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All Journal Media Statistika Matematika: Jurnal Teori dan Terapan CAUCHY: Jurnal Matematika Murni dan Aplikasi TELKOMNIKA (Telecommunication Computing Electronics and Control) BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Jurnal Cendekia : Jurnal Pendidikan Matematika International Journal of Supply Chain Management Jambura Journal of Mathematics ComTech: Computer, Mathematics and Engineering Applications Kumawula: Jurnal Pengabdian Kepada Masyarakat BERNAS: Jurnal Pengabdian Kepada Masyarakat International Journal of Business, Economics, and Social Development International Journal of Research in Community Services Operations Research: International Conference Series International Journal of Global Operations Research International Journal of Quantitative Research and Modeling SisInfo : Jurnal Sistem Informasi dan Informatika Jurnal Matematika Integratif In Search (Informatic, Science, Entrepreneur, Applied Art, Research, Humanism)
Herlina Napitupulu
Departemen Matematika, Fakultas Matematika Dan Ilmu Pengetahuan Alam, Universitas Padjadjaran
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

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PERAMALAN INDEKS ULTRAVIOLET DI KOTA BANDUNG MENGGUNAKAN METODE LONG SHORT-TERM MEMORY Satyaputra, Ida Bagus Wira Krishna; Napitupulu, Herlina; Gusriani, Nurul
Jurnal Matematika Integratif Vol 20, No 2: Oktober 2024
Publisher : Department of Matematics, Universitas Padjadjaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24198/jmi.v20.n2.58798.249-258

Abstract

Peramalan nilai indeks Ultraviolet (UV) memainkan peran penting dalam menjaga kesehatan masyarakat dan pengelolaan lingkungan. Penelitian ini bertujuan untuk menghasilkan nilai peramalan indeks UV di Kota Bandung pada tanggal 1–30 April 2024 menggunakan Metode Long Short-Term Memory (LSTM). Metode LSTM merupakan pengembangan dari metode Recurrent Neural Network (RNN). RNN diubah dengan menambahkan mekanisme gate untuk menyimpan informasi jangka panjang sehingga mengurangi resiko munculnya exploding gradients dan vanishing gradients. Model LSTM dalam penelitian ini dibangun menggunakan 1 input layer dengan 400 unit cell dan 1 output dense layer dengan fungsi update bobot adam optimizer, randomizer bobot glorot uniform distribution, dan 400 jumlah epoch. Performa model peramalan diuji menggunakan RMSE dan MAPE. Pada data training menghasilkan nilai RMSE sebesar 0,28 dan MAPE sebesar 11%. Untuk data testing menghasilkan nilai RMSE sebesar 0,48 dan MAPE sebesar 14%. Hasil peramalan indeks UV di Kota Bandung menunjukkan bahwa selama bulan April nilai rata-rata indeks UV adalah 2,27, hal ini mengartikan bahwa masyarakat Kota Bandung dapat beraktivitas diluar tanpa perlu mengkhawatirkan bahaya sinar UV.
Determination of Risk Value Using the ARMA-GJR-GARCH Model on BCA Stocks and BNI Stocks Hidayana, Rizki Apriva; Napitupulu, Herlina; Saputra, Jumadil
Operations Research: International Conference Series Vol. 2 No. 3 (2021): Operations Research International Conference Series (ORICS), September 2021
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v2i3.176

Abstract

Stocks are common investments that are in great demand by investors. Stocks are also an investment instrument that provides returns but tends to be riskier. The return time series is easier to handle than the price time series. In investment activities, there are the most important components, namely volatility and risk. All financial evaluations require accurate volatility predictions. Volatility is identical to the conditional standard deviation of stock price returns. The most frequently used risk calculation is Value-at-Risk (VaR). Mathematical models can be used to predict future stock prices, the model that will be used is the Glosten Jagannathan Runkle-generalized autoregressive conditional heteroscedastic (GJR-GARCH) model. The purpose of this study was to determine the value of the risk obtained by using the time series model. GJR-GARCH is a development of GARCH by including the leverage effect. The effect of leverage is related to the concept of asymmetry. Asymmetry generally arises because of the difference between price changes and value volatility. The method used in this study is a literature and experimental study through secondary data simulations in the form of daily data from BCA shares and BNI shares. Data processing by looking at the heteroscedasticity of the data, then continued by using the GARCH model and seeing whether there is an asymmetry in the data. If there is an asymmetric effect on the processed data, then it is continued by using the GJR-GARCH model. The results obtained on the two stocks can be explained that the analyzed stock has a stock return volatility value for the leverage effect because the GJR-GARCH coefficient value is > 0. So, the risk value obtained by using VaR measurements on BCA stocks is 0.047247 and on BNI stocks. is 0.037355. Therefore, the ARMA-GJR-GARCH model is good for determining the value of stock risk using VaR.
Determination of VaR on BBRI Stocks and BMRI Stocks Using the ARIMA-GARCH Model Napitupulu, Herlina; Hidayana, Rizki Apriva; Saputra, Jumadil
Operations Research: International Conference Series Vol. 2 No. 3 (2021): Operations Research International Conference Series (ORICS), September 2021
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v2i3.178

