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All Journal EKSAKTA: Journal of Sciences and Data Analysis Media Statistika MATEMATIKA Bionatura Statistika CAUCHY: Jurnal Matematika Murni dan Aplikasi SEMIRATA 2015 Kubik JURNAL ILMU MANAJEMEN DAN BISNIS Jurnal Matematika Sains dan Teknologi Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri BAREKENG: Jurnal Ilmu Matematika dan Terapan Zero : Jurnal Sains, Matematika, dan Terapan Teorema: Teori dan Riset Matematika International Journal of Supply Chain Management Jambura Journal of Mathematics Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Jurnal Ilmiah Matematika dan Pendidikan Matematika (JMP) InPrime: Indonesian Journal Of Pure And Applied Mathematics SMART SPORT KUBIK: Jurnal Publikasi Ilmiah Matematika 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 International Journal of Ethno-Sciences and Education Research Jurnal Matematika Integratif Agridev Journal : Agribusiness Digitalization & Community Development Mathematical Sciences and Applications Journal International Journal of Technology and Education Research Indonesian Actuarial Journal
Sukono Sukono
Jurusan Matematika, Fakultas Matematika Dan Ilmu Pengetahuan Alam, Universitas Padjadjaran, Bandung, Indonesia
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Comparison of Stock Mutual Fund Price Forecasting Results Using ARIMA and Neural Network Autoregressive Model Sianturi, Sri Novi Elizabeth; Subartini, Betty; 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.v6i2.1001

Abstract

Stock mutual funds gained popularity among the public as an investment alternative due to the convenience they offer, especially for beginner investors who have limited time and investment knowledge. Compared to money market and bond mutual funds, these mutual funds offer higher potential returns but also come with higher risks due to value fluctuations, so forecasting stock mutual fund prices is essential to minimize losses. Since stock mutual fund prices is time series data, this research employs two forecasting models such as Autoregressive Integrated Moving Average (ARIMA) and Neural Network Autoregressive (NNAR). The objective of this research is to determine the best-performing model between ARIMA and NNAR, and compare their forecasting accuracy using the Mean Absolute Percentage Error (MAPE). The data used consists of daily closing prices of stock mutual funds from March 1, 2022, to March 31, 2025, with the criteria that the selected issuers have been operating for more than five years. The results of this research show that the best ARIMA and NNAR for the RNCN are ARIMA([1],1,0) and NNAR(2,2); for TRAM are ARIMA(0,1,[1]) and NNAR(4,1); for SCHRP are ARIMA(0,1,[1]) and NNAR(4,2); for MICB are ARIMA([1],1,0) and NNAR(2,2); and for BNPP are ARIMA([1],1,0) and NNAR(5,1). The MAPE values in the same order are 6.83% and 5.49%; 6.53% and 5.75%; 8.57% and 7.10%; 8.39% and 8.75%; 8.51% and 7.30%. Based on the comparison, NNAR outperformed ARIMA in four out of five mutual funds, with lower MAPE values and also marked by the ARIMA model tend to produce stable or unchanging values over the long term. The results of this research are expected to assist investors in consederating by choosing NNAR model, both in the short and long term, to obtain better stock mutual fund price forecasts.
Implementation of Simulated Annealing Algorithm for Portfolio Optimization in Jakarta Islamic Index (JII) Stocks with Mean-VaR Riadi, Nadia Putri; Riaman, Riaman; 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.v6i2.1016

Abstract

One of the challenges for investors in the investment world is to manage the stock portfolio optimally. The main objective of portfolio optimization is to obtain maximum profit with a controlled level of risk. This study aims to find a portfolio combination that provides the best return with a more controllable risk than the conventional method, using Simulated Annealing. This research method applies the Mean-Value at Risk (Mean-VaR) approach in measuring portfolio performance and uses the application of the Simulated Annealing algorithm as an optimization method to determine the optimal investment weight on stocks in the Jakarta Islamic Index (JII), so as to obtain a portfolio with the best performance compared to a simple weighting strategy. The data used in this study is the daily closing price of stocks listed in the JII during the period January 3, 2022 - January 2, 2024. Based on the results and discussion, there are 7 stocks included in the formation of the optimal portfolio of JII index stocks, namely ADRO, ICBP, INKP, ITMG, MIKA, TPIA, and UNTR. The weight allocation of each stock generated by the Simulated Annealing method for the period is for ADRO shares 7,4177%; ICBP 1,7817%; INKP 7,3369%; ITMG 15,0006%; MIKA 2,5894%; TPIA 63,5506%; and UNTR 2,323%. The optimal portfolio of the Mean-VaR model with the Simulated Annealing method is generated when the risk tolerance is 0 (τ=0), with a return or return of 0,001923 and a VaR risk level of 0,029788. This approach is expected to be an alternative for investors in determining investment strategies based on Islamic stocks in Indonesia.
Investment Portfolio Optimization Using Genetic Algorithm on Infrastructure Sector Stocks Based on the Single Index Model Bayyinah, Ayyinah Nur; Riaman, Riaman; 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.v6i2.977

