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All Journal ETIKONOMI Jurnal Keuangan dan Perbankan JURNAL MEDIA INFORMATIKA BUDIDARMA Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Manajemen Journal of Midwifery International Journal of Engagement and Empowerment (IJE2) Jurnal Mahasiswa Karakter Bangsa Seulanga : Jurnal Pendidikan Anak Indonesian Capital Market Review eProceedings of Engineering Karimah Tauhid
Siti Saadah
Fakultas Ekonomi, Unika Atma Jaya
Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Neuroscience Social Sciences Other

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Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) Methods to Forecast Daily Turnover at BM Motor Ngawi Larasati, Larasati; Saadah, Siti; Yunanto, Prasti Eko
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 1 (2024): March 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i1.27643

Abstract

The number of motorcycles on the report of Indonesian BPS statistics from the Indonesian State Police between 2019 to 2021 by its type has increased annually. Routine motorcycle checks, services, and maintenance are essential to keep a motorcycle in good condition and more durable; therefore, buying spare parts is enlarged in line with the growth of public motorcycle ownership. The necessity of buying spare parts increases with the growth of public motorcycle ownership. Numerous stores in Ngawi offer motorcycle spare parts and check services for routine motorcycle maintenance. One of these stores is BM Motor. To develop an effective product-selling strategy, it is essential to forecast the daily turnover of the shop. To achieve this, the present research aims to analyze the daily turnover using Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM). These methods were applied to a time-series dataset, allowing for an in-depth examination of the patterns and trends in the shop's turnover. The research compares several hyperparameter tunings and scenarios to optimize the models that forecast daily turnover data at the store. The outcomes presented that the LSTM model achieved a lesser MAE score of 0.087, while the RNN model scored 0.092. These findings proved that the LSTM model achieved lower MAE than the RNN model, it means LSTM is more accurate than the RNN model.
Forecasting of GPU Prices Using Transformer Method Faisal Hadi, Risyad; Saadah, Siti; Adytia, Diditq
eProceedings of Engineering Vol. 10 No. 5 (2023): Oktober 2023
Publisher : eProceedings of Engineering

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Abstract

Abstract— GPU or VGA (graphic processing unit) is a vital component of computers and laptops, used for tasks such as rendering videos, creating game environments, and compiling large amounts of code. The price of GPU/VGA has fluctuated significantly since the start of the COVID19 pandemic in 2020. This research aims to forecast future GPU prices using deep learning-based time series forecasting using the Transformer model. We use daily prices of NVIDIA RTX 3090 Founder Edition as a test case. We use historical GPU prices to forecast 8, 16, and 30 days. Moreover, we compare the results of the Transformer model with two other models, RNN and LSTM. We found that to forecast 30 days; the Transformer model gets a higher coefficient of correlation (CC) of 0.8743, a lower root mean squared error (RMSE) value of 34.68, and a lower mean absolute percentage error (MAPE) of 0.82 compared to the RNN and LSTM model. These results suggest that the Transformer model is an effective and efficient method for predicting GPU prices.Keywords— GPU, Transformer, Forecasting, Time Series Forecasting
Prediksi Harga Dogecoin Berdasarkan Sentimen dari Twitter Menggunakan LSTM Prasetyo Nugroho, Ecky; Saadah, Siti; Afianti, Farah
eProceedings of Engineering Vol. 10 No. 5 (2023): Oktober 2023
Publisher : eProceedings of Engineering

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Abstract

Abstrak— Dogecoin adalah mata uang kripto yang diciptakan oleh Billy Markus dan Jackson Palmer, tetapi mereka membuat Dogecoin hanya untuk dibuat sebagai bahan candaan di dunia mata uang kripto. Tugas akhir ini menganalisis sentimen dan prediksi terhadap Doge dengan melakukan korelasi antara harga Doge terhadap data yang dikumpulkan dari media social Twitter mengenai Doge. Penelitian ini dilakukan menggunakan pendapat-pendapat yang disampaikan oleh pengguna jejaring sosial yang menggunakan bahasa Inggris. Metode yang digunakan adalah LSTM dengan mengacu pada penelitian-penelitian sebelumnya yang menunjukkan bahwa LSTM memberikan akurasi tertinggi. Data yang digunakan pada penelitian ini adalah harga doge dan tweet pada periode januari-april 2021. Menentukan korelasi antara doge dan tweet dilakukan dengan korelasi pearson dimana hasil korelasi tersebut menentukan korelasi positif, korelasi negatif dan tidak berkorelasi, setelah itu dilakukan prediksi harga doge close dengan LSTM. Harga Doge Close berkorelasi dengan sentimen, namun tidak kuat tidak juga lemah. Tidak ada peningkatan akurasi hasil prediksi dibandingkan pengujian pertama yang dimana pada pengujian pertama nilai RMSE sebesar 0,003 dan pengujian kedua nilai RMSE sebesar 0,008.Kata kunci— analisis sentimen, LSTM, prediksi, korelasi
Co-Authors ABADI Adytia, Diditq ANDRIYANI, EVA Andy Lasmana Azzahra, Rizqina ENTINA, SHINTA T Faisal Hadi, Risyad Farah Afianti FITRI, LENI RAHMAWATI Gia Septiana Wulandari Hendri Hendri Ida Farida Ihsan, Rivan Nur Irma Palupi Kurniawati, Elis Nia Kusumahadi, Teresia Angelia Larasati Larasati Lestari, Risma Fuji MUTIA METTY, R. RATNA Nurani, Ratih Oktaviana, Yogi Permana, Fikri C Prasetyo Nugroho, Ecky Prasti Eko Yunanto Saputra, Dian Dieniawati Satrio, M Susanti Susanti Tina Martina, Tina Triana Indrayani Utomo, Felicia Grace Ratnasari WAHYUNI, RUBIYANTI Yulianti, Yuli