Article Per Year (5 Year)
p-Index From 2021 - 2026
3.453
P-Index
This Author published in this journals
All Journal JATI (Jurnal Mahasiswa Teknik Informatika) Natural Sciences Engineering and Technology Journal International Journal of Business and Information Technology Bulletin of Information Technology (BIT) Internet of Things and Artificial Intelligence Journal Jurnal Sistem Cerdas dan Rekayasa
Didik Tristianto
Department Of Computer System, Faculty Of Computer Science, Universitas Narotama, Surabaya, Indonesia
Aerospace Engineering Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Immunology & microbiology Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology Neuroscience Physics Social Sciences Transportation

Published : 24 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 24 Documents
Search

 1   2   3 
Sistem Pembelajaran Isyarat Bahasa Indonesia (SIBI) Menggunakan Metode Convolutional Neural Network (CNN) Tristianto, Didik; Limantara, Michael Arthur
Jurnal Sistem Cerdas dan Rekayasa (JSCR) Vol 6 No 2 (2024): Jurnal Sistem Cerdas dan Rekayasa (JSCR) 2024
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Widya Kartika (LPPM UWIKA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61293/jscr.v6i2.735

Abstract

Bahasa isyarat adalah bentuk komunikasi yang mengandalkan gerakan tubuh dan ekspresi wajah untuk berinteraksi, terutama bagi penyandang tunarungu dan tunawicara. Di Indonesia, terdapat Sistem Isyarat Bahasa Indonesia (SIBI) yang digunakan sebagai bahasa isyarat yang resmi. Hingga saat ini masih terdapat kesenjangan komunikasi antara penyandang tunarungudan tunawicara dengan orang normal. Pendekatan Computer Vision diharapkan dapat mengatasi masalah tersebut dengan pengembangan sistem pengenalan bahasa isyarat. Penelitian ini berfokus pada penerapan Deep Learning dengan metode Convolutional Neural Network (CNN) atau Jaringan Saraf Konvolusional untuk mendeteksi gerakan tangan dalam bahasa isyarat abjad SIBI dan menerjemahkannya. Harapannya, hasil penelitian ini dapat menjadi landasan untuk pengembangan aplikasi pengenalan bahasa isyarat yang dioptimalkan khusus untuk SIBI, serta dapat mendukung penyandang disabilitas dan masyarakat umum untuk berkomunikasi secara lebih efektif.
SIBI Alphabet Detection System Based on Convolutional Neural Network (CNN) Method as Learning Media Arthur Limantara, Michael; Tristianto, Didik
Internet of Things and Artificial Intelligence Journal Vol. 4 No. 1 (2024): Volume 4 Issue 1, 2024 [February]
Publisher : Association for Scientific Computing, Electronics, and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/iota.v4i1.716

Abstract

Sign language is a form of communication that relies on body movements and facial expressions to interact, especially for deaf and hard-of-hearing people. The Indonesian Sign Language System (SIBI) is the official sign language in Indonesia. Until now, there is still a communication gap between deaf and hard-of-hearing people and normal people. The Computer Vision approach is expected to overcome the problem by developing a sign language recognition system. This research focuses on applying Deep Learning with the Convolutional Neural Network (CNN) method to detect hand gestures in SIBI alphabetic sign language and translate them. Hopefully, the results of this research can be the foundation for developing sign language recognition applications optimized specifically for SIBI. They can help people with disabilities and the general public communicate more effectively.
SECURITY SYSTEM ON WIFI NETWORKS BASED ON RSS (RECEIVED SIGNAL STRENGTH) CORRELATION Tristianto, Didik
International Journal of Business and Information Technology Vol. 6 No. 1 (2025): June
Publisher : LPPM STMIK Dharmapala Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47927/ijobit.v6i1.1143

Abstract

Communication systems are always evolving, including data communication that occurs wirelessly, such as through WIFI networks. However, wireless communication inherently has a low level of security, making it vulnerable to attacks from unauthorized parties. In this research, a security system for WIFI networks was created based on the correlation of RSS (Received Signal Strength) values obtained through a channel probing process between two PCs by implementing several scenarios. The RSS data was quantized using Aono's method to obtain binary bits of 1 and 0. From the test results, it can be seen that the Key Generation Rate (KGR) produced was 16bit/s for a 50ms time interval, 8bit/s for 110ms, and 7bit/s for 120ms. The Key Disagreement Rate (KDR) was 30% for 50ms, 46.59% for 110ms, and 46.71% for 120ms, where the two PCs had a high bit difference. However, with Linear Block Code, the KDR value could be reduced to 0%. From the randomness results, it has met the randomness requirement for 50ms, which is 0.3712. From the identical bits between the two PCs, a symmetric key can be generated, meaning the key on PC1 and PC2 is the same.
Microcontroller-based automatic home gate control system using a remote Tristianto, Didik; Jarek, Gamaliel K.
International Journal of Business and Information Technology Vol. 6 No. 2 (2025): December
Publisher : LPPM STMIK Dharmapala Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47927/ijobit.v6i2.1372

Abstract

Home security remains a fundamental necessity in modern living. This study presents the design and implementation of an automatic home fence security system based on a microcontroller-controlled remote access mechanism. The system utilizes an ATmega8535 microcontroller, programmed using Code Vision AVR software, to control the opening and closing of the home fence through a password-protected remote keypad. The entered password is verified with the data stored in the system’s memory. Upon successful authentication, the microcontroller activates the L293D motor driver to operate the motor that opens or closes the fence, while an LCD display provides real-time status information. The communication between the transmitter and receiver modules employs infrared (IR) serial communication using the UART protocol to ensure reliable data transfer. Experimental results demonstrate that the proposed system effectively enhances residential security and provides convenient automated control of gate operations.
Co-Authors Aji, Chandyka Wisnu Prasetyo AlFateh, Mochamad Rizky Anunwembun, Blasius Joseph Arthur Limantara, Michael Asenan, Asenan Blasius Joseph Anunwembun Christiyanto, Defis Defis Christiyanto Fadillah, Muhammad Rivky Gomes, Reinato Jarek, Gamaliel K. Kusuma, Aka Perdana Latipah Latipah Limantara, Michael Arthur Machrudin, Moch Ashar Mizanul Achlaq, Mochamad Mufid Irsyadi, Abdillah Muhammad Rivky Fadillah Nashrullah Mujibur Rahman Rachmadani, Satria Reinato Gomes Rizandy, Muhammad Julian Ilham Rusdiono, Pupy Eko Slamet Winardi Slamet Winardi