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EduMatika: Jurnal MIPA
Published by Lembaga Riset Mutiara Akbar
ISSN : -     EISSN : 28088069     DOI : -
Core Subject : Science, Education,
Chemistry Education Mathematics Physics
EduMatika : Jurnal MIPA adalah jurnal yang diterbitkan oleh Lembaga Riset Mutiara Akbar, Medan, Indonesia merupakan jurnal akses terbuka yang dapat digunakan sebagai media interaksi bagi seluruh cendikiawan di bidang Penelitian pendidikan Matematika, Biologi, Fisika dan KImia.. Jurnal ini diperuntukkan bagi, peneliti, dosen, serta mahasiswa dari berbagai institusi dan afiliasi di Indonesia.
Arjuna Subject : Umum - Umum
Articles 67 Documents
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Analysis of the Most Dominant Causing Factors of Divorce in 34 Provinces in Indonesia Using the XGBoost Algorithm Amalia, Seila; Banjarnahor, Riski Melanton; Hutapea, Risca Octaviyani; Sitorus, Gabriel Fernando; Domini, Gracia; Arnita, Arnita
EduMatika: Jurnal MIPA Vol. 5 No. 2 (2025): EduMatika: Jurnal MIPA
Publisher : Lembaga Riset Mutiara Akbar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56495/emju.v5i2.1135

Abstract

Divorce in Indonesia shows a significant increasing trend and has a broad social impact. This study aims to identify the most dominant causes of divorce in 34 provinces in Indonesia using the Extreme Gradient Boosting (XGBoost) machine learning algorithm. Secondary quantitative data from the Central Bureau of Statistics in 2024 were analyzed by pre-processing, data sharing, model training, and performance evaluation. The results showed that constant disputes and quarrels were the main causes of divorce, followed by substance abuse and forced marriage. The developed XGBoost model achieved 75% accuracy in classifying the level of divorce risk. These findings provide new insights into understanding the social factors that influence divorce and can be the basis for designing more effective prevention strategies.
The Use of Number Cards to Improve the Beginning Counting Skills of Second Grade Students at SD Muhammadiyah 01 Medan Setiawani, Indah; Elfrianto, Elfrianto
EduMatika: Jurnal MIPA Vol. 5 No. 2 (2025): EduMatika: Jurnal MIPA
Publisher : Lembaga Riset Mutiara Akbar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56495/emju.v5i2.1201

Abstract

This study was initiated by the issue of low arithmetic skills among students in mathematics learning. The objective of this research was to explore the extent to which early arithmetic skills could be improved through the implementation of number card media among second-grade students at SD Muhammadiyah 01 Medan. The method employed was Classroom Action Research (CAR), conducted over two Cycles. Each Cycle consisted of the stages of planning, action implementation, observation, and reflection. Data collection techniques included direct observation and documentation. The findings revealed that the integration of number card media had a significant impact on enhancing students’ early arithmetic skills. This was evidenced by an increase in the percentage of students achieving learning mastery from 47% in Cycle I to 96% in Cycle II. Thus, there was a 49% improvement, indicating that the predetermined success indicators had been successfully met.
Analysis of the Utilization of Mangrove Species Diversity in Jerowaru District, East Lombok Aminuddin, Muhammad
EduMatika: Jurnal MIPA Vol. 5 No. 3 (2025): EduMatika: Jurnal MIPA
Publisher : Lembaga Riset Mutiara Akbar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56495/emju.v5i3.1237

Abstract

Indonesia has the largest mangrove ecosystem in the world, but it experiences significant degradation of up to 52,000 hectares per year due to anthropogenic pressures and weak local governance. This condition demands a sustainable utilization strategy. This study examines the utilization of mangrove diversity by coastal communities in Jerowaru District, East Lombok, as a locally based conservation effort. A qualitative approach was used through participant observation, in-depth interviews, and field documentation, with thematic analysis to uncover adaptation patterns and perceptions of sustainability. The results indicate five dominant species—Rhizophora apiculata, Rhizophora mucronata, Avicennia marina, Bruguiera gymnorhiza, and Sonneratia alba—that are distributed across various ecological zones. Utilization includes construction materials, local food, herbal medicine, tourist attractions, and ritual activities, carried out collectively with traditional knowledge. Ecologically, mangroves function as abrasion barriers, waste filters, and habitats for coastal biota; socio-culturally, they serve as symbols of community identity and a space for intergenerational reflection. This study emphasizes the importance of integrating scientific and local knowledge as key to sustainable mangrove management. The utilization model in Jerowaru offers an integrated conservation approach that supports the Sustainable Development Goals (SDGs) while strengthening the foundations of a community-based green economy.
Stock Closing Price Prediction of PT Bank Central Asia Tbk (BBCA) with Long Short-Term Memory (LSTM) Tarigan, Febry Vista Kristen; Putri, Amelia; Nicolas, Jogi; Faradhilla, Anatasia; Gulo, Lirana Sapriani; Arnita, Arnita
EduMatika: Jurnal MIPA Vol. 5 No. 2 (2025): EduMatika: Jurnal MIPA
Publisher : Lembaga Riset Mutiara Akbar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56495/emju.v5i2.1104

