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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Azimut International remote sensing application journal
Dedy Fitriawan
Program Studi Geografi, Universitas Tamansiswa Padang
Astronomy Computer Science & IT Earth & Planetary Sciences Education Engineering Environmental Science Social Sciences

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USE OF MEDIUM RESOLUTION IMAGERY FOR PREDICTION MAPPING OF LAND COVER CHANGES IN SOLOK DISTRICT Sari, Yolanda Indah Permata; Fitriawan, Dedy; Antomi, Yudi; Arif, Dian Adhetya
International Remote Sensing Applied Journal Vol. 5 No. 2 (2024): International Remote Sensing Application Journal (December Edition 2024)
Publisher : Remote Sensing Technology Study Program

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/irsaj.v5i2.65

Abstract

This research aims to determine changes in land cover from 2017-2022 in Solok Regency, to find out predictions of changes in land cover until 2032 in Solok Regency, to find out the results of land cover accuracy tests in Solok Regency. This research uses the Supervised (Maximum Likelihood) method to identify changes in land cover in Solok Regency in 2017 and 2022. This research was carried out in several stages, namely the preprocessing stage including radiometric and atmospheric correction, image cropping according to the research area. The processing stage uses the Supervised (Maximum Likelihood) method to determine the classification, then creating a land cover change identification matrix, creating sample points in the field, accuracy testing, and finally making predictions using the Cellular Automata model to predict land cover in 2032. Identification results in areas there was a change in land cover from 2012 to 2017 to 2022, land cover that changed, namely primary forest in 2012 to 2017 experienced a change in 2022 to 206,362.04ha, built-up land also experienced an area change of 3,162.37ha, followed by open land experiencing changes 283.98ha, mixed plantation land experienced a change of 78,176.71ha, wetland farming experienced a change of 12,751.07ha and dry land farming experienced a change of 20,707.08ha in 2022. Then the results of land cover predictions in 2032 are forest land area primary area in Solok Regency changed to 207,382.99ha, while the area of ​​water bodies changed to 6,889.05ha, then built-up land experienced a change of 3,288.13ha, then open land cover changed to 77,912.95ha, then mixed plantation cover changed to 13,248.51 , in wetland agriculture it changed to 13,248.51ha and dryland agriculture to 19,164.11ha.
Hybrid N-gram-based framework for payload distributed denial of service detection and classification Maslan, Andi; Mohd Foozy, Cik Feresa; Bin Mohamad, Kamaruddin Malik; Hamid, Abdul; Fitriawan, Dedy; Hasugian, Joni
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4763-4774

Abstract

There are three primary approaches to DDoS detection: anomaly-based, pattern-based, and heuristic-based. The heuristic-based method integrates both anomaly- and pattern-based techniques. However, existing DDoS detection systems face challenges in performing HTTP payload-level analysis, mainly due to high false positive rates and insufficient granularity in current datasets. To address this, the study introduces a novel heuristic approach based on a hybrid N-Gram model. This hybrid combines two components: CSDPayload+N-Gram and CSPayload+N-Gram. CSDPayload represents the gap (measured via Chi-Square Distance) between a given payload and normal traffic payloads, while CSPayload reflects the similarity (measured via Cosine Similarity) between them. These metrics form a new feature set evaluated using three datasets: CIC2019, MIB2016, and H2N-Payload. The methodology begins with packet extraction and conversion of TCP/IP traffic—specifically HTTP traffic—into hexadecimal payloads. N-Gram analysis (from 1-Gram to 6-Gram) is then applied to these payloads. For each N-Gram, frequency counts are computed, followed by calculations of Chi-Square Distance (CSD), Cosine Similarity (CS), and Pearson’s Chi-Square test to classify payloads as either benign or malicious. Subsequently, feature selection is performed using weight correlation, and the resulting features are fed into three machine learning classifiers: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Neural Network. Experimental results demonstrate high detection accuracy, particularly in the 4-Gram feature category: Neural Network achieves 99.65%, KNN 95.14%, and SVM 99.73% accuracy on average.
Co-Authors Abdul Hamid Azhari Syarief Basri, Zafini Bin Mohamad, Kamaruddin Malik Cik Feresa Mohd Foozy, Cik Feresa Delta, Yusran Rizky Ananda Dian Adhetya Arif Hary Tri Senov Hasugian, Joni Heny Mariati Maslan, Andi Muhammad Ismail Novelisa Suryani Nurfadilah Nurfadilah Osronita Osronita Putri, Sri Kandi Rahma Dinda Ramadhan, Risky Rengga Permana Rengga Permana Putra Roberto Roberto Salsabila, Rania Sari, Yolanda Indah Permata syahadani, meilani Syahar, fitriana Triyatno Triyatno Wahyuni, Sri Agustia Yudi Antomi