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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
Departemen Matematika, Fakultas MIPA, Universitas Padjadjaran
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Investment Portfolio Optimization Using Genetic Algorithm on Infrastructure Sector Stocks Based on the Single Index Model Ayyinah Nur Bayyinah; Riaman Riaman; 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.v6i2.977

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%.
Comparison of Stock Mutual Fund Price Forecasting Results Using ARIMA and Neural Network Autoregressive Model Sri Novi Elizabeth Sianturi; Betty Subartini; 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.v6i2.1001

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.
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.
Portofolio Optimization of Mean-Variance Model Using Tabu Search Algorithm with Cardinality Constraints Lutfi Praditia Ma’mur; Riaman Riaman; 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.v6i2.1010

Abstract

Stock investment is increasingly attractive to Indonesians, especially through the IDX30 index, which is known to have high liquidity and solid company fundamentals. In forming an optimal stock portfolio, investors are faced with the challenge of maximizing return and minimizing risk simultaneously. An optimal portfolio is defined as a combination of assets that provides the highest expected return at a certain level of risk, or the lowest risk for the expected level of return. This study aims to form an optimal portfolio on the IDX30 index by considering cardinality constraints, which limit the maximum number of stocks in the portfolio. From 30 IDX30 stocks, 20 stocks were selected based on consistency of existence during the period February 1, 2023 to January 31, 2025. Next, 8 stocks that have positive expected return values are selected, and from these 8, 4 efficient stocks are selected using cardinality constraints. Selection is done with the Tabu Search algorithm, a memory-based metaheuristic optimization method used to find the best solution by avoiding previously explored solutions. The portfolio is formed using the Mean-Variance model, resulting in an allocation of BMRI (30,02%), PTBA (35,18%), INDF (2,48%), and BRPT (32,32%), with an expected return of 0,00207 and a variance of 0,001587.
Implementation of Simulated Annealing Algorithm for Portfolio Optimization in Jakarta Islamic Index (JII) Stocks with Mean-VaR Nadia Putri Riadi; Riaman Riaman; 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.v6i2.1016

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 the Mean-Variance Model Based on Holt-Winters Stock Price Forecasting of Food Sector in Indonesia Himda Anataya Nurdyah; Betty Subartini; 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.v6i2.1017

Abstract

The importance of the food sector to Indonesia's economy makes it one of the most attractive sectors to consider in an investment portfolio. An optimal portfolio is the best choice for investors among various efficient portfolios, aiming to maximize returns while minimizing risk. Moreover, since investment is inherently associated with fluctuating stock prices, accurate forecasting is necessary to anticipate future stock movements. This study aims to accurately predict stock prices and construct an optimal portfolio consisting of five food sector stocks listed on the Indonesia Stock Exchange, namely DMND, ICBP, HOKI, INDF, and ULTJ. Stock price predictions are generated using the Holt-Winter method, which can identify seasonal patterns and trends from historical data. The predicted stock prices are then used to calculate returns, which serve as the basis for portfolio optimization using the Mean-Variance model. The results show that the Holt-Winter method successfully produces accurate stock price forecasts, with Mean Absolute Percentage Error (MAPE) values for all stocks below 10%. These forecasts are used to calculate returns in the portfolio optimization process. The optimal portfolio composition is determined with the following weight proportions: HOKI (4%), ICBP (18%), ULTJ (21%), DMND (26%), and INDF (30%). This portfolio yields an expected return of 0.0441% and a portfolio variance of 0.0063%, reflecting a balanced trade-off between potential return and risk.
Estimation of an Optimal Portfolio Using the Constant Correlation Model: An Empirical Study on IDX Bisnis-27 Stocks Jehan Rizky Faustina Hartono; Anastasia Audrey Wijaya; Sukono
International Journal of Quantitative Research and Modeling Vol. 6 No. 4 (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.v6i4.1142

