Article Per Year (5 Year)
p-Index From 2021 - 2026
1.881
P-Index
This Author published in this journals
All Journal Telematika : Jurnal Informatika dan Teknologi Informasi JOIV : International Journal on Informatics Visualization Informatik : Jurnal Ilmu Komputer SEINASI-KESI JSR : Jaringan Sistem Informasi Robotik Jurnal Teknik Informatika (JUTIF) ABDI MOESTOPO: Jurnal Pengabdian pada Masyarakat Jurnal Pengabdian kepada Masyarakat (Nadimas) ABDIKOM
Irmanda, Helena Nurramdhani
Unknown Affiliation
Humanities Computer Science & IT Education Electrical & Electronics Engineering Energy Environmental Science Health Professions Public Health Social Sciences

Published : 13 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 13 Documents
Search

 1   2 
Sentiment Analysis of Cryptocurrency Exchange Application on Twitter Using Naïve Bayes Classifier Method Indarso, Andhika Octa; Irmanda, Helena Nurramdhani; Astriatma, Ria
Telematika Vol 20 No 1 (2023): Edisi Februari 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i1.9044

Abstract

Purpose: The growth and development of the digital currency industry also presents a variety of applications for conducting transactions using these currencies, including utilizing cryptocurrency exchanges to make investments. InI ndonesia, there are two applications that fall into the category of the largest cryptocurrency exchange and are recognized by Bappebti (Commodity Futures Trading Regulatory Agency), namely TokoCrypto and Indodax. Both applications are analyzed based on the sentiments of their users on Twitter.Design/methodology/approach: In this study the data collected is data originating from social media Twitter and has the keywords "indodax" or "#indodax" and "tokocrypto" or "#tokocrypto". The data used is between January 2021 – January 2022. The data collected from Twitter is processed using the Naïve Bayes Classifier algorithm.Findings/result: From the results of the analysis, it was found that the Indodax application has a higher positive sentiment percentage value of 9% compared to TokoCrypto.Originality/value/state of the art: The use of the Naïve Bayes algorithm in this study supports sentiment analysis of cryptocurrency exchange application users to consider which application has better positive sentiment for investing in digital currency or cryptocurrency.
Classification of Indonesian Tale Categories using Support Vector Machine and FastText Feature Extraction Irmanda, Helena Nurramdhani; Astriratma, Ria; Zaidiah, Ati; Hadi, Muhammad Rahman; Putra, Nayandra Agastia
Telematika Vol 21 No 2 (2024): Edisi Juni 2024
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v21i2.10867

Abstract

The purpose of this work is to develop a model to classify the various kinds of Indonesian folktales and to assess how well the support vector machine (SVM) approach and fastText feature extraction perform. The first phase of the study process is the gathering of data, namely the fairy tale dataset that has been annotated with categorizations for each genre of fairy tale. Following the collection of data, the pre-processing step is conducted. The purpose of the pre-processing step is to prepare the data for further processing in the subsequent stage. Following the completion of the preprocessing step, the training data and testing data are segregated. The subsequent step involves doing feature extraction using fastText. Moreover, the classification process is conducted using the Support Vector Machine (SVM) approach in order to get the ultimate outcome of the modeling process. The last phase involves assessing the performance of the constructed model. The categorization model for Indonesian fairy tales has a commendable accuracy rate of 85%, indicating its effectiveness. The aforementioned findings are substantiated by an accuracy metric of 85%, a recall metric of 85%, and an F1-score of 86%, indicating favorable outcomes.Previous researchs have not conducted any studies on the categorization of types of Indonesian fairy tales.
Enhancing Weather Prediction Models through the Application of Random Forest Method and Chi-Square Feature Selection Irmanda, Helena Nurramdhani; Ermatita, Ermatita; bin Awang, Mohd Khalid; Adrezo, Muhammad
JOIV : International Journal on Informatics Visualization Vol 8, No 3-2 (2024): IT for Global Goals: Building a Sustainable Tomorrow
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3-2.2356

Abstract

This study discovers weather forecast methodologies, concentrating mainly on the climatic issues faced by Indramayu Regency and its considerable impact on agriculture, specifically rice production and national food security. The study emphasizes the crucial need for accurate weather forecasting, especially in the context of ongoing climate change, by highlighting the region's vulnerability to weather anomalies and their possible disruption of crop output. To solve these issues, the study investigates machine learning techniques, particularly ensemble learning methods such as Random Forest in conjunction with Chi-Square feature selection. The article thoroughly outlines the research approach, including data collection from Indonesia's Meteorology, Climatology, and Geophysics Agency (BMKG), data pre-processing, feature selection processes, and data splitting. Notably, the methodology integrates the Synthetic Minority Over-sampling Technique (SMOTE) to adjust imbalanced data and uses key weather attributes for model construction (humidity, wind speed, and direction). The resulting Random Forest model performs well, with an accuracy rate of 87.6% in forecasting different types of rainfall. However, the study indicates potential overfitting in some rainfall classes, implying the need for additional data augmentation or modeling technique refining. In conclusion, this study demonstrates the potential efficacy of ensemble learning techniques in weather prediction, focusing on the Indramayu Regency. It emphasizes the need for exact forecasts in the agricultural and fisheries industries and suggests possibilities for additional investigation, such as research into alternative prediction approaches such as deep learning.
Co-Authors Andhika Octa Indarso Anjani, Margaretha Astriatma, Ria bin Awang, Mohd Khalid Ermatita - Falih, Noor Hadi, Muhammad Rahman Isnainiyah, Ika Nurlaili Matondang, Nurhafifah Muhammad Adrezo Muslim, Muhammad Panji Narissa, Tania Suzanne Niqotaini, Zatin Novi Trisman Hadi Putra, Nayandra Agastia Rahman, Puti Ayu Andhini Ria Astriratma Santoni, Mayanda Mega Sarika Afrizal, Sarika Utami, Adita Widiastiwi, Yuni Zaidiah, Ati