from PLN. The research results show that the use of the PV system can work optimally, this can be seen from the results of observations, especially at its peak, namely on June 29 2024, namely 2106 watts with a maximum voltage of 84.2V and with the installation of MPPT to ensure there is no overcharging and regulate the input voltage at 29.2V. V to batteries, efficient and effective in replacing previously used conventional energy.
Analysis of Reservoir Water Discharge at Solar Power Plant Tanjung Raja Village as a Basis for Pico Hydro Power Plant Planning in Paddy-Field Area Dinata, Yogi; Indriyani, Indriyani; Dewi, Tresna
International Journal of Advanced Science Computing and Engineering Vol. 4 No. 2 (2022)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (464.113 KB) | DOI: 10.62527/ijasce.4.2.82

Abstract

Energy is an important aspect of life as a whole. The fossil-based energy sources have dwindled over time, in line with population growth and economic growth. One approach to meeting the energy demand is to use renewable energy. According to Presidential Regulation Number 22 of 2017 on National Energy General Plan (RUEN) targets, the share of New and Renewable Energy in total national energy in 2025 will be 25%. The construction of a Hydro Power Plant is expected to reach 3.000 MW by 2025. According to NREEC statistical data from 2016, the potential for constructing a Hydro Power Plant in South Sumatera is approximately 448 MW. This paper investigates the possibility and potential of building Hydro Power Plant from the reservoir of the irrigation water system generated by a Solar Power Plant in Tanjung Raja Village, Muara Enim District, South Sumatra. The water discharge magnitude is measured, and the potential of electricity generated from the discharge is presented. The experimental data shows that water discharge from the reservoir can generate maximum electricity of  806,6488 watts with a discharge rate of 0.0653 m3/s, and power is 36,7245 watts, with a discharge of 0,0030 m3/s. The average electricity potential is approximately 375.6782 watts, with a discharge average of 0.0304 m3/s. Therefore, the experimental data shows the possibility of hybridizing the Solar Power Plant with Hydro Power Plant, which will be beneficial for the residential area in Tanjung Raja Village, Muara Enim.
Neural Network Controller Application on a Visual based Object Tracking and Following Robot Risma, Pola; Dewi, Tresna; Oktarina, Yurni; Wijanarko, Yudi
Computer Engineering and Applications Journal Vol 8 No 1 (2019)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (611.088 KB) | DOI: 10.18495/comengapp.v8i1.280

Abstract

Navigation is the main issue for autonomous mobile robot due to its mobility in an unstructured environment. The autonomous object tracking and following robot has been applied in many places such as transport robot in industry and hospital, and as an entertainment robot. This kind of image processing based navigation requires more resources for computational time, however microcontroller currently applied to a robot has limited memory. Therefore, effective image processing from a vision sensor and obstacle avoidances from distance sensors need to be processed efficiently. The application of neural network can be an alternative to get a faster trajectory generation. This paper proposes a simple image processing and combines image processing result with distance information to the obstacles from distance sensors. The combination is conducted by the neural network to get the effective control input for robot motion in navigating through its assigned environment. The robot is deployed in three different environmental setting to show the effectiveness of the proposed method. The experimental results show that the robot can navigate itself effectively within reasonable time periods.
Aplikasi CNN untuk Analisis Visual Pertumbuhan Tanaman Bitter Melon dalam Sistem Akuaponik Yurni Oktarina; Rapli Wijaya; Tresna Dewi; Pola Risma
Jurnal Rekayasa Elektro Sriwijaya Vol. 6 No. 2 (2025): Jurnal Rekayasa Elektro Sriwijaya
Publisher : Jurusan Teknik Elektro Fakultas Teknik Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/jres.v6i2.152

Abstract

Technological advances in modern agriculture face major challenges, such as limited land and climate change that affect crop productivity. One approach that is gaining popularity is the aquaponic system, which is a farming method that combines fish and plants in one controlled ecosystem. In this study, a Convolutional Neural Network (CNN) method with a transfer learning approach was used, using the ResNet50 model to classify the condition of bitter melon plants growing in an aquaponic system. The developed model aims to distinguish plants into two categories, namely Good Condition and Reject. Test results show that the model has a high level of accuracy in classifying plant conditions, with a precision of 92%, recall of 100%, and F1-score reaching 96% on training data. However, the model still faces challenges in generalizing to the test data, which indicates the possibility of overfitting. To improve the performance of the model, various optimization techniques such as data augmentation and model regulation were performed to enrich the dataset variation and improve the model's ability to recognize more diverse plant growth patterns. Although there are still obstacles in handling differences in lighting and image capture angles, this method makes a significant contribution to the development of a more efficient and accurate artificial intelligence-based monitoring system in aquaponics systems. This research can be further developed by creating a more lightweight and adaptive model, and testing its performance in various real conditions in the aquaponics environment. The implementation of this deep learning-based classification system is expected to support precision agriculture innovation and encourage the sustainability of technology-based food production.
Co-Authors A Rahman Ahmad Fudholi Alkausar, Muhammad Fajri Amalia, Kania Yusriani Amperawan Amperawan Amperawan Amperawan, Amperawan Angga Prasetia Anggraini, Citra Arissetyadhi, Iwan Auliya, Annisa Azhar, M. Sayid Bambang Tutuko Bambang, Muhammad Refo Clinton, Billi Dadi Setiadi Daniesar, Muhammad Nouval Dicky Astra Yudha Dinata, Yogi Edo Triyandi Evelina Ginting Fatahul Arifin, Fatahul Fradina Septiarini Hendra Marta Yudha Hibrizi, Dzaky Rafif Husni, Nyayu Latifah INDRAYANI INDRAYANI Indriyani Indriyani Junaedi, Ketut Juwita, Aulia Ratna Kemala Dewi Kusumanto, Raden Lukman Nul Hakim M. Muhajir Mardianto, Yudhi Mardiyati, Elsa Nurul Maulidina, Elfira Mayastri Devana Muhammad Dede Yusuf Muhammad Insan Kamil, Muhammad Insan Muhammad Nawawi Muhammad Ridho Kenawas Muhammad Roriz Muhammad Taufik Roseno Mulya, Zarqa Muslikhin Mustofa Mustofa Neta Larasati Noer, Mohammad Nawawi Nur Mutiara Syahrian Oktarina, Yurni Oktarina, Yurni Pola Risma Putri Repina Kesuma Rapli Wijaya RD Kusumanto RD Kusumanto Rinaldi Rinaldi Riyo Irawan Robiansyah Ronald Sukwadi Roseno, M. Taufik Rusdianasari Rusdianasari Rusdianasari Rusdianasari Rusdianasari Sakuraba, Takahiro Sastiani, Destri Zumar SELAMET MUSLIMIN Siproni Siproni Siproni Umar Siti Afiyah Qatrunnada Siti Nurmaini Solly Aryza Sri Rezki Artini Syahrian, Nur Mutiara Tampubolon, Debora Utami, Retyo Wizi Nafa Velia Yuliza Wahju, Marsellinus Bachtiar Wijanarko, Yudi Wijaya Pratama, Agung Yohandri Bow Yudha Wira Pratama Yudi Wijanarko Yudi Wijanarko, Yudi Yurika Islamiati Yurni Oktarina Yurni Oktarina Yurni Oktarina Yusi, Muhammad Syahirman Zarqa Mulya