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All Journal Epsilon: Jurnal Matematika Murni dan Terapan JOIV : International Journal on Informatics Visualization JOURNAL OF APPLIED INFORMATICS AND COMPUTING Data Science: Journal of Computing and Applied Informatics Jurnal Varian
Saputri, Prilyandari Dina
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Social Sciences Transportation Other

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Stock Market Index Prediction using Bi-directional Long Short-Term Memory Majid, Muhammad Althaf; Saputri, Prilyandari Dina; Soehardjoepri, Soehardjoepri
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.7195

Abstract

The IHSG (Indonesia Stock Exchange Composite Index) is a stock price index in the Indonesia Stock Exchange (BEI) that serves as an indicator reflecting the performance of company stocks through stock price movements. Therefore, IHSG becomes a reference for investors in making investment decisions. Advanced stock exchanges generally have a strong influence on other stock exchanges. Several studies have proven the influence of one global index on another. Global index is a term that refers to each country's index to represent the movement of its country's stock performance. Forecasting IHSG can be one of the analyses that help investors make wise decisions when investing. To obtain an IHSG forecasting model, an appropriate and suitable method is needed, especially for data that has a large amount. LSTM is a development of Recurrent Neural Network (RNN) which has the ability to remember information in a longer period of time, while Bi-LSTM is a development of LSTM which has the ability to remember information longer and can understand more complex patterns than LSTM. This research provide the IHSG forecasting based on global index factors. The results showed that the best Bi-LSTM model (6-9-1) had a better performance in predicting and forecasting JCI movements with a MAPE value of 0.572314% better than the best LSTM model (4-10-1) which had a MAPE of 0.74326%. With forecasting based on the Bi-LSTM model, it is expected to help investors in making decisions on the Indonesia Stock Exchange (IDX).
Preventing recession through GDP growth prediction: A classical and machine learning classification approach Saputri, Prilyandari Dina; Angrenani, Arin Berliana; Fitriana, Ika Nur Laily
Data Science: Journal of Computing and Applied Informatics Vol. 7 No. 2 (2023): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jocai.v7.i2-10507

Abstract

Classification methods are a popular method applied in many various fields of science. To represent the effect of predictor factors on categorical response variables, different machine learning classification algorithms are used, namely logistic regression, neural network (NN), random forest, support vector machine (SVM), and bayesian model averaging (BMA). Every classifier has its unique characteristic, performing well in certain datasets but not in others. Hence, it is always a quest to find the best classifier to use for a certain dataset. Economic growth, most commonly using a gross regional domestic product, is experiencing a recession or acceleration, especially before and during the COVID-19 pandemic. This research proposed a comparison of classification methods using regional GDP data for 2019-2020, before and during the COVID-19 pandemic, by predictor variables; percentage of workers, foreign direct investment (PMA), regional revenue (PAD), general allocation fund (DAU), revenue sharing fund (DBH), and the dummy of COVID-19. The results are that all selected machine learning models can classify the regional GDP growth perfectly for the training data, but, NN model outperforms the other methods with an accuracy of 100% in training and testing data. COVID-19 and the PMA are the most significant variables predicting regional GDP growth for all models. Further research relating to interpretable machine learning, such as feature interaction, global surrogate, and Shapley values, is also necessary to predict regional GDP growth using machine learning methods.
Prediction Intervals for Extreme Rainfall in Indonesia using Monotone Composite Quantile Regression Neural Networks Saputri, Prilyandari Dina; Azwarini, Rahmania; Adipradana, Dimaz Wisnu
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.3186

Abstract

Rainfall data may contain nonlinear, complex, and extreme characteristics. Weather monitoring can be performed by predicting rainfall as the cause of flooding and providing early warnings to ensure smooth evacuation. Classical methods, such as ARIMA, are unable to capture rainfall data patterns. A standard method for forecasting complex datasets is the use of neural networks. The neural network method failed to produce a prediction interval due to the limitation of the standard error calculation. The use of the Monotone Composite Quantile Regression Neural Network (MCQRNN) enables the accommodation of complex patterns and the production of interval predictions through its quantiles. The crossing problems in the quantile estimation were also resolved. In this study, we utilized four rainfall datasets from different locations: Central Java, West Java, South Sumatra, and North Sumatra. The lower and upper bounds were compiled from 2.5% and 97.5%, respectively. The point forecasts are constructed from the 50% quantile. Furthermore, the point forecast and interval prediction were compared to the standard classical forecasting method, i.e., ARIMA. The results demonstrated that the MCQRNN model outperforms the ARIMA model in terms of point forecasting. As the forecasting period is extended, the interval prediction of MCQRNN tends to become more consistent, whereas the width prediction of the ARIMA model becomes broader. Hence, the MCQRNN interval predictions are also suitable for long-term forecasting. Further research was required to evaluate the performance of prediction intervals.
PENDEKATAN MAZIMUM PENALIZED LIKELIHOOD UNTUK MENGESTIMASI FUNGSI BASELINE HAZARD PADA MODEL COX: STUDI KASUS PASIEN KANKER PAYUDARA Edina, Almira Ivah; Purnami, Santi Wulan; Sukur, Edi; Saputri, Prilyandari Dina; Febrisutisyanto, Ady; Alfajriyah, Aimmatul Ummah
EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN Vol 19, No 2 (2025)
Publisher : Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/epsilon.v19i2.17087

