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INDONESIA
(JELIKU) Jurnal Elektronik Ilmu Komputer Udayana
Published by Universitas Udayana
ISSN : 23015373     EISSN : 26545101     DOI : https://doi.org/10.24843/JLK
Core Subject : Science,
Computer Science & IT
Aim and Scope: JELIKU publishes original papers in the field of computer science, but not limited to, the following scope: Computer Science, Computer Engineering, and Informatics Computer Architecture Parallel and Distributed Computer Computer Network Embedded System Human—Computer Interaction Virtual/Augmented Reality Computer Security Software Engineering (Software: Lifecycle, Management, Engineering Process, Engineering Tools and Methods) Programming (Programming Methodology and Paradigm) Data Engineering (Data and Knowledge level Modeling, Information Management (DB) practices, Knowledge Based Management System, Knowledge Discovery in Data) Network Traffic Modeling Performance Modeling Computer Security IT Governance Networking Technology Robotic Instrumentation Information Search Engine Multimedia Security Information Retrieval Mobile Processing Natural Language Processing Artificial intelligence & soft computing and their applications Neural networks Machine Learning Reasoning and evolution Intelligence applications Computer vision and speech understanding Multimedia and cognitive informatics Data mining and machine learning tools, heuristic and AI planning strategies and tools, computational theories of learning
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 24 Documents
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Search results for , issue "Vol 12 No 3 (2024): JELIKU Volume 12 No 3, February 2024" : 24 Documents clear
Klasifikasi Serangan Application Layer Denial of Service Menggunakan Support Vector Machine (SVM) dan Chi Square Putu Agus Prawira Dharma Yuda; Cokorda Pramartha; I Komang Ari Mogi
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 12 No 3 (2024): JELIKU Volume 12 No 3, February 2024
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JLK.2023.v12.i03.p21

Abstract

In an era marked by widespread computer usage, security emerges as a critical focal point demanding meticulous attention. The spectrum of potential threats encompasses various methods of attacking computer systems, with Denial of Service (DoS) attacks being a prominent concern. This study delves into the enhancement of cybersecurity by implementing a system capable of discerning between DoS attack data and normal data, employing the Support Vector Machine (SVM) algorithm. To optimize the efficacy of the classification system, a strategic feature selection process is imperative. This research advocates for the utilization of the Chi-square method for this purpose, aiming to eliminate irrelevant features and thereby enhance system performance. The Support Vector Machine algorithm, hinging on hyperplanes for classification, gains efficiency through judicious feature selection. The empirical findings of this research unveil that employing Chi-square feature selection significantly elevates the performance of the classification system when dealing with application layer attacks. Remarkably, this enhancement is achieved without compromising the accuracy of the system. Specifically, the classification of DoS application layer attacks using SVM in tandem with Chi-square yielded identical accuracy results compared to using SVM alone. The average accuracy reached an impressive 99.9995%, with a processing time of 6.08 minutes. In contrast, the classification system without feature selection consumed a comparatively longer processing time of 6.85 minutes. This underscores the efficacy of Chi-square feature selection in optimizing the performance of cybersecurity systems, demonstrating a streamlined approach to safeguarding computer networks from malicious threats.
Implementasi CI/CD Pada Microservices Untuk Meningkatkan Availability Pada Pemrosesan Big Data Kompiang Gede Sukadharma; I Putu Gede Hendra Suputra
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 12 No 3 (2024): JELIKU Volume 12 No 3, February 2024
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JLK.2023.v12.i03.p12

Abstract

Big Data Processing needed a reliable system that not let the data loss. But sometimes we need to make the system down for a while because we need to push the newest changes of the system. The automation will help us achieve that. Continuous Integration and Continuous Deployment help us to reduce the downtime and increase the availability of the system. Thus, the implication will be led to reduce of data loss. This research focusses on the implementation of CI/CD Pipeline on single-point-of-failure service on Microservices Architecture. This research use Load-Testing to measure data loss on certain amount of time. The result on this research show that implementing CI/CD Pipeline on the Microservices that we made, make the down time will be less than 45 Second with 20 Virtual user who send the data.
Implementasi Long-Short Term Memory (LSTM) pada Klasifikasi Kategori Berita Anak Agung Ngurah Andhika Satrya Nugraha; Ida Bagus Made Mahendra
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 12 No 3 (2024): JELIKU Volume 12 No 3, February 2024
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JLK.2023.v12.i03.p07

Abstract

Karena banyaknya berita yang ada saat ini, diperlukan sebuah cara untuk memilah berita yang ingin dilihat. Salah satu cara untuk memilah berita adalah dengan membagi berita ke dalam beberapa kategori. Saat ini pembagian kategori pada berita masih dilakukan secara manual. Penelitian ini membahas tentang implementasi metode Long-Short Term Memory (LSTM) untuk mengklasifikasikan berita ke dalam 7 kategori. Terdapat dua model yang diimplementasikan pada penelitian ini, yaitu model LSTM dan model Bidirectional LSTM. Model LSTM yang dibuat berhasil mengklasifikasikan berita dengan akurasi sebesar 85.36%, model Bidirectional LSTM juga berhasil mengklasifikasikan berita dengan akurasi sebesar 84.15%.
Analisis Sentimen Pengguna Aplikasi MyPertamina Dengan Menggunakan Algoritma Naïve Bayes I Gusti Bgs Darmika Putra; Cokorda Rai Adi Pramartha
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 12 No 3 (2024): JELIKU Volume 12 No 3, February 2024
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JLK.2023.v12.i03.p17

Abstract

MyPertamina adalah aplikasi layanan keuangan digital yang di miliki oleh PT Pertamina dan anggota Badan Usaha Milik Negara (BUMN) dengan menggunakan aplikasi ini pengguna dapat untuk membeli beberapa produk buatan Pertamina, termasuk BBM, secara cashless atau nontunai.Tujuan dari aplikasi MyPertamina yaitu untuk mendata masyarakat yang telah membeli BBM bersubsidi. Klasifikasi sentiment bertujuan untuk mengatasai masalah ini dengan cara otomatis mengklasifikasikan ulasan dari pengguna dengan mengkelompokan menjadi ulasan positif atau ulasan negative. Dalam penelitian ini kami mempelajari sentimen pengguna aplikasi MyPertamina yang terdapat pada ulasan Google Playstore sebagai acuan untuk meningkatkan tingkat pelayanan dan kualitas aplikasi tersebut. Untuk mengklasifikasikan sentimen kami menerapkan metode Naïve Bayes. Hasil akhir yang akan dihasilkan adalah tingkat akurasi yang dicapai dalam melakukan analisis sentimen menggunakan metode Naïve Bayes.

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