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All Journal Aptisi Transactions on Technopreneurship (ATT) Operations Research: International Conference Series International Journal of Global Operations Research International Journal of Quantitative Research and Modeling International Journal of Applied Mathematics, Sciences, and Technology for National Defense International Journal of Mathematics, Statistics, and Computing
Ismail, Muhammad Iqbal Al-Banna
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Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Mathematics Physics Social Sciences

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Linear mixed model approach: The effect of poor people & unemployment rate on various types of crime in 34 provinces in Indonesia Atmadi, Atmadi; Tarigan, Nieldy R; Negara, I Putu Aditya Brama Putra Cakra; Syalaisa, Najlaa; Ismail, Muhammad Iqbal Al-Banna
International Journal of Applied Mathematics, Sciences, and Technology for National Defense Vol 2, No 1 (2024): International Journal of Applied Mathematics, Sciences, and Technology for Natio
Publisher : FoundAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/app.sci.def.v2i1.350

Abstract

Currently, Indonesia faces serious challenges related to increasing crime rates throughout the country. Various types of crime, such as increasing drug cases, increasingly concerning violations of decency, and disturbing acts of fraud, embezzlement, and corruption, have become a major concern for society and the government. In this context, this research plays an important role in efforts to understand the factors that drive the growth of crime and pursue the vision and mission of Indonesia Emas 2045, which sets targets to reduce poverty to reach zero percent. The main focus of the research is to link poverty and unemployment rates with three key types of crime that affect Indonesia's future. In this study, we try to understand how poverty rates and unemployment rates affect these types of crime in 34 provinces in Indonesia. The study used a statistical method called Linear Mixed Model with the support of R software. Of the 27 models analyzed, several models had a significant impact. The data used in this study was obtained from the Central Statistics Agency (BPS). The results of this study provide valuable insights into the factors influencing crime in Indonesia, which in turn can be used as a basis for designing more effective policies in an effort to achieve the vision and mission of Indonesia Emas 2045.
Understanding Technopreneurship in Agricultural E-Marketplaces Lestari, Etty Puji; Prajanti, Sucihatiningsih Dian Wisika; Adzim, Fauzul; Primayesa, Elvina; Ismail, Muhammad Iqbal Al-Banna; Lase, Sepandil Laras
Aptisi Transactions On Technopreneurship (ATT) Vol 6 No 3 (2024): November
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/att.v6i3.454

Abstract

Competition in the global market is challenging for technopreneurs to develop strategies that provide a comparative advantage to win the competition. The article aims to develop a model for applying agricultural product e-marketplaces, including the involvement of related stakeholders in Semarang and Magelang Regency, Indonesia. The study employs two primary analytical methods: the MACTOR framework, which assesses alliances, conflicts, and strategic recommendations, and the Analytical Hierarchy Process (AHP) to prioritize decision-making criteria. The results showed that developing agricultural product e-marketplaces requires collaboration from various stakeholders. Notably, consumers, who play a crucial role in the success of the e-marketplace, emerge as the most influential actors, while middlemen are identified as the most dependent. The primary challenge in developing an agricultural product e-marketplace is ensuring smooth food distribution. At the same time, alternative priorities include increasing business partnerships between local agricultural cooperatives and entrepreneurs/investors and providing infrastructure to support the development of e-marketplaces. This study emphasizes the importance of collaboration between various stakeholders in e-marketplace development and implementation of agricultural products so that they can be aligned for the success of the entire e-marketplace system.
Markov Chain Method for Calculating Insurance Premiums Dihna, Elza Rahma; Ismail, Muhammad Iqbal Al-Banna
International Journal of Quantitative Research and Modeling Vol 5, No 4 (2024)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v5i4.817

