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All Journal Indonesian Journal of Life Sciences Indonesian Actuarial Journal
Kurniawan, Yohana Joevanca
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Medicine & Pharmacology Physics

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Forecasting Rupiah-to-US Dollar Exchange Rate 2020 - 2025 Using a Fuzzy Time Series Markov Chain Model Rahmadani, Tiur Masayu; Maeni, Rosa; Hwa, Camelia Miftahur Rizki Kiem; Khasanah, Iftha Nikmatul; Yeo, Winner; Alfarisi, Kgs. M. Rifat; Kurniawan, Yohana Joevanca; Juwono, Adriano Fadlan; Tauryawati, Mey Lista
Indonesian Actuarial Journal Vol. 1 No. 1 (2025): Indonesian Actuarial Journal
Publisher : Persatuan Aktuaris Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65689/iajvol01no1pp060-072

Abstract

The exchange rate of the Indonesian Rupiah against the US Dollar experiences frequent fluctuations, making economic forecasting and financial planning more difficult. This study aims to enhance exchange rate prediction accuracy by combining Fuzzy Time Series with Markov Chain probability transitions. The approach is grounded in the idea that probabilistic modeling of state changes improves the representation of dynamic currency behavior. Using daily IDR/USD data from April 2020 to March 2025, the methodology involves two main steps: fuzzifying historical exchange rate data into linguistic variables, and applying a Markov Chain to compute transition probabilities between these fuzzy states. The model’s forecasting accuracy is evaluated using mean absolute percentage error. Results show that the hybrid model achieves a lower error rate of 0.50%, compared to 0.61% using conventional Fuzzy Time Series alone. This demonstrates the hybrid model’s ability to capture both sudden market changes and stable patterns effectively. The findings suggest that the integration of Markov Chain transitions significantly improves the predictive performance of fuzzy-based models. In conclusion, this hybrid method provides a practical and reliable forecasting tool for financial analysts and policymakers. Future research could include additional economic indicators and explore alternative probability weighting methods to further enhance model accuracy.
Comparative Analysis For CNN and MLP Models in Breast Cancer Diagnosis Nurtanio, Priscilla Natalie; Nathaniel, Darren; Sugiarto, Temmy; Angelina, Theresa; Tjandra, Raymond; Kurniawan, Yohana Joevanca; Sampe, Maria Zefanya
Indonesian Journal of Life Sciences 2026: IJLS Vol 08 No.01
Publisher : Universitas Bio Scientia Internasional Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54250/ijls.v8i01.254

Abstract

Breast cancer remains one of the most common and deadly diseases affecting women worldwide, highlighting the importance of early and accurate diagnosis to improve treatment outcomes and survival rates. However, traditional mammography techniques often fall short, failing to detect up to 20% of cases, especially in women with dense breast tissue, which makes detection more difficult. In response to these limitations, this study explores the use of neural networks to enhance diagnostic accuracy in breast cancer detection, focusing on the Convolutional Neural Network (CNN) and Multilayer Perceptron (MLP). Utilizing the Wisconsin Diagnostic Breast Cancer (WDBC) dataset, a baseline CNN model is compared against an optimized CNN refined through hyperparameter tuning using randomized search, as well as two MLP models implemented via Keras and Scikit-learn, along with their optimized versions. Each model is evaluated using key classification metrics, including accuracy, precision, recall, F1-score, and AUC, with an emphasis on minimizing false negatives, as this is critical in medical diagnosis to avoid missed malignancies. The results indicate that the optimized CNN model achieved near-perfect scores across all metrics and demonstrated the best balance between training and testing data. Therefore, it outperforms the baseline CNN and MLP models in significantly reducing false negatives, showcasing the potential of a well-tuned CNN to enhance the automation and reliability of breast cancer diagnostic processes.
Co-Authors Alfarisi, Kgs. M. Rifat Angelina, Theresa Hwa, Camelia Miftahur Rizki Kiem Juwono, Adriano Fadlan Khasanah, Iftha Nikmatul Maeni, Rosa Maria Zefanya Sampe Mey Lista Tauryawati Nathaniel, Darren Nurtanio, Priscilla Natalie Rahmadani, Tiur Masayu Sugiarto, Temmy Tjandra, Raymond Yeo, Winner