Garuda - Garba Rujukan Digital

Features for Cross Spectral Image Matching: A Survey Maulisa Oktiana; Fitri Arnia; Yuwaldi Away; Khairul Munadi
Bulletin of Electrical Engineering and Informatics Vol 7, No 4: December 2018
Publisher : Institute of Advanced Engineering and Science

In recent years, cross spectral matching has been gaining attention in various biometric systems for identification and verification purposes. Cross spectral matching allows images taken under different electromagnetic spectrums to match each other. In cross spectral matching, one of the keys for successful matching is determined by the features used for representing an image. Therefore, the feature extraction step becomes an essential task. Researchers have improved matching accuracy by developing robust features. This paper presents most commonly selected features used in cross spectral matching. This survey covers basic concepts of cross spectral matching, visual and thermal features extraction, and state of the art descriptors. In the end, this paper provides a description of better feature selection methods in cross spectral matching.

Features for Cross Spectral Image Matching: A Survey Maulisa Oktiana; Fitri Arnia; Yuwaldi Away; Khairul Munadi
Bulletin of Electrical Engineering and Informatics Vol 7, No 4: December 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v7i4.843

In recent years, cross spectral matching has been gaining attention in various biometric systems for identification and verification purposes. Cross spectral matching allows images taken under different electromagnetic spectrums to match each other. In cross spectral matching, one of the keys for successful matching is determined by the features used for representing an image. Therefore, the feature extraction step becomes an essential task. Researchers have improved matching accuracy by developing robust features. This paper presents most commonly selected features used in cross spectral matching. This survey covers basic concepts of cross spectral matching, visual and thermal features extraction, and state of the art descriptors. In the end, this paper provides a description of better feature selection methods in cross spectral matching.

Features for Cross Spectral Image Matching: A Survey Maulisa Oktiana; Fitri Arnia; Yuwaldi Away; Khairul Munadi
Bulletin of Electrical Engineering and Informatics Vol 7, No 4: December 2018
Publisher : Institute of Advanced Engineering and Science

In recent years, cross spectral matching has been gaining attention in various biometric systems for identification and verification purposes. Cross spectral matching allows images taken under different electromagnetic spectrums to match each other. In cross spectral matching, one of the keys for successful matching is determined by the features used for representing an image. Therefore, the feature extraction step becomes an essential task. Researchers have improved matching accuracy by developing robust features. This paper presents most commonly selected features used in cross spectral matching. This survey covers basic concepts of cross spectral matching, visual and thermal features extraction, and state of the art descriptors. In the end, this paper provides a description of better feature selection methods in cross spectral matching.

Identifikasi Tingkat Kematangan Kelapa Sawit Berbasis Pencitraan Termal Khusnul Azima; Khairul Munadi; Fitri Arnia; Maulisa Oktiana
Jurnal Rekayasa Elektrika Vol 15, No 1 (2019)
Publisher : Universitas Syiah Kuala

Indonesia is the biggest producer of palm oil (Elaeis guineenis jacq). The palm tree is a primary commodity that posses a high economic value. Palm oil must be considered in terms of quality to produce optimal and high-quality oil. Previously, the stipulation of the palm tree characterization used manual and visual image utilization method; it may have weaknesses due to the dependency of individual sorting and coruscation factor. Therefore, this research is aimed to improve the performance of the previous method in identifying the ripeness of palm tree based on thermal imaging. The excess of thermal imaging was not related to the coruscation since the level of ripeness was both determined by the temperature and colour. The detection method of this research deployed the colour-based features that are Dominant Colour Descriptor and Color Moment. The DCD and Color Moment was the input to the K-Nearest Neighbor (KNN) method. The percentage of identification rate was 89%, and the identification of oil palm maturity level using thermal imaging is more efficient because it is done without human intervention and does not depend on lighting assistance compared to manual method and method of using RGB visual images.

