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Desak Putu Eka Nilakusmawati
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INDONESIA
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 39 Documents
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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.
IMPLEMENTATION ON DIFFERENTIAL EQUATION OF MILNE-SIMPSON FOR PREDICTION FOR APPARENT POWER USAGE IN PLN (PERSERO) UIW NTB FITRAH RAMADHAN; RIO SATRIYANTARA; YUNITA SEPTRIANA ANWAR; I GEDE ADHITYA WISNU WARDHANA
E-Jurnal Matematika Vol. 15 No. 3 (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.i03.p509

Abstract

Electricity is one of the essential forms of energy required by humans in modern society. Consequently, the demand for electricity supply will continue to increase over time. Irregular or fluctuating electricity consumption can affect the readiness of power generation units to provide an adequate supply of electricity to consumers or the public. By predicting apparent power (VA), power companies can optimize generation efficiency, ensure grid stability, and reduce losses. This study applies the logistic equation model using annual apparent power usage data obtained from PT PLN (Persero) UIW NTB for the 2011–2024 period. The model parameters (growth rate r and carrying capacity K) were estimated directly from the historical data, and the differential equation was solved numerically using the Milne-Simpson method with initial values generated by the fourth-order Runge-Kutta approach. The logistic model is chosen for its ability to represent nonlinear growth toward a saturation capacity. Simulation results show a gradually increasing trend in VA usage that slows down as it approaches the saturation phase around 2030. Model validation, performed by comparing the numerical predictions with the actual historical data, shows very small relative errors ranging from 10⁻⁵ to 10⁻⁷, confirming that the Milne-Simpson method possesses high accuracy and stability.
ANALISIS RISIKO PERDAGANGAN BAWANG MERAH DENGAN VOLATILITAS HISTORIS DAN VALUE AT RISK YAN ADITYA PRADANA; LENNY PUSPITA DEWI; PUTRI BALQIS AL KUBRO; SATRIYO PRIYO HANDOKO; MUHAMMAD ULUL ALBAB; FRANSISKUS FIDO EKA KURNIA NUGRAHA
E-Jurnal Matematika Vol. 15 No. 3 (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.i03.p511

Abstract

Shallots are one of Indonesia's most important horticultural commodities, however their prices are highly volatile, creating significant financial risks for farmers and traders. This study aims to quantify the price risk of shallot trading in East Java Province and Nganjuk Regency using historical volatility, Value at Risk (VaR), and Conditional Value at Risk (CVaR) based on rolling windows of 10, 20, &40 days. The log-return series were found to be non-normally distributed (Shapiro–Wilk, p<0.05) but stationary (ADF, p<0.05), supporting the use of the historical simulation approach. The results show that Nganjuk exhibits substantially higher price volatility than East Java, particularly over the 10-day rolling window. Consistent with this finding, the 95% VaR indicates that Nganjuk has a higher potential short-term loss than East Java (-0.0133 compared with-0.0065). Furthermore, CVaR provides a more conservative estimate of downside risk, indicating that the expected loss beyond the VaR threshold in Nganjuk is considerably larger than that estimated by VaR alone. These findings demonstrate that combining historical volatility, VaR, &CVaR provides a more comprehensive assessment of extreme price risk than volatility analysis alone, thereby extending the application of quantitative risk measurement to agricultural commodity trading and supporting risk management and policy decisions.
EVALUASI KINERJA PORTOFOLIO SAHAM OPTIMAL DENGAN LONG SHORT-TERM MEMORY BERBASIS MODEL MARKOWITZ PADA INDEKS SMINFRA18 ANNISA FITRIANI; BANDAR ANZARI; MUHAMMAD RAFLI; FAUZAN ISMAIL MULYADI; EMBAY ROHAETI
E-Jurnal Matematika Vol. 15 No. 3 (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.i03.p510

