Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal Bulletin of Electrical Engineering and Informatics Applied Science and Technology Research Journal
Nonsi Tentua, Meilany
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
System Expert Diagnosis Iphone Cellphone Damage Web-Based Hadi Nugroho, Donny; Fairuzabadi, M.; Nonsi Tentua, Meilany; Nor Azhari Azman, Mohamed
APPLIED SCIENCE AND TECHNOLOGY REASERCH JOURNAL Vol. 1 No. 2 (2022): Applied Science and Technology Research Journal
Publisher : Lembaga Penelitian dan Pengabdian Mayarakat (LPPM) Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (801.607 KB) | DOI: 10.31316/astro.v1i2.4641

Abstract

Mobile phones (HP) or smartphones are the most popular communication tools used by the public. Based on interviews with several mobile phone users and technicians, the iPhone is currently one of the best-selling and most prestigious brands. But in reality, the iPhone user community, in general, does not understand the damage that often occurs to HP. This leads users to bring the damaged HP to the service point without knowing in advance what kind of damage occurred to the HP. The study aims to build an app to diagnose damage to web-based iPhone phones. In collecting the data needed for the study, the authors used methods of literature study, interviews, and observation. This web-based iPhone mobile damage diagnostic system application is made using the PHP programming language. The game development stage includes analysis, system design, implementation, and testing. The expert system application to diagnose damage to the web-based iPhone phone that is made can be used to find out the damage to the iPhone phone, before being taken to the service so that users know what damage is repaired. The results of system testing showed that this application is feasible and can be used as a web-based iPhone mobile damage diagnostic system application.
MAS-TENER: a modified attention score transformer encoder for Indonesian skill entity recognition Nonsi Tentua, Meilany; Suprapto, Suprapto; Afiahayati, Afiahayati
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9731

Abstract

Skill entity recognition is a crucial task for aligning educational curricula with the evolving needs of the industry, particularly in multilingual job markets. This study introduces modified attention score transformer encoder (MAS-TENER), a novel transformer-based model designed to enhance the recognition of skill entities from Indonesian job descriptions. The proposed model modifies the attention mechanism by integrating relative positional embeddings and removing the scaling factor in self-attention. These improvements enhance the context of tokens, allowing for the accurate establishment of hard skills, soft skills, and technology skills. The MAS-TENER model was pre-trained and fine-tuned using a combinF.ation of job description datasets and additional corpora, achieving an F1-score of 90.46% at the entity level. The experimental results demonstrate the model's ability to handle unstructured, mixed-language job descriptions, with significant potential for curriculum reform and the development of new workforce capabilities. The study demonstrates the efficacy of the MAS-TENER model as an effective response for any natural language processing (NLP) task in low-resource languages. Moreover, the scope of long-term job market analytics in action research has been a key skill set in the education policy arena, demonstrating collaborative workforce capabilities.
Co-Authors Afiahayati Afiahayati, Afiahayati Fairuzabadi, M. Hadi Nugroho, Donny Nor Azhari Azman, Mohamed Suprapto Suprapto