Abstract

Survival analysis is a statistical method that focuses on time-to-event variables, where the event time represents the duration a patient survives during the observation period. This study applies survival analysis to examine factors influencing the survival of breast cancer patients who are receiving treatment at C-Tech Labs Edwar Technology. The data used are right-censored survival data, referring to patients who either survived until the end of the observation period or died from unrelated causes. Risk factors analyzed include age, gender, and cancer stage, while treatment factors consist of surgery, chemotherapy, radiotherapy, and Frequency of Electro Capacitive Cancer Therapy (ECCT) usage. The Cox Proportional Hazard (PH) model combined with the Maximum Penalized Likelihood (MPL) method is used to analyze the effect of these factors on mortality risk, as well as to estimate regression coefficients and the baseline hazard function more accurately. The results indicate that age, frequency of ECCT use, and the status of additional therapies significantly affect patient survival. Older age increases the risk of death, while a higher frequency of ECCT use and the use of additional therapies help reduce that risk. Routine use of ECCT is shown to contribute to extending the survival time of breast cancer patients at C-Tech Labs Edwar Technology, Tangerang. However, potential confounding variables not examined in this study should be considered when interpreting the findings.
Evaluating Fisherman Insurance Participation using Bagging Multivariate Adaptive Regression Splines Azmi, Ulil; Soehardjoepri, Soehardjoepri; Saputri, Prilyandari Dina; Salsabila, Thalia Rizki; Iswara, Widya; Zakaria, Roslinazairimah
Jurnal Varian Vol. 8 No. 3 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v8i3.5373

Abstract

The Fishermen’s Insurance Premium Assistance Program and the Independent Fishermen’s Insurance Scheme are initiatives by the Indonesian government aimed at enhancing the protection of fishermen, whose occupations are considered high-risk compared to other professions. One of the regions actively participating in both programs is Lekok District, located in Pasuruan Regency, East Java Province. The objective of this research is to analyze the factors influencing fishermen’s participation in self-funded insurance schemes using the Multivariate Adaptive Regression Spline method. The research is based on primary data collected through direct surveys and structured questionnaires distributed to fishermen in Lekok District. The results of this research are that five key variables significantly influence participation, with the most influential factor being participation in outreach or socialization activities. Other important factors include the number of family members (X4), income (X3), and age (X1), while fishing experience (X5) does not show a significant effect. The model’s classification accuracy on the training data reached 82%, while on the test data it was 75.8%. Furthermore, applying the bootstrap aggregation technique to Multivariate Adaptive Regression Splines models significantly improved classification accuracy to 92% on the training data and 100% on the test data. The findings are expected to support stakeholders in formulating strategies to increase fishermen’s engagement in independent insurance programs. Strengthening such participation is crucial for reducing occupational risks, ensuring the sustainability of fishing activities, and improving the welfare and resilience of the fishing community. 
Co-Authors Adipradana, Dimaz Wisnu Alfajriyah, Aimmatul Ummah Angrenani, Arin Berliana Azwarini, Rahmania Edina, Almira Ivah Febrisutisyanto, Ady Fitriana, Ika Nur Laily Hakiki, M. Taufik Imam Safawi Ahmad, Imam Safawi Iswara, Widya Majid, Muhammad Althaf Mohamad Atok Muslichah Erma Widiana Salsabila, Thalia Rizki Santi Wulan Purnami Siswono, Galuh Oktavia Soehardjoepri, Soehardjoepri Sukur, Edi Ulil Azmi Widyaningrum, Mahmudah Enny Yulius Hari Zakaria, Roslinazairimah