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All Journal ComEngApp : Computer Engineering and Applications Journal Journal of Applied Smart Electrical Network and System (JASENS) International Journal of Research in Vocational Studies (IJRVOCAS)
Tresna Dewi
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Aerospace Engineering Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Social Sciences Other

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Peat Land Fire Monitoring System Using Fuzzy Logic Algorithm Nyayu Husni Latifah; Masayu Annisah; Tresna Dewi
Computer Engineering and Applications Journal Vol 8 No 3 (2019)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (308.235 KB) | DOI: 10.18495/comengapp.v8i3.297

Abstract

In this research, a fuzzy logic algorithm is implemented in a monitoring system for detecting the potential fires in peat land. The monitoring system in this research employs two sensors as the fuzzy inputs, i.e. TGS 2600 gas sensor and DHT11 temperature sensor. The outputs of the fuzzy logic are the specified conditions of motor activation (PWM) with 3600 rotations. The system is monitored through camera, which sends the monitoring result to android via web server. The result is sent when TGS 2600 and DHT11 sensors detect the determined gas concentration and surrounding temperature. Before sending the result, the rotating motor stops every five minutes to take the photograph of peat land location. The result shows that the algorithm used in this research has been successful in determining the condition of the peat lands correctly and therefore can be used as the early prevention of fires.
Simulation Design of Artificial Intelligence Controlled Goods Transport Robot Yurni Oktarina; Destri Zumar Sastiani; Tresna Dewi
Computer Engineering and Applications Journal Vol 11 No 2 (2022)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (298.753 KB) | DOI: 10.18495/comengapp.v11i2.411

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.
Integrating Temporal and Feedforward Models for Solar Energy Prediction: LSTM and ANN Hybrid Approaches Oktarina, Yurni; Zainuddin Nawawi; Bhakti Yudho Suprapto; Tresna Dewi
International Journal of Research in Vocational Studies (IJRVOCAS) Vol. 4 No. 2 (2024): IJRVOCAS - August
Publisher : Yayasan Ghalih Pelopor Pendidikan (Ghalih Foundation)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53893/ijrvocas.v4i2.317

Abstract

The increasing reliance on renewable energy, particularly solar power, necessitates accurate models for predicting energy output to optimize storage and distribution systems. Traditional methods such as Long Short-Term Memory (LSTM) networks and Artificial Neural Networks (ANNs) offer unique strengths in forecasting photovoltaic (PV) system outputs. LSTM excels in capturing temporal dependencies in time-series data, while ANNs effectively model nonlinear relationships between variables. This study aims to develop and evaluate a hybrid LSTM-ANN model for improving the accuracy of PV energy output predictions, focusing on voltage, power, and irradiance. Using data collected from a solar-powered greenhouse in Talang Kemang, Indonesia, the model was trained and validated. The hybrid model demonstrated significant improvements in prediction accuracy. For voltage, the model achieved a Mean Absolute Error (MAE) of 0.1016 and a Root Mean Squared Error (RMSE) of 0.1417, while irradiance predictions resulted in an MAE of 0.0895 and RMSE of 0.1149. Power predictions also yielded strong results, with an MAE of 0.1506 and RMSE of 0.1954. These results highlight the hybrid LSTM-ANN model's effectiveness in combining temporal and nonlinear data processing capabilities, leading to superior accuracy in predicting PV system outputs. This approach can enhance the reliability of energy forecasting models, enabling better integration of solar power into electrical grids. The model holds promise for broader applications in renewable energy systems, improving their efficiency and sustainability
The Implementasi Robot Line Follower Sebagai Peghntar Makanan / Minuman Di Restoran Modern Ariski, Ariski Sapni Putra; Tresna Dewi; Pola Risma
Journal of Applied Smart Electrical Network and Systems Vol 5 No 1 (2024): Vol 5 No 1 (2024): Vol 5 No 1, June 2024
Publisher : Indonesian Society of Applied Science (ISAS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/jasens.v5i1.934

Abstract

Laporan ini membahas implementasi Robot Line Follower untuk mengantarkan makanan atau minuman di restoran modern. Penelitian ini bertujuan untuk merancang dan mengimplementasikan robot yang dapat mengikuti garis dan mengantarkan pesanan secara otomatis ke meja pelanggan. Robot ini menggunakan sensor garis untuk mendeteksi lintasan, sensor ultrasonik untuk menghindari rintangan, dan sensor warna untuk mendeteksi meja. Sistem kontrol pada robot ini menggunakan mikrokontroler ESP32. Hasil penelitian menunjukkan bahwa robot dapat berfungsi dengan baik dalam mengantarkan makanan ke dua meja yang berbeda dan kembali ke titik awal setelah pesanan diambil oleh pelanggan. Namun, robot masih memerlukan perbaikan pada sensor dan pemrograman untuk meningkatkan kinerjanya. Penelitian ini diharapkan dapat berkontribusi pada pengembangan teknologi otomasi di industri layanan makanan.
Co-Authors Ariski, Ariski Sapni Putra Bhakti Yudho Suprapto Destri Zumar Sastiani Husni, Nyayu Latifah Masayu Annisah Pola Risma Yurni Oktarina Zainuddin Nawawi