Article Per Year (5 Year)
p-Index From 2020 - 2025
1.285
P-Index
This Author published in this journals
All Journal Jambura Journal of Mathematics International Journal of Research in Community Services International Journal of Quantitative Research and Modeling Pattimura Proceeding : Conference of Science and Technology International Journal of Ethno-Sciences and Education Research
Rizki Apriva Hidayana
Program Studi Magister Matematika, Fakultas MIPA, Universitas Padjadjaran, Indonesia
Religion Agriculture, Biological Sciences & Forestry Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Physics Social Sciences Transportation Other

Published : 8 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 8 Documents
Search

 1 
SIMULASI PERGERAKAN HARGA SAHAM MENGGUNAKAN MODEL GERAK BROWN GEOMETRIK DENGAN R STUDIO Ahmad Fawaid Ridwan; Rizki Apriva Hidayana; Budi Nurani Ruchjana
Pattimura Proceeding 2021: Prosiding KNM XX
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (980.48 KB) | DOI: 10.30598/PattimuraSci.2021.KNMXX.559-564

Abstract

Saham merupakan surat berharga sebagai bukti penyertaan atau kepemilikan individu maupun instansi dalam suatu perusahaaan. Keuntungan berinvestasi saham dapat dilihat dari besarnya return saham. Model matematis dapat diterapkan untuk memodelkan pergerakan harga saham agar investor memiliki pengetahuan untuk memprediksi harga saham di masa mendatang. Salah satu pemodelan yang dapat digunakan untuk melihat pergerakan harga saham yaitu dengan model gerak Brown geometrik. Penelitian ini bertujuan untuk mengetahui tingkat akurasi prediksi pergerakan harga saham menggunakan gerak Brown geometrik. Gerak Brown merupakan sebuah proses stokastik yang bersifat kontinu dan sering disebut sebagai proses Wiener. Gerak Brown dapat dibentuk dari sebuah Random Walk yang simetris, yaitu dengan mencari nilai limit dari distribusi Random Walk tersebut. Model Gerak Brown Geometrik merupakan modifikasi dari gerak Brown dimana perubahan relatifnya berbentuk kombinasi dari pertumbuhan deterministik ditambah dengan perubahan acak yang berdistribusi normal. Metode penelitian menggunakan studi literatur dan studi eksperimental melalui simulasi pada data sekunder berupa data harian harga saham suatu perusahaan dari tanggal 4 Mei 2020 sampai 30 April 2021. Simulasi data menggunakan gerak Brown geometrik dengan R Studio menunjukkan bahwa data return saham berdistribusi normal serta menghasilkan prediksi pergerakan harga saham dengan tingkat akurasi yang baik. Hal ini ditunjukkan dengan nilai rata-rata MAPE dari 20 kali percobaan simulasi menggunakan R Studio, yaitu sebesar 13,904 %. Oleh karena itu, model gerak Brown geometrik dapat digunakan oleh para investor atau manager investasi untuk memprediksi pergerakan harga saham suatu perusahaan dalam rentang waktu tertentu
Investment Portfolio Optimization with a Mean-Variance Model Without Risk-Free Assets Syifa Nur Rasikhah Daulay; Nurfadhlina Abdul Halim; Rizki Apriva Hidayana
International Journal of Quantitative Research and Modeling Vol 3, No 3 (2022)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v3i3.345

Abstract

Investment is an allocation of money, stocks, mutual funds, or other valuable resources provided by someone at the present time and held from being used until a specified period to get a profit (return). The higher the return received, the higher the risk. This study studied the Mean-Variance investment portfolio optimization model without risk-free assets to obtain the optimum portfolio. Five shares are used, namely BMRI, AMRT, SSMS, MLPT, and ANTM. The research results obtained optimal portfolio stocks with respective weights BMRI = 0.45741; AMRT=0.17852; SSMS=0.23300; MLPT=0.08475 and ANTM=0.04632. An optimal portfolio composition produces an average return = 0.00207 and variance = 0.00020.
Investment Portfolio Optimization Model Using The Markowitz Model Emmanuel Parulian Sirait; Yasir Salih; Rizki Apriva Hidayana
International Journal of Quantitative Research and Modeling Vol 3, No 3 (2022)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v3i3.344

