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All Journal Jurnal Ilmu Komputer dan Informasi Prosiding KOMMIT TELKOMNIKA (Telecommunication Computing Electronics and Control) Journal of Applied Data Sciences International Journal of Engineering, Science and Information Technology Jurnal Sistem Informasi
Rina Refianti
Faculty of Computer Science and Information Technology, Gunadarma University
Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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IMPLEMENTATION OF QUANTUM IMAGE HALFTONING ALGORITHM Oktorianti, Anastasia Monika; Mutiara, Achmad Benny; Refianti, Rina
Prosiding KOMMIT 2014
Publisher : Prosiding KOMMIT

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

Abstract

Developing new computing methods and signal processing algorithms byborrowing from the principle of quantum mechanics is a very interesting and newresearch topic. By utilizing quantum algorithm in the process of computing, it isexpected that this algorithm can solved the problems of image processing fasterthan using a classical algorithm. In this paper, a quantum digital imagehalftoning algorithm will be presented.The image would be processed usingbitmap image (.bmp) and the applications would be created using Matlab R2009a.Test phase of the applications is using an image, lena.bmp. This application willshow three new images as results of three halftoning methods: binary thresholdmethod, ordered dithering method, and method with quantum algorithm. Themethod with quantum algorithm shows better than two others methods.
Musical Genre Classification Using SVM and Audio Features Achmad Benny Mutiara; Rina Refianti; Nadia R.A. Mukarromah
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 3: September 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v14i3.3281

Abstract

The need of advance Music Information Retrieval increases as well asa huge amount of digital music files distribution on the internet.Musical genres are the main top-level descriptors used to organize digital music files. Most of work in labeling genre done manually. Thus, an automatic way for labeling a genre to digital music files is needed.The most standard approach to do automatic musical genre classification is feature extraction followed by supervised machine-learning. This research aims to find the best combination of audio features using several kernels of non-linear Support Vector Machines (SVM). The 31 different  combinations of proposed audio features are dissimilar compared in any other related research. Furthermore, among the proposed audio features, Linear Predictive Coefficients (LPC) has not been used in another works related to musical genre classiffication. LPC was originally used for speech coding. An experimentation in classifying digital music file into a genre is carried out. The experiments are done by extracting feature sets related to timbre, rhythm, tonality and LPC from music files. All possible combination of the extracted features are classified using three different kernel of SVM classifier that are Radial Basis Function (RBF), polynomial and sigmoid.The result shows that the most appropriate kernel for automatic musical genre classification is polynomial kernel and the best combination of audio features is the combination of musical surface, Mel-Frequency Cepstrum Coefficients (MFFC), tonality and LPC. It achieves 76.6 % in classification accuracy.
SIMULATION OF QUANTUM SEARCH ALGORITHM Rina Refianti; Achmad Benny Mutiara
Jurnal Ilmu Komputer dan Informasi Vol 6, No 2 (2013): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (765.019 KB) | DOI: 10.21609/jiki.v6i2.227

Abstract

The rapid progress of computer science has been accompanied by a corresponding evolution of computation, from classical computation to quantum computation. As quantum computing is on its way to becoming an established discipline of computing science, much effort is being put into the development of new quantum algorithms. One of quantum algorithms is Grover's algorithm, which is used for searching an element in an unstructured list of N elements with quadratic speed-up over classical algorithms. In this work, Quantum Computer Language (QCL) is used to make a Grover's quantum search simulation in a classical computer document.
AGENTS-BASED COMMODITY MARKET SIMULATION WITH JADE Rina Refianti; Achmad Benny Mutiara; Hendra Gunawan
Jurnal Sistem Informasi Vol. 9 No. 1 (2013): Jurnal Sistem Informasi (Journal of Information System)
Publisher : Faculty of Computer Science Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1077.002 KB) | DOI: 10.21609/jsi.v9i1.345

Abstract

A market of potato commodity for industry scale usage is engaging several types of actors. They are farmers, middlemen, and industries. A multi-agent system has been built to simulate these actors into agent entities, based on manually given parameters within a simulation scenario file. Each type of agents has its own fuzzy logic representing actual actors' knowledge, to be used to interpreting values and take appropriated decision of it while on simulation. The system will simulate market activities with programmed behaviors then produce the results as spreadsheet and chart graph files. These results consist of each agent's yearly finance and commodity data. The system will also predict each of next value from these outputs.
Sentiment Analysis Using Convolutional Neural Network Method to Classify Reviews on Zoom Cloud Meetings Application Based on Reviews on Google Playstore Refianti, Rina; Anggraeni, Novia
International Journal of Engineering, Science and Information Technology Vol 3, No 3 (2023)
Publisher : Department of Information Technology, Universitas Malikussaleh, Aceh Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v3i3.463

