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All Journal INTECOMS: Journal of Information Technology and Computer Science Journal Innovations Computer Science
Notonegoro, Danendra Satriyohadi
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Computer Science & IT

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Optimasi Keamanan Jaringan VPN IPSec Tunnel Fortigate Dengan AES Abdillah, Gipari Pradina; Notonegoro, Danendra Satriyohadi; Susanto, Hermawan; Mulyana, Dadang Iskandar
INTECOMS: Journal of Information Technology and Computer Science Vol 7 No 5 (2024): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/intecoms.v7i5.11499

Abstract

VPN merupakan teknologi komunikasi yang menggunakan jaringan pribadi yang dapat dihubungkan ke jaringan publik. Dengan cara ini maka izin dan pengaturan yang sama seolah-olah berada di jaringan lokal yang menggunakan jaringan publik. Penelitian ini menggunakan enkripsi AES pada IKEv2 IPSec Tunnel. AES (Advanced Encryption Standard) merupakan algoritma enkripsi yang biasa digunakan di IPSec (Internet Protocol Security) untuk melindungi transmisi data di terowongan. Oleh karena itu, untuk menambahkan lapisan enkripsi AES, digunakan protokol enkripsi IPSec dan IKEv2. Itu sebabnya penerapan di Fortigate ini memungkinkan pengguna membuat jalur pengoptimalan yang aman antara dua jaringan berbeda. Semua lalu lintas yang melewati jalur optimasi dienkripsi menggunakan AES. Implementasi ini menunjukkan tingkat keamanan yang tinggi. Enkripsi AES, yang merupakan standar keamanan yang kuat, berfungsi secara efektif dalam melindungi komunikasi melalui tunnel IPSec. Kata Kunci: IPSec, VPN, Terowongan, AES, IKEv2, Fortigate
Support Vector Machine and Histogram of Oriented Gradients-Based Classification System for Waste Type Identification Notonegoro, Danendra Satriyohadi; Mulyana, Dadang Iskandar
Journal Innovations Computer Science Vol. 4 No. 2 (2025): November
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v4i2.315

Abstract

This study examines the effectiveness of classical computer vision methods for modern waste classification by combining Histogram of Oriented Gradients (HOG) for feature extraction with Support Vector Machine (SVM) for classification. The TrashNet dataset, consisting of five categories—cardboard, glass, metal, paper, and plastic—was used as the primary benchmark. To address data limitations and improve generalization, augmentation techniques such as random rotations, horizontal flipping, and brightness adjustments were applied. Hyperparameter optimization was further conducted using GridSearchCV with the RBF kernel to determine the most effective configuration. The optimized model achieved an accuracy of 84.36%, representing a substantial improvement from the 60% baseline. These findings confirm that non-deep learning approaches remain relevant and can serve as computationally efficient alternatives to CNNs, which typically require GPUs and extensive training time. Challenges persist in classifying reflective materials such as glass and metal, where HOG descriptors are less effective. Future work should integrate complementary descriptors, including color and texture-based features, to enhance robustness and scalability. Overall, the study demonstrates that an optimized HOG-SVM pipeline offers a practical, resource-efficient solution for automated waste classification, with strong potential to support sustainable waste management in real-world applications.
Support Vector Machine and Histogram of Oriented Gradients-Based Classification System for Waste Type Identification Notonegoro, Danendra Satriyohadi; Mulyana, Dadang Iskandar
Journal Innovations Computer Science Vol. 4 No. 2 (2025): November
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v4i2.315

Abstract

This study examines the effectiveness of classical computer vision methods for modern waste classification by combining Histogram of Oriented Gradients (HOG) for feature extraction with Support Vector Machine (SVM) for classification. The TrashNet dataset, consisting of five categories—cardboard, glass, metal, paper, and plastic—was used as the primary benchmark. To address data limitations and improve generalization, augmentation techniques such as random rotations, horizontal flipping, and brightness adjustments were applied. Hyperparameter optimization was further conducted using GridSearchCV with the RBF kernel to determine the most effective configuration. The optimized model achieved an accuracy of 84.36%, representing a substantial improvement from the 60% baseline. These findings confirm that non-deep learning approaches remain relevant and can serve as computationally efficient alternatives to CNNs, which typically require GPUs and extensive training time. Challenges persist in classifying reflective materials such as glass and metal, where HOG descriptors are less effective. Future work should integrate complementary descriptors, including color and texture-based features, to enhance robustness and scalability. Overall, the study demonstrates that an optimized HOG-SVM pipeline offers a practical, resource-efficient solution for automated waste classification, with strong potential to support sustainable waste management in real-world applications.
Co-Authors Abdillah, Gipari Pradina Dadang Iskandar Mulyana` Hermawan Susanto