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All Journal Journal of Data Analysis ELIPS: Jurnal Pendidikan Matematika International Journal of Applied Mathematics, Sciences, and Technology for National Defense Limits: Journal of Mathematics and Its Applications
Ikhsan Maulidi
Universitas Syiah Kuala
Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Mathematics Physics

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Beberapa Subgrup dari SL(2,3) Mahmudi Mahmudi; Ikhsan Maulidi; Saiful Amri
Journal of Data Analysis Volume 2, Number 2, December 2019
Publisher : Department of Statistics, Syiah Kuala University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (329.702 KB) | DOI: 10.24815/jda.v2i2.15788

Abstract

Artikel ini membahas mengenai SL(2,3) dengan rincian elemen-elemennya. Dengan bantuan Tabel Cayley dibuktikan bahwa SL(2,3) merupakan grup dan memiliki beberapa subgrup siklik dan subgrup tidak siklik sesuai dengan Teorema Lagrange. Lebih jauh, juga dibuktikan bahwa SL(2,3) tidak memiliki subgrup berorder 12.This article discusses about SL(2,3)with its detail elements. By using Cayley Table, we prove that SL(2,3)is a group and has cyclic subgroup and noncyclic subgroup according to The Lagrange Theorem. Futher, we also give a detail proof that SL(2,3)has no subgroup of order 12.
Model Persamaan Beda Pada Pendapatan Nasional Radhiah Radhiah; Ikhsan Maulidi; Nani Maulida; Intan Syahrini
ELIPS: Jurnal Pendidikan Matematika Vol 4 No 1 (2023): ELIPS, Maret 2023
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47650/elips.v4i1.893

Abstract

Penelitian ini menerapkan persamaan perbedaan dengan pendapatan nasional dalam ekonomi terbuka menggunakan metode akar karakteristik. Tujuan dari penelitian ini adalah untuk mengetahui kondisi stabilitas titik ekuilibrium dari model persamaan perbedaan terhadap pendapatan nasional dan memberikan grafik simulasi. Simulasi dilakukan dalam penelitian ini dengan memilih beberapa nilai parameter berdasarkan teorema koefisien kondisi. Model persamaan beda yang dihasilkan dalam menentukan pendapatan nasional dalam ekonomi terbuka adalah persamaan beda orde dua dengan koefisien konstan. Berdasarkan hasil penelitian ini nilai parameter yang sudah memenuhi kondisi stabilitas dalam teorema kondisi koefisien, grafik solusi pendapatan nasional stabil dan akan selalu menyatu di sekitar keadaan titik ekuilibrium.
Model Kredibilitas Bühlmann dengan Frekuensi Klaim Berdistribusi Binomial Negatif-Lindley Ikhsan Maulidi; Vina Apriliani
Limits: Journal of Mathematics and Its Applications Vol. 18 No. 1 (2021): Limits: Journal of Mathematics and Its Applications Volume 18 Nomor 1 Edisi Me
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

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Abstract

In this article, we develop a parametric Bühlmann credibility model with the frequency of claims that are assumed following the Negative Binomial- Lindley distribution. The Estimator of the quantities in the Bühlmann model have provided for this distribution using methods commonly used in the greatest accuracy credibility. The premium estimation that resulted in this model is a linear combination of the past claims which gives a minimum error square. The momen function of the Binomial-Lindley distribution is very helpful to determine these Bühlmann’s quantities. Application simulations of this model are also given for simple data claims along with the algorithm. However, it gives an appreciable credibility factor value, this model requires many past claims to get a good premium estimation.
Average-based fuzzy time series for forecasting blood bag availability: Implications for health resilience and emergency preparedness in Banda Aceh, Indonesia Ikhsan Maulidi; Nurafni Fazriani; Radhiah Radhiah; Vina Apriliani; Sarbaini
International Journal of Applied Mathematics, Sciences, and Technology for National Defense Vol. 4 No. 1 (2026): International Journal of Applied Mathematics, Sciences, and Technology for Nati
Publisher : FoundAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/app.sci.def.v4i1.1138