Abstract

The stock portfolio is related to how someone allocates several shares in various types of investments so that the results achieve maximum profit. By implementing a diversification system or portfolio optimization on several stocks, investors can reduce the level of risk and simultaneously optimize the expected rate of return. This study aims to determine which stocks listed on the Indonesia Stock Exchange (IDX) and included in the portfolio for the 2021-2022 period are eligible to be included in the optimal portfolio and to determine the proportion of funds for each share in the formation of the optimal portfolio. The population in this study are all shares included in the Indonesia Stock Exchange (IDX) listed on the Indonesia Stock Exchange (IDX) for the 2021-2022 period. The sample of this research is five stocks that are candidate portfolios. The sampling method uses a purposive sampling method with the criteria of 5 stocks with the highest positive ratio. The population in this study was all 30 companies included in the IDX30, while the samples were five companies. Data were analyzed using a mean-variant optimization model with a research duration between May 2021 and May 2022. Based on the results of the investment portfolio optimization analysis on the 5 (five) selected stocks, this study shows that, out of 23 stocks, five stocks are eligible to enter the optimal portfolio with their respective proportions, namely PT Adaro Energy Indonesia Tbk (ADRO) 20%, PT Astra International Tbk (ASII) 26%, PT Merdeka Copper Gold Tbk (MDKA) 10%, PT XL Axiata Tbk (EXCL) 19%, PT Bukit Asam Tbk (PTBA) 25%. The portfolio of these stocks generates an expected return of 0.00217 at a risk level of 0.00022. It is hoped that this research can be helpful to add to the literature on investment optimization models, especially the concentration of Mathematics in Finance, and serve as an additional reference for further research, as well as an alternative for investors in optimizing investment portfolios.
The Study of Value-At-Risk Calculation and Back-testing Using the ARMA-GARCH Model Based on Stock Returns: An Overview Rizki Apriva Hidayana; Subiyanto Subiyanto; Sudradjat Supian
International Journal of Research in Community Services Vol 3, No 4 (2022)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijrcs.v3i4.368

Abstract

Stocks are investment instruments that provide returns but tend to be risky. The most important component of investing is volatility, where volatility is identical to the standard conditional deviation of stock price return. The important thing in investing in addition to return is a risk. Value-at-Risk (VaR) is a statistical method of estimating maximum losses. To evaluate the quality of VaR estimates, models should always be back-tested with appropriate methods. Back-testing is a statistical procedure in which actual gains and losses are systematically compared to appropriate VaR estimates. To evaluate the quality of VaR estimates, models should always be back-tested with appropriate methods. Back-testing is a statistical procedure in which actual gains and losses are systematically compared to appropriate VaR estimates. The goal of the study was to estimate the Autoregressive Moving Average-Generalized Conditional Heteroscedastic (ARMA-GARCH) model to determine Value-at-Risk and back-testing. ARMA is a combination of AR and MA models, while GARCH is a time series model with symmetrical properties. The method in this study is systematic browsing of libraries. Systematic library tracing is an attempt to identify, evaluate, and interpret all research relevant to a particular phenomenon.  
Investment Portfolio Optimization With Mean-Variance Investment Portfolio Optimization Model Without Risk Free Assets Wilda Nur Rahmalia; Dwi Susanti; Rizki Apriva Hidayana
International Journal of Quantitative Research and Modeling Vol 3, No 4 (2022)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v3i4.360

Abstract

Forming a portfolio is a strategy that is often carried out by investors in risky investment conditions. Five non-risk free stocks were selected, namely PTBA, IPCM, ANTM, BUMI, and ADMF. The purpose of forming this portfolio is to determine the composition of the weight (proportion) of the allocation of funds in each of these shares in forming the optimum portfolio. The method used is the Mean-Variance investment portfolio optimization model without risk-free assets using the Markowitz approach. Based on the results obtained by the optimum portfolio of the Mean-Variance model without risk-free assets, the average return is 0.00105 and the variance is 0.000067 with a portfolio ratio value of 14.65256. The proportion of fund allocation to PTBA shares = 0.28872; IPCM=0.02484; ANTM=0.00016; EARTH=0.13501; and ADMF=0.55126. It is hoped that the formation of this portfolio optimization model will be useful as an alternative for investors in optimizing the investment portfolio to make it more profitable in the future. 
How to Teach English to Children Early on Rizki Apriva Hidayana; Usman A. Yakubu
International Journal of Ethno-Sciences and Education Research Vol 2, No 4 (2022)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijeer.v2i4.387

