Article Per Year (5 Year)
p-Index From 2021 - 2026
3.477
P-Index
This Author published in this journals
All Journal Format : Jurnal Imiah Teknik Informatika IJISCS (International Journal Of Information System and Computer Science) Building of Informatics, Technology and Science Jurnal Pengabdian Kepada Masyarakat (JPKM) Tabikpun JTIKOM: Jurnal Teknik dan Sistem Komputer Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Data Mining dan Sistem Informasi Journal Social Science And Technology For Community Service ICTEE (Engineering Journals of Information, control, telecommunication and electrical) Jurnal Ilmiah Sistem Informasi Akuntansi (JIMASIA) Paradigma Journal of Engineering and Information Technology for Community Service Jurnal Ilmiah Informatika dan Ilmu Komputer Jurnal Media Celebes Journal of Data Science and Information System Journal of Artificial Intelligence and Technology Information Journal of Information Technology, Software Engineering and Computer Science Swarnadwipa Jurnal Informatika dan Rekayasa Perangkat Lunak
Gunawan, Rakhmat Dedi
Universitas Teknokrat Indonesia
Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences Other

Published : 27 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Building of Informatics, Technology and Science

 1 
Perbandingan Random Forest dan XGBoost Untuk Prediksi Penjualan Produk E-Commerce Rumah Madu Hayatunnisa, Destaria; Permata, Permata; Priandika, Adhie Thyo; Gunawan, Rakhmat Dedi
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8491

Abstract

Inventory management is one of the main challenges for small and medium enterprises (SMEs), including Rumah Madu in Bandar Lampung, where honey stock levels are often determined based on estimation rather than precise calculation. This study aims to analyze and compare the performance of the Random Forest and XGBoost algorithms in predicting honey sales to achieve more measurable stock management. The dataset consists of 1,699 honey sales transactions that have undergone cleaning, feature transformation, and standardization processes. The variables used include honey type, unit price, day, month, holiday status, and promotion indicators. Modeling was conducted using a time-series split approach, where historical data served as the training set and recent data as the testing set. The evaluation results show that Random Forest achieved an MAE of 24.35, RMSE of 29.04, and R² of -0.9685, while XGBoost achieved an MAE of 25.50, RMSE of 30.58, and R² of -1.1825. The negative R² values indicate that both models were unable to explain data variation optimally, with performance falling below a simple baseline. Nevertheless, the feature importance analysis revealed that unit price and honey type were the dominant factors influencing sales. This study highlights the need for further model development through parameter optimization and improved data quality to enhance prediction accuracy.
Perbandingan Kinerja Model ARIMA dan LSTM dalam Peramalan Harga Crypto Solana (SOL-USD) Berbasis Data Yahoo Finance Wadiyan, Wadiyan; Permata, Permata; Priandika, Adhie Thyo; Gunawan, Rakhmat Dedi
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9444

Abstract

The extreme volatility and non-linear patterns of Solana (SOL) data, driven by its unique consensus mechanism and massive transaction volume, demand accurate forecasting methods to mitigate investment risks. This study compares the statistical method Autoregressive Integrated Moving Average (ARIMA) and Deep Learning Long Short-Term Memory (LSTM) using daily closing price data of SOL-USD from April 2020 to March 2025 obtained from Yahoo Finance. The ARIMA model was developed with optimal parameters (0,1,0), while the LSTM architecture utilized 50 hidden layer units with a 60-day timestep. Evaluation results indicate that the LSTM model significantly outperforms ARIMA, achieving an RMSE of 13.1352 and a MAPE of 6.07% (classified as highly accurate), compared to ARIMA's RMSE of 31.1241 and MAPE of 14.03%. The study concludes that neural network approaches are more effective and adaptive than traditional statistical methods in capturing the highly volatile price dynamics of crypto assets.
Co-Authors Abim Ari Bimantara Ade Surahman Agung Setiawan Aldi Nugraha Anggun Maylani Annisa Anggraini Ariany, Fenty Arshad, Muhammad Waqas Bagus Abadi Deddy Rudhistiar Dedi Darwis Dimas Rizki Mahendra Edwin Dwi Andriano Esy Ervina Yanti Fenty ariany Fuad Surya Mawinar Hayatunnisa, Destaria Heni Sulistiani Imam Ahmad Irma Hanifah Lathifah Lathifah Mico Fahrizal Muhamad Odhie Prasetio Muhtad Fadly Novriyadi Novriyadi Novriyadi Patricia Evericho Mountaines Permata Permata Permata, Permata Prakoso, Ryo Cahyo Praptiwi, Riska Amalia Prasetyawan, Purwono Priandika, Adhie Thyo Rido Febryansyah Riduwan Napianto Roby Satria Rohmat Indra Borman Ryan Andre Nugroho Ryan Randy Suryono Saputra, Thesa Adi Stevani Natasiyah suaidah suaidah SUAIDAH SUAIDAH, SUAIDAH Temi Ardiansah Tri Widodo Ulum, Faruk Wadiyan, Wadiyan Wahyudi, Agung Deni Wang, Junhai Wijayanto, Danur Yuri Rahmanto Zaenal Abidin