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Desak Putu Eka Nilakusmawati
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E-Jurnal Matematika
Published by Universitas Udayana
ISSN : -     EISSN : 23031751     DOI : https://doi.org/10.24843/MTK
Core Subject : Education,
Mathematics
The scope of the E-Jurnal Matematika includes analysis, algebra, topology, graphics, numerical simulation approaches or what is known as numerical analysis, optimal control, queuing problems, optimization, finance, biomathematics, industrial mathematics, financial mathematics, and others.
Arjuna Subject : Matematika - Matematika Terapan
Articles 7 Documents
 1 
Search results for , issue "vol. 15 no. 2 (2026)" : 7 Documents clear
PENERAPAN MODEL PROSES RENEWAL DALAM MANAJEMEN PERSEDIAAN BAHAN PANGAN DI HOTEL MERCURE RESORT SANUR, BALI DIVA FELIZIA BAGINDO; MADE IREINA DWIANDRA DIVAYANTI; NARISKA DESWITA WINANDI; CHRISTIAN MELANIA LEU; MADE AYU DWI OCTAVANNY
E-Jurnal Matematika Vol. 15 No. 2 (2026)
Publisher : Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MTK.2026.v15.i02.p503

Abstract

The daily operation of a hotel kitchen relies heavily on the consistent availability of staple goods. This study analyzes the inventory management of key staples such as rice, cooking oil, milk, and eggs at Mercure Resort Sanur Bali. The research aims to determine optimal safety stock levels and reorder points to handle uncertain demand and market fluctuations. Using a renewal process model, the study analyzes the hotel’s inventory patterns based on usage and restocking data. The results show that Rice C4 should be reordered when stock reaches 40 kg, with a safety stock of 7 kg. Cooking Oil (18 LT) should be replenished at 2 jars, with 1 jar as safety stock. Milk UHT full cream should be reordered at 43 liters remaining, with an 8 liters safety stock. Chicken Eggs should be reordered at 30 kg, with a 6 kg safety stock. These findings demonstrate that applying a renewal process helps maintain the availability of essential food items and supports smooth, continuous kitchen operations at the resort.
PENERAPAN BIRTH-AND-DEATH PROCESS PADA KASUS TERINFEKSI DAN KEMATIAN COVID-19 KALIMANTAN TIMUR JANUARI 2021 WINA CHRISTIANI PARHUSIP; NI KOMANG KHARINA GAYATRI; DEWA AYU RATIH ISAKA PUTRI; GINA CORNELIA BR TARIGAN; MADE AYU DWI OCTAVANNY
E-Jurnal Matematika Vol. 15 No. 2 (2026)
Publisher : Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MTK.2026.v15.i02.p504

Abstract

The birth and death process is a powerful Markov process for modeling population changes due to two events: births and deaths. In this study, the birth and death events are described as the number of new infections and deaths due to the Covid-19 virus in East Kalimantan for the period 1–31 January 2021. In this study, the birth and death processes are used to analyze the data with a focus on estimating the parameters of the birth rate and death rate and observing the ratio between the two parameters. The results show that the estimated parameter values ​​for the birth (λ) and death (µ) are 0.0139132 and 0.00024230, respectively, and the ratio of the two parameters is 57.42. This indicates that the birth and death rates in this case are still in the supercritical category. This means that without significant external interventions such as emergency PPKM (restrictions on community activities), mass vaccination, or drastic increases in testing and tracing capacity, the number of active cases is likely to increase indefinitely in the long term.
COMPARATIVE EVALUATION OF SVM AND LSTM FOR TOURISM SENTIMENT CLASSIFICATION: STUDY CASE TANAH LOT TOURISM DESTINATION, BALI DEWA MADE ALIT ADINUGRAHA; JERY CHRISTIANTO
E-Jurnal Matematika Vol. 15 No. 2 (2026)
Publisher : Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MTK.2026.v15.i02.p502

Abstract

This study presents a comparative evaluation of Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) models for tourism sentiment classification, using YouTube comments related to Tanah Lot, Bali. The dataset manually cleaned comments labeled as Positive, Neutral, or Negative. Both models achieved identical overall accuracy (0.95), but class-wise analysis revealed substantial differences: LSTM exhibited strong bias toward the majority class (Neutral), failing to detect minority sentiments, while SVM retained partial sensitivity to Positive and Negative classes. These findings highlight the limitations of deep learning architectures under low-resource and imbalanced conditions and underscore the importance of context-aware model selection. Class-wise evaluation metrics are essential for capturing minority sentiment, which is critical for destination governance and informed decision-making in tourism management.
PERAMALAN SUHU MINIMUM BULANAN KABUPATEN MALANG MENGGUNAKAN METODE HOLT–WINTERS UNTUK ANALISIS POLA MUSIM DINGIN TROPIS ANISAH; AMALIYATUL HASANAH; A.DZAKIYYURAYHAN
E-Jurnal Matematika Vol. 15 No. 2 (2026)
Publisher : Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MTK.2026.v15.i02.p505

