Article Per Year (5 Year)
p-Index From 2021 - 2026
5.831
P-Index
This Author published in this journals
All Journal Jurnal Pseudocode Journal of Information Systems Engineering and Business Intelligence Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Eksplora Informatika JITK (Jurnal Ilmu Pengetahuan dan Komputer) Techno Nusa Mandiri : Journal of Computing and Information Technology JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Teknoinfo Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Infomedia JURNAL PengaMAS MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Jurnal Informatika dan Rekayasa Elektronik Jurnal Riset Sistem Informasi dan Teknologi Informasi (JURSISTEKNI) Information System Journal (INFOS) JTECS : Jurnal Sistem Telekomunikasi Elektronika Sistem Kontrol Power Sistem dan Komputer Jurnal Pengabdian Mitra Masyarakat (JPMM) International Journal of Advanced Science Computing and Engineering SmartComp Fahma : Jurnal Informatika Komputer, Bisnis dan Manajemen Scientific Journal of Informatics SWAGATI: Journal of Community Service Jurnal Pengabdian Masyarakat Inovasi Indonesia
Yoga Pristyanto
Universitas Amikom Yogyakarta
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Medicine & Pharmacology Public Health Social Sciences Other

Published : 44 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : JOIV : International Journal on Informatics Visualization

 1 
Stock Price Time Series Data Forecasting Using the Light Gradient Boosting Machine (LightGBM) Model Anggit Dwi Hartanto; Yanuar Nur Kholik; Yoga Pristyanto
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.4.01740

Abstract

In the world of stock investment, one of the things that commonly happens is stock price fluctuations or the ups and downs of stock prices. As a result of these fluctuations, many novice investors are afraid to play stocks. However, on the other hand, stocks are a type of investment that can be relied upon during disasters or economic turmoil, such as in 2019, namely the Covid-19 pandemic. For stock price fluctuations to be estimated by investors, it is necessary to carry out a forecasting activity. This study builds stock price forecasting using the Light Gradient Boosting Machine (LightGBM) algorithm, which has high accuracy and efficiency. To forecast stock price time series, the model used is the LightGBM ensemble. At the same time, they were optimizing the determination of hyperparameters using Grid Search Cross Validation (GSCV). This study will also compare the LGBM algorithm with other algorithms to see which model is optimal in forecasting price stock data. In this study, the test used the RMSE metric by comparing the original data (testing data) with the predicted results. The experimental results show that the LightGBM model can compete with and outperform boosting-based forecasting models like XGBoost, AdaBoost, and CatBoost. In comparing forecasting models, the same dataset is used so that the results are accurate, and the comparisons are equivalent. In future research, paying attention to the data during pre-processing is necessary because it has many outliers. In addition, it is necessary to include exogenous variables and external variables, which are determined to involve many parties.
Extreme Gradient Boosting Algorithm to Improve Machine Learning Model Performance on Multiclass Imbalanced Dataset Pristyanto, Yoga; Mukarabiman, Zulfikar; Nugraha, Anggit Ferdita
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1102

Abstract

Unbalanced conditions in the dataset often become a real-world problem, especially in machine learning. Class imbalance in the dataset is a condition where the number of minority classes is much smaller than the majority class, or the number is insufficient. Machine learning models tend to recognize patterns in the majority class more than in the minority class. This problem is one of the most critical challenges in machine learning research, so several methods have been developed to overcome it. However, most of these methods only focus on binary datasets, so few methods still focus on multiclass datasets. Handling unbalanced multiclass is more complex than handling unbalanced binary because it involves more classes than binary class datasets. With these problems, we need an algorithm with features that can support adjustments to the difficulties that arise in multiclass unbalanced datasets. One of the algorithms that have features for adjustment is the ensemble algorithm, namely Xtreme Gradient Boosting. Based on the research, our proposed method with Xtreme Gradient Boosting showed better results than the other classification and ensemble algorithms on eight datasets with five evaluation metrics indicators such as balanced accuracy, the geometric-mean, multiclass area under the curve, true positive rate, and true negative rate. In future research, we suggest combining methods at the data level and Xtreme Gradient Boosting. With the performance increase in Xtreme Gradient Boosting, it can be a solution and reference in the case of handling multiclass imbalanced problems. Besides, we also recommended testing with datasets in the form of categorical and continuous data.
Co-Authors Acihmah Sidauruk Aditya Yoga Pratama Afrig Aminuddin Aisha Shakila Iedwan Akhmad Dahlan Alvin Rahman Al Musyaffa Andi Sunyoto Anggi Thoat Ariyanto Anggit Dwi Hartanto Anggit Dwi Hartanto Anggit Dwi Hartanto, Anggit Dwi Anggita, Sharazita Dyah Anna Baita Arif Nur Rohman arif nur rohman Asti Astuti, Ika Atik Nurmasani ATIK NURMASANI Atik Nurmasani Barus, Herianta Bety Wulan Sari Bety Wulan Sari, Bety Wulan Bligania Bligania Cherfly Kaope Donni Prabowo, Donni Dwi Hartanto, Anggit Dyah Anggita, Sharazita Eli Pujastuti, Eli Eza Nanda Fadhilah Dwi Ananda Fajri, Ika Nur Fauzy, Marwan Noor Gagah Gumelar Gita Cahyani Hendra Kurniawan Heri Sismoro Hidayat, Kardilah Rohmat Ibnu Hadi Purwanto Iedwan, Aisha Shakila Ike Verawati Ikmah Ikmah Irfan Pratama Istikomah Khoiruddin, Lukman Kono, Maria Fatima Kristianti, Fanny Novatriana Lucky Adhikrisna Wirasakti Mambaul Hisam Marcheilla Trecya Anindita Mauliza, Nia Mukarabiman, Zulfikar Mulia Sulistiyono Nia Mauliza Nia Mauliza Nugraha, Anggit Ferdita Nuri Cahyono Nurindah A Amari Purwati, Sintia Eka Putra, Frahma Aditya Rahman Saputra, Rahman Rifda Faticha Alfa Aziza Rizky Hafizh Jatmiko Rohmad Fajarudin Rohman, Arif Nur Romadhon, Ibrahim Aji Fajar Rospita, Andri Sabella, Cindy Dinda Sifa’ul Husna, Siti Okta Sumarni Adi Windarni, Vikky Aprelia Wirantanu, Dipa Wirasakti, Lucky Adhikrisna Wiwi Widayani Yanuar Nur Kholik Yudiyanto, Muhammad Resa Arif Yuli Astuti Zein, Aditya Ahmad