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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 488 Documents
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Analisis Forensik Digital pada Aplikasi Twitter di Android sebagai Bukti Digital dalam Penanganan Kasus Prostitusi Online Pratama, Sang Putu Febri Wira; Putra, I Gusti Ngurah Anom Cahyadi; Hamid, Muhammad Akbar; Christian, Calvin; Merdana, I Ketut Kusuma
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 10 No 3 (2022): JELIKU Volume 10 No 3, February 2022
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.2022.v10.i03.p03

Abstract

At this time the use of social media from time to time has experienced rapid development, one of which is the social media twitter. Twitter social media has many benefits such as making tweets about daily activities. However, Twitter social media has a negative side in its use, one of which is online prostitution. Prostitution is an act of cyber crime that violates the rules and norms that exist in society. Therefore, to overcome these cyber crimes, the necessary action is to review online prostitution on Twitter social media. In this study, a digital forensic analysis was conducted on Twitter social media on smartphones related to acts of prostitution using the National Institute of Justice (NIJ). Based on the research conducted, digital evidence is obtained that can be accounted for by the perpetrators.
Analisis Keamanan Aplikasi Android Dengan Metode Vulnerability Assessment Ardita, I Kadek Aldy Oka; Anom Cahyadi Putra, I Gusti Ngurah; Kustiadie, Mohammad Rizky; Dika Varuna, Gusti Ngurah Made; Eka Prananda, Made Yayang
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 10 No 3 (2022): JELIKU Volume 10 No 3, February 2022
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.2022.v10.i03.p04

Abstract

Seiring berkembangnya beragam aplikasi maka sistem Android haruslah tahan terhadap bebagai serangan malware dengan mengamati izin akses yang diberikan oleh pengguna. Penyerang dapat menggunakan kerentanan dalam aplikasi untuk mencuri berbagai informasi penting. Informasi merupakan aset penting dan berharga berupa rekaman suara, rekaman video, catatan, dll. Oleh karena itu, diperlukan suatu analisis keamanan. dari aplikasi yang digunakan dengan tes / tindakan pada tingkat keamanan aplikasi. Dalam melakukan Vulnerability test atau proses identifikasi celah keamanan pada aplikasi android dilakukan dua teknik yaitu dengan MobSF dan dengan frida. Hasil dari Analisis MobSF sangat terlihat perbedaannya antara mendownload aplikasi melalui pihak ketiga dengan mendownload aplikasi melalui Play Store. Dimana nilai hash yang didapat sangat berbeda baik dari md5, sha1, atau sha256, dari hasil tersebut dapat diketahui bahwa ada perubahan pada file yang disediakan oleh penyedia pihak ketiga. Pada security score didapatkan bahwa aplikasi yang di download melalui pihak ketiga terdapat banyak server dan aktivitas mencurigakan, sedangkan aplikasi yang terdapat di playstore terdapat 2 server yang asli. Pada size, ukuran file yang disediakan oleh pihak ketiga, ukuran file asli hanya 20.37MB sedangkan file yang di sediakan oleh pihak ketiga berukuran 61.33MB. Pada analisis menggunakan frida dilakukan proses penyerangan yaitu bypass login. Dimana pada aplikasi pihak ketiga sudah memiliki email yang telah diinputkan oleh penyedia aplikasi. Dari hasil analisis yang dilakukan maka lebih baik untuk mendownload aplikasi melalui playstore agar lebih aman. Karena sebagai pengguna awam tidak akan tahu perubahan file apa yang dilakukan dan beresiko atau tidaknya perubahan tersebut terhadap perangkat tersebut
Comparison of K-Nearest Neighbor And Modified K-Nearest Neighbor With Feature Selection Mutual Information And Gini Index In Informatics Journal Classsification
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 10 No 3 (2022): JELIKU Volume 10 No 3, February 2022
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.2022.v10.i03.p05

Abstract

With the rapid development of informatics where thousands of informatics journals have been made, a new problem has occured where grouping these journals manually has become too difficult and expensive. The writer proposes using text classification for grouping these informatics journals. This research examines the combinations of two machine learning methods, K-Nearest Neighbors (KNN) and Modified K-Nearest Neighbors with two feature selection methods, Gini Index (GI) and Mutual Information (MI) to determine the model that produces the higherst evaluation score. The data are informatics journals stored in pdf files where they are given one of 3 designated labels: Information Retrieval, Database or Others. 252 data were collected from the websites, neliti.com and garuda.ristekbrin.go.id. This research examines and compares which of the two methods, KNN and MKNN at classifying informatics journal as well as determining which combination of parameters and feature selection that produces the best result. This research finds that the combination of method and feature selection that produces the best evaluation score is MKNN with GI as feature selection producing precision score, recall score and f1-score of 97.7%
Application of Steganography for Copyright Protection Using the Least Significant Bit (LSB) Method
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 10 No 4 (2022): JELIKU Volume 10 No 4, May 2022
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.2022.v10.i04.p03

