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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Ilmiah Pangabdhi INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi MUST: Journal of Mathematics Education, Science and Technology BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Zeta - Math Journal J Statistika : Jurnal Ilmiah Teori Dan Aplikasi Statistika Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) ComTech: Computer, Mathematics and Engineering Applications Jurnal ABDINUS : Jurnal Pengabdian Nusantara G-Tech : Jurnal Teknologi Terapan Inferensi Contemporary Mathematics and Applications (ConMathA) Jurnal Statistika dan Aplikasinya Elastisitas Jurnal Ekonomi Pembangunan Journal of Mathematics Education and Science Indonesian Community Journal Eksponensial Limits: Journal of Mathematics and Its Applications
Elly Pusporani
Universitas Airlangga
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Pertamina Geothermal Energy Stock Price Prediction and Risk Analysis: ARIMA-GARCH and VaR with Cornish-Fisher Expansion M. Fariz Fadillah Mardianto; Doni Muhammad Fauzi; Idrus Syahzaqi; Elly Pusporani
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 10, No 3 (2026): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v10i3.37866

Abstract

The geothermal energy sector makes a strategic contribution to supporting long-term domestic energy sustainability and attracts investor attention due to high market volatility. Therefore, analysis that can accurately describe stock price dynamics and risks is needed. This study aims to model and predict the share price of PT Pertamina Geothermal Energy (PGEO) and estimate the associated investment risk. This study uses a quantitative time series approach with ARIMA–GARCH modeling and the Value at Risk method using Cornish–Fisher Expansion. This study uses weekly closing price data for PGEO stocks from February 2023 to September 2025. The methods used include ARIMA-GARCH modeling for stock price prediction and Cornish–Fisher Expansion based Value at Risk to estimate investment risk. The results indicate that the ARIMA(2,2,0)–GARCH(2,0) model provides the most adequate representation of PGEO stock price dynamics and volatility, achieving an RMSE value of 258.33 and a MAPE of 16.21% as measures of forecasting performance. Meanwhile, risk measurement using the Cornish–Fisher Expansion Value at Risk method produced a VaR value that increased along with the holding period and confidence level, with a risk range of 8.21% to 19.95%. The novelty of this research lies in the integration of ARIMA–GARCH volatility modeling and the Value at Risk method using Cornish–Fisher Expansion, thereby providing a more comprehensive analytical framework for price prediction and investment risk estimation in renewable energy stocks. The findings of this study are expected to serve as an empirical reference for investors and policymakers in assessing potential risks and supporting more informed investment decisions within the renewable energy sector.
Optimalisasi Pemasaran Digital dan Penguatan Legalitas Usaha bagi UMKM Desa Tambaksawah: Implementasi Pelatihan dan Pendampingan Berbasis Media Sosial Sediono Sediono; Elly Pusporani; Sa’idah Zahrotul Jannah; M. Fariz Fadillah Mardianto; Alfredi Yoani; Helfira Lady Ari Pramesti; Ainaya Zakiyah Nabila; Sasy Okti Karima
I-Com: Indonesian Community Journal Vol 5 No 4 (2025): I-Com: Indonesian Community Journal (Desember 2025)
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/i-com.v5i4.8282

Abstract

Kabupaten Sidoarjo merupakan kawasan industri dengan potensi besar pada sektor UMKM, termasuk Desa Tambaksawah yang telah mengembangkan berbagai produk makanan dan kerajinan tangan. Namun, pelaku UMKM masih menghadapi kendala dalam perizinan usaha, pemasaran terbatas, serta kurangnya pemahaman mengenai legalitas usaha. Kegiatan Pengabdian Masyarakat yang dilaksanakan pada Maret – September 2023 bertujuan memperluas jangkauan pemasaran melalui digital marketing dan meningkatkan pemahaman terkait legalitas usaha. Kegiatan meliputi tahap persiapan, pelatihan, pendampingan, monitoring, dan evaluasi. Sebanyak 9 UMKM berhasil membuat akun TikTok dan 30 video promosi diproduksi sebagai strategi branding digital. Selain itu, UMKM mulai beralih dari pemasaran berbasis WhatsApp menuju penggunaan media sosial yang lebih luas, sehingga meningkatkan visibilitas produk dan peluang perluasan pasar. Secara umum, kegiatan berjalan baik, ditunjukkan oleh hasil kuesioner yang mayoritas menyatakan pelaksanaan sangat baik. Meski demikian, penerapan informasi perizinan masih perlu ditingkatkan, karena baru sekitar 50% pelaku UMKM yang telah mendaftarkan usahanya.
UNILEVER STOCK PRICES FORECASTING WITH ENSEMBLE AVERAGING APPROACH ARIMA-GARCH AND SUPPORT VECTOR REGRESSION Elly Pusporani; Alfi Nur Nitasari; Fatiha Nadia Salsabila; Irma Ayu Indrasta; M. Fariz Fadillah Mardianto
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0137-0154

