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All Journal ComEngApp : Computer Engineering and Applications Journal Jupiter JSI: Jurnal Sistem Informasi (E-Journal) Jurnal Edukasi dan Penelitian Informatika (JEPIN) Annual Research Seminar Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS) Jurnal Linguistik Komputasional Sriwijaya Journal of Informatics and Applications The Indonesian Journal of Computer Science
Mastura Diana Marieska
Universitas Sriwijaya, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Other

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Journal : Jupiter

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Sistem Pengamanan Data Menggunakan Kriptografi AES dan Blockchain Berbasis Android Dhiya Calista; Al Farissi; Mastura Diana Marieska
JUPITER (Jurnal Penelitian Ilmu dan Teknologi Komputer) Vol 13 No 2 (2021): JUPITER Edisi Oktober 2021
Publisher : Teknik Komputer Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/3927.jupiter.2021.10

Abstract

Data or information security is a very important thing for internet users to pay attention to now, so that the data or information owned is not attacked by irresponsible parties. So, in this research, an implementation of a combination of Blockchain and AES cryptography will be carried out in order to avoid active and passive attacks by attackers. Blockchain method can detect data changes from attackers quickly and easily. However, Blockchain method can still be attacked passively, therefore AES method is combined with Blockchain as a complement that is used to encrypt data from plaintext to ciphertext so that existing data or information can be avoided from active or passive attacks. In this research, the software development method is using Rational Unified Process (RUP) method and the tests carried out are Blockchain resistance to modification attacks testing and Avalanche Effect testing on AES method. Keywords— Cryptography, Blockchain, AES, RUP, Avalanche Effect
Optimasi Fuzzy Time Series Chen Pada Prediksi Kasus Covid-19 Di Sumatera Selatan Menggunakan Particle Swarm Optimization HAFIZH SHAFWAN RAFA; Dian Palupi Rini; Mastura Diana Marieska
JUPITER (Jurnal Penelitian Ilmu dan Teknologi Komputer) Vol 14 No 2-c (2022): Jupiter Edisi Oktober 2022
Publisher : Teknik Komputer Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281./4949/5.jupiter.2022.10

Abstract

At the beginning of its appearance, COVID-19 made the whole community become worried about the possibility that would happen in the future. Prediction of COVID-19 cases is a solution that can be done to reduce this worry. This study uses the Fuzzy Time Series Chen method to predict COVID-19 cases in the future, but on the other hand this method has shortcomings in determining the length of the interval which can result in the prediction accuracy being less good, so a Particle Swarm Optimization algorithm is needed to optimize the length. intervals that will later be used to predict cases of COVID-19, so that the results of the predictions will be better. Prediction accuracy is calculated using Mean Absolute Percentage Error. Based on testing the MAPE error value generated from Fuzzy Time Series Chen which is optimized for 26.380%, while for predictions without optimization it produces a value of 30.057%.
Analisis Sentimen di Twitter Menggunakan Algoritma Artificial Neural Network Novi Yusliani; Armenia Yuhafiz; Mastura Diana Marieska; Alvi Syahrini Utami
JUPITER (Jurnal Penelitian Ilmu dan Teknologi Komputer) Vol 15 No 1d (2023): Jupiter Edisi April 2023
Publisher : Teknik Komputer Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281./6603/15.jupiter.2023.04

Abstract

Along with the development of social media, the amount of data in the form of opinions is increasing. The opinions in social media can be used to find out the assessments of social media users regarding something, one of which is the assessment of a candidate in politics. In general, the opinions in social media can be classified into two categories, namely positive and negative. Sentiment analysis is one of the research topics in the field of Natural Language Processing which aims to classify opinions into one of these categories. The opinions in social media that are often used as research objects are the opinions of Twitter users. This study uses an Artificial Neural Network (ANN) algorithm to be implemented in sentiment analysis system. The dataset used in this study is 1088 tweets consisting of 700 tweets labeled positive and 388 tweets labeled negative. The test results show that the best performance is produced when the data is divided into 80% for training and 20% for testing. The resulting percentages for each performance parameter used are accuracy is 61.3%, recall is 67.9%, precision is 75.1%, and f1-score is 71.3% using 0.01 for learning rate and 150 for epoch.
Perbandingan Metode Mapreduce Berbasis Single Node Hadoop Pada Aplikasi Word Count Marieska, Mastura Diana; Utami, Alvi Syahrini; Oktaviani, Elvira
JUPITER (Jurnal Penelitian Ilmu dan Teknologi Komputer) Vol 16 No 1 (2024): Jurnal Penelitian Ilmu dan Teknologi Komputer (JUPITER)
Publisher : Teknik Komputer Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.11097045

Abstract

In the context of Big Data processing, Hadoop MapReduce serves as a framework used to develop software and process large-scale data in parallel. Word Count is a type of task used to count the occurrences of unique words in a text file. Considering processing time is crusial in adhering to standards of Big Data Processing. The conducted research involved the processing of text files using the MapReduce method on the Hadoop Distributed File System (HDFS) using a single node, comparing the results of Word Count processing with and without the use of MapReduce. The research findings indicate that the implementation of Word Count without using MapReduce offers better speed in processing Indonesian language text data on a Hadoop single node. Additionally, the comparison of processing time between the Word Count program with Hadoop MapReduce and the Word Count program without MapReduce shows that the latter has faster processing time. A significant reduction in processing time, up to 95% for a 5 MB file size, can be achieved by using the Word Count method without MapReduce. However, the level of reduction decreases with increasing file size.
Co-Authors A.A. Ketut Agung Cahyawan W Abdiansah Abdiansah, Abdiansah Abdiansyah Al Farissi Alvi Syahrini Utami Armenia Yuhafiz Aruda, Syechky Al Qodrin Arya Yunanta Buchari, Muhammad Ali Danny Matthew Saputra Desty Rodiah Dhiya Calista Dian Palupi Rini Dian Palupi Rini Dian Palupi Rini Fressy Arlind HAFIZH SHAFWAN RAFA Harisatul Aulia Kanda Januar Miraswan Mahendra, Calvin Muhammad Qurhanul Rizqie Novi Yusliani Oktadini, Nabila Rizky Oktaviani, Elvira Plakasa, Gerald Raisha Fatiya Reyhan Navind Shaquille Saputra, Danny Mathew Saputra, Danny Matthew Sari Dwi Septiani YUNITA Yunita Yunita