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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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Application of Mathematical Model in Bioeconomic Analysis of Skipjack Fish in Pelabuhanratu, Sukabumi Regency, Jawa Barat Nurkasyifah, Fathimah Syifa; Supriatna, Asep K.; Napitupulu, Herlina
International Journal of Quantitative Research and Modeling Vol. 5 No. 1 (2024)
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 Syahla, Raynita; Susanti, Dwi; Napitupulu, Herlina
International Journal of Quantitative Research and Modeling Vol. 5 No. 1 (2024)
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 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.
Optimization of Investment Portfolio Mean-Variance Model Using Genetic Algorithm Syahla, Raynita; Susanti, Dwi; Napitupulu, Herlina
International Journal of Business, Economics, and Social Development Vol. 5 No. 2 (2024)
Publisher : Rescollacom (Research Collaborations Community)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijbesd.v5i2.654

Abstract

The optimization of investment portfolio is aimed at finding the optimal combination of each stock with the goal of maximizing returns while minimizing risk through diversification. However, the question is how much funds should be invested to achieve the minimum risk. One of the approaches that has proven effective in building an optimal investment portfolio is the Mean-Variance model. The aim of this research is to determine the weights of the optimal portfolio components with the minimum risk. The data used consists of stocks included in the LQ45 index for the period from February 2020 to July 2021. Based on the research results, there are five stocks that form the optimal portfolio, namely ADRO, AKRA, BBCA, CPIN, and EXCL. The allocated weights for each stock are ADRO 9.896%, AKRA 32.049%, BBCA 30.749%, CPIN 13.949%, and EXCL 13.357%. The optimal portfolio generated by the Genetic Algorithm method has a risk of 0.000472 and an expected return of 0.000492.
Investment Portfolio Optimization of Mean-Entropic-VaR Model on the Top Ten Stocks from LQ45 in the Indonesian Capital Market Suhaimi, Nurnisaa binti Abdullah; Napitupulu, Herlina; Sukono, Sukono
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 1 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim 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.
Systematic Literature Review (SLR) on Annuity Modeling of Plantation Replanting Cost Reserves Based on the Cobb-Douglas Model Fasa, Rayyan Al Muddatstsir; Napitupulu, Herlina; Sukono, Sukono
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 9, No 1 (2024): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/ca.v9i1.25831

Abstract

Annuity is a financial concept that involves a series of periodic payments or receipts. In oil palm plantation management, the annuity concept is adapted to model and estimate the reserves required for replanting costs over time. The Cobb-Douglas model is a model that considers the contribution of various factors in the production process. This model can be used to estimate the income of plantations. This study discusses the Systematic Literature Review on Annuity Modeling of Plantation Replanting Cost Reserves through the application of the Cobb-Douglas Model using the Reporting Method of Choice for Systematic Review and Meta-Analysis (PRISMA) method. The study systematically collected and analyzed relevant literature from Scopus, Science Direct, Dimensions, and SAGE databases. The review followed a structured methodology that included four main stages: Identification, Screening, Eligibility, and Inclusion. Analysis was conducted on the datasets obtained at the Eligibility and Inclusion stages. Statistical techniques facilitated by the "bibliometrix" package in RStudio software were used to process the findings. In addition, the results can be accessed through the "biblioshiny ()" command, allowing easy access through a web interface for in-depth exploration. Based on the inclusion and exclusion criteria carried out in this study, it can be concluded that there is no research that discusses the topic of annuity modeling of plantation replanting cost reserves using the Cobb-Douglas model specifically. This can be further research on this topic. 
Metode Transformasi Diferensial untuk Menentukan Solusi Persamaan Diferensial Linear Nonhomogen Firosi, Valeska Isma; Napitupulu, Herlina; Supriatna, Asep Kuswandi
Jurnal Matematika Integratif Vol 19, No 2: Oktober 2023
Publisher : Department of Matematics, Universitas Padjadjaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24198/jmi.v19.n2.48876.183-200

