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All Journal JTAM (Jurnal Teori dan Aplikasi Matematika) Square : Journal of Mathematics and Mathematics Education Journal Focus Action of Research Mathematic (Factor M)
Haris, M. Al
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Projection of PT Aneka Tambang Tbk Share Risk Value Based on Backpropagation Artificial Neural Network Forecasting Result Haris, M. Al; Setyaningsih, Laras Indah; Fauzi, Fatkhurokhman; Amri, Saeful
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 2 (2024): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i2.20267

Abstract

PT Aneka Tambang Tbk (ANTAM) received an award as the most sought-after stock issuer in Indonesia in 2016. That stock continued to attract investors in 2022 due to a 105% increase in net profit and a 19% increase in sales from the previous year. Despite the upward trend, investors still had doubts due to the fluctuating movement of ANTAM's stock prices. Therefore, forecasting was needed to determine the future movement of stock prices. The Backpropagation Neural Network method had good capabilities for fluctuating data types. However, this method has the disadvantage of a lengthy iteration process. To handle this limitation, The Nguyen-Widrow weighted setting was applied to address this constraint. The expected Shortfall (ES) method used the forecasting results to measure investment risk. This research uses ANTAM stock closing price data from May 2, 2018, to May 31, 2023. Based on the analysis results, the best architecture was obtained with a configuration of 5-11-1, using Nguyen-Widrow weight initialization and a combination of a learning rate of 0.5 and momentum of 0.9. This architecture yielded a prediction error based on the Mean Absolute Percentage Error (MAPE) of 1.9947%. Risk measurement with the ES method based on the prediction for the next 60 periods showed that at a 95% confidence level, the risk value was 0.002181; at a 90% confidence level, it was 0.002165; at an 85% confidence level, it was 0.002148, and at an 80% confidence level, it was 0.002132.
Pemodelan ARIMA dan ARIMAX untuk Memprediksi Jumlah Produksi Padi di Kota Magelang Amri, Ihsan Fathoni; Ramadhan, Wulan Nur; Ainurrofiah, Safira; Haris, M. Al
Square : Journal of Mathematics and Mathematics Education Vol. 5 No. 2 (2023)
Publisher : UIN Walisongo Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21580/square.2023.5.2.17059

Abstract

Memprediksi atau meramalkan perilaku observasi biasanya menggunakan pemodelan time series yang dilakukan secara berurutan. Prediksi jumlah produksi padi diharapkan dapat memberikan masukan bagi pemerintah dan dimanfaatkan oleh siapa saja sebagai pengembangan pada sektor pertanian serta sebagai bahan ajar penggunaan metode ARIMA (Autoregressive Integrated Moving Average) dan ARIMAX (Autoregressive Integrated Moving Average with Exogenous Variable). Tujuan dari penelitian ini sebagai perbandingan dalam menemukan model terbaik dari metode ARIMA dan ARIMAX untuk memprediksi jumlah produksi padi pada tahun 2023 di kota Magelang. Data yang digunakan merupakan data produksi padi dan luas lahan tanam sebagai variabel eksogen di Kota Magelang pada bulan Januari 2019 sampai Desember 2022. Berdasarkan hasil analisis, diperoleh model ARIMA terbaik untuk meramalkan jumlah produksi padi di kota Malang adalah ARIMA (0,1,1), sedangnkan model ARIMAX terbaik adalah ARIMAX (0,0,1). Perbandingan kedua model tersebut berdasarkan nilai MAPE, model ARIMAX (0,0,1) menjadi model terbaik untuk meramalkan jumlah produksi padi di Kota Malang karena menghasilkan MAPE terkecil 6,31%. Hasil peramalan menggunakan model ARIMAX (0,0,1) menunjukkan data cenderung mengalami pola trend turun. Hal ini dikarenakan lahan pertanian yang semakin sempit setiap tahunnya sehingga menyebabkan jumlah produksinya semakin menurun.Kata Kunci: Pemodelan, metode ARIMA, metode ARIMAX, Produksi Padi.
Pemodelan ARIMAX untuk Meramalkan Harga Minyak Mentah Dunia Amri, Ihsan Fathoni; Wulandari, Ayu; Abidah, Khansa Ni'mal; Irawan, Alfian Chandra; Haris, M. Al
Square : Journal of Mathematics and Mathematics Education Vol. 5 No. 1 (2023)
Publisher : UIN Walisongo Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21580/square.2023.5.1.17074