Abstract

Stocks are investment instruments that are much in demand by investors as a basis in financial storage. Return and risk are the most important things in investing. Return is a complete summary of investment and the return series is easier to handle than the price series. The movement of risk of loss is obtained from stock investments with profits. One way to calculate risk is value-at-risk. The movement of stocks is used to form a time series so that the calculation of risk can use time series. The purpose of this study was to find out the Value-at-Risk value of BBRI and BMRI stock using the ARIMA-GARCH model. The data used in this study was the daily closing price for 3 years. The time series method used is the Autoregressive Integrated Moving Average (ARIMA)-Generalized Autoregressive Conditional Heteroscedastic (GARCH) model. The stage of analysis is to determine the prediction of stock price movements using the ARIMA model used for the mean model and the GARCH model is used for volatility models. The average value and variants obtained from the model are used to calculate value-at-risk in BBRI and BMRI stock. The results obtained are the ARIMA(3,0,3)-GARCH(1,1) and ARIMA(2,0,2)-GARCH(1,1) model so with a significance level of 5% obtained Value-at-Risk of 0.04058 to BBRI stock and 0.10167 to BMRI stock.
Determination of Value-at-Risk in UNVR Stocks Using ARIMA-GJR-GA RCH Model Hidayana, Rizki Apriva; Napitupulu, Herlina; Sukono, Sukono
Operations Research: International Conference Series Vol. 2 No. 4 (2021): Operations Research International Conference Series (ORICS), December 2021
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v2i4.181

Abstract

Stocks are investment instruments that are in great demand by investors as a basis for storing finances. The most important thing in investing is the return and risk of loss obtained from investing in stocks. Risk measurement is carried out using Value-at-Risk and Conditional Value-at-Risk. The stock movements used are historical data and in the form of time series, so that a model can be formed to predict the next movement of stocks and risk measurements can be carried out. The purpose of this study is to determine the value of risk obtained by investors using time series analysis. The data used in this study is the daily closing price of stocks for 3 years. The stages of the analysis carried out to predict stock movements are to determine the ARIMA model for the mean model and the GJR-GARCH model for the volatility model. The mean value and variance are used to calculate the risk value of VaR. Based on the results of the Value-at-Risk calculation obtained, UNVR shares have a risk value of 0.01217. This means that if an investment is made in UNVR shares of IDR 100,000,000.00, the estimated maximum loss of potential loss that occurs is estimated to reach IDR 1,217,000.
Value-at-Risk Estimation of Indofood (ICBP) and Gas Company (PGAS) Stocks Using the ARMA-GJR-GARCH Model Napitupulu, Herlina; Hidayana, Rizki Apriva; Saputra, Jumadil
Operations Research: International Conference Series Vol. 2 No. 4 (2021): Operations Research International Conference Series (ORICS), December 2021
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v2i4.183

Abstract

Stocks are one of the most widely used financial market instruments by investors in investing. The most important component of any investment is volatility. Volatility is a conditional measure of variance in stock returns and is important for risk management. In addition to volatility, the important things in investing are return and risk. Risk can be measured using Value-at-Risk (VaR) and can estimate the maximum loss that occurs. The purpose of this study is to determine VaR using the Autoregressive Moving Average-Glosten Jagannatan Runkle-Generalized Autoregressive Conditional Heteroscedasticity (ARMA-GJR-GARCH) model. The stages of data analysis used are estimating the ARMA model and the GARCH model, then estimating the GJR-GARCH model by looking at the heteroscedasticity and asymmetric effects on the GARCH model. Next, determine the VaR value from the estimated mean and variance (volatility) using the ARMA-GJR-GARCH model. The results of the model estimator obtained are based on the return data for the four stocks analyzed, namely the ARMA (5,5)-GJR-GARCH (1,1) model for ICBP stocks and ARMA (1,2)-GJR-GARCH (1,1) for PGAS shares. The Value-at-Risk values of each stock are 0.060427 and 0.024724. This research can be used by investors as a consideration in making investment decisions.
Peramalan Data Univariat Menggunakan Metode Long Short Term Memory Helma Syifa Izzadiana; Herlina Napitupulu; Firdaniza Firdaniza
SisInfo Vol 5 No 2 (2023): SisInfo
Publisher : Universitas Informatika dan Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37278/sisinfo.v5i2.669

