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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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A COMPARISON OF CENTRALITY MEASURES IN SUSTAINABLE DEVELOPMENT GOALS Ariesandy, Sena; Carnia, Ema; Napitupulu, Herlina
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 14 No 3 (2020): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1092.17 KB) | DOI: 10.30598/barekengvol14iss3pp309-320

Abstract

The Millennium Development Goals (MDGs), which began in 2000 with 8 goal points, have not been able to solve the global problems. The MDGs were developed into Sustainable Development Goals (SDGs) in 2015 with 17 targeted goal points achieved in 2030. Until now, methods for determining the priority of SDGs are still attractive to researchers. Centrality measure is one of the tools in determining the priority goal points on a network by using graph theory. There are four measurements of centrality used in this paper, namely degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. The calculation results obtained from the four measurements are compared dan analyzed, to conclude which goal points are the most prior and the least prior. Furthemore, in this paper we present other example with simple graph to show that each different centrality calculation possibly resulted different priority node, the calculation of this illustration is done using a Python’s library named NetworkX
COMPARATIVE ANALYSIS OF TIME SERIES FORECASTING MODELS USING ARIMA AND NEURAL NETWORK AUTOREGRESSION METHODS Melina, Melina; Sukono, Sukono; Napitupulu, Herlina; Mohamed, Norizan; Chrisnanto, Yulison Herry; Hadiana, Asep ID; Kusumaningtyas, Valentina Adimurti; Nabilla, Ulya
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 4 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss4pp2563-2576

Abstract

Gold price fluctuations have a significant impact because gold is a haven asset. When financial markets are volatile, investors tend to turn to safer instruments such as gold, so gold price forecasting becomes important in economic uncertainty. The novelty of this research is the comparative analysis of time series forecasting models using ARIMA and the NNAR methods to predict gold price movements specifically applied to gold price data with non-stationary and non-linear characteristics. The aim is to identify the strengths and limitations of ARIMA and NNAR on such data. ARIMA can only be applied to time series data that are already stationary or have been converted to stationary form through differentiation. However, ARIMA may struggle to capture complex non-linear patterns in non-stationary data. Instead, NNAR can handle non-stationary data more effectively by modeling the complex non-linear relationships between input and output variables. In the NNAR model, the lag values of the time series are used as input variables for the neural network. The dataset used is the closing price of gold with 1449 periods from January 2, 2018, to October 5, 2023. The augmented Dickey-Fuller test dataset obtained a p-value = 0.6746, meaning the data is not stationary. The ARIMA(1, 1, 1) model was selected as the gold price forecasting model and outperformed other candidate ARIMA models based on parameter identification and model diagnosis tests. Model performance is evaluated based on the RMSE and MAE values. In this study, the ARIMA(1, 1, 1) model obtained RMSE = 16.20431 and MAE = 11.13958. The NNAR(1, 10) model produces RMSE = 16.10002 and MAE = 11.09360. Based on the RMSE and MAE values, the NNAR(1, 10) model produces better accuracy than the ARIMA(1, 1, 1) model.
DETERMINATION OF INSURANCE PREMIUMS FOR CHILI PLANTATION USING THE BLACK-SCHOLES MODEL WITH CLAYTON COPULA APPROACH Sutisna, Sarah; Sukono, Sukono; Napitupulu, Herlina
MEDIA STATISTIKA Vol 18, No 1 (2025): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.18.1.13-24

Abstract

Agriculture is a vulnerable sector to the risk of crop damage due to climate change and other environmental factors. One source of risk in agriculture is rainfall, which significantly affects productivity and farmers’ income. Traditional insurance premium calculations often rely on assumptions of normal distribution and linear dependency, which may not accurately capture the complex and non-linear relationships between climatic and agricultural variables. This research presents a novel contribution to agricultural risk management by applying the Clayton Copula to model the dependency structure between rainfall and chili crop production output in the context of crop insurance pricing. The estimation of Copula parameters was conducted using Maximum Likelihood Estimation, yielding a parameter θ value of -0.1252, which indicates the dependency structure between the variables. The predictive accuracy of the Copula Clayton model was evaluated using the Mean Absolute Error, with a result of 0.01291, demonstrating strong relevance in describing the dependency between precipitation and yield. Furthermore, the research integrates the Copula-based rainfall modeling with the Black-Scholes model for determining insurance premiums. The findings reveal that premium prices depend on rainfall index values, where higher rainfall percentages correspond to higher premium costs.
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.
Analisis Komparatif Prediksi Kelembaban di Kota Bandung Menggunakan Metode Long Short Term Memory (LSTM) dan Gated Recurrent Unit (GRU) Budiman, Muhammad Arief; Darmawan, Muhammad Rizky; Akmal, Muhammad Novrizal; Napitupulu, Herlina
Jurnal Matematika Integratif Vol 21, No 2: Oktober 2025
Publisher : Department of Matematics, Universitas Padjadjaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24198/jmi.v21.n2.68198.203-212

