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All Journal Bulletin of Electrical Engineering and Informatics Makara Journal of Technology International Journal of Electrical, Computer, and Biomedical Engineering (IJECBE)
Mia Rizkinia
Department Of Electrical Engineering, Faculty Of Engineering, Universitas Indonesia, Depok 16424, Indonesia
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology

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 1 
ALOS/PALSAR Image Processing Using Dinsar and Log Ratio for Flood Early Detection in Jakarta Based on Land Subsidences Sudiana, Dodi; Rizkinia, Mia
Makara Journal of Technology Vol. 15, No. 2
Publisher : UI Scholars Hub

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Abstract

Flood that occurred in Jakarta is not only influenced by rainfall, urban planning system and drainage alone, but also may be involved land subsidence (LS). LS is possible in because Jakarta stands on top of layers of sediments and the presence of ground water consumption in very large quantities. In this research, the Advanced Land Observing Satellite (ALOS)/Phased Array type L-band Synthetic Aperture Radar (PALSAR) data was processed to determine the level of LS in Jakarta area and its relation to flood potential area. Differential interferometry method (DInSAR) was performed on two PALSAR data with different acquisition years, i.e. 2007 and 2008, respectively. DInSAR processing generated images containing information that can be converted into LS. To find the elevation changing area, log ratio algorithm was applied to those images as the additional analysis. The log ratio image is superimposed on the DInSAR result and Jakarta inundation map of 2009, to acquire the relationship between LS and the flood and flood vulnerability map of Jakarta based on LS. It is found that lands on the flooded area of 10.57 cm on the average, with a minimum and maximum of 5.25 cm and 22.5 cm, respectively. The greater the value of LS, inundation area also tend to widen, except in a few areas that have special conditions, such as reservoirs, river flow solution, water pump system and sluices. Accuracy of DInSAR result image is quite high, with the difference of 0.03 cm (0.18%) to 0.55 cm (3.37%) as compared to those from GPS measurements. These results can be recommended to the local government of Jakarta to minimize the potential risk of flood, as well as the subject of city planning for the future.
Observation of Center Disaster Damage on Pariaman and Wasior Using Differential Sar Interferometry (Dinsar) Sudiana, Dodi; Rizkinia, Mia
Makara Journal of Technology Vol. 16, No. 2
Publisher : UI Scholars Hub

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Abstract

This study focuses on disaster observations in Pariaman (West Sumatera) and Wasior (Papua) using remote sensing techniques (differential SAR interferometry). Differential interferometry (DInSAR) method was performed on two PALSAR data sets with different acquisition months, i.e. about a month after and before disaster, respectively. The center damage of Pariaman earthquake and Wasior flood can be determined by deriving Land Subsidence using DInSAR method.
Evaluation of Primal-Dual Splitting Algorithm for MRI Reconstruction Using Spatio-Temporal Structure Tensor and L1-2 Norm Rizkinia, Mia; Okuda, Masahiro
Makara Journal of Technology Vol. 23, No. 3
Publisher : UI Scholars Hub

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Abstract

Magnetic resonance imaging (MRI) is an essential medical imaging technique which is widely used for medical research and diagnosis. Dynamic MRI provides the observed object visualization through time and results in a spatiotemporal signal. The image sequences often contain redundant information in both spatial and temporal domains. To utilize this characteristic, we propose a spatio-temporal reconstruction approach based on compressive sensing theory. We apply spatio-temporal structure tensor using nuclear norm, in addition to the wavelet sparsity regularization. The spatio-temporal structure tensor is a matrix that consists of gradient components of the MRI data w.r.t the spatial and temporal domains. For the wavelet sparsity, we use L1 – L2 instead of L1 norm. We propose the algorithm using primaldual splitting (PDS) approach to solve the convex optimization problem. In the experiment, we investigate the potential benefit of adding the two regularizations to the compressive sensing problem. The algorithm is compared with PDSbased algorithm using conventional regularizations, i.e., wavelet sparsity and total variation. Our proposed algorithm performs superior results in terms of reconstruction accuracy and visual quality.
Real-time stress detection and monitoring system using IoT-based physiological signals Atika Hendryani; Dadang Gunawan; Mia Rizkinia; Rinda Nur Hidayati; Frisa Yugi Hermawan
Bulletin of Electrical Engineering and Informatics Vol 12, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i5.5132

Abstract

Currently, medical experts use psychophysiological questionnaires to evaluate human stress levels during counseling or interviews. Typically, biochemical samples use urine, saliva, and blood samples to identify the effects of stress on the human body. This research explains that stress detection can be done by analyzing psychological signals and the importance of monitoring stress levels. The authors develop research on stress detection based on psychological signals. The system then processes the recorded data; the android application displays the calculation results. The database can also be accessed as a spreadsheet via a web application. The design of real-time stress detection and monitoring using internet of things (IoT) can work well.
Implementation of Xception and EfficientNetB3 for COVID-19 Detection on Chest X-Ray Image via Transfer Learning Novalina, Nadya; Rizkinia, Mia
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 1 No. 2 (2023)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v1i2.14

