Ahmad Amsyar Khan Sarjankan

Motivated and meticulous software engineering fresher looking for a demanding position where utilization of current coding talents on actual projects. Seeking to apply and enhance technical skills in a challenging and dynamic work environment by applying a variety of programming languages and tools. Familiar with knowledge of data structures and algorithms and possesses expertise in web development. Has a keen interest in venturing into a data analyst or software tester position.

• Place of Birth: Hospital Sarikei, Sarikei, Sarawak
• Date of Birth: 05/31/01
• Gender: Male
• Nationality: Malaysian
• Driving License: D
• Marital Status: Single

• Programming League National (Finalist), 2023

• Dean List for 5 Bachelorette Semesters (1st-4th Semester & 6th Semester), 2021-2023

• Foundation In Computer Technology's Best Student, 2019-2020

Programming League National 2023 Finalist

• President of Basketball Club, 01/01/23 - 12/31/23

• Member of UNIKL MIIT Badminton Club, 01/01/21 - 12/31/22

• Member of Software Engineering Club, 01/01/23 - 12/31/23

• Member of UNIKL MIIT Language Club, 01/01/23 - 12/31/23

• Member of UNIKL MIIT e-Sport Club, 01/01/23 - 12/31/21

• Vice President of Basketball Club, 01/01/20 - 12/31/20

e-BISI: Built-In Sign Interpreter

e-BISI is a web-based application for learning sign language, equipped with a visual built-in sign language interpreter. It shall allow users to learn and practice sign language and would ease users to learn and practice whatever they learn directly in one place. It was developed by using PHP and Python language. The proposed system functions similarly to an e-learning website with various available course plans. Learners can navigate to every page available on the web application. As for the online course, once users start their course, they can view the syllabus video and learn from the video. Along with that, the highlight of the website would be the AI Sign Language Interpreter module which functions to detect the sign language being shown by users through camera vision. The AI interpreter will be fed with image data by using the concept of supervised machine learning and is taught to analyze the meaning of gestures being shown by users. Labeled datasets will be used to train algorithms to classify data or predict outcomes accurately.

• Norshaharizan binti Puteh, +6012-303 2216, norshaharizan@unikl.edu.my