Abstract

Investment is a strategic step in managing assets to gain profits in the future by allocating some funds in the present. However, behind the promising potential returns, investment also contains risks that cannot be ignored. One way to reduce the level of risk in investing is to implement a portfolio diversification strategy, which is to form an optimal portfolio by allocating investments to various stocks. This study aims to identify the stocks that form the optimal portfolio, determine the optimal weight of each stock, and calculate the expected return and risk of the portfolio. The portfolio optimization process is carried out using Genetic Algorithm, with the calculation of expected return and risk using the Single Index Model (SIM) approach. The data used includes data on stocks in the infrastructure sector for the period July 1, 2023 to June 30, 2024. The results showed that there were six stocks selected in forming the optimal portfolio with the weight of each stock: PGEO 15.0023%, ISAT 32.1522%, GMFI 4.7822%, EXCL 15.3236%, JSMR 29.7379, and OASA 3.0018%. This optimal portfolio provides an expected return of 0.1167% with a portfolio risk of 0.0152%.
Sustainable Cultural Tourism Development Strategy in Karuhun Eco Park Village, Sumedang Regency, West Java, Indonesia Ratnasari, Dewi; Tiswaya, Waway; Riaman, Riaman; Sukono, Sukono; Hidayana, Rizki Apriva; Laksito, Grida Saktian
International Journal of Business, Economics, and Social Development Vol. 6 No. 3 (2025)
Publisher : Rescollacom (Research Collaborations Community)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijbesd.v6i3.989

Abstract

The development of cultural arts tourism as a sustainable tourism destination is a priority considering the features and nature of these locations, which highlight a number of elements such the local economy, environment, culture, preservation, and community empowerment. One of the cultural tourism areas in Indonesia, to be precise the province of West Java, namely Kampung Ladang in Sumedang Regency which is located at the top of Pasir Peti hill - Marga Laksana Sumedang Village. Kampung Ladang is one of Sumedang's cultural tourism centres by collaborating with the local community to maintain and preserve traditional Sundanese culture and procedures, especially Sumedang culture and traditions. This research uses qualitative methods and uses IFAS / EFAS and SWOT Analysis which aims to identify the potentials developed by Kampung Ladang with data collection carried out by observation, indepth interviews, and documentaries. The results of this study indicate that Kampung Ladang has three potentials that are ready to be developed in attracting tourists to visit such as potentials that need to be improved, namely attractions and activities, external supporting potentials, namely accessibility infrastructure consisting of information boards and other supporting facilities, and potentials that are not yet available, namely the provision of tour packages through marketing and promotion strategies that can attract tourists to visit and can develop community-based sustainable tourism that is community-centred to improve community welfare, besides that government support is needed in carrying out development and maintenance. In order to meet the needs of travellers, businesses, the environment, and tourism management communities, among other stakeholders, sustainable tourism management is a type of tourism management that will play a significant role in both the present and the future economic and social conditions.
Systematic Literature Review: Optimal Stopping and Investment Optimization for Bankruptcy Risk Management in Sharia Insurance Okta Yohandoko, Setyo Luthfi; Chaerani, Diah; Sukono, F
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (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.v10i2.35523

Abstract

The increasing demand for Sharia-compliant financial services in Muslim-majority countries such as Indonesia has driven the rapid development of Sharia insurance (Takaful). Despite its growth, the Sharia insurance sector faces significant challenges in managing investment portfolios and mitigating bankruptcy risks. Addressing these challenges requires a comprehensive understanding of the existing mathematical and financial models configured according to Islamic principles. Several studies have introduced stochastic approaches to model surplus processes, investment returns, and risk probabilities in insurance operations. Among these, the Cramér–Lundberg model has been widely used to estimate surplus dynamics and bankruptcy risks, while the Vasicek model provides a stochastic framework for modeling investment returns. Quadratic programming has also been applied to optimize asset allocation under specific constraints. However, these methodologies have typically been explored in isolation, which limits their ability to provide an integrated and effective framework for simultaneous bankruptcy risk mitigation and Sharia-compliant investment optimization. This methodological gap constrains the advancement of comprehensive, practically applicable, and theoretically sound solutions that are specifically designed to address the distinctive operational characteristics of Shariainsurance. The objective of this systematic review of the literature is to synthesize and critically analyze the methods used in previous research and to explore how they can be systematically integrated to form a comprehensive risk and investment management framework for Sharia insurance. The review identifies the strengths, limitations, and potential for combining optimal stopping theory, stochastic surplus modeling, and investment optimization to support robust financial decision making. This review contributes by offering a structured research agenda for the development of integrated models that simultaneously address the complexities of bankruptcy risk and Sharia-compliant investment strategies. Furthermore, this study provides valuable information for academics and practitioners seeking to improve the financial sustainability of the Islamic Insurance industry.
Numerical Solution of the Time-Fractional Black-Scholes Equation and Its Application to European Option Pricing Dihna, Elza Rahma; Rusyaman, Endang; Sukono, Sukono
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (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.v10i2.35248

