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All Journal Indonesian Journal on Computing (Indo-JC) IJoICT (International Journal on Information and Communication Technology) Sistemasi: Jurnal Sistem Informasi JETT (Jurnal Elektro dan Telekomunikasi Terapan) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA KOMPUTIKA - Jurnal Sistem Komputer Jurnal Teknologi Bahan dan Barang Teknik Charity : Jurnal Pengabdian Masyarakat eProceedings of Engineering Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) LOGIC: Jurnal Penelitian Informatika Jurnal Pemanfaatan Teknologi untuk Masyarakat
Sidik Prabowo
Universitas Telkom
Arts Humanities Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Social Sciences

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Journal : indonesian journal on computing indo-jc

 1 
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.
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
Indonesian 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.
Co-Authors Aditya Bhagus Aria Hutomo Aji Gautama Putra Aji Gautama Putrada Ananda Muhammad Nuriawan Andrian Rakhmatsyah Angga Juliat Saputra Arif Mahmudi Aziz Nurul Iman Aziza Hayupratiwi Bagus Aditya Bagus Yoga Permana Putra Bayu Erfianto Chando Anggara Natanael Batubara Dadang Setiawan Dimas Rizki Agusta Dita Oktaria Doan Perdana Endro Ariyanto Erwid M Jadid Fadhlillah Fadhlillah Fadhlurahman Irwan Faiz Syukri Fakhri Akbar Pratama Fazmah Arif Yulianto Fitra Ilham Haris Hamdani Latif Hilal Hudan Nuha Ikke Dian Oktaviani Ikke Dian Oktaviani Indrasari, Elizabeth Sastrina Kevin Bastian Sirait Kurnia Wisuda Aji Luthfi Kusuma Maman Abdurohman Mas'ud Adhi Saputra Mas'ud Adhi Saputra Maulina Ngalimatul Ghoniya Mohamad Nurkamal Fauzan Muhammad Arief Nugroho Muhammad Dio Khairunnas Muhammad Ibraihan Diviantama Novian Anggis Suwastika Nur Alamsyah NUR ALAMSYAH Qotrunnada Firly Ramadhanty R. Ismoyojati R. Ismoyojati Rahmat Irianto Rahmat Suryoputro Rahmat Yasirandi Randy Agustyo Raharjo Rezy Noerdyah Ayu Saputri Ryandito Danuansa Saiful Anwar Satria Dzaky Raihan Setiawan, Muhammad Ilham Setyorini Siti Amatullah Karimah Taufik Akbar Tri Retno Pamungkas Sujarwo