Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal Journal of Applied Business and Technology Luxury: Landscape of Business Administration
Johan Johan
Institut Bisnis dan Teknologi Pelita Indonesia, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Social Sciences

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
The Role of Artificial Intelegensia Technology in Improving the Quality of Education Wilda Susanti; Rahma Widi; Torkis Nasution; Johan Johan; Unung Verawardina
Journal of Applied Business and Technology Vol. 6 No. 1 (2025): Journal of Applied Business and Technology
Publisher : Institut Bisnis dan Teknologi Pelita Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35145/jabt.v6i1.178

Abstract

The future of education is aligned with advances in artificial intelligence (AI) technology that significantly change how we learn, teach, and manage educational systems. This article reviews the critical role of AI technology in improving the quality of education in the digital era. AI technology allows for better personalization of education according to each student's needs and interests and changes how teachers teach and students learn. By using machine learning algorithms and data analysis learning algorithms and data analysis, education systems can identify patterns in student learning behavior, predict individual needs, and provide timely interventions. The article also highlights the challenges and opportunities in implementing AI technology in schools, including data privacy concerns, digital divides, and new skills required by educators. By understanding AI technology's role in improving education quality, we can design a more inclusive, responsive, and effective education system for a better future.
Transfer Learning Model Evaluation on CNN Algorithm: Indonesian Sign Language System (SIBI) Deny Jollyta; Prihandoko Prihandoko; Johan Johan; William Ramdhan; Erick Santoso
Journal of Applied Business and Technology Vol. 6 No. 2 (2025): Journal of Applied Business and Technology
Publisher : Institut Bisnis dan Teknologi Pelita Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35145/jabt.v6i2.213

Abstract

In Indonesia as much as elsewhere, the deaf can communicate using sign language. The Indonesian Sign Language System (SIBI) is one of the sign language systems used in Indonesia. A model produced by the Convolutional Neural Network (CNN) method can be used in computer science for the recognition of sign language. By using the Transfer Learning paradigm, CNN's performance may be enhanced. However, not many researches have been conducted to assess the effectiveness of transfer learning on sign language models, particularly those that use the TensorFlow library. In fact, the evaluation results can influence the selection of the transfer learning model together with CNN. This study aims to evaluate the efficacy of using the CNN model for SIBI sign language through Transfer Learning. The data used are images of 24 SIBI alphabets and are processed through the TensorFlow library. The images will be recognized through the transfer learning performance of 6 models, namely VGG16, VGG19, Resnet50, Desenet121, Inception-V3 and MobileNet-V2. The results of the study found that through the TensorFlow library, Mobilenetv2 had the highest accuracy of 78% after 20 epochs.
Creating Business Value through IoT-Based Waste Management Web Dashboard Wilda Susanti; Nicholas Renaldo; Achmad Tavip Junaedi; Mukhsin Mukhsin; Johan Johan; Yulvia Nora Marlim; Gusrio Tendra; Wahyu Joni Kurniawan; Fauzan Azim
Luxury: Landscape of Business Administration Vol. 4 No. 1 (2026): Luxury: Landscape of Business Administration
Publisher : First Ciera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61230/luxury.v4i1.139

Abstract

The rapid growth of urbanization and industrial activities has increased the complexity of waste management operations within business organizations, leading to rising costs and managerial challenges. While the Internet of Things (IoT) has enabled real-time data collection in waste management, business value creation depends on how such data are integrated into managerial information systems. This study aims to develop and evaluate an IoT-based waste management web dashboard that supports operational monitoring and managerial decision-making to create business value. Using a Design Science Research (DSR) methodology, this study designs an integrated system that collects real-time waste volume data through IoT-enabled sensors and presents the information via a centralized web dashboard. The system is evaluated using a mixed approach combining operational performance indicators and user perceptions based on Information Systems Success Theory. The results indicate improvements in operational efficiency, resource allocation, and decision-making quality following system implementation. The findings demonstrate that the proposed web dashboard functions as a strategic managerial information system rather than a purely technical solution. By enhancing information quality and managerial control, the system contributes to business value creation through improved operational performance and supports digital transformation and sustainability-oriented business practices. This study contributes to the business and management literature by shifting the focus of IoT-based waste management research from technical performance to managerial relevance and value creation.
Co-Authors Achmad Tavip Junaedi Deny Jollyta Erick Santoso Fauzan Azim Gusrio Tendra Mukhsin Mukhsin Nicholas Renaldo Prihandoko Prihandoko Rahma Widi Torkis Nasution Unung Verawardina Wahyu Joni Kurniawan Wilda Susanti William Ramdhan Yulvia Nora Marlim