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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 BULLET : Jurnal Multidisiplin Ilmu In Search (Informatic, Science, Entrepreneur, Applied Art, Research, Humanism)
Herlina Napitupulu
Departemen Matematika, Fakultas Matematika Dan Ilmu Pengetahuan Alam, Universitas Padjadjaran
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Analysis of Determining The Cost of Replanting for Smallholder Oil Palm Plantations Using Annuities Model with Python Rayyan Al Muddatstsir Fasa; Herlina Napitupulu; Sukono Sukono
International Journal of Quantitative Research and Modeling Vol. 4 No. 4 (2023): International Journal of Quantitative Research and Modeling
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v4i4.547

Abstract

Palm oil replanting is a necessary activity to enhance the productivity of aging oil palm trees. However, the high costs associated with replanting often create a financial burden for farmers. To address this issue, the study proposes the implementation of a contribution or levy system for smallholder farmers while their oil palm plantations are still productive, which would alleviate the financial burden of replanting. The research methodology employed includes a literature review and primary data collection through a survey of smallholder farmers, with the data being processed to create a mathematical model and simulated using the Python programming language. The results of this study include the development of a mathematical model for the levy and distribution of replanting costs, along with a simulation of the proposed system. This model could help smallholder farmers prepare for replanting costs, enhance the sustainability of palm oil production, and ultimately increase productivity.
Application of Mathematical Model in Bioeconomic Analysis of Skipjack Fish in Pelabuhanratu, Sukabumi Regency, Jawa Barat Fathimah Syifa Nurkasyifah; Asep K. Supriatna; Herlina Napitupulu
International Journal of Quantitative Research and Modeling Vol. 5 No. 1 (2024): International Journal of Quantitative Research and Modeling
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v5i1.598

Abstract

Presently, sustainability has emerged as a crucial and compelling concern across diverse sectors, evolving into a long-term agenda championed by the United Nations through the implementation of the Sustainable Development Goals (SDGs). Within the SDGs, particularly under point 14 addressing life below water, emphasis is placed on ensuring sustainability in aquatic ecosystems, encompassing the fisheries sector. The concept of Maximum Sustainable Yield (MSY) holds significance in the bioeconomic analysis of fisheries, influencing decision-making processes aimed at preserving sustainability. Regrettably, several studies have identified inaccuracies in the determination of MSY, leading to instances of overfishing in various regions. Conversely, it is imperative to give due attention to Maximum Economic Yield (MEY) to ensure that economic considerations remain integral to decision-making processes. Consequently, a more comprehensive and detailed bioeconomic analysis, incorporating mathematical models, becomes essential. Among these models, the logistic growth rate model and the Gompertz growth rate model stand out as significant contributors. 
The Comparison of Investment Portfolio Optimization Result of Mean-Variance Model Using Lagrange Multiplier and Genetic Algorithm Raynita Syahla; Dwi Susanti; Herlina Napitupulu
International Journal of Quantitative Research and Modeling Vol. 5 No. 1 (2024): International Journal of Quantitative Research and Modeling
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v5i1.611

Abstract

Investment portfolio optimization is carried out to find the optimal combination of each stock with the aim of maximizing returns while minimizing risk by diversification. However, the problem is how much proportion of funds should be invested in order to obtain the minimum risk. One approach that has proven effective in building an optimal investment portfolio is the Mean-Variance model. The purpose of this study is to compare the results of the Mean-Variance model investment portfolio optimization using Lagrange Multiplier method and Genetic Algorithm. The data used are stocks that are members of the LQ45 index for the period February 2020-July 2021. Based on the research results, there are five stocks that form the optimal portfolio, namely ADRO, AKRA, BBCA, CPIN, and EXCL stocks. The optimal portfolio generated by the Lagrange Multiplier method has a risk of 0.000606 and a return of 0.000726. Meanwhile, using the Genetic Algorithm resulted in a risk of 0.000455 and a return of 0.000471. Thus, the Genetic Algorithm method is more suitable for investors who prioritize lower risk. Meanwhile, the Lagrange Multiplier method produces a relatively higher risk, making it less suitable for investors who expect a small risk. 
Stock Investment Portfolio Optimization Using Mean-Variance Model Based on Stock Price Prediction with Long-Short Term Memory Popy Febrianty; Herlina Napitupulu; Sukono Sukono
International Journal of Quantitative Research and Modeling Vol. 6 No. 2 (2025): International Journal of Quantitative Research and Modeling
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.
Clustering of Banking Sector Stocks using Integration of Fourier Transform, Spectral Clustering, and Fuzzy C-Means as a Basis for Mean-Variance Portfolio Optimization Ricardo, Dimitri Salsabila Fakhriyah; Gusriani, Nurul; Napitupulu, Herlina
Jurnal Matematika Integratif Vol 22, No 1: April 2026
Publisher : Department of Matematics, Universitas Padjadjaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24198/jmi.v22.n1.69287.59-72

