Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal BERKALA SAINSTEK Journal of Current Studies in SDGs Journal of Current Studies in SDGs
Rachmawati, Arum Vonie
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemistry Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Mathematics Physics Social Sciences Other

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Analysis of the Time of Dew Occurrence on the DCDB Panel at PLN GIS 150 kV Gunungsari Surabaya Rachmawati, Arum Vonie; Agustinur, Satya Cantika; Indralaksono, Rio; Syahriannanda, Kinanta; Riandana, I Made Niantara; Alim, Andu Mahdy; Yantidewi, Meta; Dzulkiflih, Dzulkiflih
BERKALA SAINSTEK Vol. 13 No. 1 (2025)
Publisher : Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/bst.v13i1.45408

Abstract

The DCDB panel is a panel for distributing direct current electricity. Inside the panel there are cables to carry current which affects the temperature of the panel. The greater the current flowing, the greater the resulting temperature. Dew that occurs on the DCDB panel can cause various kinds of electrical disturbances, so the DCDB panel must be kept dry and protected from condensation. The dew parameter is the dew point. In order to minimize the occurrence of dew on the DCDB panel, it is necessary to know the prediction or estimate of the occurrence of dew on the DCDB panel by evaluating the Rectifier room temperature with the external environmental weather temperature. Therefore, research was carried out for 10 days in the PT. PLN (Persero) GIS 150 kV Gunungsari Rectifier room, Surabaya, which contained a DCDB panel. The data obtained is temperature monitoring data in the Rectifier room, DCDB panel body, and DCDB panel door. From this data, observations are made to see the temperature changes that occur in the panel. Environmental weather data is obtained via the Weather Underground website. The research data obtained was analyzed using RMSE and MAPE to check errors and graph plotting using SPSS. Based on the research conducted, it can be concluded that the dew point is estimated to occur at 03.30-05.00 in July with a temperature range of 21.3℃-23.3℃.
Design Optimization of Rectangular Microstrip Antenna Using Deep Neural Network for 3 GHz Applications in Support of SDG 9 Ramadani, Riski; Nikmah, Afiyah; Rachmawati, Arum Vonie; Firdaus, Rohim Aminullah Firdaus; Ramadhani, Noer Risky
Journal of Current Studies in SDGs Vol. 2 No. 1 (2026): March
Publisher : Sekolah Tinggi Agama Islam Sabilul Muttaqin Mojokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63230/jocsis.2.1.130

Abstract

Objective: This study aims to investigate the effectiveness of Deep Neural Networks (DNN) for optimizing the design of a rectangular microstrip antenna operating at a target frequency of 3 GHz. The research focuses on improving antenna design efficiency by predicting antenna performance parameters based on geometric characteristics. Method: The study employed a computational simulation approach combined with machine learning techniques. A synthetic dataset consisting of 6000 antenna configurations was generated using analytical microstrip antenna equations. The dataset included geometric parameters such as dielectric constant, substrate thickness, patch width, patch length, and inset feed position. A Deep Neural Network model was trained to predict resonant frequency, return loss, and input impedance. The trained model was then used as a surrogate model to evaluate 30,000 candidate antenna designs and identify the optimal configuration. Result: The proposed model achieved high predictive accuracy with values of 0.9987 for resonant frequency prediction and 0.9988 for input impedance prediction. The optimized antenna design produced a resonant frequency of 2.996 GHz, return loss of −18.70 dB, and input impedance of 53.95 Ω, which closely match the target specifications for S-band wireless applications. Novelty: The study demonstrates that Deep Neural Networks can significantly accelerate antenna design optimization by replacing repetitive electromagnetic simulations with data-driven prediction models.
Design Optimization of Rectangular Microstrip Antenna Using Deep Neural Network for 3 GHz Applications in Support of SDG 9 Ramadani, Riski; Nikmah, Afiyah; Rachmawati, Arum Vonie; Firdaus, Rohim Aminullah Firdaus; Ramadhani, Noer Risky
Journal of Current Studies in SDGs Vol. 2 No. 1 (2026): March
Publisher : Sekolah Tinggi Agama Islam Sabilul Muttaqin Mojokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63230/jocsis.2.1.130

Abstract

Objective: This study aims to investigate the effectiveness of Deep Neural Networks (DNN) for optimizing the design of a rectangular microstrip antenna operating at a target frequency of 3 GHz. The research focuses on improving antenna design efficiency by predicting antenna performance parameters based on geometric characteristics. Method: The study employed a computational simulation approach combined with machine learning techniques. A synthetic dataset consisting of 6000 antenna configurations was generated using analytical microstrip antenna equations. The dataset included geometric parameters such as dielectric constant, substrate thickness, patch width, patch length, and inset feed position. A Deep Neural Network model was trained to predict resonant frequency, return loss, and input impedance. The trained model was then used as a surrogate model to evaluate 30,000 candidate antenna designs and identify the optimal configuration. Result: The proposed model achieved high predictive accuracy with values of 0.9987 for resonant frequency prediction and 0.9988 for input impedance prediction. The optimized antenna design produced a resonant frequency of 2.996 GHz, return loss of −18.70 dB, and input impedance of 53.95 Ω, which closely match the target specifications for S-band wireless applications. Novelty: The study demonstrates that Deep Neural Networks can significantly accelerate antenna design optimization by replacing repetitive electromagnetic simulations with data-driven prediction models.
Co-Authors Agustinur, Satya Cantika Alim, Andu Mahdy Dzulkiflih, Dzulkiflih Firdaus, Rohim Aminullah Firdaus Indralaksono, Rio Nikmah, Afiyah Ramadani, Riski Ramadhani, Noer Risky Riandana, I Made Niantara Syahriannanda, Kinanta Yantidewi, Meta