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All Journal Jurnal Radiologi Dentomaksilofasial Indonesia (JRDI) Makara Journal of Technology
Tati Latifah Mengko, Tati Latifah
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Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Dentistry Electrical & Electronics Engineering Energy Engineering Health Professions Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology Physics

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Analysis of Arm Movement Prediction by Using the Electroencephalography Signal Darmakusuma, Reza; Prihatmanto, Ary Setijadi; Indrayanto, Adi; Mengko, Tati Latifah; Andarini, Lidwina Ayu; Idrus, Achmad Furqon
Makara Journal of Technology Vol. 20, No. 1
Publisher : UI Scholars Hub

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Various technological approaches have been developed in order to help those people who are unfortunate enough to be afflicted with different types of paralysis which limit them in performing their daily life activities independently. One of the proposed technologies is the Brain-Computer Interface (BCI). The BCI system uses electroencephalography (EEG) which is generated by the subject’s mental activity as input, and converts it into commands. Some previous experiments have shown the capability of the BCI system to predict the movement intention before the actual movement is onset. Thus research has predicted the movement by discriminating between data in the “rest” condition, where there is no movement intention, with “pre-movement” condition, where movement intention is detected before actual movement occurs. This experiment, however, was done to analyze the system for which machine learning was applied to data obtained in a continuous time interval, between 3 seconds before the movement was detected until 1 second after the actual movement was onset. This experiment shows that the system can discriminate the “pre-movement” condition and “rest” condition by using the EEG signal in 7-30 Hz where the Mu and Beta rhythm can be discovered with an average True Positive Rate (TPR) value of 0.64 ± 0.11 and an average False Positive Rate (FPR) of 0.17 ± 0.08. This experiment also shows that by using EEG signals obtained nearing the movement onset, the system has higher TPR or a detection rate in predicting the movement intention.
Hybrid Brain-Computer Interface: a Novel Method on the Integration of EEG and sEMG Signal for Active Prosthetic Control Darmakusuma, Reza; Prihatmanto, Ary Setijadi; Indrayanto, Adi; Mengko, Tati Latifah
Makara Journal of Technology Vol. 22, No. 1
Publisher : UI Scholars Hub

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This paper describes a novel method for controlling active prosthetics by integrating surface electromyography (sEMG) and electroencephalograph signals to improve its intuitiveness. This paper also compares the new method (RTA-2) with other existing methods (AND and OR) for controlling active prosthetics. Based on analysis, RTA-2 features higher true positive rate (TPR) and balanced accuracy (BA) than AND method. On the other hand, the new method (RTA-2) yields lower false detection rate (FPR) than OR method. Analysis also shows that RTA-2 possesses equal TPR, FPR, and BA with the detection of movement intention using sEMG-based system. Although the RTA-2 method shows equal performance with the sEMG-based system, it presents an advantage for driving active prosthetics to move faster and to reduce its total time response by generating more movement commands.
Development of cephalometric radiography in orthodontic imaging: a literature review Prativi, Shinta Amini; Suksmono, Andriyan Bayu; Mengko, Tati Latifah; Danudirdjo, Donny
Jurnal Radiologi Dentomaksilofasial Indonesia (JRDI) Vol 9 No 2 (2025): Jurnal Radiologi Dentomaksilofasial Indonesia (JRDI)
Publisher : Ikatan Radiologi Kedokteran Gigi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32793/jrdi.v9i2.1344

Abstract

Objectives: This review article aims to discuss the development of lateral cephalometric radiography use in science until now. Review: The search for studies on the identification of lateral cephalometric anatomical landmarks based on artificial intelligence was conducted by involving four databases: PubMed, IEEE Xplore, Google Scholar, and Scopus. The article selection was conducted using the keywords "Cephalometric Radiograph," "Automatic Cephalometric," "Cephalometric Landmarking," and "Cephalometric Digital" from January 2000 to March 2022. A total of 11 articles were obtained for this study. Cephalometric radiography is a radiographic technique that shows a picture of the skull and is widely used in dentistry to analyze and assess the relationship between teeth, jaws, and facial bones. Cephalometric analysis can be done by identifying anatomical landmark points and measuring angles on lateral cephalometric radiographs. The development of cephalometric radiography in biomedical imaging, especially in terms of the processing of cephalometric radiograph images from the process of forming X-rays to their potential use in the process of determining automatic anatomical landmark points. Conclusion: The results of the literature review of the development of dental radiology, especially digital cephalometric radiography, continue to increase, and its development is supported by computing technology, especially Artificial Intelligence. Keywords: Lateral cephalometric; biomedical imaging; artificial intelligence
Co-Authors Achmad Furqon Idrus, Achmad Furqon Adi Indrayanto Andriyan Bayu Suksmono, Andriyan Bayu Ary Setijadi Prihatmanto Danudirdjo, Donny Lidwina Ayu Andarini, Lidwina Ayu prativi, shinta amini Reza Darmakusuma, Reza