Abstract

Kelembaban udara memiliki pengaruh yang signifikan pada berbagai sektor industri, terutama yang bergantung pada kondisi lingkungan, seperti tekstil dan farmasi. Kota Bandung sebagai salah satu kota industri juga sangat dipengaruhi oleh tingkat kelembaban udaranya. Oleh karena itu, peramalan kelembaban udara penting untuk mendukung pengambilan keputusan yang tepat. Dalam penelitian ini, dilakukan analisis komparatif dua metode pemodelan berbasis Recurrant Neural Network (RNN), yaitu Long Short Term Memory (LSTM) dan Gated Recurrent Unit (GRU), untuk memprediksi kelembaban udara di Kota Bandung. Data yang digunakan dalam penelitian ini adalah data kelembaban dari 1 Juli 2019 hingga 1 Juli 2024, yang diambil dari sumber data sekunder di situs web NASA. Proses pemodelan dilakukan dengan menggunakan Google Colab, dan akurasi model diukur dengan dua metrik utama, yaitu Mean Absolute Percentage Error (MAPE) dan Root Mean Squared Error (RMSE). Hasil penelitian menunjukkan bahwa model GRU menghasilkan kinerja yang lebih baik dibandingkan LSTM, dengan 1 layer dan 25 node, nilai MAPE yang diperoleh adalah 2,831% dan RMSE adalah 0,579. Sementara itu, LSTM optimal dalam penggunaan 1 layer dengan 75 node dan memiliki MAPE sebesar 2,929% dan RMSE sebesar 0,593. Dari segi efisiensi waktu, GRU juga unggul dengan waktu pengoperasian yang lebih pendek, yaitu 2525,489 detik dibandingkan dengan LSTM yang membutuhkan waktu 3410,316 detik. Oleh karena itu, GRU adalah pilihan yang lebih baik dalam memprediksi kelembaban udara, baik dari segi akurasi maupun efisiensi waktu.Kata kunci: Kelembaban; Long Short Term Memory; Gated Recurrent Unit;
Inventory Control for Eyeglass Supply Using the P Model Based on Sales Products Sales Forecasting (Case Study: Merry Optic Bandung) Adi Suripto; Julita Nahar; Herlina Napitupulu
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.v4i4.494

Abstract

Inventory is a resource owned by the company to be used in the production process to meet consumer demand. Companies must be able to control inventory appropriately in order to avoid excess or shortage of inventory by using inventory control. Inventory control is a necessary part of a company that requires an appropriate inventory policy to meet uncertain needs. Based on this background, this study discusses the single item inventory model in the form of photochromic glasses at Merry Optik to find the optimal total inventory cost. In meeting the uncertain needs of the company, the Additive Decomposition forecasting method is used in order to find out the forecast sales data pattern in the future. Uncertain demand causes the inventory system to be probabilistic, so it is necessary to carry out probabilistic inventory control. The P model of the case of back orders was chosen because the range of ordering periods is fixed and the company can buy inventory when it runs out before the time the inventory order is made so that buyers can wait until the inventory arrives. By using Model P for the case of back orders, the company can obtain the period between orders, the total cost of inventory, and the optimal level of service. Based on the results of this study, a pattern of sales forecast data is obtained which repeats every 12 months. Companies must order glasses within a period of 32 days between orders so that it is optimal and able to provide a reduction in the total inventory cost of IDR 21,828,771 with a service level of 95%. Companies can save on inventory costs if they use shorter periods between orders. The total cost of inventory can be more optimal if the company reduces the cost of storing inventory in the warehouse.
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