Article Per Year (5 Year)
p-Index From 2021 - 2026
4.333
P-Index
This Author published in this journals
All Journal Swabumi (Suara Wawasan Sukabumi) : Ilmu Komputer, Manajemen, dan Sosial Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Perspektif : Jurnal Ekonomi dan Manajemen Universitas Bina Sarana Informatika Jurnal Teknik Komputer AMIK BSI Paradigma Information System for Educators and Professionals : Journal of Information System JIEET (Journal of Information Engineering and Educational Technology) Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Riset Informatika INTI Nusa Mandiri Jurnal Infortech Computer Science (CO-SCIENCE) JUSTIAN - Jurnal Sistem Informasi Akuntansi J-SAKTI (Jurnal Sains Komputer dan Informatika) Reputasi: Jurnal Rekayasa Perangkat Lunak Jurnal Abdimas Komunikasi dan Bahasa TECHSI - Jurnal Teknik Informatika Jurnal Insan Indonesian Journal Computer Science (ijcs) Jurnal Pengabdian Kreatif Cemerlang Indonesia Bianglala Informatika JUSS (Jurnal Sains dan Sistem Informasi)
Yesni Malau
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

Published : 41 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 41 Documents
Search

 1   2   3   4   5 
PERBANDINGAN PENERAPAN ALGORITMA DEEP LEARNING DALAM PREDIKSI HARGA EMAS Muhammad Fahmi Julianto; Muhammad Iqbal; Wahyutama Fitri Hidayat; Yesni Malau
INTI Nusa Mandiri Vol. 19 No. 1 (2024): INTI Periode Agustus 2024
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v19i1.5559

Abstract

Digital investment is trending because advancements in information technology make access easy through smartphones. Various digital investment instruments attract much interest from the public. Post COVID-19 pandemic, the economic impact of the pandemic is still felt until the end of 2022, requiring people to be smart in managing their finances. Gold investment is considered profitable due to its high value and tendency to increase, unlike the fluctuating stocks. Although easily accessible, investments carry risks, so investors must have sufficient knowledge to maximize profits. This research aims to predict gold prices using several deep learning models, namely Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM). The dataset used was taken from the Kaggle website, which includes historical gold price data. In this research, various deep learning models were applied and evaluated to determine the best model for predicting gold prices. The results show that the CNN model with Adam optimization and Mean Squared Error (MSE) loss function provides the best performance. The CNN model achieved the lowest Mean Absolute Error (MAE) of 0.004848717761305338, the lowest MSE of 4.3451079619612133, and the lowest Root Mean Squared Error (RMSE) of 0.006591743291392053. These results indicate that the CNN model is more effective in predicting gold prices compared to the ANN, RNN, and LSTM models on the used dataset.
Co-Authors Adi Surya Putra Ari Ariyanto Adi Surya Putra Ari Ariyanto - STMIK Nusa Mandiri Ahmad Setiadi Aldi Hermawan Pratama Asep Sayfulloh Cep Adiwiharja Eka Wulansari Fridayanthie, Eka Wulansari Eni Pudjiarti Fintri Indriyani Harsih Rianto Hasan Basri Juando, Raden Rivan Julianto, Muhammad Fahmi Kartika Handayani khairul rizal Maulida, Ulfa Mochammad Imron Awalludin Mohammad Syamsul Azis Muhammad Fahmi Julianto Muhammad Iqbal Oky Irnawati Pradita Pusfitaningrum Pusfitaningrum, Pradita Rachmat Adi Purnama Riswandi Ishak Rizki, Achmad Guntur Al Sri Watmah Syaiful Anwar Syaputra, Syahiena Maulana Tati Mardiana Tubagus Aom Somadiningrat D.W.K Wahyutama Fitri Hidayat Wawan Nugroho Yoseph Tajul Arifin