Article Per Year (5 Year)
p-Index From 2021 - 2026
0.553
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This Author published in this journals
All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Computer Science and Information Technologies
Hollanda Arief Kusuma
University of Maritime Raja Ali Haji
Computer Science & IT Engineering

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Development and performance evaluation of a CNN model for seagrass species classification in Bintan, Indonesia Nurul Hayaty; Hollanda Arief Kusuma
Computer Science and Information Technologies Vol 7, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v7i1.p20-29

Abstract

This study presents the development and evaluation of a convolutional neural network (CNN) model for automated seagrass species classification in Bintan, Indonesia. The objective of this research is to examine how different train-validation data split ratios affect model accuracy and generalization performance. The CNN was trained under four configurations (60:40, 70:30, 80:20, and 90:10) to analyze the influence of training data volume on learning convergence and predictive capability. The results indicate that all configurations achieved high validation accuracy, with the best performance reaching 98.53% when using the 90:10 split. Evaluation on unseen data demonstrated that the 60:40 configuration provided the most consistent and reliable generalization. Performance variations were also affected by the morphological similarity between the classified species, which increases the challenge in correctly distinguishing certain classes. Overall, the findings confirm the effectiveness of CNN-based classification for supporting marine biodiversity monitoring and underline the importance of dataset composition in achieving optimal performance. Future improvements will focus on expanding data variability to enhance robustness in real-world scenarios.
Comparing global system for mobile and G-NetTrack signal strength in drive test study Hollanda Arief Kusuma; Dwi Eny Djoko Setyono
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 2: April 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i2.26130

Abstract

The quality of wireless signals is determined by the received signal strength indicator (RSSI), which can be measured using various tools, including Android apps and global system for mobile (GSM) modules. To enhance the measurement of signal quality and communication reliability, this study aimed to develop a complementary instrument consisting of a microcontroller, global positioning system (GPS) module, GSM module, and microSD card. The firmware was developed using C++ code and compiled using the Arduino integrated development environment (IDE). In addition, this study utilized the Kolmogorov-Smirnov test (K-S test) normality and the Mann-Whitney U test to investigate any differences in RSSI quality between the GSM module and global positioning system network track (G-NetTrack). The results showed that the GSM module consistently produced higher RSSI values than G-NetTrack in most locations along the route. Furthermore, the K-S test normality suggested that the RSSI values obtained from both tools were not normally distributed, and the Mann-Whitney U test revealed a significant difference between the two samples, with G-NetTrack having lower values than the GSM module (U=14730, p<0.05). This study demonstrated that the GSM module provides stronger signal strength measurements than G-NetTrack during the drive test, and highlighted the importance of using appropriate statistical tests to analyze RSSI data.
Co-Authors Dwi Eny Djoko Setyono Nurul Hayaty