Abstract

Peramalan data univariat mengacu pada kegiatan meramalkan nilai pada data dengan satu variabel independen yang mungkin muncul di masa depan berdasarkan nilai-nilai yang ada di masa lalu. Penelitian ini bertujuan untuk memperoleh model yang dibangun menggunakan pendekatan deep learning jenis supervised learning yaitu metode Long Short Term Memory (LSTM) yang diterapkan pada data univariat. Metode LSTM merupakan pengembangan dari metode Recurrent Neural Network (RNN) dengan menambahkan 3 gate yang mampu memilih informasi yang dibutuhkan untuk pelatihan sel sehingga mampu mengurangi kemungkinan exploding gradients dan vanishing gradients. Model dibangun dengan input layer LSTM dengan unit sel dan output dense layer dengan tambahan hyperparameter tuning yang diset menggunakan optimizer, fungsi aktivasi dan , dan nilai epoch. Performa model peramalan diuji menggunakan mean absolute percentage error (MAPE).
Analisis Sentimen Menggunakan Metode Klasifikasi Support Vector Machine (SVM) dan Seleksi Fitur Chi-Square Ewen Hokijuliandy; Herlina Napitupulu; Firdaniza Firdaniza
SisInfo Vol 5 No 2 (2023): SisInfo
Publisher : Universitas Informatika dan Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37278/sisinfo.v5i2.670

Abstract

Analisis sentimen adalah teknik komputasi untuk mengidentifikasi opini, sikap, emosi, dan maksud seseorang terhadap suatu subjek melalui ulasan yang diberikan. Studi sebelumnya menunjukkan teknik analisis sentimen menggunakan machine learning, seperti metode klasifikasi Support Vector Machine (SVM) telah terbukti efektif dalam mengklasifikasi opini. Penerapan metode seleksi fitur dapat meningkatkan performa model dan efisiensi model. Salah satu metode yang sering digunakan untuk seleksi fitur adalah metode Chi-Square. Penelitian ini bertujuan untuk memperoleh model SVM dari data teks yang telah melewati tahap seleksi fitur Chi-Square. Analisis sentimen dilakukan dengan kerangka kerja yang terdiri dari text preprocessing, representasi kata Term Frequency Inverse Document Frequency (TF-IDF), seleksi fitur Chi-Square, klasifikasi menggunakan metode SVM, evaluasi performa model, dan hyperparameter tuning.
Sistem Rekomendasi Menggunakan Item-Based Collaborative Filtering Berbasis Mobile iOS Bagas Ilham Rabbani; Herlina Napitupulu; Elis Hertini
SisInfo Vol 5 No 2 (2023): SisInfo
Publisher : Universitas Informatika dan Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37278/sisinfo.v5i2.671

Abstract

Kecerdasan buatan menjadi topik yang sering diperbincangkan dalam beberapa tahun terakhir. Salah satu implementasi kecerdasan buatan adalah pembuatan sistem yang dapat memberikan rekomendasi. Adanya sistem rekomendasi yang bisa memberikan saran seperti pada rekomendasi restoran, kursus daring, mata kuliah, dan lain sebagainya. Pilihan-pilihan yang direkomendasikan tersebut akan membantu user terutama untuk cakupan pilihan yang sangat banyak. Penelitian ini bertujuan untuk menghasilkan perhitungan nilai kemiripan antar item dalam bahasa pemrograman Swift, perhitungan prediksi nilai rating dalam bahasa pemrograman Swift, dan implementasi sistem rekomendasi dalam aplikasi mobile iOS.
CRYPTOCURRENCY TIME SERIES FORECASTING MODEL USING GRU ALGORITHM BASED ON MACHINE LEARNING Melina, Melina; Sukono, Sukono; Napitupulu, Herlina; Mohamed, Norizan; Herry Chrisnanto, Yulison; ID Hadiana, Asep; Kusumaningtyas, Valentina Adimurti
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp1317-1328

