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Zeta - Math Journal
Published by Universitas Islam Madura
ISSN : 24599948     EISSN : 25795864     DOI : -
Core Subject : Education,
Mathematics
Zeta - Math Journal is a mathematical journal published twice a year by the Mathematics Department, Faculty of Mathematics and Science, Islamic University of Madura. Journal includes research papers, literature studies, analysis, and problem solving in Mathematics (Algebra, Analysis, Statistics, Computing and Applied). It cordially invites contributions from researcher, lecturer, and teacher of related disciplines. The language used in this journal is Indonesian and English.
Arjuna Subject : -
Articles 98 Documents
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Special Form Hankel Matrix Inverses (n+1)×(n+1),n≥3 With 2×2 Block Matrices Rahma, Ade Novia; Azzahra, Frista; Aryani, Fitri; Rahmawati, Rahmawati
Zeta - Math Journal Vol 10 No 1 (2025): May
Publisher : Universitas Islam Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31102/zeta.2025.10.1.1-10

Abstract

This study aims to determine the inverse of a special form Hankel matrix using a block matrix. In this study, some steps are carried out. The first step will be given a special form Hankel matrix which will then be blocked into a block matrix. Next, determine the inverse of the invertible submatrix of the Hankel matrix so that the general form is obtained. The last step is seen from the inverse pattern of the two ways of blocking the Hankel matrix of the unique structure of order to so that the general shape of the inverse Hankel matrix of special form is obtained. The results obtained will be obtained in the general structure of the Hankel matrix inverse special form, using a block matrix.
Application of Economic Production Quantity (EPQ) Method and Just in Time Method (JIT) in Bread Raw Material Inventory Control Kisty, Teshi Amelia Hakilla; Safitri, Elfira; Basriati, Sri; Soleh, Mohammad
Zeta - Math Journal Vol 10 No 1 (2025): May
Publisher : Universitas Islam Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31102/zeta.2025.10.1.30-38

Abstract

Retno bakery Bread Shop is one of the shops that produces various types of bread. This bakery has not used a inventory control system in the production process so it cannot meet customer needs. For this reason, Retno Bkery Bakery needs to control inventory so that the company's performance and profits are more optimal. This study discusses the control of the inventory of each bread raw material such as flour, sugar, milk, yeast, salt, butter and eggs. The purpose of this study is to obtain the results of the total cost of bread raw material inventory using the Economic Production Quantity (EPQ) method and the Just in Time (JIT) method as well as the comparison of the two methods used. The methods used are the Economic Production Quantity (EPQ) and Just in Time (JIT) methods. Based on the results of the study, the percentage difference in cost for each raw material was obtained using the Economic Production Quantity (EPQ) method and the Just in Time (JIT) method, namely for flour raw materials of 42.05%, sugar 47.31%, milk 51.63%, yeast 51.83%, salt 47.65%, butter 51.38% and eggs 51%. Thus, the Just in Time (JIT) method results in a smaller total cost than the Economic Production Quantity (EPQ) method.
Music Artist Recommendation System Based on Listening History Using SVD and MICE Imputation Approaches Martal, David Vijanarco; Najib, Mohamad Khoirun
Zeta - Math Journal Vol 10 No 1 (2025): May
Publisher : Universitas Islam Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31102/zeta.2025.10.1.70-80

Abstract

In the digital era, music streaming platforms face challenges in providing relevant music recommendations to users. This research aims to develop a music artist recommendation system based on the user's listening history using the SVD and MICE methods. In this research, MICE was applied together with ALS predictive model. SVD is used to identify latent patterns between users and artists, while MICE address the problem of missing data in listening history. The data used comes from the online music platform Last.fm. Analysis was carried out with Julia 1.8.5 software. The results show that the model with MICE provides more accurate and consistent recommendations compared to SVD, especially in the context of missing data. Accuracy using the MICE model provides results of up to 96%, while the SVD model provides an accuracy of 90,22%. This approach can increase the relevance of recommendations, helping users find artists according to their preferences. These findings support the application of MICE in music recommendation systems, with the potential to improve user experience on music streaming platforms.
Implementation of the K-Means Method for Beverage Clustering Based on Calorie and Protein Rewina, Anggita Eka; Hapsari, Rinci Kembang; Putri, Chatarina Natassya; Lande, Gamaliel Virani Fofid; Aditya, Andre Fransisco; Alamsyah, Mochamad Tegar Bagas
Zeta - Math Journal Vol 10 No 1 (2025): May
Publisher : Universitas Islam Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31102/zeta.2025.10.1.19-29

Abstract

Recently, the number of coffee shops in big cities in Indonesia has increased. This makes it easier for coffee lovers to enjoy it. With the increasing public awareness of the importance of healthy drinking patterns in preventing diabetes and other diseases, consuming low-calorie drinks has become a prominent trend. This study aims to group the coffee drink menu at Starbucks based on the calorie and protein content of Starbucks drinks. It is grouped into 2 clusters, namely, high and low clusters. In this study, the clustering process of Starbucks drink menu data was carried out by applying the K-Means algorithm. The clustering results can identify members of Cluster 1 and members of Cluster 2. From the tests that have been carried out, it can group the drink menu into 2 clusters based on the amount of protein and calories from Starbucks drinks and help the public choose which drinks are better to consume.
Application of Support Vector Regression in Time Series Analysis of Dior Stock Prices Sari, Adma Novita; Zuleika, Talitha; Mardianto, M. Fariz Fadillah; Pusporani, Elly
Zeta - Math Journal Vol 10 No 1 (2025): May
Publisher : Universitas Islam Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31102/zeta.2025.10.1.51-60

