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All Journal Seminar Nasional Informatika (SEMNASIF) Indonesian Journal on Computing (Indo-JC) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Building of Informatics, Technology and Science Journal of Computer System and Informatics (JoSYC) eProceedings of Engineering
Setyorini
Telkom University
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Deteksi Malware Android Menggunakan Pengklasifikasi Pembelajaran Mesin Paralel Mukhamad Rafi Galih Saputro; Setyorini Setyorini; Siti Amatullah Karimah
eProceedings of Engineering Vol 10, No 5 (2023): Oktober 2023
Publisher : eProceedings of Engineering

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Abstract

Abstrak — Perkembangan android semakin pesat, sehingga mendorong pertumbuhan malware android. Data malware android memiliki dimensi tinggi, dibutuhkan algoritme untuk melakukan deteksi. Support Vector Machine (SVM) adalah algoritme pembelajaran mesin yang cocok untuk data malware android. Namun SVM memiliki keterbatasan dari segi waktu komputasi untuk data dalam jumlah besar, karena membutuhkan solusi dari masalah pengoptimalan Quadratic Programming (QP). Penelitian ini mengusulkan Parallel Support Vector Machine (PSVM) dengan metode dekomposisi Sequential Minimal Optimization (SMO) untuk melakukan deteksi atau klasifikasi malware android menggunakan dataset DREBIN. algoritme SMO yang dijamin memecahkan QP, dengan menggunakan teknik dekomposisi yaitu mendistribusikan tugas ke beberapa prosesor untuk dieksekusi secara paralel. Evaluasi berdasarkan kinerja perbandingan model Parallel SVM-SMO 4 dekomposisi dan Non-Parallel SVM-SMO dengan analisis fitur menggunakan Correlation Coefficient. Pada pengujian metrik performa dan akurasi fitur paling optimal 14 fitur dengan rata-rata akurasi 78%. Pada pengujian kinerja model, fitur paling optimal 27 fitur dengan rata-rata percepatan 9.58 dan efisiensi 2.39 pada kernel linier.Kata Kunci— DREBIN, SVM, PSVM, SMO, Dekomposisi, Koefisien Korelasi
Comparative Analysis of Max-Throughput and Proportional Fair Scheduling Algorithms in 5G Networks St. Nur Hikmah Damayanti; Siti Amatullah Karimah; Setyorini Setyorini
Journal of Computer System and Informatics (JoSYC) Vol 4 No 2 (2023): Februari 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v4i2.3073

Abstract

Mobile network service use is growing, especially after the Covid-19 pandemic. To improve the quality of mobile network services, 5G comes as a network service ecosystem with low latency, which is 1ms, or about 10 times lower than 4G, making 5G able to provide more efficient access, especially in real-time network utilization. Maximum speed can be obtained by sharing limited bandwidth. The limited amount of available bandwidth causes the need for a Packet Scheduler, which aims to improve the efficiency and fairness of bandwidth usage. This research uses two packet schedulers comparing the Proportional Fair algorithm and the Max-Throughput algorithm using test scenarios for changes in the number of users and user speed. The resulting output value analyzes resource limits such as frequency, power, speed, and time in each scenario to allocate resources so that their use remains efficient with a Quality of Service that remains stable and maintained. In simulation testing using the 5G-air-simulator, the average value obtained in the delay is 1,394 ms, throughput is 0,636, and fairness index is 0,967.
Co-Authors Aditya Bhagus Aria Hutomo Arief Goeritno Arif Mahmudi Bayu Erfianto Devina Adinda Hartono Dodi Wisaksono Sudiharto Endro Ariyanto Erwid M Jadied Fadma Sari Y.E.G. M Firmansyah Arrozi Muhammad Ali Said Mukhamad Rafi Galih Saputro Rayhan Muhammad Rendy Munadi Satrio Nurcahya Seno Adi Putra Sidik Prabowo Siti Amatullah Karimah St. Nur Hikmah Damayanti Trisya Indah Pravitasari Yuggo Afrianto