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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 Penelitian dan Pengkajian Ilmu Pendidikan: e-Saintika 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 SmartComp Jurnal Polimesin International Journal of Mechanics, Energy Engineering and Applied Science (IJMEAS) TECHNOLOGIC Jurnal Elektronika dan Telekomunikasi
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Politeknik Negeri Sriwijaya
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Model Prediksi Deep Learning dengan Pendekatan Feedforward Neural Network Lukman Nul Hakim; Tresna Dewi; Pola Risma; Yurni Oktarina
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.v6i2.158

Abstract

Penelitian ini bertujuan untuk menerapkan model deep learning, khususnya Feedforward Neural Network (FNN), dalam meramalkan nilai irradiance berdasarkan data waktu. Solar irradiance sangat penting dalam pengembangan sistem energi terbarukan seperti panel surya untuk meningkatkan efisiensi sistem tenaga surya dan mengoptimalkan perencanaan sumber daya energi. Penggunaan model ini diharapkan dapat memberikan prediksi yang lebih akurat dan handal, sehingga mendukung pengambilan keputusan dalam pengelolaan energi terbarukan secara berkelanjutan. Untuk meningkatkan akurasi prediksi, penelitian ini menerapkan teknik preprocessing data yang mencakup penghapusan nilai hilang dan normalisasi menggunakan MinMaxScaler guna meningkatkan stabilitas pelatihan model. Model FNN yang diusulkan terdiri dari beberapa lapisan tersembunyi dengan aktivasi non-linear untuk menangkap pola kompleks dalam data, serta lapisan output untuk menghasilkan prediksi akhir. Pelatihan model dilakukan menggunakan algoritma optimasi seperti Adam, dengan fungsi aktivasi ReLU untuk meningkatkan konvergensi. Evaluasi model dilakukan menggunakan metrik RMSE, MSE, MAE, dan R-squared (R²) sebagai indikator utama keakuratan model dalam peramalan irradiance. Hasil evaluasi menunjukkan bahwa model ini mampu memberikan prediksi yang akurat terhadap pola irradiance, dengan nilai RMSE dan MAE yang rendah serta R² mendekati satu, menandakan kinerja yang sangat baik dalam menangkap dinamika data.
Simulation Design of Artificial Intelligence Controlled Goods Transport Robot Oktarina, Yurni; Sastiani, Destri Zumar; Dewi, Tresna; Kusumanto, RD
Computer Engineering and Applications Journal (ComEngApp) Vol. 11 No. 2 (2022)
Publisher : Universitas Sriwijaya

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

Abstract

Technological advances enable scientists and researchers to develop more automated systems for life's convenience. Transportation is among those conveniences needed in daily activities, including warehouses. The easy-to-build and straightforward transport robot are desired to ease human workers' working conditions. The application of artificial intelligence (AI), Fuzzy Logic Controller, and Neural Network ensures the robot is able to finish assigned tasks better and faster. This paper shows the concept design of an AI-controlled good transport robot applied in the warehouse. The design is made as fast and straightforward forward possible, and the feasibility of the proposed method is proven by simulation in Scilab FLT and Neuroph.
Aplikasi Sensor Ultrasonik Dalam Pembacaan Level Air Pada Sistem Pertanian Aquaponic Daniesar, Muhammad Nouval; Dewi, Tresna; Oktarina, Yurni
JURNAL TELISKA Vol 16 No I (2023): TELISKA Maret 2023
Publisher : Teknik Elektro Polsri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.8031245

Abstract

SYNERGY OF GREEN ENERGY AND SMART TECHNOLOGY: APPLICATION OF RECURRENT NEURAL NETWORKS IN SOLAR-POWERED AGRICULTURE Maulidina, Elfira; Dewi, Tresna; Kusumanto, Raden
International Journal of Mechanics, Energy Engineering and Applied Science (IJMEAS) Vol. 3 No. 2 (2025): IJMEAS - May
Publisher : Yayasan Ghalih Pelopor Pendidikan (Ghalih Foundation)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53893/ijmeas.v3i2.406

Abstract

In an effort to improve energy efficiency and sustainability in the agricultural sector, smart technology has been integrated into the greenhouse system. The research utilizes the Recurrent Neural Network (RNN) algorithm to forecast values of irradiance on a time principal. The RNN algorithm is chosen for its ability to handle time-series data and predict patterns based on historical data. By using the RNN algorithm, the system can predict real-time needs and then use this information to optimally distribute power from solar power plants. Additionally, this system is equipped with Internet of Things (IoT)-based monitoring capabilities, allowing remote monitoring and control of the research object. Connected IoT sensors collect real-time environmental data and send it to the data server for analysis. The data is also used to update the model of RNN, making supply prediction more accurate over time. The implementation results show increased energy efficiency and reduced operational costs in Green House management. By leveraging AI and IoT technology, model evaluation is conducted using RMSE, MSE, MAE, and R-squared (R²) metrics as important indicators of model accuracy. The evaluation results indicate that this model can provide accurate predictions of irradiance patterns, with low RMSE and MAE values and R² approaching one, signifying excellent implementation in capturing data dynamics.
Internet of Things Based Temperature and pH Stabilization Control System in The Pome Biodigester Fermentation Process at PLTBg Mustofa; Dewi, Tresna; Bow, Yohandri
International Journal of Mechanics, Energy Engineering and Applied Science (IJMEAS) Vol. 3 No. 3 (2025): IJMEAS - September
Publisher : Yayasan Ghalih Pelopor Pendidikan (Ghalih Foundation)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53893/ijmeas.v3i3.433