Abstract

Stock price volatility remains one of the key challenges for investors in making accurate investment decisions in Indonesia’s capital market. To address this issue, predictive approaches based on machine learning—such as the Long Short-Term Memory (LSTM) algorithm—are increasingly utilized due to their effectiveness in processing time series data. This study aims to develop a model for predicting the closing price of PT Bank Central Asia Tbk (BBCA) shares using the LSTM method. The dataset consists of historical daily stock prices of BBCA from 2015 to mid-2025, obtained from Yahoo Finance. The research stages include data preprocessing, normalization, sequence generation, LSTM model construction, training and validation, and performance evaluation using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The results show that the LSTM model successfully predicted closing stock prices with high accuracy, as indicated by a very low validation loss and prediction curves that closely follow actual price trends. This suggests that LSTM has a strong generalization ability and is effective in capturing complex stock movement patterns. The novelty of this research lies in the practical implementation of LSTM for BBCA stock price prediction and its potential application in real-time decision support systems for investors.
Development of Deep Learning Model Based on Convolutional Neural Network (CNN) for Brain Tumor Classification Using MRI Images Sinaga, May Rani Tabitha; Tampubolon, Bungaria; Daulay, Nurfitri Humayro; Triana, Dinie; Hani, Aulia; Simanjuntak, Ferdyanto Abangan; Arnita, Arnita
EduMatika: Jurnal MIPA Vol. 5 No. 2 (2025): EduMatika: Jurnal MIPA
Publisher : Lembaga Riset Mutiara Akbar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56495/emju.v5i2.1105

Abstract

Brain tumor classification using MRI images presents a critical challenge in medical radiology. This study develops a deep learning model based on Convolutional Neural Network (CNN) to classify brain MRI images into four categories: Normal, Glioma, Meningioma, and Pituitary. A publicly available dataset from Kaggle consisting of 20,672 images was used, with preprocessing and data augmentation applied. The model architecture includes convolutional, pooling, flatten, dense, and dropout layers, optimized using the Adam optimizer and categorical crossentropy loss function. The evaluation results show that the model achieved an overall accuracy of 96% with high f1-scores across all classes, particularly for the Pituitary class (0.98). The main contribution of this study lies in the integration of diverse data augmentation techniques and Explainable AI (XAI) methods, enabling the visualization of key areas in MRI images that support classification decisions. The proposed model is not only accurate but also demonstrates strong generalization and interpretability, making it a promising tool for clinical decision support systems in brain tumor diagnosis.
Comparison of Cox Proportional Hazards and Weibull Regression Models in Survival Analysis of Heart Failure Patients Using UCI Repository Data Sianturi, Ardicha Appu; Hutapea, Risca Octaviyani; Sinaga, May Rani Tabitha
EduMatika: Jurnal MIPA Vol. 5 No. 3 (2025): EduMatika: Jurnal MIPA
Publisher : Lembaga Riset Mutiara Akbar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56495/emju.v5i3.1327

Abstract

Heart failure is a leading cause of death worldwide, with a high mortality rate due to decreased heart function and systemic complications. Survival analysis is used to understand factors that influence patient survival and estimate the risk of death based on clinical characteristics. This study aims to analyze factors that influence survival time in heart failure patients and compare the performance of the Cox Proportional Hazards (CoxPH) model with the Weibull Accelerated Failure Time (AFT) in predicting the risk of death. Data are from the Heart Failure Clinical Records Dataset (UCI Repository) which includes 299 patients with variables such as age, anemia, hypertension, serum creatinine levels, and ejection fraction. The analysis was performed using the Kaplan–Meier, CoxPH, and Weibull AFT methods with evaluation through AIC and C-index values. The results show that age, anemia, hypertension, and creatinine increase the risk of death, while ejection fraction is protective. The CoxPH model performed better (AIC 958.46; C-index 0.741) than the Weibull AFT (AIC 1282.24; C-index 0.259). Therefore, CoxPH is recommended for estimating relative risk between patients, while Weibull AFT is more suitable for estimating absolute survival duration.
Application of the TOPSIS Method for Electronic Supplier Selection Based on Multi-Criteria Selviani, Chairunnisa; Nasution, Dewi Aslamiah; Amelia, Nabila; Zulida, Nadya Putri; Salsabila, Nisya Aulia; Syahputra, Muhammad Romi
EduMatika: Jurnal MIPA Vol. 5 No. 4 (2025): EduMatika: Jurnal MIPA
Publisher : Lembaga Riset Mutiara Akbar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56495/emju.v5i4.1381

Abstract

Supplier selection in the electronics industry is highly complex due to the numerous and often conflicting assessment parameters. These parameters include economic aspects, product quality, delivery reliability, and environmental impact. This study uses the TOPSIS method to objectively rank suppliers based on four main criteria with their respective weights: product quality (30%), price (25%), delivery accuracy (25%), and sustainability (20%). Simulation data is used as an example to demonstrate how this method is applied. The assessment results show that PT Green Component (S5) is ranked first with a closeness coefficient of  0.7777, followed by PT Techno Supply (S3) with a score of 0.7577. Sensitivity tests using four weighting change scenarios show stable results, with S5 and S3 remaining in the top two rankings. These findings indicate that balancing product quality and environmental sustainability is a key factor in differentiating competitive advantage in the modern supply chain management era. The TOPSIS approach has proven effective as a structured and transparent framework to assist decision-making in the supplier selection process in the electronics industry.

Page 7 of 7 | Total Record : 67

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