Abstract

COVID-19 is a highly contagious infectious disease caused by the SARS-CoV-2 virus that can cause respiratory issues. The utilization of X-ray imaging has the potential to serve as an alternative means of detecting COVID-19 by offering insights into the condition of the lungs. Rapid and automated analysis of medical images and patterns can be achieved through deep learning techniques. In this study, we propose methods for the automatic classification of COVID-19 from Chest X-Ray images using CNN with transfer learning techniques, namely Xception and EfficientNetB3 architectures, as well as an ensemble of both architectures working in parallel. Additionally, we use Grad-CAM to visualize the regions of the X-ray image that are most important for the classification decision. The classification of COVID-19 is carried out for four types of classes: COVID-19, normal, bacterial pneumonia, and viral pneumonia. The proposed classifier models achieve an overall accuracy of 94.44% for the Xception classifier, 95.28% for the EfficientNetB3 classifier, and 94.44% for the parallel classifier. The accuracy value is higher than the other comparison classifiers accuracy values. Overall, the proposed classifiers can be recommended as tools to assist radiologists and clinical practitioners in diagnosing and following up with COVID-19 cases.
Implementation of Diffusion Variational Autoencoder for Stock Price Prediction with the Integration of Historical and Market Sentiment Data Ardisurya; Rizkinia, Mia
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 2 No. 2 (2024)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v2i2.55

Abstract

This study aims to predict stock prices using a Diffusion Variational Autoencoder (D-VAE) model that integrates technical data and market sentiment. Technical data is obtained from historical stock prices and trading volume, while sentiment data is derived from financial news analyzed using the IndoBERT model for sentiment classification. The research findings indicate that the integration of sentiment data in the D-VAE model enhances the accuracy of stock price predictions compared to a model that uses only technical data. Model evaluation is conducted using metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared (R²). The model with sentiment data integration has an MSE of 2753.204, MAE of 42.751, and R² of 0.94489, which are better than the model without sentiment data integration. This study demonstrates that the use of sentiment analysis can significantly contribute to improving stock price prediction performance using machine learning technology.
Implementation of U-Net for Paddy Field Mapping Using Very High-Resolution Satellite Imagery Isbat, Faiz Khairul; Rizkinia, Mia; Sudiana, Dodi
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 2 No. 3 (2024)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v2i3.57

Abstract

Mapping rice fields using remote sensing is one method that can be used to determine the number of rice fields, especially in Indonesia. Using this method can increase effectiveness in agricultural resource management. This research uses Pleiades optical satellite image data with very high resolution which is capable of displaying data information on a larger scale. The rice field classification model in this study uses U-net to classifier between rice fields and non-rice fields. The performance of applying this model for the classification of paddy fields and non-rice fields is 96%. These results show that the U-net model is capable of classifying small rice fields with high accuracy
Benchmarking machine learning algorithm for stunting risk prediction in Indonesia Novalina, Nadya; Aksar Tarigan, Ibrahim Amyas; Kayla Kameela, Fatimah; Rizkinia, Mia
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8997

Abstract

Stunting is a condition caused by poor nutrition that results in below-average height development, potentially leading to long-term effects such as intellectual disability, low learning abilities, and an increased risk of developing chronic diseases. One effort to reduce stunting is to apply a machine learning algorithm with a data science approach to develop risk prediction models based on factors in stunting. The study used the current cross industry standard process for data mining (CRISP-DM) framework to gain insight and analyzed 1561 records of data collected from the Indonesia family life survey (IFLS) for the prediction models. Two sampling methods, random undersampling, and oversampling synthetic minority oversampling technique (SMOTE), were employed and compared to overcome the data imbalance problem. Four machine learning classifier algorithms were trained and tested to determine the best-performing model. The experiment results showed that the algorithms yielded an average accuracy of more than 75%. Using the undersampling technique, the accuracy obtained by logistic regression, k-nearest neighbor (KNN), support vector classifier (SVC), and decision tree classifier were 95.21%, 78.91%, 92.97%, and 86.26% respectively. Meanwhile, the oversampling technique reached 96.17%, 88.50%, 93.29%, and 95.21%, respectively. Logistic regression emerges as the best classification, with oversampling yielding superior performance.
Development of frequency modulated continuous wave radar antenna to detect palm fruit ripeness Rahmawati, Yosy; Rizkinia, Mia; Zulkifli, Fitri Yuli
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8749

Abstract

Oil palm fruits farmers in Indonesia have determined the ripeness of oil palm fruits in the traditional way, namely using human eye visuals, which have the weakness of inconsistent levels of accuracy and are prone to errors. The development of increasingly sophisticated technology will help oil palm fruits farmers recognize the characteristics of fruit maturity. Advanced technology, such as frequency modulated continuous wave (FMCW) radar, can assist farmers in accurately identifying fruit maturity. To ensure high accuracy and sensitivity, an antenna with low side lobe level (SLL), high gain, and wide bandwidth in the 23-26 GHz range is required. Using CST Microwave Studio 2023, a designed and simulated antenna achieved an SLL of 24 dB, a gain of 15 dBi, and a bandwidth of 2.5 GHz. These results indicate that higher gain enhances energy directionality and overall antenna performance. Additionally, a smaller angular value improves the antenna’s radiation focus, making it more effective for precision sensing in oil palm fruit ripeness detection.
Co-Authors Aksar Tarigan, Ibrahim Amyas Ardisurya Atika Hendryani Dadang Gunawan Dodi Sudiana Fitri Yuli Zulkifli Frisa Yugi Hermawan Isbat, Faiz Khairul Kayla Kameela, Fatimah Novalina, Nadya Okuda, Masahiro Rinda Nur Hidayati Yosy Rahmawati