Abstract

Perdagangan secara umum dikelompokkan menjadi dua yaitu, ekspor dan impor. Salah satu contoh perdagangan tersebut adalah minyak mentah. Diketahui saat ini harga pasar minyak mentah dunia mempengaruhi tingkat perekonomian global. Harga minyak yang terus berubah, tentu saja menjadi sumber kekhawatiran dan perhatian tersendiri, terutama dalam industri minyak. Dalam penelitian ini, akan mengkaji harga minyak mentah menggunakan model ARIMAX (Autoregressive Integrated Moving Average with Exogenous Variables). Model ARIMAX dipilih karena mampu mengintegrasikan variabel eksternal, seperti volume nilai tukar rupiah dan produksi minyak, yang mempengaruhi harga minyak mentah. Tujuan utama penelitian ini adalah untuk mengembangkan model prediktif yang akurat dengan mempertimbangkan pengaruh produksi minyak (Richard et al., 2021)dan nilai tukar rupiah terhadap harga minyak mentah dunia. Berdasarkan hasil analisis model ARIMAX (0,1,2) merupakan model terbaik dalam meramalkan harga minyak mentah dunia karena memiliki nilai AIC dan MAPE terkecil, yaitu AIC sebesar 408,49 dan MAPE 8,88. Berdasarkan hasil tersebut peramalan dengan model model ARIMAX (0,1,2) dapat dikategorikan sangat baik.Kata Kunci: ARIMAX, Perekonomian, Harga minyak mentah dunia, Nilai tukar rupiah, Produksi minyak.
Forecasting the Rupiah exchange rate against the US Dollar using the LSTM algorithm Multiyaningrum, Riska; Dawi, Herculianus Rowa; Hartanto, Raka Nurhaq Mulya; Haris, M. Al; Amri, Ihsan Fathoni
Journal Focus Action of Research Mathematic (Factor M) Vol. 8 No. 2 (2025): Vol. 8 No. 2 (2025)
Publisher : Universitas Islam Negeri (UIN) Syekh Wasil Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30762/f_m.v8i2.6530

Abstract

Exchange rates are a vital indicator of an economy's balance. The fluctuations of Indonesia's currency, the rupiah, against the USD influenced trade patterns, investment, and both monetary and fiscal policy. Exchange rate fluctuations affect international trade, investment, inflation, and overall economic stability. The high volatility of the Rupiah against the USD, driven by macroeconomic and monetary factors, has a significant impact on national economic policy, necessitating research that utilizes the latest data and adaptive models. To capture the nonlinear and complicated behavior of exchange rates, an advanced methodology for forecasting is needed. This journal utilizes the Long Short-Term Memory (LSTM) neural network model to forecast the exchange rate of the rupiah towards the dollar from March 1, 2022, up to February 28, 2025, in daily data. The data used in this research are sourced from www.bi.go.id, which provides the official daily exchange rate of USD to IDR. The Long Short-Term Memory method was chosen for modeling long-term dependencies within time series. After normalization, an 80/20 split is performed for training and testing on the dataset. The network runs optimization using three hidden layers with 50 neurons each and a batch size of 32 for 200 epochs. The optimal configuration, achieved through experimental trials, consisted of two hidden layers with 50 neurons, a batch size of 32, and 200 epochs. This is manifest in the fact that LSTM effectively captures movements in exchange rates, with an RMSE of 0.6226 and a MAPE of 0.3031%. This degree of accuracy enables the model to inform economic policy decisions based on data.
Co-Authors Abidah, Khansa Ni'mal Ainurrofiah, Safira Amri, Saeful Ayu Wulandari Dawi, Herculianus Rowa Fauzi, Fatkhurokhman Hartanto, Raka Nurhaq Mulya Ihsan Fathoni Amri Irawan, Alfian Chandra M. Al Haris Multiyaningrum, Riska Ramadhan, Wulan Nur