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Klasifikasi Citra Wajah Untuk Rentang Usia Menggunakan Metode Artificial Neural Network Anggraini, Lusiana; Yamasari, Yuni
Journal of Informatics and Computer Science (JINACS) Vol. 5 No. 02 (2023)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jinacs.v5n02.p185-192

Analysis of the Development of E-Commerce Transactions in the 6 Highest Transaction Countries in Southeast Asia A'yun, Indanazulfa Qurrota; Anggraini, Lusiana; Asmara, Gea Dwi; Khoirunnisa, Rikha Muftia
Journal of Economics Research and Social Sciences Vol 8, No 2: August 2024
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jerss.v8i2.22033

The background of this study is driven by the significant growth of the internet, particularly in the Southeast Asian region, which has led to technological advancements and the emergence of e-commerce. E-commerce has become crucial for meeting consumer needs, highlighting the necessity to further enhance the potential of economic digitalization through e-commerce. This study aims to examine the impact of e-commerce development factors on the growth of e-commerce transaction values in six Southeast Asian countries. It utilizes secondary data sourced from the official e-Conomy SEA website and DataReportal. The research employs panel data regression analysis using the Random Effects Model (REM) approach. The findings indicate that increases in population, number of social media users, and mobile phone users significantly contribute to the growth of e-commerce transaction values, urging platform developers and policymakers to enhance digital infrastructure and marketing strategies to maximize the digital economy potential in Southeast Asia.

Analysis of the Development of E-Commerce Transactions in the 6 Highest Transaction Countries in Southeast Asia A'yun, Indanazulfa Qurrota; Anggraini, Lusiana; Asmara, Gea Dwi; Khoirunnisa, Rikha Muftia
Journal of Economics Research and Social Sciences Vol. 8 No. 2: August 2024
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jerss.v8i2.22033

The background of this study is driven by the significant growth of the internet, particularly in the Southeast Asian region, which has led to technological advancements and the emergence of e-commerce. E-commerce has become crucial for meeting consumer needs, highlighting the necessity to further enhance the potential of economic digitalization through e-commerce. This study aims to examine the impact of e-commerce development factors on the growth of e-commerce transaction values in six Southeast Asian countries. It utilizes secondary data sourced from the official e-Conomy SEA website and DataReportal. The research employs panel data regression analysis using the Random Effects Model (REM) approach. The findings indicate that increases in population, number of social media users, and mobile phone users significantly contribute to the growth of e-commerce transaction values, urging platform developers and policymakers to enhance digital infrastructure and marketing strategies to maximize the digital economy potential in Southeast Asia.

Pre-trained convolutional neural network-based algorithms: application for recognizing the age category Yamasari, Yuni; Anggraini, Lusiana; Qoiriah, Anita; Eka Putra, Ricky; Agustin Tjahyaningtijas, Hapsari Peni; Ahmad, Tohari
IAES International Journal of Artificial Intelligence (IJ-AI) 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/ijai.v14.i5.pp3576-3587

Cybercrime is a major issue in the current digital era, with one of its branches-cyber pornography-notably affecting Indonesia. Various efforts have been made to suppress or prevent this problem. One alternative solution involves using technological advances to recognize age ranges based on facial recognition. This age range recognition can be implemented to prevent users from accessing content that is not appropriate for their age. An optimal age-range recognition system is essential for this purpose. However, limited research has focused on this domain. Therefore, our research aimed to develop the best possible system. The proposed method applies a trained convolutional neural network (CNN) as a feature extractor to the artificial neural network (ANN) and k-nearest neighbor (K-NN) methods for age recognition based on facial images. By incorporating computational learning techniques, the system's performance is significantly enhanced, leveraging advanced algorithms to improve accuracy. The test results show that the performance of the pre-trained CNN-based ANN model is superior. This is indicated by the model's accuracy and F1-score, which were 11% and 0.11 higher, than the pre-trained CNN-based K-NN model. The error rate of the pre-trained CNN-based ANN model was also reduced by 0.11.

Development of Electronic Worksheet Based on Scientific Approach to Train Critical Thinking Skills on Membrane Transport Topic Anggraini, Lusiana; Lisdiana, Lisa
Berkala Ilmiah Pendidikan Biologi (BioEdu) Vol. 13 No. 1 (2024)
Publisher : Program Studi Pendidikan Biologi, FMIPA, Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/bioedu.v13n1.p1-11

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