Abstract

The classical Black-Scholes model is widely used in option pricing but relies on idealized assumptions such as constant volatility and memoryless market dynamics, which limit its accuracy in capturing real-world financial behavior. To overcome these limitations, the time-fractional Black-Scholes model incorporates a fractional-order derivative—specifically the Caputo derivative—which introduces memory effects and accommodates time-varying volatility. This study focuses on numerically solving the time-fractional Black-Scholes equation using the finite difference method (FDM) and applying the results to the pricing of European call options. The model is discretized using an implicit finite difference scheme to ensure stability and accuracy over the time domain. Numerical simulations are conducted for various values of the fractional order α, illustrating that the option price is sensitive to the fractional parameter. Lower values of α tend to increase option prices, highlighting the influence of memory effects on pricing behavior. The results confirm that the finite difference method is an effective numerical tool for solving fractional partial differential equations and demonstrate that the fractional Black-Scholes model offers improved flexibility and realism in option  valuation, particularly in markets characterized by irregular volatility and non-Markovian features.
Stock Portfolio Optimization of IDX30 using Agglomerative Hierarchical Clustering and Ant Colony Optimization Algorithm Firdaus, Muhammad Rayhan; Subartini, Betty; Sukono, Sukono
International Journal of Global Operations Research Vol. 6 No. 3 (2025): International Journal of Global Operations Research (IJGOR), August 2025
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v6i3.394

Abstract

The stock market offers high profit opportunities but also entails significant risks, making portfolio optimization essential to help investors manage risk and maximize returns. This study aims to cluster IDX30 stocks to form a more diversified portfolio, determine the optimal stock weights, and evaluate portfolio performance. The method employed is Agglomerative Hierarchical Clustering (AHC) with Ward linkage for clustering stocks based on financial ratios, with the silhouette score used to evaluate cluster quality. Subsequently, the Ant Colony Optimization (ACO) algorithm is applied to optimize stock weights in the portfolio based on the clustering results. The findings indicate that the best portfolio is obtained in clusters 5 and 6, with a maximum fitness value of 0.064555 and a portfolio return of 0.000814. Portfolio performance evaluation using the Sharpe ratio yields a value of 0.044767 for both clusters, indicating that the resulting portfolios are efficient. This research is expected to contribute to the development of more accurate and practical data-driven investment strategies for investors.
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.
A Systematic Literature Review on Mean-CVaR Based Financial Asset Portfolio Weight Allocation Using K-Means Clustering Wahid, Alim Jaizul; Riaman, Riaman; Sukono, Sukono
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (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.v10i2.36590