Abstract

Background: Blood availability remains a major challenge in healthcare systems, particularly in developing countries where the demand for blood often exceeds the available supply. Accurate forecasting of blood collection is therefore important to support effective blood inventory management at blood transfusion units. Aims: This study aims to apply the Average-Based Fuzzy Time Series method to forecast the number of collected blood bags at the Blood Transfusion Unit (UTD) of the Indonesian Red Cross in Banda Aceh, both in total and by blood type. Method: Monthly blood collection data from January 2016 to September 2020 were analyzed using the Average-Based Fuzzy Time Series model. The forecasting procedure involved constructing fuzzy intervals using the average-based approach, forming fuzzy logical relationships, and performing defuzzification. Model performance was evaluated using Mean Squared Error (MSE) and Average Forecasting Error Rate (AFER). Result: The second-order model provided the best forecasting performance with an AFER value of 13.67% and an accuracy of approximately 86.33%, producing a prediction of 2054 blood bags for October 2020. Forecasting by blood type yielded predictions of 529 (A), 702 (O), 738 (B), and 154 (AB) blood bags. Conclusion: The results indicate that the Average-Based Fuzzy Time Series method is effective for forecasting blood bag availability and can support planning and management of blood supply at blood transfusion units. Furthermore, the proposed approach has potential applications in defense and emergency contexts by supporting medical logistics planning, improving preparedness, and enhancing the resilience of blood supply systems during military operations and disaster response.
Hybrid random forest–catboost ensemble for heart disease prediction on imbalanced datasets: Toward applications in military health systems Mahyus Ihsan; Zahnur; Iftahul Fadlan; Ikhsan Maulidi
International Journal of Applied Mathematics, Sciences, and Technology for National Defense Vol. 4 No. 1 (2026): International Journal of Applied Mathematics, Sciences, and Technology for Nati
Publisher : FoundAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/app.sci.def.v4i1.1148

Abstract

ackground: Heart disease is one of the main causes of death worldwide, with cases increasing every year. This situation highlights the urgent need for early detection systems that are not only fast but also accurate and reliable. In recent years, machine learning has emerged as a promising alternative approach for analyzing medical data, particularly for disease classification and risk prediction tasks. Aims: This study aims to develop a heart disease prediction model by integrating Random Forest and CatBoost in a hybrid ensemble framework and evaluating its performance on an imbalanced medical dataset. Method: This study employs a quantitative approach based on supervised learning using the Behavioral Risk Factor Surveillance System (BRFSS) 2021 dataset, which consists of more than 300,000 observations. Data preprocessing includes duplicate removal, BMI categorization, encoding of categorical variables, and exploratory analysis. To address class imbalance, the Borderline-SMOTE technique was applied before splitting the dataset using an 80:20 train-test split. Random Forest and CatBoost models were trained and combined using a soft voting ensemble. Result: The evaluation results indicate that Random Forest achieved the highest accuracy of 0.94, with well-balanced precision and recall across all classes. CatBoost demonstrated relatively stable performance with accuracy around 0.84. The ensemble approach achieved an accuracy of 0.91 with strong metric stability and good sensitivity to positive cases. Conclusion: The results indicate that Random Forest performs best for the dataset used in this study, while the ensemble model provides a balanced compromise between predictive performance and robustness. The analysis also shows that Age Category, General Health, and BMI are the most influential predictors of heart disease risk. This model can support early cardiovascular risk detection in military personnel, contributing to maintaining operational readiness in defense systems. Furthermore, the proposed approach provides a reliable decision-support tool for large-scale medical screening in resource-constrained healthcare environments.
Co-Authors Alim Misbullah Bonno Andri Wibowo Iftahul Fadlan Intan Syahrini Mahmudi Mahmudi Mahyus Ihsan Muhamad Syazali Nani Maulida Nurafni Fazriani Radhiah Radhiah Radhiah Radhiah Rini Oktavia Saiful Amri Sarbaini Uswah Uswah Vina Apriliani Windiani Erliana Zahnur