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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 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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Mean-Variance Optimal Portfolio Selection with Risk Aversion on Transportation and Logistics Sector Stocks Based on Multi-Criteria Decision-Making Putri, Aulya; Riaman, Riaman; Sukono, Sukono
International Journal of Quantitative Research and Modeling Vol 6, No 1 (2025)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v6i1.895

Abstract

The importance of the transportation and logistics sector to a country's economy, coupled with the growth of this sector in Indonesia, requires investment support for this sector to continue to grow. Therefore, stocks in the transportation and logistics sector are attractive for investment portfolio consideration. The optimal portfolio selection is to minimize the risk with the expected return. In the formation of an investment portfolio, the problem is how to determine the weight of capital allocation in order to get the maximum return while still considering the risk in each stock, by considering several criteria in decision making. This study was conducted to determine the best stock selection in the transportation and logistics sector listed on the Indonesia Stock Exchange, and determine the optimal weight in the investment portfolio. The method used is Multi-Criteria Decision Making (MCDM), namely Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) using 15 financial metrics as relevant criteria in stock selection. Furthermore, to determine the allocation weight to form an optimal stock portfolio using the Mean-Variance model with Risk Aversion. The stocks analyzed were 28 stocks in the transportation and logistics sector. The results of research based on MCDM selected 9 stocks, namely MITI, BIRD, HATM, TMAS, JAYA, PPGL, BPTR, ASSA, and RCCC. However, TMAS, PPGL, and BPTR stocks are not included in portfolio formation because they have a negative average return. Based on the optimization results, the allocation weights of the 6 stocks included in the optimal portfolio are BIRD (37.7%), JAYA (24.6%), MITI (12.9%), HATM (9.9%), ASSA (7.5%), and RCCC (7.4%). The results of this study are expected to be a consideration in making investment decisions.
CRYPTOCURRENCY TIME SERIES FORECASTING MODEL USING GRU ALGORITHM BASED ON MACHINE LEARNING Melina, Melina; Sukono, Sukono; Napitupulu, Herlina; Mohamed, Norizan; Herry Chrisnanto, Yulison; ID Hadiana, Asep; Kusumaningtyas, Valentina Adimurti
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp1317-1328

Abstract

The cryptocurrency market is experiencing rapid growth in the world. The high fluctuation and volatility of cryptocurrency prices and the complexity of non-linear relationships in data patterns attract investors and researchers who want to develop accurate cryptocurrency price forecasting models. This research aims to build a cryptocurrency forecasting model with a machine learning-based time series approach using the gated recurrent units (GRU) algorithm. The dataset used is historical Bitcoin closing price data from January 1, 2017, to July 31, 2024. Based on the gap in previous research, the selected model is only based on the accuracy value. In this study, the chosen model must fulfill two criteria: the best-fitting model based on the learning curve diagnosis and the model with the best accuracy value. The selected model is used to forecast the test data. Model selection with these two criteria has resulted in high accuracy in model performance. This research was highly accurate for all tested models with MAPE < 10%. The GRU 30-50 model is best tested with MAE = 867.2598, RMSE = 1330.427, and MAPE = 1.95%. Applying the sliding window technique makes the model accurate and fast in learning the pattern of time series data, resulting in a best-fitting model based on the learning curve diagnosis.
Multinomial Logistic Regression Model to Analysis Traffic Accident on Indonesia’s Regional Data Agustini Tripena Br Surbakti; Yhenis Apriliana; Agus Sugandha; Agung Prabowo; Sukono, Sukono
International Journal of Technology and Education Research Vol. 2 No. 01 (2024): January - March, International Journal of Technology and Education Research(IJ
Publisher : International journal of technology and education research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63922/ijeter.v2i01.944

