Article Per Year (5 Year)
p-Index From 2021 - 2026
1.642
P-Index
This Author published in this journals
All Journal Agrikultura Jurnal Konseling dan Pendidikan Jurnal Simetris Teika JRSI (Jurnal Rekayasa Sistem dan Industri) JURNAL NUSANTARA APLIKASI MANAJEMEN BISNIS INFORMASI (Jurnal Informatika dan Sistem Informasi) INOTEK
Chairuddin
STMIK IM
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences Other

Published : 12 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 12 Documents
Search

 1   2 
Evaluasi Kelayakan dan Validitas Perbandingan Model Deep Learning Lintas Domain: Studi Kasus YOLO dan RNN Chairuddin; Yudhi Widya Arthana Rustam; Muhammad Haniif Muzaki
INFORMASI (Jurnal Informatika dan Sistem Informasi) Vol 18 No 1 (2026): INFORMASI (Jurnal Informatika dan Sistem Informasi)
Publisher : LPPM STMIK Indonesia Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37424/informasi.v18i1.573

Abstract

The rapid development of deep learning has encouraged the use of various neural network architectures for diverse computational tasks. However, there is a growing tendency to compare the performance of models with different characteristics and objectives without a clear methodological framework, which can lead to scientific misconceptions. This study aims to analyze the validity of a direct comparison between Recurrent Neural Networks (RNN) and You Only Look Once (YOLO). A mixed-method approach was employed, combining a conceptual analysis of fundamental differences including model objectives, data types, output spaces, and evaluation metrics with limited empirical proof within each architecture's respective task domain. The results indicate that RNN and YOLO operate in entirely different representation spaces; RNN is designed to model temporal dependencies in sequential data, whereas YOLO focuses on spatial data processing for object detection. Therefore, it is concluded that a direct comparison between these two architectures is methodologically invalid, as image data lacks meaningful temporal dimensions for RNN processing, and sequential data lacks the spatial annotations required as ground truth for YOLO. Deep learning model evaluation must always be aligned with its original task domain to avoid biased and misleading conclusions.
Improving E-Sport Player Loyalty: An Overview of the Game Industry in JABOTABEK, Indonesia William Widjaja; Andika Samudra; Chairuddin; Adryan Rachman; Meilisa Alvita
Jurnal Nusantara Aplikasi Manajemen Bisnis Vol 9 No 2 (2024): Jurnal Nusantara Aplikasi Manajemen Bisnis
Publisher : UNIVERSITAS NUSANTARA PGRI KEDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/nusamba.v9i2.21176

Abstract

Research Aim: This study investigates the impact of experiential values ​​and promotional activities on player satisfaction, which affects player loyalty in the e-sports industry in the Jabodetabek area. Approach: This study uses the Structural Equation Modeling-Partial Least Squares (SEM-PLS) approach to test the proposed hypotheses and achieve the research objectives. Questionnaires were distributed to 100 respondents who identified as e-sports players in the Jabodetabek area. Research Findings: This study found that experiential values ​​have a positive and significant effect on player satisfaction in the e-sports industry in the Jabodetabek area. However, promotional activities do not significantly affect e-sports player satisfaction. This means that esports companies' promotional efforts may not significantly impact player satisfaction. Theoretical Contribution: This finding suggests that the theory of satisfaction on loyalty also applies to the Jabodetabek esports industry. Practical Implications: This study has significant practical implications for e-sports companies looking to enhance the value of the player experience by increasing the complexity of their games' features. Research Limitations: This study is limited to the Jabodetabek area and may not reflect the same conditions in other areas.
Co-Authors Adryan Rachman Andika Samudra Ardi Azhar Nampira arie sairoen Decka Azqia Abad Gede Bagia Wijaya Adyana Haryoso Wicaksono HENDRA GUNAWAN B11211055 Jarot Budiasto Meilisa Alvita Moch. Ali Ramdhani Muhammad Haniif Muzaki Muhammad Maksum Nana Suryana Putra, Yogi Kurnia Rio Ariswendi Sevi Nurafni William Widjaja Yudhi Widya Arthana Rustam