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All Journal International Journal of Advances in Intelligent Informatics Jurnal INSYPRO (Information System and Processing) ILKOM Jurnal Ilmiah Science, Technology, and Communication Journal
Setiaji, Haris
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology

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PENDEKATAN BARU PENTERJEMAH BAHASA ISYARAT INDONESIA DINAMIS MENGGUNAKAN METODE GATE RECURRENT UNIT Setiaji, Haris; Indra Syahyadi, Asep; Afif, Nur; Ridwang
Jurnal INSYPRO (Information System and Processing) Vol 9 No 1 (2024)
Publisher : Prodi Sistem Informasi UIN Alauddin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/insypro.v9i1.50395

Abstract

Indonesian Sign Language (BISINDO) and the Indonesian Sign Language System (SIBI) are forms of communication used by the deaf community in Indonesia. However, the use of BISINDO is considered less effective in the sign language translation system due to variations in body movements in each community. On the other hand, SIBI is considered more effective because it is an adaptation of American Sign Language (ASL) and has been officially recognized by the Indonesian government. This research aims to develop a deep learning-based sign language translation system to support communication with the deaf community using Indonesian Sign Language (SIBI). The research methodology involves acquiring a sign language data set, preprocessing the data using the Mediapipe library, training the model using Gated Recurrent Neural Networks (GRU), and evaluating model performance using the Confusion Matrix method. The test results show that the developed model succeeded in achieving an accuracy level of 94% in classifying SIBI sign language signs. This shows the potential of the system in assisting communication and increasing accessibility for deaf people who use Indonesian Sign Language. This research makes a significant contribution to technological developments aimed at improving the quality of life and social inclusion for the deaf community
UI/UX design thinking adoption for integrated AI point-of-sale system (Case study: Plastic Poultry Wholesale Store) Okfalisa, Okfalisa; Fahruddin, Fahruddin; Setiaji, Haris; Pratama, M Farhan Aulia; Finaldhi, Harry; Delifah, Nur
Science, Technology, and Communication Journal Vol. 5 No. 3 (2025): SINTECHCOM Journal (June 2025)
Publisher : Lembaga Studi Pendidikan dan Rekayasa Alam Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59190/stc.v5i3.311

Abstract

The development of artificial intelligence (AI) technology drives the need for a point of sales (POS) system that is not only efficient, but can also provide adaptive information according to the user's sudden wishes. This research implements the design thinking method in designing a user interface (UI/UX) for a smart POS system integrated with conversational AI features. A case study was conducted at a Plastic Poultry Wholesale Store to gain in-depth insights related to field needs and operational challenges faced. The design thinking method was chosen because of its user-centered approach, through the stages of empathize, define, ideate, prototype, and testing, it is hoped that the final results obtained can be aligned with the concrete needs of users, so that the output of the system that has been designed will not be abandoned, but will always be used. In this design, the implementation of conversational AI is used to enhance the user experience through a virtual assistant feature that is able to answer dynamic questions according to the wishes of the user, so that users can freely explore any information in detail related to their overall business performance. The implementation results show that this system not only increases operational efficiency, but also improves user experience through more intuitive interactions when they want to see their business performance. This research contributes to integrating AI technology with a user-centered design approach for smart, responsive, and adaptive POS system solutions.
Detecting signal transtition in dynamic sign language using R-GB LSTM method Ridwang, Ridwang; Adriani, Adriani; rahmania, Rahmania; Sahrim, Mus’ab; Syahyadi, Asep Indra; Setiaji, Haris
International Journal of Advances in Intelligent Informatics Vol 10, No 2 (2024): May 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v10i2.1445

Abstract

Sign Language Recognition (SLR) helps deaf people communicate with normal people. However, SLR still has difficulty detecting dynamic movements of connected sign language, which reduces the accuracy of detection. This results from a sentence's usage of transitional gestures between words. Several researchers have tried to solve the problem of transition gestures in dynamic sign language, but none have been able to produce an accurate solution. The R-GB LSTM method detects transition gestures within a sentence based on labelled words and transition gestures stored in a model. If a gesture to be processed during training matches a transition gesture stored in the pre-training process and its probability value is greater than 0.5, it is categorized as a transition gesture. Subsequently, the detected gestures are eliminated according to the gesture's time value (t). To evaluate the effectiveness of the proposed method, we conducted an experiment using 20 words in Indonesian Sign Language (SIBI). Twenty representative words were selected for modelling using our R-GB LSTM technique. The results are promising, with an average accuracy of 80% for gesture sentences and an even more impressive accuracy rate of 88.57% for gesture words. We used a confusion matrix to calculate accuracy, specificity, and sensitivity. This study marks a significant leap forward in developing sustainable sign language recognition systems with improved accuracy and practicality. This advancement holds great promise for enhancing communication and accessibility for deaf and hard-of-hearing communities.
Smart Verification of High School Student Reports Using Optical Character Recognition and BERT Models Syahyadi, Asep Indra; Afif, Nur; Yusuf, Ahmad; Setiaji, Haris; Ridwang, Ridwang; Irfan, Mohammad
ILKOM Jurnal Ilmiah Vol 17, No 3 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i3.2764.252-261

Abstract

This study proposes an intelligent framework for verifying high school report cards with diverse layouts by integrating Optical Character Recognition (OCR) and a fine-tuned BERT model. While previous works primarily address document formats with uniform structures, this research specifically tackles the heterogeneity of report cards that differ in subject arrangement, naming conventions, and grade presentation across schools. The system was trained and evaluated using 1,000 Indonesian high school report card pages encompassing 20 subjects, both core (e.g., Mathematics, Indonesian History, Religious Education) and non-core (e.g., Arts and Culture, Physical Education). OCR was employed to extract textual content from scanned or image-based report cards, while BERT handled contextual mapping between subjects and corresponding grades. The dataset was divided into 80% for training and 20% for validation, and the model was fine-tuned on the IndoBERT-base architecture. Experimental results showed that the proposed OCR–BERT pipeline achieved an average accuracy of 97.7%, with per-subject accuracies ranging from 96% to 99%. The model exhibited high robustness in handling inconsistent layouts and minimizing deviations between actual and detected grades. Comparative analysis indicated that this hybrid approach outperforms traditional OCR-only or CNN-based methods, which are typically constrained by fixed template assumptions and lack contextual understanding. The proposed system demonstrates practical relevance for large-scale admission platforms such as SPAN-PTKIN, where manual verification of thousands of report cards is laborious and error-prone. By automating the verification process, the framework reduces human workload, enhances accuracy, and supports fairer, data-driven admission decisions. Future research will explore multimodal integration of textual and visual features, expansion to broader datasets, and application to other academic documents such as transcripts and diplomas. Overall, this work contributes a scalable, accurate, and context-aware solution for educational data verification in heterogeneous document environments.
Co-Authors ADRIANI ADRIANI AFIF, NUR Ahmad Yusuf Delifah, Nur Fahruddin Fahruddin Finaldhi, Harry Indra Syahyadi, Asep Mohammad Irfan Okfalisa Okfalisa Pratama, M Farhan Aulia Rahmania Rahmania Ridwang Ridwang Ridwang, Ridwang Sahrim, Mus’ab SYAHYADI, ASEP INDRA