Abstract

The cryptocurrency market is experiencing rapid growth in the world. The high fluctuation and volatility of cryptocurrency prices and the complexity of non-linear relationships in data patterns attract investors and researchers who want to develop accurate cryptocurrency price forecasting models. This research aims to build a cryptocurrency forecasting model with a machine learning-based time series approach using the gated recurrent units (GRU) algorithm. The dataset used is historical Bitcoin closing price data from January 1, 2017, to July 31, 2024. Based on the gap in previous research, the selected model is only based on the accuracy value. In this study, the chosen model must fulfill two criteria: the best-fitting model based on the learning curve diagnosis and the model with the best accuracy value. The selected model is used to forecast the test data. Model selection with these two criteria has resulted in high accuracy in model performance. This research was highly accurate for all tested models with MAPE < 10%. The GRU 30-50 model is best tested with MAE = 867.2598, RMSE = 1330.427, and MAPE = 1.95%. Applying the sliding window technique makes the model accurate and fast in learning the pattern of time series data, resulting in a best-fitting model based on the learning curve diagnosis.
Stock Investment Portfolio Optimization Using Mean-Variance Model Based on Stock Price Prediction with Long-Short Term Memory Febrianty, Popy; Napitupulu, Herlina; Sukono, Sukono
International Journal of Quantitative Research and Modeling Vol 6, No 2 (2025)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v6i2.1002

Abstract

Stock investment in the technology sector in Indonesia offers high potential returns. However, like any other investment instruments, the associated risks cannot be overlooked. Therefore, an appropriate portfolio optimization strategy is needed to enable investors to achieve optimal returns while managing risk. In this study, the author combines stock price prediction approaches with portfolio optimization methods to construct an efficient portfolio. The Long-Short Term Memory (LSTM) model is used to predict daily closing stock prices, with model performance evaluated using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) metrics. An optimal LSTM model is obtained with a batch size hyperparameter of 16 for ISAT, MTDL, MLPT, and EDGE stocks, and a batch size of 32 for DCII stock. For all stocks, the average prediction error from the actual values falls within the range of 1.53% ≤ MAPE ≤ 3.52%. The optimal portfolio is constructed using the Mean-Variance risk aversion model to maximize expected returns while considering risk. The resulting optimal portfolio composition consists of a weight allocation of 19.7% for ISAT stock, 36.8% for MTDL stock, 34.8% for MLPT stock, 3.6% for EDGE stock, and 15% for DCII stock. This portfolio yields an expected portfolio return of 0.001249 and a portfolio variance of 0.000311.
Co-Authors Adi Suripto Adi Suripto, Adi Agus Santoso Aisyah Nurul Aini Aisyah, Ranti Rivani Akmal, Muhammad Novrizal Albert Raja Harungguan Alit Kartiwa Ariesandy, Sena Asep K. Supriatna Asep K. Supriatna Asep Kuswandi Supriatna Aulia Wanda Puspitasari Bagas Ilham Rabbani Balqis, Viona Prisyella Betty Subartini Darmawan, Muhammad Rizky Diah Chaerani Dwi Purnomo Dwi Susanti Dwi Susanti Dwi Susanti Edi Kurniadi Elis Hertini Ema Carnia Eman Lesmana Erwin Harahap Ewen Hokijuliandy Fasa, Rayyan Al Muddatstsir Fathimah Syifa Nurkasyifah Fauziyah, Wida Nurul Febrianty, Popy Firdaniza Firdaniza Firdaus, Hamidah 'Alina Firosi, Valeska Isma Ghazali, Puspa Liza Hadiana, Asep Id Helma Syifa Izzadiana Hidayana, Rizki Apriva Ida Widianingsih Ira Sumiati Ismail Bin Mohd Jeane R. M. D. P Chantique Julita Nahar Melina Melina Michael Lim Michelle Selina Buntara Muhammad Arief Budiman Muhammad Deni Johansyah Muhammad Helambang Prakasa Yudha Muhammad Ribhan Hadiyan Nabilla, Ulya Norizan Mohamed Novitasari, Ela Nursanti Anggriani Nurul Gusriani Popy Febrianty Rahmadini, Nurhaliza Raynita Syahla Rayyan Al Muddatstsir Fasa Riaman Riaman Ridwan Pandiya Saprilian Hidayat Saputra, Jumadil Satyaputra, Ida Bagus Wira Krishna Siti Aizal Yasni Ellena Sudrajat Supian Suhaimi, Nurnisaa binti Abdullah Sukono Sukono Supian, Sudradjat Supian, Sudrajat Sutisna, Sarah Syahla, Raynita Thania Nur Salsabila Valentina Adimurti Kusumaningtyas Valerie ​Valerie Valerie ​Valerie Viona Prisyella Balqis Wida Nurul Fauziyah Yosza Dasril Yudha, Muhammad Helambang Prakasa Yulison Herry Chrisnanto Yuyun Hidayat