Abstract

Monthly minimum temperature is an important indicator in climate analysis, particularly for identifying tropical winter patterns in Malang Regency. This study aims to forecast monthly minimum temperature using the additive Holt–Winters method and to analyze the resulting tropical winter pattern. The data used consist of monthly minimum temperature from 2019 to 2025. The model parameters were optimized, resulting in = 0.23314, = 0.0, and = 0.59085. Forecast accuracy was evaluated using Mean Absolute Deviation (MAD), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE). The results show that the model achieves a very high level of accuracy with a MAPE value of 8.00626%. The 2026 forecast indicates that the lowest minimum temperatures occur in July and August, reflecting a tropical winter pattern in mid-year. Therefore, the additive Holt–Winters method is capable of representing the annual seasonal pattern and is suitable for forecasting monthly minimum temperature in Malang Regency.
AN APPLICATION OF GREY MODEL IN MODELLING SOCIO-ECONOMIC VARIABLES WITH LIMITED DATA IN NORTH KALIMANTAN RAY SASTRI; ARBI SETIYAWAN
E-Jurnal Matematika Vol. 15 No. 2 (2026)
Publisher : Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MTK.2026.v15.i02.p508

Abstract

New administrative regions often face a severe data scarcity precludes the use of conventional econometric models and data-intensive machine learning techniques. This study evaluates the performance of Grey System Theory, specifically the classical GM(1,1) and the extended model EXGM(1,1) in modeling &forecasting socio-economics variables under limited data conditions. Utilizing official time-series data from 2013–2021, models were developed using an 8-year training set and validated against a 1-year testing set. Performance was measured using the Mean Absolute Percentage Error (MAPE). Mathematical findings reveal that both models achieve highly accurate performance with MAPE under 5% for variables with near-monotonic trends, such as Gross Regional Domestic Product (GRDP) and unemployment rate. While EXGM(1,1) demonstrated superior mathematical fit during the training phase due to optimized background values. The classical GM(1,1) proved more resilient during the testing phase, particularly for volatile indicators like poverty rates. Specifically, EXGM(1,1) exhibited a risk of overfitting to grey noise in non-monotonic datasets, leading to higher forecasting errors compared to the GM(1,1). It concludes that while EXGM(1,1) offers superior flexibility for tracking turning points in stable decaying trends, the classical GM(1,1) remains the more reliable tool for general policy interpolating due to its predictive stability and resistance to stochastic fluctuations.
PERHITUNGAN PREMI KOMODITAS PADI BERBASIS INDEKS CURAH HUJAN DENGAN DISTRIBUSI WEIBULL DAN DISTRIBUSI EKSPONENSIAL CAMPURAN I KADEK WIDIADNYANA; KOMANG DHARMAWAN; NI KETUT TARI TASTRAWATI
E-Jurnal Matematika Vol. 15 No. 2 (2026)
Publisher : Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MTK.2026.v15.i02.p506

Abstract

Agricultural insurance is insurance that exists in the agricultural sector. Agricultural insurance is relatively recently introduced in Indonesia, there are three types of agricultural insurance, agricultural insurance based on losses, price-based agricultural insurance, and index-based agricultural insurance. In this study, the type of agricultural insurance used is index-based insurance, especially based on the rainfall index. The purpose of this study was to determine the procedures required to determine the price of agricultural insurance premiums based on the rainfall index and calculate agricultural insurance premiums using the Weibull distribution or mixed exponential distribution. Furthermore, where the premium price is relatively smaller and close to the insurance premium recommended by the government. In this study, the premium price generated is different for the two distributions, with a not too large difference in the range of rainfall resulting from the simulation of rainfall obtained by using a mixed exponential distribution to get a smaller premium value.
PEMODELAN GEOGRAPHICALLY WEIGHTED RIDGE REGRESSION PADA KASUS TUBERKULOSIS DI INDONESIA NI PUTU EKA MARTINI; NI LUH PUTU SUCIPTAWATI; ANGGUN YULIARUM QUR’ANI
E-Jurnal Matematika Vol. 15 No. 2 (2026)
Publisher : Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MTK.2026.v15.i02.p507

Abstract

Tuberculosis remains a major public health issue in Indonesia, with substantial variation in case notification rates (CNR) across provinces, indicating the presence of spatial heterogeneity and potential multicollinearity among influencing factors. This study aims to model CNR of tuberculosis in Indonesia in 2024 using the Geographically Weighted Ridge Regression (GWRR) method, which simultaneously addresses spatial heterogeneity and multicollinearity. Secondary data from 38 provinces were analyzed, including variables such as population percentage, GERMAS implementation, smoking prevalence, HIV cases, poverty rate, hospitals, and malnutrition among infants. The analysis began with Ordinary Least Squaress (OLS), followed by diagnostic tests revealing heteroskedasticity and multicollinearity, justifying the use of GWRR. The results show that GWRR produces stable local parameter estimates across provinces with a high coefficient of determination (97%) and relatively low RMSE, indicating strong model performance. Spatial analysis reveals that dominant factors vary by region, with malnutrition and smoking showing strong influence in several provinces. Overall, GWRR proves effective in capturing spatial variation and improving model stability in tuberculosis analysis.

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