Abstract

This study applies the Markov Chain method to calculate insurance premiums based on the dynamic health status of policyholders over time. The model considers three health states Healthy, Mild Illness, and Severe Illness each associated with a specific insurance premium. The transition probabilities between these states are represented in a transition matrix, capturing the likelihood of a policyholder remaining in their current health state or transitioning to another state in a given period. Using this approach, the steady-state distribution, which reflects the long-term probabilities of being in each health state, is calculated. This distribution is then used to determine the expected monthly premium by taking a weighted average of the premiums for each state. The methodology incorporates real-world scenarios where a policyholder's health condition may change over time, impacting the premiums they are required to pay. The Markov Chain model provides an effective framework for estimating these premiums by considering the "memoryless" nature of health state transitions, where future states depend only on the current state and not on prior health history. By solving the steady-state equations pi P=pi and ensuring the total probabilities sum to one, the model yields a robust estimation of long-term health state distributions. These distributions, combined with the associated premiums, produce an accurate calculation of expected insurance costs. The results demonstrate the flexibility and accuracy of the Markov Chain method in assessing risks and setting premiums. Insurers benefit from this approach as it enables dynamic pricing strategies tailored to individual risk profiles. For policyholders, the model provides transparency in understanding how health status influences premiums. Overall, this study highlights the practicality of using Markov Chains in health insurance pricing and underscores their importance in creating equitable and sustainable insurance systems.
Health Sector Portfolio Optimization Using the Markowitz Approach with Risk Aversion and Risk Tolerance Parameters Ismail, Muhammad Iqbal Al-Banna; Pirdaus, Dede Irman
Operations Research: International Conference Series Vol. 5 No. 4 (2024): Operations Research International Conference Series (ORICS), December 2024
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v5i4.352

Abstract

This study analyzes the optimal portfolio formation of health sector stocks listed on the Indonesia Stock Exchange using the Markowitz approach with dual risk parameters. Unlike traditional mean-variance optimization, this research incorporates both risk aversion (ρ) and risk tolerance (τ) parameters to better accommodate varying investor risk preferences. Using daily closing price data from six health sector stocks during the period January 2022 to December 2023, this study employs web scraping techniques for data collection and implements portfolio optimization calculations. The results show that the dual risk parameters approach produces consistent portfolio weights across both risk measures, with SIDO.JK receiving the highest allocation (approximately 41.6%) followed by SOHO.JK (23.0%) and SILO.JK (16.9%). The efficient frontier analysis demonstrates portfolio risk ranges from 0.015 to 0.030 with returns between 0.10% to 0.45%. This study contributes to the literature by demonstrating how incorporating dual risk parameters can provide more nuanced portfolio allocations while maintaining the fundamental benefits of diversification.
Analysis of the Application of the Integrated Catastrophe Risk Model Method for the Flood Insurance Program Afifah, Nur; Joebaedi, Khafsah; Ismail, Muhammad Iqbal Al-Banna
International Journal of Global Operations Research Vol. 3 No. 4 (2022): International Journal of Global Operations Research (IJGOR), November 2022
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v3i4.188

Abstract

As the flood rises continue to grow, well-designed insurance programs are becoming an important instrument in flood risk management. One of the obstacles in the flood insurance program is the method used to calculate the premium value. This thesis refers to the Integrated Catastrophe Risk Model (ICRM) which consists of two probability events and stochastic optimization procedures with respect to observation of site-specific risk. The application of the model is illustrated in the study area simulation data. In this thesis, analysis of various aspects of trade-off, new ex-post variables, opportunity occurrence 1 and 2 and minimization of loss function. From the results of research based on these four aspects it can be concluded that the use of Integrated Catastrophe Risk Model method in the optimal flood insurance program.
Application Of Economic Mathematics In Periodical Installation System Ismail, Muhammad Iqbal Al-Banna; Hernawati, Depie; Supena, Yusup
International Journal of Global Operations Research Vol. 3 No. 4 (2022): International Journal of Global Operations Research (IJGOR), November 2022
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v3i4.189

Abstract

This paper provides financial planning aspect by modeling the impact of mortgage rate changes on the size of payments for ARMS. This study uses a simulation approach to model the choice between a fixed rate mortgage (FRM} and an adjustable rate mortgage (ARM). Our simulations help assess therisks and benefits of choosing an ARM rather than a FRM. We represent the risk of the ARM with distributions of present value cost for a variety of mortgage lifeperiods.
Comparison of Activation Functions in Recurrent Neural Network for Litecoin Cryptocurrency Price Prediction Yuningsih, Siti Hadiaty; Ismail, Muhammad Iqbal Al-Banna
International Journal of Global Operations Research Vol. 6 No. 2 (2025): International Journal of Global Operations Research (IJGOR), May 2025
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v6i2.381