Abstract

This study aims to identify and analyze the application of the Mean-Conditional Value-at-Risk (Mean-CVaR) model in the allocation of financial asset portfolio weights combined with the K-Means Clustering algorithm. The Systematic Literature Review (SLR) method is used with the PRISMA protocol through the stages of identification, screening, eligibility, and inclusion. Data is obtained from Scopus, ScienceDirect, and Dimensions databases, then selected up to six relevant primary articles. The results of the study indicate that CVaR is the dominant risk measure in portfolio optimization, while K-Means Clustering serves as a method of grouping assets to increase diversification. The optimization methods used include Genetic Algorithm, Particle Swarm Optimization, Teaching Learning-Based Optimization, and Stochastic Programming. However, direct integration between Mean-CVaR and K-Means within a portfolio weight allocation framework is still rare. This research emphasizes the need to develop a hybrid model that combines both approaches in an integrated manner, applied to a multi-asset portfolio, and validated under various market conditions to produce an optimal, adaptive, and resilient investment strategy against extreme risks.
Co-Authors Abdul Talib Bon Abiodun Ezekiel Owoyemi Achmad Bachrudin Adhitya Ronnie Effendie, Adhitya Ronnie Agung Prabowo Agung Prabowo Agung Prabowo Agus Santoso Agus Santoso Agus Sugandha Agustini Tripena Br Surbakti Aisyah Nurul Aini Amalia, Hana Safrina Amitarwati, Diah Paramita Apipah Jahira, Juwita Asep K Supriatna Asep Saepulrohman Asep Solih Awalluddin, Asep Solih Asri Rula Hanifah Audina, Maudy Afifah Aulia Kirana Aziza Ayu Nurjannah Bakti Siregar Banowati, Puspa Dwi Ayu Basuki , Basuki Basuki Bayyinah, Ayyinah Nur Betty Subartini Bimasota Aji Pamungkas bin Mamat, Mustafa Budi Pratikno Candra Budi Wijaya Carissa, Katherine Liora Dara Selvi Mariani Dedy Rosadi Dedy Rosadi Dewi Ratnasari DEWI RATNASARI Dhika Surya Pangestu Diah Chaerani Diah Paramita Amitarwati Diana Ekanurnia Dianti, Estu Putri Dihna, Elza Rahma Dini Aulia Dwi Susanti Dwi Susanti Dwi Susanti Dwi Susanti Dwi Susanti Dwi Susanti Eddy Djauhari Edi Kurniadi Ema Carnia Emah Suryamah, Emah Eman Lesmana Endang Rusyaman Endang Soeryana Hasbullah Fasa, Rayyan Al Muddatstsir Febrianty, Popy Firdaus, Muhammad Rayhan Forman Ivana S. S. S. Ghazali, Puspa Liza Grida Saktian Laksito Hadiana, Asep Id Haq, Fadiah Hasna Nadiatul Hasbullah, Soeryana Hasriati Hasriati Hazelino Rafi Pradaswara Herlina Napitupulu Herlina Napitupulu Hidayana, Rizki Apriva Ibrahim M Sulaiman Ihda Hasbiyati Iin Irianingsih Ira Sumiati Ismail Bin Mohd Januaviani, Trisha Magdalena Adelheid Jumadil Saputra Jumadil Saputra Kahar, Ramadhina Hardiva kalfin Kalfin Kalfin, Kalfin Khairi, M. Ihsan Kusumaningtyas, Valentina Adimurti Labitta, Kirana Fara Laksito, Grida Saktian M. Ihsan Khairi Maraya, Nisrina Salsabila Maulana Malik Maulida, Ghafira Nur Ma’mur, Lutfi Praditia Melina Melina Melina Melina, Melina Mochamad Suyudi Mohamad Nurdin, Dadang Muhammad Arief Budiman Muhammad Iqbal Al-Banna Ismail Mustafa Mamat Mustafa Mamat Mustafa Mamat Mustafa Mamat Mustafa Mamat Nabilla, Ulya Nahda Nabiilah Nita Rulianah Noriszura Ismail Norizan Mohamed Novianti, Saqila Novieyanti, Lienda Novinta S, Fujika Novitasari, Ela Nugraha, Dwita Safira Nur Mahmudah Nurdyah, Himda Anataya Nurfadhlina Abdul Halim Nurul Fadilah Okta Yohandoko, Setyo Luthfi Pardede, Ester Priyatna, Yayat Puspa Liza Ghazali Putri, Aulya Putri, Linda Damayanti Putri, Sherina Anugerah Raharjanti, Amalia Rahman, Rezki Aulia Ramdhania, Tya Shafa Ratih Kusumadewi Riadi, Nadia Putri Riaman Riaman Riaman Riaman Riaman Riaman Riaman Riaman, Riaman Riaman, Riaman Rini Cahyandari Riza Adrian Ibrahim Rosadi, D. - Rulianah, Nita Saefullah, Rifki Salamiah, Mia Salih, Yasir Sampath, Sivaperumal Saputra, Jumadil Shindi Adha Gusliana Sianturi, Sri Novi Elizabeth Sisilia Sylviani Siti Sabariah Abas Soeryana Hasbullah Sri Purwani Stanley Pandu Dewanto Subanar - Subanar . Subanar Subanar Subiyanto Subiyanto Sudradjat Supian Suhaimi, Nurnisaa binti Abdullah Sulastri, S Sumiati, Ira Supian, Sudradjat Supriyanto Supriyanto Suroto Suroto Susanto, Sunarta Sutiono Mahdi Sutisna, Sarah Suyudi, Mochamad Suyudi, Mochammad T.P Nababan Tampubolon, Carlos Naek Tua Tika Fauzia Tiswaya, Waway Titi Purwandari Titin Herawati Umar A Omesa Valentina Adimurti Kusumaningtyas Verrany, Maria Jatu Vimelia, Willen Wahid, Alim Jaizul Wan Muhamad Amir W Ahmad Widyani, Azizah Rini Wiliya Wiliya Yasir Salih Yasmin, Arla Aglia Yhenis Apriliana Yulianus Brahmantyo Yulison Herry Chrisnanto Yuningsih, Siti Hadiaty Yuyun Hidayat Zahra, Ami Emelia Putri Zinedine Amalia Noor Mauludy Reihan