Abstract

The Government of Indonesia is striving to reduce dependence on fossil fuels by increasing the use of renewable energy (RE), with a target of achieving 23% by 2025. Biogas Power Plants (PLTBg) are one of the solutions that utilize palm oil mill effluent (POME) to produce biogas through anaerobic fermentation. The currently operating systems face problems and challenges in monitoring and operating mesophilic digesters, particularly covered lagoon types, which are managed conventionally, resulting in frequent drops in temperature and pH levels. To address these issues, automation was implemented in the temperature and pH control systems, with the expectation of increasing biogas production. This study adopted an approach that involved recording temperature and pH data, analyzing their upper and lower thresholds, and developing a laboratory-scale model that simulates industrial conditions. This model was equipped with a temperature and pH control system, along with monitoring and control coding. In the laboratory-scale POME digester prototype using actual POME liquid as the test medium, it was demonstrated that temperature and pH could be effectively monitored and controlled by automatically regulating the POME pump motors. The study results show that temperature control was maintained within the range of 38–41 °C, and pH control within the range of 6.5–8. The temperature drop from 40 °C to 35 °C occurred over 274 minutes. The temperature control response time was 5.6 seconds. The pH decrease rate due to the addition of fresh POME was 2.04, with a pH control response time of 8.5 seconds.
SIMULASI KEMACETAN LALU LINTAS PADA LOKASI BUNDARAN BALTOS BANDUNG Dewi, Tresna; Badruzzaman, Farid; Fajar, Yusuf; Suhaedi, Didi; Harahap, Erwin
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 9, No 2 (2020): Smart Comp :Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v9i2.1768

Abstract

Pada masa kini perkembangan teknologi telah memacu meningkatnya kepemilikan kendaraan, terutama di beberapa kota seperti Jakarta, Bandung, Yogyakarta, dan kota besar lainnya. Situasi ini menimbulkan masalah yaitu kemacetan lalu lintas dimana salah satu penyebabnya adalah kepemilikan kendaraan yang semakin bertambah setiap tahun. Kondisi ini tidak didukung dengan keadaan infrastruktur dan sumber daya yang terbatas, disamping fasilitas lalu lintas yang pengoperasiannya masih belum optimal. Penyebab lain dari kemacetan lalu lintas ini adalah banyaknya angkutan umum yang sering berhenti sembarangan, keluar masuk area parkir, dan persilangan kendaraan di persimpangan. Oleh karena itu pada artikel ini dilakukan penelitian untuk mengetahui penyebab dalam masalah kemacetan dan diharapkan ditemukan solusi pemecahan masalah. Dalam penelitian ini, dirancang sistem simulasi arus lalu lintas dengan menggunakan aplikasi SimEvents yang dijalankan pada software MATLAB Simulink. Berdasarkan simulasi, dapat diprediksi penyebab kemacetan lalu lintas khususnya di lokasi Bundaran Balubur Town Square (BALTOS) Kota Bandung Jawa Barat.Kata kunci : kemacetan, lalu lintas, simevents, matlab
Eligibility Study on Floating Solar Panel Installation over Brackish Water in Sungsang, South Sumatra Sasmanto, Andri Agus; Dewi, Tresna; Rusdianasari
EMITTER International Journal of Engineering Technology Vol 8 No 1 (2020)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v8i1.514

Abstract

Electric generation using the photovoltaic (PV) effect is considered ideal in South Sumatra as a response to the government policy to increase the utilization of renewable energy to support the depletion of conventional energy. PV panels can be installed in a fishing village in the Sungsang Estuary. This paper examined the eligibility analysis for the installation of PV panels on brackish water. In this research, two Panels are installed, the first one is floating over a water body, and the second is ground mounted as a comparison of electricity produced and efficiency. The Jsc floating and ground mounting differ in 0.4435 A. The measured Jload in floating PV panels is 0.3900 A higher than the ground mounting. The measured Voc at the floating PV panels is 0.2935 V higher, and the Vload of the floating PV panel is 3.0742 V higher than the ground mount. The differences are due to the floating PV panel surface temperature being lower than ground mounting. Electricity generated by floating PV panels is averagely 11.89 Watt higher, and the efficiency is 4% higher than that of ground installation. This experiment also shows that PV panels can be installed over brackish water in the fishing village of Sungsang Estuary.
IMPLEMENTASI ALGORITMA POLYNOMIAL REGRESSION UNTUK PREDIKSI PERTUMBUHAN TANAMAN PAKCOY PADA SISTEM HIDROPONIK Yurni Oktarina; Rendi Dwi Yanto; Tresna Dewi
JURNAL TELISKA Vol 19 No I (2026): TELISKA Maret 2026
Publisher : Teknik Elektro Polsri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36257/teliska.v19iI.11816