Abstract

Christian Dior (Dior) is a multinational company focusing on luxury goods, including fashion products, cosmetics, and accessories. In 2020–2024, Dior's share price will experience significant fluctuations influenced by financial performance, global market trends, etc. These fluctuations require investors to implement appropriate strategies to minimize the risk of losses and support sustainable economic growth. This step aligns with goal 8 of the Sustainable Development Goals (SDGs), emphasizing the importance of sustainable economic growth through investment and infrastructure development for economic prosperity. One of the effective methods for modeling and predicting stock prices is Support Vector Regression (SVR). By applying SVR using the Radial Basis Function (RBF) kernel, this study shows that the model can generate predictions with a MAPE value of 2.5864% on the test data. The SVR method is expected to provide accurate predictions, making it a helpful tool for investors and market analysts to make better investment decisions.
A Robustness Study of Multi-Layer Perceptrons and Logistic Regression to Data Perturbation: MNIST Dataset Thahiruddin, Muhammad; Khotijah, Siti; Fajar, Moh.; Farras, Adib El
Zeta - Math Journal Vol 10 No 1 (2025): May
Publisher : Universitas Islam Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31102/zeta.2025.10.1.39-50

Abstract

This study systematically evaluates the robustness of Multi-Layer Perceptrons (MLPs) And Logistic Regression (LR) models against data pertubations using the MNIST handwritten digit dataset. While MLPs and LR are foundational in machine learning, their comparative resilience to diverse pertubations-noise, geometric distortions, and adversarial attacks-remains underexplored,despite implications for real-world applications with imperfect data., whe test three pertubations categories : Gaussian noise (σ=0.1 to 1.0), salt and pepper noise (p=0.1 to 0.5), rotational distorsions (5° to 30°), and adversial attacks (FGSM with ϵ=0.005 to0.30). both models were trained on 60.000 MNIST samples and tested on 10.000 pertubed images. Results demonstrate that MLPs exhibit superior robustness under moderate noise and rotations, achieving baseline accuracies of 97.07% (vs. LR’s 92.63%). For Gaussian noise (σ=0.5), MLP retained 35.35% accuracy compared to LR’s 23.91% . however, adversarial attacks (FGSM, ϵ= 0.30) reduced MLP accuracy to 0.20%, revealing critical vulnerabilities. Statistical analysis (paired t-test, p < 0.05) confirmed significant performance differences across pertubations levels. Alinear regressions (R^2 = 0.98) further quantified MLP’s predictable accuracy decline with Gaussian noise intensity. These findings underscore MLP’s suitability for noise-prone environments but highlight urgent needs for adversarial defense mechanisms. Practitioners are advised to prioritize MLPs for tasks with moderate distortions, while future work should integrate robustness enhancements like adversarial training.
Forecasting the Number of BMT NU Lenteng Branch Customers Using the Single Exponential Smoothing Method Munawwarah, Siti; Sarifah, Luluk
Zeta - Math Journal Vol 10 No 1 (2025): May
Publisher : Universitas Islam Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31102/zeta.2025.10.1.11-18

Abstract

The presence of financial institutions greatly helps the country in terms of economy, including Islamic financial institutions such as BMT NU Lenteng Branch which was established in 2014. For BMT NU, the existence of customers greatly influences the continuity of the work process. Therefore, in order to facilitate the preparation of the next work plan, a customer forecasting technique is needed to determine the number of saving customers in the next period, which can fluctuate every year. For this study, data on the number of savers was used from 2014-2023, then for forecasting using the Single Exponential Smoothing method, a method that focuses on finding stability values. The advantage of this method lies in its ease of operation which is relatively simple. To determine the level of accuracy obtained from the forecasting results, the Mean Absolute Deviation (MAD), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE) methods are used. From the results of the research that has been carried out, it was found that the best alpha value for forecasting is at alpha 0,9 with a forecasting result of 10.065,3. The error calculation obtained for the last 10 years of data is MAD = 1.050,037676, MSE = 1.622.018,167, and MAPE = 25%. While for the last 5 years of data, MAD = 1.415,6342, MSE = 2.528.041,621, and MAPE = 19%.
A District/City Profiling Based on Poverty Indicators in East Nusa Tenggara Using the Centroid Linkage Algorithm Dani, Andrea Tri Rian; Candra, Yossy; Putra, Fachrian Bimantoro; Fauziyah, Meirinda
Zeta - Math Journal Vol 10 No 2 (2025): November
Publisher : Universitas Islam Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31102/zeta.2025.10.2.81-91

Abstract

Poverty is a complex multidimensional phenomenon that significantly impacts human life. Poverty has always been a problem that the government has discussed regionally, centrally, and internationally. The issue of poverty is interesting to approach and analyze using a statistical approach, namely cluster analysis. Cluster analysis is used to group objects based on their level of similarity. In this research, the algorithm used is the Centroid Linkage Algorithm. The Centroid Linkage algorithm was chosen based on its advantages in the grouping process. Distance similarity measurement uses Squared Euclidean. The data used are district/city poverty indicators in East Nusa Tenggara Province. The analysis results show that two optimal clusters were obtained with their distinguishing characteristics. Hopefully, the results of this analysis can be used as a reference in formulating policies for alleviating poverty.

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