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All Journal IJoICT (International Journal on Information and Communication Technology) JRSI (Jurnal Rekayasa Sistem dan Industri) JURNAL INSTEK (Informatika Sains dan Teknologi)
Aradea
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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The The Recognition of American Sign Language Using CNN with Hand Keypoint Muhamad Asep Ridwan; Aradea; Husni Mubarok
International Journal on Information and Communication Technology (IJoICT) Vol. 9 No. 2 (2023): Vol.9 No. 2 Dec 2023
Publisher : School of Computing, Telkom University

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Abstract

Sign Language is a method used by the deaf community for their communication. In line with the advances of deep learning, researchers have widely interpreted neural networks for language recognition in recent years. Many models and hardware have been developed to help get high accuracy in language recognition, but generally, the problem of accuracy is still a concern of researchers, even the accuracy problem related to American language or American sign language (ASL) still requires further research to solve. This paper discusses a method to improve ASL recognition accuracy using Convolutional Neural Network (CNN) with hand keypoint. Pre-trained Keypoint detector is used to generate hand keypoints on the massey dataset as an input for classification in the CNN model. The results show that the accuracy of the proposed method is better than the previous studies, obtaining an accuracy of 99.1% in recognizing the 26 statistical signs of the ASL alphabet.
The The Recognition of American Sign Language Using CNN with Hand Keypoint Ridwan, Muhamad Asep; Aradea; Mubarok, Husni
International Journal on Information and Communication Technology (IJoICT) Vol. 9 No. 2 (2023): Vol.9 No. 2 Dec 2023
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Sign Language is a method used by the deaf community for their communication. In line with the advances of deep learning, researchers have widely interpreted neural networks for language recognition in recent years. Many models and hardware have been developed to help get high accuracy in language recognition, but generally, the problem of accuracy is still a concern of researchers, even the accuracy problem related to American language or American sign language (ASL) still requires further research to solve. This paper discusses a method to improve ASL recognition accuracy using Convolutional Neural Network (CNN) with hand keypoint. Pre-trained Keypoint detector is used to generate hand keypoints on the massey dataset as an input for classification in the CNN model. The results show that the accuracy of the proposed method is better than the previous studies, obtaining an accuracy of 99.1% in recognizing the 26 statistical signs of the ASL alphabet.
The Application of Template Matching Algorithm to Improve Accuracy of Plate Number Recognition R Moch Dysa Satria Anggara; Aradea; Rianto
Jurnal Rekayasa Sistem & Industri Vol 9 No 01 (2022): Jurnal Rekayasa Sistem & Industri
Publisher : School of Industrial and System Engineering, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/jrsi.v9i01.514

Abstract

Pattern Recognition is a part research of Image Processing. In term implementation of Pattern Recognitionused for variety research needs such as Identify Transportation License Plate Number. Identifying licenseplate number is one process to reduce crime, accident, and support related agencies to process informationtraffic data. Taking picture of license plate number by camera create image that can be processed by OpticalCharacter Recognition (OCR). OCR is conversion process of picture that contain alphabet and numericobject into ASCII character. In creating of OCR, there’s many kind of algorithm, we used TemplateMatching algorithm as main research. Process of Template Matching algorithm is fitting all part of picturetaken by camera with image that has been processed into Template Data. However like another algorithm,this algorithm had a deficiency about accuracy and time to recognition process. To create and enhance ofaccuracy in Template Matching algorithm, we create 3 scenarios to take a picture for compare the result ofaccuracy and time to OCR process also comparing Template Matching algorithm with Learning VectorQuantization (LVQ) as accuracy comparison method. Based on result of Template Matching algorithm had84% accuracy and 0,942 Milliseconds to process OCR.
FEDERATED XGBOOST FOR ETHEREUM AML: PERFORMANCE EFFICIENCY TRADE OFF IN A MULTI EXCHANGE SETTING Nazwa Auliarahman; Nur Widiyasono; Aradea
Jurnal INSTEK (Informatika Sains dan Teknologi) Vol 11 No 1 (2026): APRIL
Publisher : Department of Informatics Engineering, Faculty of Science and Technology, Universitas Islam Negeri Alauddin, Makassar, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/instek.v11i1.66853

Abstract

Penelitian ini mengevaluasi efektivitas federated learning dalam mendeteksi alamat ilegal pada blockchain Ethereum untuk Anti Money Laundering (AML). Studi ini membandingkan XGBoost centralized dan federated dalam simulasi multi exchange, data dibagi secara horizontal menjadi 3 subset yang merepresentasikan entitas bursa independen. Untuk mengisolasi pengaruh pelatihan terdistribusi, pembagian data dilakukan secara homogen (IID), sehingga analisis difokuskan pada perbedaan mekanisme pembelajaran tanpa dipengaruhi heterogenitas data. Hasil menunjukkan bahwa model federated mencapai performa yang kompetitif dengan PR AUC 0,9962 dan akurasi 97,11%, dibandingkan model terpusat dengan PR AUC 0,9975 dan akurasi 97,75%. Namun, performa tersebut disertai peningkatan durasi pelatihan 4,01 detik dibandingkan 3,39 detik, yang disebabkan oleh beban komunikasi selama proses pembaruan model. Temuan ini menegaskan adanya trade off antara kinerja dan efisiensi dalam penerapan federated learning. Meskipun mampu mendekati performa pembelajaran centralized, pendekatan ini menimbulkan biaya operasional tambahan. Studi ini merupakan evaluasi dasar dalam kondisi terkontrol dan belum merepresentasikan skenario dunia nyata, sehingga diperlukan penelitian lanjutan pada data non IID dan skala sistem yang lebih besar.
Co-Authors Husni Mubarok Husni Mubarok Muhamad Asep Ridwan Nazwa Auliarahman Nur Widiyasono R Moch Dysa Satria Anggara Rianto Ridwan, Muhamad Asep