Abstract

Zoom Cloud Meetings is an application that is used to conduct video conferencing. On the Google Play Store, the Zoom Cloud Meeting application received a rating of 3.8, with 500 million more downloads as of March 2021. The application has many advantages, such as not being disturbed by pauses in conversation and having good video and audio quality. The advantages possessed by these applications require development so that application services are getting better. For this reason, user reviews are needed to see user satisfaction with the application so that they can determine services that can be developed in the future. Based on this, this research was created to create a web-based application that can classify user reviews of the Zoom Cloud Meetings application using the Convolutional Neural Network (CNN) method and calculate the accuracy value. This application is built using the Flask framework and the Python programming language. Model training is carried out using the TensorFlow library. Applications that have been made are then tested using two stages of testing, namely system testing with black box and data testing. Based on system testing, it was found that the website can run well, and for data testing using test data, the accuracy result is 91.5%.
A Lexicon-Based Long Short-Term Memory (LSTM) Model for Sentiment Analysis to Classify Halodoc Application Reviews on Google Playstore Refianti, Rina; Mutiara, Achmad Benny; Putra, Ryan Arya
Journal of Applied Data Sciences Vol 5, No 1: JANUARY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i1.160

Abstract

The development of information and communication technology is developing very quickly, has made many new breakthroughs. One of these technological advances is in the health sector, the creation of telemedicine applications. During the Covid-19 pandemic, it is difficult for people to get access to health. Therefore, telemedicine applications are needed. Halodoc is one of the telemedicine applications that has successfully become the top health application on the Google PlayStore. The application has been used by more than ten million users throughout Indonesia and received a rating of 4.6. To be able to see ratings and satisfaction from the public, user reviews are needed. The very large number of reviews often contain errors, making them difficult to decipher. Based on this, this research aims to create a web application, which can classify user reviews of the Halodoc application, using a proposed lexicon-based Long Short-Term Memory (LSTM) Model. Application is built using the Flask framework and the Python programming language. Models are created and trained using the TensorFlow library. The results of the model evaluation get an accuracy of 85.3% with an average precision value of 85.3%, a recall value of 85.6% and an f1-score of 85.3%. The proposed LSTM model can be used to classify Halodoc review sentiment classes.
Data Visualization of Climate Patterns in Indonesia Using Python and Looker Studio Dashboard: A Visual Data Mining Approach Refianti, Rina; Mutiara, Achmad Benny; Ariyanto, Ananda Satria
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.420

Abstract

Climate has a significant impact on the lives of Indonesian people. Information about climate patterns, when presented visually and interactively, can greatly enhance understanding of climate conditions in Indonesia. This study aims to produce a visualization of climate pattern data in Indonesia that can be accessed online by the general public, serving as a valuable resource for climate information. The study highlights the ability to display historical trends for a 10-year period (2010-2020) through interactive visuals, which load information according to user-defined filters, enabling diverse presentations of data. The research employs the Visual Data Mining method, encompassing Project Planning, Data Preparation, and Data Analysis phases. Additionally, Exploratory Data Analysis techniques were utilized in the data analysis phase. The data was cleaned and processed using the Python programming language with libraries such as pandas, numpy, seaborn, and matplotlib. Visualizations were created using Looker Studio tools and published on a website, providing accessible climate pattern information in Indonesia via the Internet. The final results of this research indicate that the developed climate visualization dashboard successfully delivers detailed insights into sunlight duration, temperature, humidity, rainfall, and wind speed across various Indonesian regions. Users can effectively monitor climate trends and weather changes. The dashboard also demonstrates significant seasonal variations and differences in climate patterns between provinces. Performance metrics reveal that the dashboard meets Key Performance Indicators, achieving a click-through ratio of 40.1%, the average page position in search engines is 4.8 top positions, and receiving positive user experience scores. Further development and research on the Climate Pattern Dashboard in Indonesia still have room for enhancement. Important aspects include expanding data coverage to include multiple decades for observing significant climate patterns and applying sophisticated prediction methods like machine learning algorithms for future climate change projections.
Sentiment Analysis of Vidio Application Based on Reviews on Google Play Store Using Bidirectional Encoder Representations from the Transformers Method Refianti, Rina; Senjaya, Andrian
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.1124

Abstract

Sentiment analysis is a computational study that aims to process, extract, summarise, and analyse the information contained in the text so it can conclude the emotions and points of view given by the author from the text and share the emotional tendencies in the text through the subjective information contained in it. Vidio is a video streaming site that allows users to watch and enjoy various videos and other services, such as live chat and playing games over the internet, and broadcast them by live streaming and video on demand. The analysis process uses the Bidirectional Encoder Representations from Transformers (BERT) method to classify comments into positive, neutral, and negative sentiments using the Python programming language, and based on the results of the tests that have been carried out from the amount of comment data—as much as 6000 data with training data as much as 4019 data, validation data as many as 1154 data, and test data as many as 569 data—an accuracy result of 76%.
Co-Authors Achmad Benny Mutiara Anastasia Monika Oktorianti Anggraeni, Novia Ariyanto, Ananda Satria HENDRA GUNAWAN B11211055 Jayawarsa, A.A. Ketut Nadia R.A. Mukarromah Putra, Ryan Arya Senjaya, Andrian