Abstract

Lontar sebagai salah satu naskah (naskah kuno) yang merupakan warisan budaya nenek moyang yang memiliki keunikan dalam cara pencatatan dan transfer pengetahuan tradisional yang dilakukan melalui tulisan di media Lontar. Namun lambatnya waktu lontar membuat naskah naskah sangat rentan karena berbagai sebab. Kerusakan tersebut berdampak pada peningkatan informasi yang terdapat pada Lontar, kemudian dilakukan media ke digital. Jika berbagai jenis aksara tersebut didigitalkan, apa yang akan menjadi bukti keaslian bahwa aksara lontar ini adalah milik-Nya? Selain itu, digitalisasi ini juga dapat digunakan untuk mengembangkan akses kepada triplet dan masyarakat umum terhadap ilmunya. Dari permasalahan tersebut pada penelitian ini dilakukan untuk membangun sebuah aplikasi berbasis desktop yang mengimplementasikan steganografi dengan menyembunyikan pesan rahasia ke dalam ekstensi PNG dengan memanfaatkan metode Least Significant Bit (LSB) sebagai sarana perlindungan hak cipta pada Naskah Lontar Bali.
Diagnosis Penyakit Ginjal Kronis dengan Algoritma C4.5, K-Means dan BPSO
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 10 No 4 (2022): JELIKU Volume 10 No 4, May 2022
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.2022.v10.i04.p07

Abstract

Chronic kidney disease or Chronic Kidney Disease (CKD) is a disorder of the kidneys that results in the kidneys not being able to perform their functions properly due to decreased kidney performance. Classification is a data mining technique that can be used in diagnosing chronic kidney disease. In this study, the classification was carried out using the C4.5 algorithm. K-Means Clustering is used to discretize numeric type data. Binary Particle Swarm Optimization (BPSO) serves to select a subset of features that are redundant and less informative in the dataset or what is known as feature selection. The test was carried out using the 10-fold cross validation method on the Chronic Kidney Disease (CKD) dataset obtained from the UCI Machine Learning Repository. The test results in this study found that the application of feature selection with BPSO was able to increase the performance of the C4.5 classification with the values ??of accuracy, precision, recall and f-measure, respectively, namely 96%, 96.869%, 96.8% and 96.781% as well as computation time. which is obtained is 62.56 ms. While in BPSO parameter testing, the best parameter values ??obtained with the number of particles is 15, the number of iterations is 40, the value of c1 is 1 and c2 is 1.2 and the value of inertia weight (w) is 0.9.
Rancang Bangun Portal Lowongan Pekerjaan Berbasis Web Dengan Fitur Rekomendasi
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 10 No 3 (2022): JELIKU Volume 10 No 3, February 2022
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.2022.v10.i03.p06

Abstract

Tingginya tingkat pengangguran di Indonesia yang disebabkan oleh dampak pertumbuhan penduduk yang padat dan sulitnya mencari pekerjaan yang mengakibatkan pertumbuhan ekonomi tidak stabil menjadi tantangan bagi pemerintah untuk menanggulangi masalah pengangguran. Salah satu faktor yang mempengaruhi tingkat pengangguran yang tinggi adalah penyebaran informasi lowongan kerja yang kurang merata. Pelamar kerja sering kesulitan untuk mendapatkan informasi pekerjaan yang sesuai dengan kemampuan dan keterampilan yang dimiliki. Dengan dibuatnya sistem rekomendasi lowongan kerja, diharapkan dapat menyelesaikan masalah yang dialami seorang pelamar yang ingin melamar pekerjaan yang sesuai dengan kemampuannya. Dengan menggunakan algoritma euclidean distance, sistem diharapkan dapat memberikan hasil rekomendasi berdasarkan beberapa parameter yang sesuai dengan keinginan pelamar. Parameter yang dimaksudkan adalah usia, jenis kelamin, pendidikan, jurusan, gaji, keterampilan, pengalaman kerja, dan kategori pekerjaan yang diinginkan. Penelitian ini menghasilkan kesimpulan yaitu berdasarkan hasil pengujian precision recall sistem rekomendasi lowongan kerja menggunakan metode euclidean distance menghasilkan nilai precision sebesar 0,94 atau 94% dan nilai recall sebesar 0,94 atau 94%.
Identifikasi Ekspresi Idiomatik Menggunakan Distributional Semantic Based Approach dan Truth Discovery
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 10 No 4 (2022): JELIKU Volume 10 No 4, May 2022
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.2022.v10.i04.p04

