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All Journal Jurnal Buana Informatika Jurnal Aplikasi Bisnis Kesatuan Jurnal Informatika Kesatuan
Damatraseta, Febri
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Languange, Linguistic, Communication & Media Library & Information Science Social Sciences

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Real-time BISINDO Hand Gesture Detection and Recognition with Deep Learning CNN Damatraseta, Febri; Novariany, Rani; Ridhani, Muhammad Adlan
Jurnal Informatika Kesatuan Vol. 1 No. 1 (2021): JIKES Edisi Agustus 2021
Publisher : LPPM IBI Kesatuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37641/jikes.v1i1.774

Abstract

BISINDO is one of Indonesian sign language, which do not have many facilities to implement. Because it can cause deaf people have difficulty to live their daily life. Therefore, this research tries to offer an recognition or translation system of the BISINDO alphabet into a text. The system is expected to help deaf people to communicate in two directions. In this study the problems encountered is small datasets. Therefore this research will do the testing of hand gesture recognition, by comparing two model CNN algorithms, that is LeNet-5 and Alexnet. This test will look for which classification technique is better if the dataset conditions in an amount that does not reach 1000 images in each class. After testing, the results found that the CNN technique on the Alexnet architectural model is better to used, this is because when doing the testing process by using still-image and Alexnet model data which has been released in training process, Alexnet model data gives greater prediction results that is equal to 76%. While the LeNet model is only able to predict with the percentage of 19%. When that Alexnet data model used on the system offered, only able to predict correcly by 60%. Keywords: Sign language, BISINDO, Computer Vision, Hand Gesture Recognition, Skin Segmentation, CIELab, Deep Learning, CNN.
Comparative Analysis Of Efficient Image Segmentation Technique For Text Recognition And Human Skin Recognition Cahyadi, Septian; Damatraseta, Febri; S, Lodryck Lodefikus
Jurnal Informatika Kesatuan Vol. 1 No. 1 (2021): JIKES Edisi Agustus 2021
Publisher : LPPM IBI Kesatuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37641/jikes.v1i1.775

Abstract

Computer Vision and Pattern Recognition is one of the most interesting research subject on computer science, especially in case of reading or recognition of objects in realtime from the camera device. Object detection has wide range of segments, in this study we will try to find where the better methodologies for detecting a text and human skin. This study aims to develop a computer vision technology that will be used to help people with disabilities, especially illiterate (tuna aksara) and deaf (penyandang tuli) to recognize and learn the letters of the alphabet (A-Z). Based on our research, it is found that the best method and technique used for text recognition is Convolutional Neural Network with achievement accuracy reaches 93%, the next best achievement obtained OCR method, which reached 98% on the reading plate number. And also OCR method are 88% with stable image reading and good lighting conditions as well as the standard font type of a book. Meanwhile, best method and technique to detect human skin is by using Skin Color Segmentation: CIELab color space with accuracy of 96.87%. While the algorithm for classification using Convolutional Neural Network (CNN), the accuracy rate of 98% Key word: Computer Vision, Segmentation, Object Recognition, Text Recognition, Skin Color Detection, Motion Detection, Disability Application
Tinjauan Perhitungan, Pemotongan, Dan Pelaporan PPh Pasal 23 Atas Jasa Pada PT. Yudhistira Ghalia Indonesia Nurmilawati, Nurmilawati; Riyadi, Rizal; Damatraseta, Febri
Jurnal Aplikasi Bisnis Kesatuan Vol. 5 No. 2 (2025): JABKES Edisi Agustus 2025
Publisher : Program Vokasi dan LPPM IBI Kesatuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37641/jabkes.v5i2.1905

Abstract

Income tax is a tax imposed on the income of a taxpayer or entity. Income tax article 23 is one of the income taxes imposed on income from capital, delivery of services, and other activities that are not included in income tax article 21. PT Yudhistira Ghalia Indonesia is a taxable company that uses the services of other companies, so the transaction is subject to income tax article 23 which will be deducted by the company. The purpose of this study is to determine how the implementation of the calculation, deduction, and reporting of Income Tax Article 23 on services at PT Yudhistira Ghalia Indonesia. The results of the review in this study indicate that PT Yudhistira Ghalia Indonesia has calculated, deducted, and reported Income Tax Article 23 on services with rates and procedures following applicable tax regulations. The company has calculated the rate for services which is 2% and has used the e-bupot unification application properly and correctly so that tax payments can be made easily.
Penerapan Graph Neural Network dalam Pengenalan Alfabet BISINDO dengan Fokus pada Gerakan Dinamis Damatraseta, Febri; Alfan, Muhammad; Yuliandi
Jurnal Buana Informatika Vol. 16 No. 2 (2025): Jurnal Buana Informatika, Volume 16, Nomor 02, Oktober 2025
Publisher : Universitas Atma Jaya Yogyakarta

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Abstract

Sebagian besar studi pengenalan alfabet Bahasa Isyarat Indonesia (BISINDO) masih terbatas pada gesture statik, meskipun beberapa huruf seperti R dan J memiliki karakteristik gerakan dinamis yang tidak dapat direpresentasikan secara statis. Penelitian ini menggunakan MediaPipe untuk mendeteksi 21 keypoints tangan sebagai input fitur. Titik-titik ini dimodelkan dalam bentuk graf dan diproses menggunakan Graph Neural Networks (GNNs) guna mengenali alfabet secara simultan, termasuk huruf-huruf dinamis. Proses pelatihan menggunakan K-Fold Cross Validation untuk menguji konsistensi performa model. Model GNN menghasilkan akurasi sebesar 96% pada pengujian data alfabet BISINDO. Prototipe sistem dalam bentuk aplikasi web berhasil mengenali 26 huruf BISINDO secara dinamis dengan tingkat akurasi prediksi mencapai 91%, menunjukkan potensi implementasi nyata dari pendekatan GNN dalam mendukung aksesibilitas komunikasi inklusif.
Co-Authors Alfan, Muhammad Cahyadi, Septian Novariany, Rani Nurmilawati, Nurmilawati Ridhani, Muhammad Adlan Riyadi, Rizal S, Lodryck Lodefikus Yuliandi