Abstract

Traffic accident is one of the highest causes of death in Indonesia following coronary disease and tuberculosis. Traffic accident is classified into three: mild, moderate and severe. The aim of this research was to determine the significant factors causing traffic accidents in Cilacap Regency using multinomial logistic regression. The data used were secondary data from the Resort Police of Cilacap Regency. The research’s response variable was accident classification deemed as a nominal scale, while the predictor variables were day of occurrence, time of occurrence, accident type, accident location, situation, weather problem, number of vehicles involved, and number of victims of nominal, ordinal and ratio scales. The research results show that the accident type, accident location, situation, and number of victim variables significantly influence the three accident classifications.
Time Series Seasonal Autoregressive Integrated Moving Average Model for Analysis of Rainfall Forecasting: Implementation on Agricultural Insurance Agustini Tripena Br Surbakti; Zinedine Amalia Noor Mauludy Reihan; Agung Prabowo; Suroto, Suroto; Agus Sugandha; Sukono, Sukono
International Journal of Technology and Education Research Vol. 2 No. 01 (2024): January - March, International Journal of Technology and Education Research(IJ
Publisher : International journal of technology and education research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63922/ijeter.v2i01.945

Abstract

Cilacap is the largest regency located in the southwest of Central Java Province. The rain fall in Cilacap has seasonal variation for monthly period, thus information on rainfall is very important for the Government of Cilacap Regency its people, especially farmers. The usefulness of forecasting method in predicting the volume of rainfall is important. It motivates development of a system that can predict future amount of rainfall. A fluctuation analysis on forecasting result can be used for local government’s policy making purpose. This paper analyses and presents SARIMA method to develop a forecasting model which may support and predict rainfall volume. The dataset for model development was collected from time series data published by Meteorology, Climatology and Geophysics Agency Tunggul Wulung Station from January 2009 to December 2022. The data were divided into data training (to December 2021) and data testing (January to December 2022) groups. The use of data training produced SARIMA model (2,0,2)(0,0,1)12 as the selected model. The model achieved 0.70% for MAPE using data testing. It indicated the final model’s capability to closely represent and made prediction based on the rainfall history dataset. The model produced was used to forecast the rainfall from January 2023 to December 2024. The forecast results were analyzed in relation to agricultural insurance program.
Ruin Probability Model for Disaster Insurance Companies: A Systematic Literature Review Saefullah, Rifki; Riaman, Riaman; Sukono, Sukono
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 1 (2025): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v9i1.24371

Abstract

Ruin probability modelling is a crucial aspect for insurance companies that is very important and urgent to maintain the company's solvency. Ruin probability modelling helps identify insolvency risks, so companies can take timely preventive measures before it is too late. This research will present a systematic literature review (SLR) using a bibliometric analysis approach with the support of VOSviewer software, utilising the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method. The data sources used in this study came from 3 databases, namely Scopus, ScienceDirect and Dimensions, which resulted in 9 articles relevant to the topic under study. The results identified research gaps that could be an opportunity for future exploration. This research is expected to provide academic and practical contributions in developing ruin risk mitigation strategies in disaster insurance companies facing natural disaster uncertainty.
Investment Portfolio Optimization Using the Mean-Variance Model Based on Holt-Winters Stock Price Forecasting of Food Sector in Indonesia Nurdyah, Himda Anataya; 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.v6i2.1017

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

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.
Analysis of the French Five Factors Fama Model on Excess Return of Stocks Listed on IDXBUMN20 for the Period 2020-2023 Putri, Linda Damayanti; 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.v6i2.966

Abstract

Excess return is the difference between the rate of return earned on an investment and the rate of risk-free return in a given period. This shows how much return is received because they are willing to take risks in investing. This study aims to analyze the Fama French Five Factor model on the excess return of stocks listed in IDXBUMN20 2020-2023 period. The factors in the model are market factors, size factors, book to market ratio, profitability, and investment. The population in this study amounted to 20 companies registered in the IDXBUMN20 index, the sample selection in this study used the purposive sampling method and a sample of 12 companies was obtained. The data used in the study are close price, number of shares outstanding, Bank Indonesia (BI) interest rate, and company financial statements. The analysis method used was the Common Effect Model (CEM) panel data regression analysis. Based on hypothesis testing, market factors were obtained which only had an effect on excess returns. This factor shows the influence of the ups and downs of market performance on the price of a stock.
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.
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 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 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