Abstract

Portfolio optimization is an essential aspect of investment decision-making, as investors aim to achieve an optimal trade-off between expected return and risk. However, the traditional Markowitz portfolio model requires the estimation of a large variance–covariance matrix, which becomes computationally complex as the number of assets increases. To address this limitation, this study applies the Constant Correlation Model (CCM), which simplifies portfolio construction by assuming a constant correlation among asset returns. This study aims to estimate an optimal stock portfolio using the CCM approach based on stocks included in the IDX Bisnis-27 index, representing companies with strong business fundamentals listed on the Indonesia Stock Exchange. The data consist of daily closing prices of 28 stocks for the period from January to December 2023. The analysis involves calculating stock returns, expected returns, standard deviations, Excess Return to Standard Deviation (ERS), constant correlation, and the cut-off rate (C*). The results show that the average constant correlation among the selected stocks indicates a moderate level of interdependence, suggesting that diversification benefits still exist. Based on the CCM selection criteria, only one stock, ANTM, has an ERS value exceeding the cut-off rate and is therefore included in the optimal portfolio with a weight of 100%. These findings indicate that ANTM exhibits the strongest risk-adjusted performance among IDX Bisnis-27 stocks during the observation period. This study provides practical insights for investors in constructing optimal portfolios using simplified correlation assumptions in emerging markets.
Outlier-Resistant Claim Reserving Using a Robust Chain Ladder Method with a 2.5 Interquartile Range Criterion Naia Rafida Mumtaz; Jessica Sie; Sukono
International Journal of Quantitative Research and Modeling Vol. 7 No. 1 (2026): 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.v7i1.1144

Abstract

Claim reserving plays an essential role in evaluating outstanding liabilities in credit insurance portfolios, which are commonly characterized by irregular claim development patterns and the occurrence of extreme claim values. Such characteristics may influence the stability of reserve estimates when historical paid claim data exhibit high variability across development periods. Therefore, an appropriate reserving approach is required to accommodate these data features while maintaining a consistent estimation process. This study estimates claim reserves for a credit insurance portfolio using the Robust Chain Ladder method based on paid claim data. The analysis is conducted by constructing incremental and cumulative run-off triangles from historical claim payments. To address the presence of extreme observations, a residual-based outlier detection procedure is applied using an interquartile range criterion with a 2.5 multiplier. This approach aims to reduce the influence of extreme claim values while preserving plausible large claims inherent in credit insurance portfolios. The reserving process is performed iteratively until no further extreme observations are identified, resulting in an adjusted run-off triangle used for reserve estimation. Based on the adjusted run-off triangle, the total estimated claim reserve amounts to IDR 120,773,423,681. This value represents an actuarial estimate of outstanding claim liabilities across all accident periods derived from historical claim development under the applied reserving assumptions. The results provide an overview of reserve levels for credit insurance portfolios and illustrate the application of a robust reserving approach in the presence of irregular and volatile claim patterns.
Comparison between Holt Winter Additive and Holt Winter Multiplicative Methods in Forecasting Bank Central Asia (BBCA) Stock Price in Indonesia Stock Exchange Alem Huga Martono; Ruben Clynton Oey; Sukono
International Journal of Quantitative Research and Modeling Vol. 7 No. 1 (2026): 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.v7i1.1237

Abstract

Stock market investment plays a pivotal role in the Indonesian economy as a source of capital formation and wealth creation for investors. As a financial instrument, the performance of stock markets is determined by the ability to predict future price movements accurately to minimize investment risks and maximize returns. Bank Central Asia (BBCA) is one of the largest private banks in Indonesia and is strategically positioned as one of the most actively traded and liquid stocks in the Indonesia Stock Exchange (IDX), consistently included in the LQ45 index. This research aims to determine the proper forecasting method for the existing data patterns of BBCA stock prices and to provide more accurate forecasting results for investment decision-making. The methods used include Holt Winter Additive and Holt Winter Multiplicative exponential smoothing techniques. The dataset comprises daily closing prices of BBCA stock from December 2, 2024, to December 5, 2025, totaling 241 trading days. From these two methods, the forecasting accuracy was evaluated using Mean Absolute Percentage Error (MAPE) and Mean Squared Error (MSE). The results show that the Holt Winter Additive method has the smallest MAPE value of 0.86% (MSE: 5,043.71) compared to the Holt Winter Multiplicative method with MAPE of 1.01% (MSE: 6,789.32), indicating that the Additive model provides superior forecasting performance for BBCA stock price prediction in the observed period.
The Charactherization Criterion of g-quasi-Frobenius Lie Algebra Corresponding to Inner Derivation Budiman, Muhammad Arief; Kurniadi, Edi; Sukono, Sukono
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 1 (2026): 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.v11i1.41601