Abstract

The rapid advancement of information technology and digitalization has significantly transformed the financial sector, particularly with the emergence of cryptocurrencies characterized by high price volatility and complex movement patterns. Accurate price prediction of these crypto assets is essential to support investment decision-making and risk management. This study aims to compare the performance of six activation functions ReLU, Tanh, Sigmoid, Softplus, Swish, and Mish in a Simple Recurrent Neural Network (RNN) model for predicting the price of Litecoin, a widely traded cryptocurrency. Using historical daily closing price data from May 2020 to April 2025, the data were preprocessed through Min-Max normalization and sliding window sequence formation to fit the RNN input requirements. Each activation function was applied in the RNN model under consistent training conditions, and model performance was evaluated using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (R²). Results indicate that the Swish activation function outperforms others by achieving the lowest RMSE of 4.58 and the highest R² score of 0.9578, demonstrating superior prediction accuracy and stable convergence. Tanh also showed competitive results, while Sigmoid and Softplus performed less effectively. In conclusion, Swish is recommended as the most suitable activation function for RNN-based cryptocurrency price forecasting due to its balance of accuracy and computational efficiency.
Optimization of Machine Learning Models for Sentiment Analysis of TikTok Comment Data on the Progress of the Ibu Kota Nusantara as New Capital City of Indonesia Saputra, Renda Sandi; Dwiputra, Muhammad Bintang Eighista; Saputra, Moch Panji Agung; Ismail, Muhammad Iqbal Al-Banna
International Journal of Mathematics, Statistics, and Computing Vol. 3 No. 3 (2025): International Journal of Mathematics, Statistics, and Computing
Publisher : Communication In Research And Publications

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijmsc.v3i3.232

Abstract

Sentiment analysis plays a crucial role in understanding public opinion on social media platforms, especially in discussions related to government policies such as the relocation of Indonesia’s new capital city, known as Ibu Kota Nusantara (IKN). While machine learning algorithms like Naïve Bayes, Support Vector Machine (SVM), and Logistic Regression (LR) are widely used for sentiment classification tasks, previous studies often focus on performance comparisons without addressing the impact of data imbalance or regularly optimizing model parameters. These issues can lead to suboptimal classification performance, especially in real-world social media data. This study aims to improve the accuracy and robustness of sentiment classification by applying two enhancement strategies: text data augmentation and hyperparameter tuning. Three models Naïve Bayes, SVM, and Logistic Regression were trained and evaluated in three experimental stages: (1) using original data, (2) after applying augmentation, and (3) after augmentation combined with hyperparameter tuning via GridSearchCV. The evaluation results show progressive improvements across the three stages. In the first stage (original data), Logistic Regression achieved the highest accuracy of 80.41%, while Naïve Bayes and SVM reached 79.73% and 76.98%, respectively. However, all models struggled to classify the minority class (positive sentiment), as reflected in their lower recall and F1-scores. After applying augmentation, performance improved significantly across all models. SVM, in particular, reached an accuracy of 92.77%, followed by Logistic Regression (86.57%) and Naïve Bayes (86.22%), with better balance between precision and recall for both sentiment classes. hyperparameter tuning further optimized model performance. Logistic Regression became the best-performing model, achieving an accuracy of 93.80%, along with high precision, recall, and F1-scores for both classes. SVM and Naïve Bayes also showed stable improvements, with accuracies of 92.88% and 87.72%, respectively.
Co-Authors Atmadi, Atmadi Dihna, Elza Rahma Dwiputra, Muhammad Bintang Eighista Etty Puji Lestari Hernawati, Depie Khafsah Joebaedi Lase, Sepandil Laras Muhammad Afif Negara, I Putu Aditya Brama Putra Cakra Pirdaus, Dede Irman Primayesa, Elvina Saputra, Renda Sandi Sucihatiningsih Dian Wisika Prajanti Supena, Yusup Syalaisa, Najlaa Tarigan, Nieldy R Yuningsih, Siti Hadiaty