Abstract

Infrastructure and facilities should be sufficient to support the early introduction of English. To properly implement learning management, the teacher, who is a part of the learning process manager, must be aware of the early childhood thinking framework (AUD). Learning should be controlled to produce engaging and fulfilling learning activities. To make learning English for young children meaningful and enjoyable, singing (song) is used as a teaching tool. Children can be engaged in learning English by using a variety of approaches, tactics, games, and learning resources that will make them feel like they are playing rather than learning. Even the local culture can be useful for making learning enjoyable for AUD. The topic of learning English for AUD, in general, and through singing, in particular, will be covered in this article. A positive attitude toward learning English will keep AUD from being disinterested, dissatisfied, or even terrified by it.
Peramalan Return Saham Menggunakan Model Integrated Moving Average Rizki Apriva Hidayana; Budi Nurani Ruchjana
Jambura Journal of Mathematics Vol 5, No 1: February 2023
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3360.988 KB) | DOI: 10.34312/jjom.v5i1.17381

Abstract

A popular investment that is in great demand among investors is stocked. Stocks are another type of financial instrument offering returns but carrying a higher risk level. Price time series are more difficult to manage than return time series. To equip investors with the knowledge to forecast future stock prices, mathematical models can be used to simulate stock price fluctuations. The time series method, especially the Integrated Moving Average (IMA) model, is a model that can be used to observe changes in stock prices. The Integrated Moving Average (IMA) model will be used in this study to simulate stock returns. The Integrated Moving Average (IMA) model is a Moving Average model that is carried out with a differencing process or an Autoregressive Integrated Moving Average model with a value of Autoregressive being zero. This study uses secondary data simulations from secondary sources, such as data on daily business stock prices for one year, to conduct a literature review and test experiments. The Integrated Moving Average (IMA) model is used in data processing, especially to test the differencing data process. The results obtained are the IMA (1,1) model with the following equation Zˆt = Zt-1 + 0, 5782at-1, which can be used to anticipate future stock returns. Based on these results, it is expected that investors can predict the value of shares within a certain period of time.
Application of Mathematics Learning Methods to Students of SMP N 11 Bengkulu Rizki Apriva Hidayana; Jumadil Saputra
International Journal of Ethno-Sciences and Education Research Vol 3, No 1 (2023)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijeer.v3i1.406

Abstract

Mathematics is a clear and precise language, quantitative in character, and a method of logical thinking. It significantly impacts the advancement of all branches of science. This study aims to ascertain the instructional strategies used by teachers of SMP N 11 Bengkulu. A descriptive case study was chosen as the research methodology. The participants in this study were three math teachers from SMPN 11 Bengkulu who taught various classes using various teaching resources. Analysis of data using qualitative or non-statistical methods. The lecture and question-and-answer formats were employed to introduce the lesson in light of the findings of the field observations. During the core activities, the question-and-answer, the assignment, the lecture, the discussion, and the demonstration approach are all used. At the end of the lesson, the math teacher employs the lecture technique, question-and-answer method, and assignment method. The assignment and question-and-answer methods are the techniques the teacher uses while evaluating student learning. The technique used in the class results in an average mark that satisfies the academic completeness criteria. 
Co-Authors Ahmad Fawaid Ridwan Budi Nurani Ruchjana Dwi Susanti Emmanuel Parulian Sirait Jumadil Saputra Nurfadhlina Abdul Halim Subiyanto Subiyanto Sudradjat Supian Syifa Nur Rasikhah Daulay Usman A. Yakubu Wilda Nur Rahmalia Yasir Salih