Identifikasi Tingkat Kematangan Kelapa Sawit Berbasis Pencitraan Termal Khusnul Azima; Khairul Munadi; Fitri Arnia; Maulisa Oktiana
Jurnal Rekayasa Elektrika Vol 15, No 1 (2019)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v15i1.12963

Indonesia is the biggest producer of palm oil (Elaeis guineenis jacq). The palm tree is a primary commodity that posses a high economic value. Palm oil must be considered in terms of quality to produce optimal and high-quality oil. Previously, the stipulation of the palm tree characterization used manual and visual image utilization method; it may have weaknesses due to the dependency of individual sorting and coruscation factor. Therefore, this research is aimed to improve the performance of the previous method in identifying the ripeness of palm tree based on thermal imaging. The excess of thermal imaging was not related to the coruscation since the level of ripeness was both determined by the temperature and colour. The detection method of this research deployed the colour-based features that are Dominant Colour Descriptor and Color Moment. The DCD and Color Moment was the input to the K-Nearest Neighbor (KNN) method. The percentage of identification rate was 89%, and the identification of oil palm maturity level using thermal imaging is more efficient because it is done without human intervention and does not depend on lighting assistance compared to manual method and method of using RGB visual images.

Optimization of Solar Panel Installation Potential Mapping Based on Convolutional Neural Network Maulisa Oktiana; Rika Sri Utami; Ilham Maulana; Annisa Gusti Ananda
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 2, May 2026
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i2.2584

The increasing global energy demand and the depletion of fossil fuel resources have accelerated the transition toward renewable energy. Solar energy is considered one of the most promising sustainable energy sources. However, identifying suitable locations for solar panel installation remains challenging due to geographic and environmental variability across different regions. This study proposes a Convolutional Neural Network (CNN)-based approach to map potential solar panel installation areas using high-resolution satellite imagery. The model is designed to extract spatial features from land surfaces, including land cover characteristics, building density, and reflectance patterns derived from Sentinel-2 imagery obtained through Google Earth Engine. The proposed framework utilizes a VGG19-based architecture with transfer learning to improve feature extraction and classification performance. Experimental results demonstrate that the proposed model achieves an accuracy of 94.2% in classifying areas suitable for solar panel installation. These findings indicate that deep learning–based spatial analysis can provide an effective approach to support large-scale solar energy planning and decision-making.

Performance Analysis of a Bayesian Regularization Neural Network for Short-Term Electricity Load Forecasting Ahmad Fauzi; Tarmizi Tarmizi; Khairun Saddami; Maulisa Oktiana; Ramzi Adriman
JET (Journal of Electrical Technology) Vol 11, No 3 (2026): Edisi Oktober
Publisher : Universitas Islam Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30743/jet.v11i3.14370

Short-term electricity load forecasting (STLF) is important for supporting reliable and efficient power system operation. Bayesian Regularization Neural Network (BRNN) can model nonlinear relationships in electricity load data while controlling model complexity. This study analyzes the performance of a previously developed BRNN model by examining the effects of network architecture and forecasting horizon. Four input variables, namely hour, air temperature, air humidity, and previous-day electricity load, were used to forecast the following-day electricity load. Five BRNN configurations with 5, 10, 15, 20, and 25 hidden units were evaluated using Mean Squared Error (MSE) and correlation coefficient (R), while forecasting performance was assessed using Mean Absolute Percentage Error (MAPE) for horizons h+1 to h+7. The 4-10-1 architecture achieved the lowest training MSE of 0.000933 and was selected as the reference configuration based on the predefined minimum-training-MSE criterion. The testing R values ranged from 0.930 to 0.952 across the evaluated architectures. For the selected configuration, MAPE values across h+1 to h+7 were 8.46%, 7.76%, 8.16%, 5.28%, 2.45%, 8.02%, and 6.11%, respectively. The lowest MAPE of 2.45% occurred at h+5. These results indicate that BRNN performance varies with network architecture and forecasting horizon, while the observed performance should be interpreted within the evaluated dataset and experimental conditions.

Title

Found 7 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 7 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 7 Documents
Search