Abstract

Investment, mainly in stock prices, plays a significant role in the Indonesian economy. Accurate stock price forecasting can help investors make informed decisions. Unilever Indonesia Tbk (UNVR) exhibits high volatility in its closing stock prices, making it crucial to develop a reliable forecasting model. This study applies an ensemble averaging method that integrates the ARIMA-GARCH model and Support Vector Regression (SVR) to predict UNVR's closing stock prices from January 6, 2019, to November 5, 2023. The results indicate that the data can be modeled using ARIMA (0,2,1). However, the squared residuals of the model show heteroscedasticity, necessitating variance modeling using the ARCH-GARCH approach. The best combination of mean and variance modeling is achieved with ARIMA (0,2,1) – GARCH (1,1), yielding a Mean Absolute Percentage Error (MAPE) of 2.865%. Additionally, a nonparametric SVR model with parameters C = 4 and ε = 0 is applied, resulting in a MAPE of 2.94%. An ensemble averaging approach is implemented to optimize forecasting accuracy further, combining ARIMA-GARCH and SVR models. This ensemble approach improves predictive performance, achieving a final MAPE of 1.682%. These findings demonstrate that ensemble averaging effectively enhances stock price forecasting accuracy by leveraging linear and nonlinear modeling techniques.
PREDICTION OF THE INDONESIA COMPOSITE INDEX (ICI) USING THE ARCH GARCH APPROACH AND THE FOURIER SERIES M. Fariz Fadillah Mardianto; Hanny Valida; Farah Fauziah Putri; Doni Muhammad Fauzi; Elly Pusporani
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0271-0286

Abstract

The Indonesia Composite Index (ICI) is a key indicator of stock market performance in Indonesia, often experiencing high volatility due to various domestic and global economic factors. In recent years, ICI has shown a significant upward trend, influenced by both local and international factors. In 2024, from June to October, the ICI saw a notable increase, reaching its highest value since 2020 at Rp 7,670. Despite fluctuations in stock prices, the rise in ICI reflects a positive outlook for the Indonesian stock market, attracting both domestic and foreign investors. This study aims to predict ICI movements using ARIMA-GARCH and Fourier Series approaches. The ARIMA model is employed to analyze time series data, while the ARCH-GARCH model addresses heteroskedasticity in residual variance. For comparison, the Fourier Series Estimator is applied to capture seasonal patterns in the data. Although ICI volatility is driven by a range of external macroeconomic and geopolitical factors, this study focuses on univariate modeling to evaluate the predictive capability of the index’s own historical movements, without involving exogenous variables. The data used comes from Investing.com. Weekly ICI data from March 2020 to June 2024 is used, split into training and testing sets. The analysis results indicate that the ARIMA-GARCH method provides higher accuracy, with a Mean Absolute Percentage Error (MAPE) of 5% (out-sample), compared to the Fourier Series method, which has a MAPE of 8.57%. This suggests that ARIMA-GARCH is more effective in predicting ICI trends, reflecting its ability to account for volatility and market changes more accurately.
Co-Authors Adinda Tries Melati Afifah Nur Makkiyah Ailsa Shafa Salsabila Ain, Dzuria Hilma Qurotu Ainaya Zakiyah Nabila Alexandra, Victoria Anggia Alfi Nur Nitasari Alfredi Yoani Ana, Elly Andreas, Christopher Antonio Nikolas Manuel Bonar Simamora Audilla, Marfa Aufa Muhammad Yogi Riyanto Aulia Ramadhanti Ayuning Dwis Cahyasari Ayuning Dwis Cahyasari Azizah Dewi Ariyani Azzah Nazhifa Wina Ramadhani Bagas Maulana Cantika Dhiya Deby Victoria Diana Nurlaily Dita Amelia Doni Muhammad Fauzi Doni Muhammad Fauzi Dwitya, Shabrina Nareswari Elly Ana Fajrina, Sofia Andika Nur Farah Fauziah Putri Farida Nur Hayati Farizi, Muhammad Fikry Al Fatiha Nadia Salsabila Ferissa Maulida Ismi Fidela Sahda Ilona Ramadhina Fitri, Marfa Audilla Fitriana Nur Afifa Ganesya Intantalia Grace Lucyana Koesnadi Hanny Valida Haq, Affan Fayzul Helfira Lady Ari Pramesti I Kadek Pasek Kusuma Adi Putra Idrus Syahzaqi Idrus Syahzaqi Irhamah - Irma Ayu Indrasta Ismi, Ferissa Maulida Jannah, Sa'idah Zahrotul Julia Widiyanti Koesnadi, Grace Lucyana Lu'lu'a, Na'imatul Lu’lu’a, Na’imatul M. Fariz Fadillah Mardianto M. Fariz Fadillah Mardianto M. Fariz Fadillah Mardianto Marcel Laverda Subiyanto Marcelena Vicky Galena Marcelena Vicky Galena Maula, Sugha Faiz Al Maulana Syah Putra Ramadhani Mochamad Rasyid Nabila Rahma Na’ifa, Ariza Nadya Lovita Hana Trisa Nashwa Carista Na’imatul Lu’lu’a Nitasari, Alfi Nur Nurrohmah, Zidni ‘Ilmatun Nurul Fajriah Deswani Sangadji Permana, Made Riyo Ary Pratama, Bagas Shata Previan, Anggara Teguh Putri, Ferdiana Friska Rahmana Putri, Refa Berliana Rahmat Agung Ibrahim Rani, Lina Nugraha Rohayah, Dewi Sa'idah Zahrotul Jannah Sa'idah Zahrotul Jannah Salsabila, Fatiha Nadia Sari, Adma Novita Sari, Adma Novita Sasy Okti Karima Sa’idah Zahrotul Jannah Sediono, Sediono Setiawan, Nicoletta Almira Dyah Sheila Sevira Asteriska Naura Siregar, Naufal Ramadhan Al Akhwal Siti Maghfirotul Ulyah Siti Qomariyah Sri Endah Nurhidayati Steven Soewignjo Suliyanto Toha Saifudin Tsabita Amalia Shofa, Nayla Wieldyanisa, Ezha Easyfa Yuliati, Intan Yuniar, Muhammad Alvito Dzaky Putra Zah, Alfian Iqbal Zuleika, Talitha Zuleika, Talitha