Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal JATI (Jurnal Mahasiswa Teknik Informatika) Journal of Artificial Intelligence and Engineering Applications (JAIEA)
Arya Gunawan
Unknown Affiliation
Automotive Engineering Computer Science & IT Control & Systems Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
OPTIMALISASI KONEKTIVITAS JARINGAN KAMPUS MELALUI SIMULASI ARP DAN DHCP MENGGUNAKAN CISCO PACKET TRACER Pribadi Fitrian, Harry; Anisa, Fitri; Agustina, Mayasari; Masitoh, Neneng; Gunawan, Arya
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 2 (2025): JATI Vol. 9 No. 2
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i2.12846

Abstract

Tujuan dari penelitian ini adalah untuk menganalisis optimalisasi konektivitas jaringan kampus melalui simulasi protokol ARP dan DHCP menggunakan Cisco Packet Tracer. Mengingat pentingnya jaringan yang efisien untuk mendukung kegiatan akademis dan administratif, penelitian ini mengidentifikasi beberapa masalah umum, termasuk kemacetan jaringan dan kesulitan dalam mengelola alamat IP. Metodologi yang digunakan meliputi analisis persyaratan jaringan, desain topologi, simulasi dan evaluasi kinerja jaringan berdasarkan parameter throughput, latensi, dan keamanan. Penelitian telah menunjukkan bahwa penerapan protokol ARP untuk resolusi alamat dan DHCP untuk manajemen alamat IP dapat secara signifikan meningkatkan efisiensi koneksi, mengurangi kemacetan, dan membuat jaringan berskala besar lebih mudah dikelola. Studi ini menghasilkan hasil yang sangat baik dalam menilai kinerja jaringan, yang menunjukkan bahwa Cisco Packet Tracer merupakan alat yang efektif untuk merancang dan menguji model jaringan kampus sebelum implementasi sebenarnya, sehingga meminimalkan risiko kesalahan dan biaya. Disimpulkan bahwa ini merupakan alat.
Mitigating Imbalanced Citrus Disease Image Datasets with Oversampling Gunawan, Arya; Suarna, Nana; Bahtiar, Agus; Marthanu, Indra Wiguna; Kaslani
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1862

Abstract

Dataset imbalance is a critical challenge in plant disease image classification because it causes bias towards the majority class. This study evaluates the effectiveness of augmentation-based oversampling techniques on the classification performance of citrus leaf images using the MobileNetV2 architecture. The four leaf disease classes classified include Greening, Fresh, Canker, and Blackspot. The dataset was obtained from a public repository and processed through preprocessing (resize, normalization) and augmentation (rotation, flipping, zoom) stages. The model was trained and tested in two scenarios: baseline (unbalanced data) and mitigation (data balanced through augmentation). The experimental results show that the mitigation approach was able to increase accuracy from 91.92% to 93.94%. The F1-score, precision, and recall values also increased significantly, especially in the minority class. Evaluation using a confusion matrix reinforced the finding that augmentation-based oversampling is effective in reducing classification errors. This study shows that the integration of augmentation techniques and MobileNetV2-based transfer learning can significantly improve classification performance and contribute to the development of early detection systems for plant diseases in precision agriculture.
Co-Authors Agustina, Mayasari Anisa, Fitri Kaslani Marthanu, Indra Wiguna Masitoh, Neneng Pribadi Fitrian, Harry Suarna, Nana