Abstract

This study aims to apply the polynomial regression algorithm to model and predict the growth of pakcoy plants in a hydroponic system. The observed growth parameters include plant height, plant width, and number of leaves, with plant age used as the independent variable. Data were collected over two planting periods, with weekly observations conducted from the seedling stage until harvest. In addition to morphological parameters, Total Dissolved Solids (TDS) and water temperature were recorded as supporting parameters to ensure stable cultivation conditions throughout the study. The non-linear relationship between growth parameters and plant age was represented using a second-order polynomial regression model. The modeling results indicate a good level of fit, with coefficients of determination (R²) of 0.989 for plant height, 0.946 for plant width, and 0.970 for number of leaves, respectively. The relatively low Root Mean Square Error (RMSE) values for each parameter indicate that the model is capable of providing predictions with low estimation error. These findings demonstrate that second-order polynomial regression is a simple and effective approach for modeling the growth dynamics of pakcoy plants in hydroponic systems with limited data availability
Object Detection Approach Using YOLOv5 For Plant Species Identification Billi Clinton; Amperawan Amperawan; Tresna Dewi
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.
Multistage Fertile Egg Prediction via Texture Using Convolutional Neural Network Bimo, Muhammad; Dewi, Tresna; Maulidda, Renny; Oktarina, Yurni; Risma, Pola; Yudha, Hendra Marta
Jurnal Rekayasa Elektro Sriwijaya Vol. 7 No. 2 (2026): 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/q58ezz91

Abstract

Accurate early detection of egg fertilisation status is necessary for effective incubation management in chicken production in order to avoid energy waste and decreased hatchery productivity brought on by infertile or non-viable eggs. Due to their comparable perceptual traits, conventional candling inspection relied on manual observation, which introduced subjectivity and made it challenging to distinguish between fertilised and blighted eggs early on. This study suggested an automated multistage fertilisation prediction method based on candling image analysis, utilising a convolutional neural network framework to get around this restriction. Rather than using traditional binary classification, the suggested system allowed for progressive monitoring of embryonic growth. On incubation days 1, 7, 14, and 21, candling photos were taken from native chicken eggs and classified into three groups: fertilised, infertile, and blighted. To enhance feature extraction efficiency under constrained dataset conditions, a transfer learning technique utilising the MobileNetV2 architecture was implemented. To guarantee consistent learning performance, image preprocessing, augmentation, model training, and validation were carried out. Precision, recall, F1-score, and classification accuracy were used as assessment measures. According to experimental findings, the suggested model produced consistent classification results for both fertilised and infertile eggs, with validation accuracy ranging from 90 to 95% throughout the incubation period. The results of multistage prediction showed consistent decision-making throughout the observation of embryo development. However, during intermediate incubation stages, visual uncertainty with fertilised eggs led to decreased performance in recognising blighted eggs. All things considered, the suggested method showed great promise as a nondestructive intelligent system for early fertilisation prediction. To increase the accuracy of blighted egg classification, more dataset expansion and model improvement were still required.
Co-Authors A Rahman Agum Try Wardhana Agung Tantowi Junior Ahmad Fudholi Alkausar, Muhammad Fajri Amalia, Kania Yusriani Amperawan Amperawan Amperawan Amperawan, Amperawan Angga Liwijaya Angga Prasetia Anggraini, Citra Arissetyadhi, Iwan Assyifa Mourlina Faraquinnsha Aulia, M Habib Auliya, Annisa Azhar, M. Sayid Badruzzaman, Farid Bambang Tutuko Bambang, Muhammad Refo Billi Clinton Bimo, Muhammad Clinton, Billi Dadi Setiadi Daniesar, Muhammad Nouval Denny Juraijin Dicky Astra Yudha Didi Suhaedi Dinata, Yogi Dini Septiyani AR Edo Triyandi Erwin Harahap Evelina Ginting Fajar, Yusuf Fatahul Arifin, Fatahul Fradina Septiarini Hendra Marta Yuda 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 Amri Yahya Muhammad Billy Akbar 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 Ragil Alfarizi Rapli Wijaya RD Kusumanto RD Kusumanto Rendi Dwi Yanto Renny Maulidda, Renny Rinaldi Rinaldi Riyo Irawan Robiansyah Ronald Sukwadi Roseno, M. Taufik Rusdianasari Rusdianasari Rusdianasari Rusdianasari Rusdianasari Sakuraba, Takahiro Sasmanto, Andri Agus Sastiani, Destri Zumar SELAMET MUSLIMIN Siproni Siproni Siproni Umar Siti Afiyah Qatrunnada Siti Nurmaini 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