Abstract

Idiomatic expressions are phrases that consist of a sequence of two or more words that have a meaning that cannot be predicted from the meaning of the individual words that compose it. Idiomatic expressions exist in almost all languages ??but are difficult to extract because there is no algorithm that can precisely decipher the structure of idiomatic expressions, so most rule-based machine translation systems generally translate idiomatic expressions by translating word for word their constituents, but the translation results do not produce the true meaning of the idiomatic expression. Based on this problem, the author tries to do research on the identification of the use of idiomatic expressions in Indonesian sentences. First, the author conducts the sentence classification process using BERT to find out whether the sentence contains idiomatic expressions or not. Furthermore, idiomatic expressions are identified based on distributional semantic based approach and then validated automatically using the Truth Discovery method. From the research conducted, the identification of idiomatic expressions in Indonesian sentences using Distributional Semantic Based Approach and Truth Discovery obtained an accuracy of 0.82; precision 1.0; recall 0.64 and f1-score 0.78.
Cover & Table of Contents Vol. 10 No. 3
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 10 No 3 (2022): JELIKU Volume 10 No 3, February 2022
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Implementasi LSTM Pada Analisis Sentimen Review Film Menggunakan Adam Dan RMSprop Optimizer
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 10 No 4 (2022): JELIKU Volume 10 No 4, May 2022
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.2022.v10.i04.p05

Abstract

Movies are an entertainment that is in great demand by many groups from children, teenagers, adults, and parents. In the current digital era, various films can be watched on television to digital streaming services. Public opinion on the films watched can be in the form of positive opinions or negative opinions. Sentiment analysis is one of the fields of Natural Language Processing (NLP) which is able to build a system to recognize and extract opinions in the form of text, sentiment analysis is usually used to find out people's opinions or assessments of a products, services, politics, or other topics. Through sentiment analysis from the collection of reviews, the public can get various recommendations for films that can be watched. The method implemented to classify review data into positive reviews and negative reviews in this study is LSTM by comparing two different optimizers, namely Adam and RMSprop. This study succeeded in providing sentiment predictions with different optimizers with accuracy values ??for the LSTM application with Adam Optimizer reaching 77.11% and the LSTM application with RMSprop reaching 80.07%.
Implementasi Generalized Learning Vector Quantization (GLVQ) dan Particle Swarm Optimization (PSO) Untuk Klasifikasi Kanker Payudara
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 10 No 4 (2022): JELIKU Volume 10 No 4, May 2022
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.2022.v10.i04.p01

Abstract

Breast cancer (BC) is the biggest cause of death from cancer every year. Delayed early detection is one the causes of the high incidence of BC. Machine learning-based classification has been widely used for automatic early detection and classifying cancer types. GLVQ was used to classify BC into benign and malignant. GLVQ has sensitivity to the initialization of a random weight vector which affects the accuracy of the results. PSO is used to optimize the initial weight vector on GLVQ, so that the optimal initial weight vector is produced. The data processing stage includes handling outliers with Winsorizing method, z-score normalization, and dimensionality reduction using PCA. Changes in the parameters cognition learning rate, social learning rate, and inertia in PSO affect the results of the optimization of the weight vector which is indicated by the average value of the resulting fitness. The combination of cognition learning rate=2,4, social learning rate=2,1, and inertia=0,6 produces the highest average fitness value of 0,91868. Changes in the parameters learning rate and number of prototype per class in GLVQ affect the level of accuracy and error rate of the resulting breast cancer classification results. The combination of learning rate=0,1, number of prototype per class=5, maximum epoch of 100, and minimum error tolerance of 0,0000001 produces the highest average accuracy value of 0,956044. PSO-GLVQ performance provides higher accuracy, recall, and F2-Score values than GLVQ.

Page 25 of 49 | Total Record : 488

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