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Journal : Indonesian Journal on Computing (Indo-JC)

Implementasi Algoritma Penjadwalan untuk pengelolaan Big Data dengan Hadoop Sidik Prabowo; Maman Abdurohman
Indonesia Journal on Computing (Indo-JC) Vol. 2 No. 2 (2017): September, 2017
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/INDOJC.2017.2.2.189

Abstract

Paper ini mengusulkan skema scheduler hadoop pada penyelesaikan tipe job yang sesuai untuk peningkatan kinerja Hadoop. Kesesuaian jenis scheduler dan tipe job yang dikerjakan dapat meningkatkan throughput dan menurunkan waktu rata-rata penyelesaian job. Masalah utama pada eksekusi job adalah ketidaksesuaian antara scheduler dengan tipe job yang dikerjakan. Pada paper ini telah dilakukan pengujian terhadap beberapa algoritma scheduler Hadoop yaitu FIFO, Fair, SARS dan COSHH scheduler dengan beberapa jenis job yang ditangani dalam lingkungan hadoop. Jenis-jenis job yang diujikan adalah word count, random text writer dan grep. Pengujian dilakukan dua skenario, yaitu job homogen (satu jenis) dan heterogen (beberapa jenis job) dikerjakan bersama. Hasil pengujian menunjukan bahwa algoritma SARS cocok digunakan pada penyelesaian job yang sifatnya homogen. Sementara itu algoritma COSHH cocok digunakan pada penyelesaian job gabungan yang heterogen. 
QUIDS: A Novel Edge-Based Botnet Detection with Quantization for IoT Device Pairing Aji Gautama Putrada; Nur Alamsyah; Mohamad Nurkamal Fauzan; Sidik Prabowo; Ikke Dian Oktaviani
Indonesia Journal on Computing (Indo-JC) Vol. 8 No. 3 (2023): December 2023
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2023.8.3.878

Abstract

Advanced machine learning has managed to detect IoT botnets. However, conflicts arise due to complex models and limited device resources. Our research aim is on a quantized intrusion detection system (QUIDS), an edge-based botnet detection for IoT device pairing. Using knearest neighbor (KNN) within QUIDS, we incorporate quantization, random sampling (RS), and feature selection (FS). Initially, we simulated a botnet attack, devised countermeasures via a sequence diagram, and then utilized a Kaggle botnet attack dataset. Our novel approach includes RS, FS, and 16-bit quantization, optimizing each step empirically. The test results show that employing a mean decrease in impurity (MDI) by FS reduces features from 115 to 30. Despite a slight accuracy drop in KNN due to RS, FS, and quantization sustain performance. Testing our model revealed 1200 RS samples as optimal, maintaining performance while reducing features. Quantization to 16-bit doesn’t alter feature value distribution. Implementing QUIDS increased the compression ratio (CR) to 175×, surpassing RS+FS threefold and RS by 13 times. This novel method emerges as the most efficient in CR.