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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Engineering Science Letter
Muhammad Ilham
Telkom University
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology

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Experimenting with the Hyperparameter of Six Models for Glaucoma Classification Muhammad Ilham; Angga Prihantoro; Iqbal Kurniawan Perdana; Rita Magdalena; Sofia Saidah
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26331

Abstract

Glaucoma, being one of the leading causes of blindness worldwide, often presents without noticeable symptoms, making early detection crucial for effective treatment. Numerous studies have been conducted to develop glaucoma detection systems. In this particular study, a glaucoma detection system using the CNN method was developed. The models employed in this study include AlexNet, Custom Layer, MobileNetV2, EfficientNetV1, InceptionV3, and VGG19. For training, an augmented RIM-ONE DL dataset was utilized. Hyperparameter experiments were conducted to determine the most optimal parameters for each model, specifically testing batch size, learning rate, and optimizer. The hyperparameter optimization process yielded the optimal parameters for each model. However, it is important to note that the MobileNetV2, InceptionV1, and VGG19 models exhibited signs of overfitting in the training graph results. Among the models, the custom layer model achieved the highest accuracy of 93%, while InceptionV3 attained the lowest accuracy at 83.5%. Testing of the models was performed using data from Cicendo Eye Hospital and the RIM-ONE DL testing dataset. Based on the testing results, it was found that InceptionV3 outperformed the other models in predicting images accurately. Therefore, the study concluded that high accuracy in training does not necessarily indicate superior performance in testing, particularly when limited variation exists in the training dataset.
Experimenting with the Hyperparameter of Six Models for Glaucoma Classification Muhammad Ilham; Angga Prihantoro; Iqbal Kurniawan Perdana; Rita Magdalena; Sofia Saidah
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26331

Abstract

Glaucoma, being one of the leading causes of blindness worldwide, often presents without noticeable symptoms, making early detection crucial for effective treatment. Numerous studies have been conducted to develop glaucoma detection systems. In this particular study, a glaucoma detection system using the CNN method was developed. The models employed in this study include AlexNet, Custom Layer, MobileNetV2, EfficientNetV1, InceptionV3, and VGG19. For training, an augmented RIM-ONE DL dataset was utilized. Hyperparameter experiments were conducted to determine the most optimal parameters for each model, specifically testing batch size, learning rate, and optimizer. The hyperparameter optimization process yielded the optimal parameters for each model. However, it is important to note that the MobileNetV2, InceptionV1, and VGG19 models exhibited signs of overfitting in the training graph results. Among the models, the custom layer model achieved the highest accuracy of 93%, while InceptionV3 attained the lowest accuracy at 83.5%. Testing of the models was performed using data from Cicendo Eye Hospital and the RIM-ONE DL testing dataset. Based on the testing results, it was found that InceptionV3 outperformed the other models in predicting images accurately. Therefore, the study concluded that high accuracy in training does not necessarily indicate superior performance in testing, particularly when limited variation exists in the training dataset.
Performance Evaluation of 5G C-V2X for V2V in Semi-Enclosed Environments Ridha Muldina Negara; Muhammad Alfian Alfarizi; Mochammad Hakim Al Irsyad; Muhammad Ilham; Nicholai Dandy Nainggolan; Rohmat Tulloh
Engineering Science Letter Vol. 5 No. 03 (2026): Engineering Science Letter - Articles in Press
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/IISTR.esl.002268

Abstract

The rapid advancement of 5G technology has accelerated the development of vehicular networks, where reliable Vehicle-to-Vehicle (V2V) communication is essential for intelligent transportation systems. This study evaluates the performance of Cellular Vehicle-to-Everything (C-V2X)-based V2V communication using a trace-driven simulation approach in a semi-enclosed environment. A realistic mobility model is generated using Simulation of Urban Mobility (SUMO) based on OpenStreetMap data and integrated with the WiLabV2Xsim simulator implementing the 3GPP C-V2X Mode 4 standard. The evaluation is conducted using Packet Reception Ratio (PRR), Channel Busy Ratio (CBR), and packet delay under varying vehicular densities. The results show that increasing vehicle density leads to a significant degradation in PRR while increasing CBR and delay, indicating higher channel congestion and communication interference. Increasing bandwidth improves communication reliability, whereas higher transmission power provides limited benefits in dense scenarios. These findings highlight the importance of efficient resource allocation and congestion-aware mechanisms to ensure reliable V2V communication in 5G-enabled vehicular networks. However, this study is limited to simulation-based evaluation and does not consider real-world deployment constraints. The study contributes to understanding the scalability and performance behavior of C-V2X systems in realistic vehicular environments.
Co-Authors Angga Prihantoro Iqbal Kurniawan Perdana Mochammad Hakim Al Irsyad Muhammad Alfian Alfarizi Nicholai Dandy Nainggolan Ridha Muldina Negara Rita Magdalena ROHMAT TULLOH SOFIA SAIDAH