Abstract

The structure of a g-quasi-Frobenius Lie Algebra can be realized as a quasi-Frobenius Lie Algebra modules over a Lie Algebra g. This research discusses a special condition of the g-quasi-Frobenius Lie Algebra, namely when g acts on its self. This condition supports the construction of an inner derivation on g. The criterion investigated is the characterization of the g-quasi-Frobenius Lie Algebra in relation to the inner derivation. The result obtained is a criterion: a g-quasi-Frobenius Lie Algebra can be constructed on g itself if and only if the inner derivation is zero. Furthermore, several concrete examples are provided to test this criterion.
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 Alem Huga Martono Amalia, Hana Safrina Amitarwati, Diah Paramita Anastasia Audrey Wijaya Apipah Jahira, Juwita Arla Aglia Yasmin Asep K Supriatna Asep Saepulrohman Asep Solih Awalluddin, Asep Solih Asri Rula Hanifah Aulia Kirana Aulya Putri Ayyinah Nur Bayyinah Aziza Ayu Nurjannah Azizah Rini Widyani Bakti Siregar Banowati, Puspa Dwi Ayu Basuki , Basuki Basuki Bayyinah, Ayyinah Nur Betty Subartini Betty Subartini 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 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 Estu Putri Dianti Fadia Irsya Septiana Fasa, Rayyan Al Muddatstsir Febrianty, Popy Firdaus, Muhammad Rayhan Forman Ivana S. S. S. Gani Gunawan Ghazali, Puspa Liza Grida Saktian Laksito Hadiana, Asep Id Hana Safrina Amalia Haq, Fadiah Hasna Nadiatul Hasbullah, Soeryana Hasriati Hasriati Hazelino Rafi Pradaswara Herlina Napitupulu Hidayana, Rizki Apriva Himda Anataya Nurdyah Ibrahim M Sulaiman Ihda Hasbiyati Iin Irianingsih Ira Sumiati Ismail Bin Mohd Januaviani, Trisha Magdalena Adelheid Jehan Rizky Faustina Hartono Jessica Novia Sitepu Jessica Sie Jumadil Saputra Jumadil Saputra Kahar, Ramadhina Hardiva kalfin Kalfin Kalfin, Kalfin Katherine Liora Carissa Khairi, M. Ihsan Kirana Fara Labitta Labitta, Kirana Fara Laksito, Grida Saktian Linda Damayanti Putri Lutfi Praditia Ma’mur M. Ihsan Khairi Maraya, Nisrina Salsabila Maudy Afifah Audina Maulana Malik Maulida, Ghafira Nur Ma’mur, Lutfi Praditia 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 Nadia Putri Riadi Nahda Nabiilah Naia Rafida Mumtaz Nisrina Salsabila Maraya 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 Popy Febrianty Priyatna, Yayat Puspa Liza Ghazali Putri, Aulya Putri, Linda Damayanti Putri, Sherina Anugerah Raharjanti, Amalia Rahman, Rezki Aulia Ramdhania, Tya Shafa Ratih Kusumadewi Rayyan Al Muddatstsir Fasa Riadi, Nadia Putri Riaman Riaman Riaman Riaman Rini Cahyandari Riza Adrian Ibrahim Rosadi, D. - Ruben Clynton Oey 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 Novi Elizabeth Sianturi 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 Titi Purwandari Titin Herawati Umar A Omesa Valentina Adimurti Kusumaningtyas Verrany, Maria Jatu Vimelia, Willen Wahid, Alim Jaizul Wan Muhamad Amir W Ahmad Waway Tiswaya Widyani, Azizah Rini Wiliya Wiliya Willen Vimelia 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