Abstract

Persamaan diferensial merupakan salah satu topik dalam matematika yang banyak digunakan dalam memodelkan masalah kehidupan nyata. Misalkan pemodelan penyakit, perkembangan bakteri, pemodelan gelombang, persamaan panas dan lain sebagainya. Secara umum, ada dua jenis persamaan diferensial, yaitu Persamaan Diferensial Biasa (PDB) dan Persamaan Diferensial Parsial (PDP). Pada praktiknya, penyelesaian PDB maupun PDP secara analitik memiliki tantangan tersendiri, sehingga solusi dengan metode semi-analitik (pendekatan dengan kombinasi antara analitik dan pendekatan numerik) merupakan alternatif yang sampai saat ini menarik untuk dikaji. Metode Transformasi Diferensial (MTD) adalah salah satu metode numerik semi-analitik yang dapat digunakan untuk menyelesaikan persamaan diferensial. Metode ini didasarkan pada perluasan deret Taylor, dimana persamaan diferensial diubah menjadi relasi rekurensi untuk mendapatkan solusi deret dalam bentuk polinomial. Pada penelitian ini dibahas secara rinci bagaimana pengaplikasian metode transformasi diferensial untuk penyelesaian PDB linear nonhomogen dan PDP linear nonhomogen untuk beberapa contoh kasus tertentu yang belum pernah dibahas pada penelitian terdahulu. Pertama, digunakan MTD untuk menyelesaikan masalah nilai awal serta masalah nilai batas untuk PDB linear nonhomogen. Selanjutnya, digunakan MTD Dua Dimensi untuk menyelesaikan masalah nilai awal dan batas untuk PDP linear nonhomogen. Hasil yang diperoleh dengan MTD dibandingkan dengan solusi analitik dari PDB yang diubah ke bentuk deret Taylor. Demikian pula, hasil yang diperoleh MTD Dua Dimensi dibandingkandengan solusi analitik PDB yang diubah ke bentuk deret Taylor. Perbandingan solusi analitik dan solusi MTD diberikandalam bentuk perbandingan grafik solusi dengan \textit{software} Maple serta dilakukan perhitungan galat. Berdasarkan perhitungan galat, solusi dari PDB dan PDP ini mendekati solusi analitik dengan galat yang relatif kecil, terlebih ketika banyaknya iterasi ditingkatkan pada MTD dan MTD dua dimensi. 
Systematic Literature Review Robust Graph Coloring on Electric Circuit Problems Balqis, Viona Prisyella; Chaerani, Diah; Napitupulu, Herlina
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 6, No 4 (2022): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v6i4.9446

Abstract

Graph Coloring Problem (GCP) is the assignment of colors to certain elements in a graph based on certain constraints. GCP is used by assigning a color label to each node with neighboring nodes assigned a different color and the minimum number of colors used. Based on this, GCP can be drawn into an optimization problem that is to minimize the colors used. Optimization problems in graph coloring can occur due to uncertainty in the use of colors to be used, so it can be assumed that there is an uncertainty in the number of colored vertices. One of the mathematical optimization methods in the presence of uncertainty is Robust Optimization (RO). RO is a modeling methodology combined with computational tools to process optimization problems with uncertain data and only some data for which certainty is known. This paper will review research on Robust GCP with model validation to be applied to electrical circuit problems using a systematic review of the literature. A systematic literature review was carried out using the Preferred Reporting Items for Systematic reviews and Meta Analysis (PRISMA) method. The keywords used in this study were used to search for articles related to this research using a database. Based on the results of the search for articles obtained from PRISMA and Bibliometric R Software, it was found that there was a relationship between the keywords Robust Optimization and Graph Coloring, this means that at least there is at least one researcher who has studied the problem. However, the Electricity keyword has no relation to the other two keywords, so that a gap is obtained and it is possible if the research has not been studied and discussed by other researchers. Based on the results of this study, it is hoped that it can be used as a consideration and a better solution to solve optimization problems.
Co-Authors Adi Suripto, Adi Agus Santoso Aisyah, Ranti Rivani Albert Raja Harungguan Alit Kartiwa Alit Kartiwa Ariesandy, Sena Asep K. Supriatna Asep K. Supriatna Asep Kuswandi Supriatna Balqis, Viona Prisyella Betty Subartini Diah Chaerani Dwi Purnomo Dwi Susanti Dwi Susanti Edi Kurniadi Edi Kurniadi Edi Kurniadi Ema Carnia Eman Lesmana Erwin Harahap Ewen Hokijuliandy Fasa, Rayyan Al Muddatstsir 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, Julita Kusumaningtyas, Valentina Adimurti Melina Melina Melina Melina, Melina Michael Lim Michelle Selina Buntara Muhammad Deni Johansyah Muhammad Helambang Prakasa Yudha Muhammad Ribhan Hadiyan Nabilla, Ulya Norizan Mohamed Novitasari, Ela Nurkasyifah, Fathimah Syifa Nursanti Anggriani Nurul Gusriani Nurul Gusriani, Nurul Rahmadini, Nurhaliza Riaman Riaman Ridwan Pandiya Saprilian Hidayat Saputra, Jumadil Satyaputra, Ida Bagus Wira Krishna Siti Aizal Yasni Ellena Sudrajat Supian Suhaimi, Nurnisaa binti Abdullah 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