Abstract

The Indonesian capital market has experienced significant growth accompanied by high volatility, particularly in the banking sector whichholds a substantial contribution to market capitalization. Extremevolatility during the 2019-2024 period triggered by the impact of theCOVID-19 pandemic, economic recovery phases, and global macroeconomic challenges has created complexities in investment decisionmaking and portfolio optimization, which often produces unstable solutions under uncertain market conditions. Various studies have applied frequency domain analysis to uncover hidden patterns in stockprice movements or clustering to group stocks based on their characteristics to support optimal investment decision-making, however theintegration of these two approaches remains limited in its application togenerate robust portfolio optimization solutions in the Indonesian capital market. This study aims to generate clustering of banking sectorstocks through the integration of Fourier Transform, spectral clustering, and Fuzzy C-Means and to construct an optimal portfolio using theMean-Variance method based on the clustering results. This study usesclosing price data of 41 banking sector stocks on the Indonesia StockExchange for the 2019-2024 period through an integrated approach ofFourier Transform to extract frequency patterns, spectral clustering asa basis for grouping, Fuzzy C-Means to generate cluster membership degrees, and Mean-Variance for portfolio optimization. The results showthat the integration of these methods produces four optimal stock clusters consisting of nine stocks with a medium risk-low return profile,six stocks with a high risk-high return profile, fifteen stocks with a lowrisk-low return profile, and eleven stocks with a medium risk-high return profile. Based on the clustering results, four representative stockswere selected from each cluster for portfolio optimization, resulting inan optimal portfolio at a risk aversion value of ρ = 6.83 with a portfolio ratio of 3.4128877. This optimal portfolio is constructed from fourrepresentative stocks with weight allocations of 11.48% BMAS, 11.76%ARTO, 72.08% BNGA, and 4.68% BBHI, with an expected return valueof 0.0263613 and a portfolio variance of 0.0077241.
Investment Portfolio Optimization of Mean-Entropic-VaR Model on the Top Ten Stocks from LQ45 in the Indonesian Capital Market Nurnisaa binti Abdullah Suhaimi; Herlina Napitupulu; Sukono Sukono
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 1 (2025): 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.v10i1.30794

Abstract

In an investment portfolio, investors certainly choose a portfolio according to their preferences for return and risk. The problem is the allocation of investment weights in forming a portfolio, if the risk is in the form of Entropic-Value-at-Risk (EVaR). The purpose of this study is to determine the allocation of investment weights that maximize returns and minimize portfolio risk. The method used in this study is through investment portfolio optimization in the form of Mean-EVaR. The stages carried out are selecting the ten best stocks in the LQ45 index, estimating and testing the suitability of the return distribution, determining expectations, variance and covariance between stock returns, and optimizing the allocation of investment portfolio weights using the Mean-EVaR model. Based on the results of the analysis, it was obtained that the optimal portfolio weight allocation is 0.01073, 0.23284, 0.04617, 0.08052, 0.00470, 0.09021, 0.14669, 0.00427, 0.22672 and 0.15715, to be allocated successively to the stocks ACES, BBRI, EXCEL, ITMG, PTBA, ADRO, BBTN, GGRM, KLBF and AKRA. In this optimal portfolio, the average portfolio return is obtained at 0.00055 with an EVaR risk of 0.01632. It is hoped that the results of this study can provide a significant contribution to investors in making investments, especially in the ten stocks analyzed.
Perbandingan Hasil Peramalan XGBoost Tanpa Dan Dengan Optimisasi Hyperparameter Menggunakan Whale Optimization Algorithm Berbasis Recursive Feature Elimination with Cross-Validation (Studi Kasus: Data Curah Hujan Dasarian Kabupaten Pati) Adeliya Fernanda; Herlina Napitupulu; Nurul Gusriani
BULLET : Jurnal Multidisiplin Ilmu Vol. 5 No. 3 (2026): BULLET : Jurnal Multidisiplin Ilmu (INPRESS)
Publisher : CV. Multi Kreasi Media

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

Abstract

Pati Regency is one of Indonesia's major salt-producing regions, where production remains highly dependent on rainfall conditions. The wet dry-season phenomenon has increased uncertainty in production schedules, highlighting the need for accurate rainfall forecasting. This study aims to identify the most influential features, compare the performance of XGBoost models with and without hyperparameter optimization using the Whale Optimization Algorithm (WOA), and forecast ten-day rainfall in Pati Regency for the next six periods. The research began with feature engineering on historical ten-day rainfall data, followed by feature selection using Recursive Feature Elimination with Cross-Validation (RFECV). The selected features were used to develop both the baseline XGBoost model and the WOA-optimized XGBoost model. Model performance was evaluated using the Root Mean Square Error (RMSE). The optimal feature subset consisted of 12 features: lag 1, lag 2, lag 3, lag 5, lag 6, rolling mean 3, rolling mean 6, rolling standard deviation 6, rolling mean 9, rolling mean 18, rolling standard deviation 18, and the dasarian sine feature. The optimized XGBoost-WOA model achieved a lower RMSE (44.37) than the baseline XGBoost model (50.12). Forecasted rainfall for the next six ten-day periods was 12.65, 32.77, 33.23, 42.66, 48.25, and 48.25 mm per ten-day period, indicating that dry-season conditions remain favorable for salt production despite increasing rainfall toward the end of the forecast horizon. Therefore, XGBoost-WOA provides a promising alternative for ten-day rainfall forecasting to support salt production planning in Pati Regency.
Co-Authors Adeliya Fernanda 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 Nurnisaa binti Abdullah Suhaimi Nursanti Anggriani Nurul Gusriani Popy Febrianty Rahmadini, Nurhaliza Raynita Syahla Rayyan Al Muddatstsir Fasa Riaman Riaman Ricardo, Dimitri Salsabila Fakhriyah Ridwan Pandiya Saprilian Hidayat Saputra, Jumadil Satyaputra, Ida Bagus Wira Krishna Siti Aizal Yasni Ellena Sudrajat Supian Sukono Sukono Supian, Sudradjat Supian, Sudrajat Sutisna, Sarah Syahla, Raynita Valentina Adimurti Kusumaningtyas Valerie ​Valerie Valerie ​Valerie Viona Prisyella Balqis Wida Nurul Fauziyah Yosza Dasril Yudha, Muhammad Helambang Prakasa Yulison Herry Chrisnanto Yuyun Hidayat