Abstract

This study aims to determine the optimal stock portfolio within the SMinfra18 index using the Markowitz model and forecast the dominant stock price using the Long Short-Term Memory (LSTM) method. Monthly closing prices of 18 SMinfra18 stocks from January 2024 to December 2025 were analyzed. The study consisted of three main stages: (1) calculating stock returns and selecting stocks with positive expected returns; (2) optimizing the portfolio using the Markowitz model to construct an Equal Weight Portfolio, a Minimum Variance Portfolio, and an Optimal Portfolio based on the maximum Sharpe Ratio; and (3) forecasting the dominant stock price using LSTM. Seven stocks met the selection criteria: WIFI, SSIA, PGAS, UNTR, ELSA, MEDC, and PGEO. The optimal portfolio achieved a monthly expected return of 5.04% with a risk of 6.26% and a Sharpe Ratio of 0.8044, comprising UNTR (37.50%), PGAS (29.85%), SSIA (13.92%), WIFI (12.51%), and ELSA (6.21%). The LSTM model achieved a Mean Absolute Percentage Error (MAPE) of 1.36%, while a 5-day forecast for UNTR indicated stable prices between Rp 29,421.87 and Rp 29,371.21. These results demonstrate the effectiveness of integrating the Markowitz model and LSTM for portfolio optimization and stock price forecasting.
MODEL SISTEM ANTREAN PADA KEDATANGAN DAN KEPERGIAN PELANGGAN DI CIRCLE K SURAPATI DENPASAR DESAK MADE NIRMALA YONI; NI LUH MEILIANA; MADE AYU DWI OCTAVANNY
E-Jurnal Matematika Vol. 15 No. 3 (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.i03.p516

Abstract

This study aims to analyze the queueing model based on the customer arrival and departure dynamics at Circle K Surapati, Denpasar. The study uses primary data obtained by recording customer arrival and departure times during a single observation period from 15:00 to 17:30 WITA. The results show that the arrival rate increases from 1.6 customers per 10 minutes at state 0 to 18 customers per 10 minutes at state 3, while the departure rate decreases from 4.4 customers per 10 minutes at state 1 to 1.5 customers per 10 minutes at state 4. The highest steady-state probability occurs at state 4 (0.8279), indicating that this condition most frequently appears in the long run. Furthermore, the Chi-Square goodness-of-fit test reveals that both interarrival and service time do not follow exponential distributions. Consequently, the system is best represented by a general distribution queueing model. The system is identified as a :  queueing model, utilizing two active service facilities under a first come-first served discipline
ANALISIS PEMILIHAN MODA TRANSPORTASI MAHASISWA FAKULTAS MIPA UNIVERSITAS UDAYANA MENGGUNAKAN RANTAI MARKOV WIRYA CHANDRA ADIWIJAYA; NI PUTU AMAYLIA PRADNYANDARI; FAUZIAH NURHASNA NINGSIH; MARULI SIMATUPANG; MADE AYU DWI OCTAVANNY
E-Jurnal Matematika Vol. 15 No. 3 (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.i03.p514

Abstract

Student mobility on university campuses often depends on multiple transportation options that may shift over time. This study analyzes the dynamics of transportation mode choices among undergraduate students of the Faculty of Mathematics and Natural Sciences at Udayana University using a discrete-time Markov chain approach. The research aims to identify weekly transition patterns among four transportation modes, namely private vehicles, public transportation, online transportation, and walking, and to estimate their long-term usage probabilities. Primary data were obtained through a structured questionnaire distributed to 114 students selected using proportional simple random sampling. The observed transition frequencies were used to construct a one-step transition matrix, whose stochastic properties were then assessed to ensure ergodicity before computing the steady-state distribution to identify the long-run behavioral tendencies. The results indicate that private vehicles remain the dominant mode of transportation both in the current period and in long-term projections, with a steady-state probability of 77.05 percent, while the other modes contribute relatively small proportions. These findings suggest a strong reliance on private transportation among students and highlight the need for improved mobility planning, strengthened alternative transportation options, and the development of more sustainable mobility strategies within the campus environment.

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