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All Journal Pythagoras: Jurnal Matematika dan Pendidikan Matematika CAUCHY: Jurnal Matematika Murni dan Aplikasi Dimas: Jurnal Pemikiran Agama untuk Pemberdayaan Jurnal Pendidikan Matematika RAFA Journal of Natural Sciences and Mathematics Research Phenomenon : Jurnal Pendidikan MIPA Edu Sains: Jurnal Pendidikan Sains dan Matematika Sinkron : Jurnal dan Penelitian Teknik Informatika BAREKENG: Jurnal Ilmu Matematika dan Terapan Unisda Journal of Mathematics and Computer Science (UJMC) JTAM (Jurnal Teori dan Aplikasi Matematika) Jambura Journal of Mathematics Delta: Jurnal Ilmiah Pendidikan Matematika Jurnal Informatika dan Rekayasa Perangkat Lunak Square : Journal of Mathematics and Mathematics Education Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Journal of Multidisciplinary Applied Natural Science Journal of Mathematics: Theory and Applications Edumatica: Jurnal Pendidikan Matematika Journal Focus Action of Research Mathematic (Factor M) Indonesian Journal of Statistics and Its Applications
Emy Siswanah
Universitas Islam Negeri Walisongo Semarang
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Energy Engineering Environmental Science Immunology & microbiology Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Comparative Study of Recurrent Neural Network (RNN) and Extreme Learning Machine (ELM) in Predicting Bank Central Asia’s Stock Price Mukharomah, Rizanatul; Siswanah, Emy
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 2 (2025): September
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Predicting stock prices is an important financial topic, especially for investors who want to maximize profit and minimize risk. This research compares two machine-learning capabilities, a Recurrent Neural Network (RNN) and an Extreme Learning Machine (ELM), in predicting Bank Cental Asia (BBCA) stock prices. These two are chosen for their capabilities in handling time-series data. This research uses the data of BBCA’s daily prices over a certain period and involves several steps such as data collecting, data pre-processing, model training, and calculation of accuracy value. This accuracy calculation will be evaluated using Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). This research shows ELM has better accuracy than RNN in predicting BBCA’s stock prices. ELM shows lower MSE and MAPE values than RNN, indicating the capability of ELM to predict with smaller errors. This research also concludes ELM is better in accuracy than RNN in predicting BBCA’s stock prices. Thus, ELM is the recommended method to predict stock prices.
Option Pricing Using Modification of Black Scholes Merton Model with GJR-GARCH Fatimah Oktaviani; Emy Siswanah
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 1 (2026): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v11i1.37426

Abstract

Option pricing is an important topic in modern finance as it plays a role in investment strategy and risk management. The Black-Scholes-Merton (BSM) model introduced in 1973has become the standard in option pricing, but the assumption of constant volatility and symmetry makes this model often less suitable for volatile and asymmetric market conditions. This study aims to modify the BSM by incorporating volatility estimated through the Glosten-Jagannathan-Runkle GARCH (GJR-GARCH) model, and compare its performance with the Fractional Black-Scholes-Merton (FBSM) model. The data used is the daily closing price of Apple Inc. shares for the period January 7, 2022 to March 7, 2025 obtained from Yahoo Finance. The research procedure includes stationarity test using Augmented Dickey-Fuller (ADF), ARIMA identification, heteroscedasticity testing, and volatility estimation with GJR-GARCH (1,1) model. European call option prices are estimated by BSM and FBSM using both historical volatility and GJR-GARCH volatility. The results show that the FBSM model with GJR-GARCH (1,1) volatility provides the most accurate estimation with a Mean Absolute Percentage Error (MAPE) of 4.08%. In contrast, the BSM with historical volatility yields a MAPE of 17.25%. These findings confirm that the integration of FBSM with GJR-GARCH volatility is more realistic and reliable in option pricing under dynamic and asymmetric market conditions.
Low Welfare Status Modeling Using Mixed Geographically Weighted Regression Method with Fixed Tricube Weighting Function: Pemodelan Status Sejahtera Rendah Menggunakan Metode Mixed Geographically Weighted Regression Dengan Fungsi Pembobot Fixed Tricube Tri Yuliyanti; Emy Siswanah; Lulu Choirun Nisa
Indonesian Journal of Statistics and Applications Vol 6 No 2 (2022)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v6i2p213-227

Abstract

Mixed Geographically Weighted Regression (MGWR) is a method for analyzing spatial data in regression that produces local and global parameters. Parameter estimation using WLS with a fixed tricube weighting function. The object of research in this study is poor population (X1), female household heads (X2), the education (X3), individuals with disabilities (X4), individuals having chronic disease (X5), individuals works (X6), uninhabitable houses (X7), and low welfare status (Y). This reseach applied to the low welfare status (Y) of each district/town in Central Java in 2019, and produced local variables are X1, X3, X5 and global variables are X2, X4, X6, and X7. However, only X1, X4, and X7 have a significant effect on Y in each district/town in Central Java, and X3 has a significant effect on only a few districts/cities, the other, X2, X5, and X6 have no significant effect on the model. The predictor variable has an effect of 98.92% on the model while the remaining 1.18% affected by other factors. The MGWR method divides 2 groups based on significant variables, (a) The first, a district/town whose low welfare status affected by X1, X3, X4, X7 covering Cilacap, Purbalingga, Kendal, Batang, Brebes, Pekalongan Town, and Tegal Town, (b) The second, districts/town whose low welfare status affected by X1, X4, X7 covering Banjarnegara, Purworejo, Temanggung, Kudus, Wonosobo, Pekalongan, Pemalang, Jepara, Wonogiri, Boyolali, Tegal, Magelang, Sukoharjo, Banyumas, Grobogan,  Klaten, Karanganyar,  Kebumen, Blora,  Semarang Town, Pati, Sragen, Demak, Magelang Town, Salatiga Town, Surakarta Town, Semarang, and Rembang.
Co-Authors Abdurakhman Abdurakhman Ahmad Aunur Rohman Ahmad Aunur Rohman Ahmad Mutawaslih Idrus Anggraini, Agustina Di Asih I Maruddani Dwi Noviyanti Dyan Falasifa Tsani Dyan Falasifa Tsani Fara Lukita Umul Amaliah Fatimah Oktaviani Fika Riza Syifamillah Fitriani, Ulliya Furqon, Syifa'ul Izzatul Yazidah, Izzatul Linda Nur Chabibah Lulu Choirun Nisa M. Ismail M. Ismail Maulida, Via Minhayati Saleh Muhammad Malik Hakim Muhammad Malik Hakim, Muhammad Malik Mukharomah, Rizanatul Multiningsih, Multiningsih Nabila Wafa’, Jihan Nisa, Eva Khoirun Rohman, Ahmad Aunur Rosidah, Zakiyatur Seftina Diyah Miasary Siti Maslihah Sulis Azizahroh Mahmudah Syifa'ul Furqon Tafrikan, Mohamad Tauki Rohman Muzaki Tri Yuliyanti Tsani, Dyan Falasifa Ulna, Jihan Ramadhani Ar-Raafi’ Umami, Riza Lathifatul Via Maulida Yustisio, Dheva