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INDONESIA
JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI
Published by Universitas Hasanuddin
ISSN : 18581382     EISSN : 26148811     DOI : -
Core Subject : Education,
Mathematics
Jurnal ini mempublikasikan paper-paper original hasil-hasil penelitian dibidang Matematika, Statistika dan Komputasi Matematika.
Arjuna Subject : -
Articles 496 Documents
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The Forecasting Result Study of the Poverty Line and Number of Poor Population in DIY using DES and ARIMA Shazia Ayesha Azzahra; Wiranti Nugrah Andini; Achmad Fauzan; Irwan Sutisna
Jurnal Matematika, Statistika dan Komputasi Vol. 21 No. 2 (2025): JANUARY 2025
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v21i2.36734

Abstract

The poverty rate in DIY, based on BPS data, stands at 11.04%, which remains above the national average of 9.36%. This study aims to predict poverty patterns in the Special Region of Yogyakarta (DIY) using DES and ARIMA methods. The data utilized in this research is sourced from BPS, focusing on poverty line data and the number of impoverished individuals. The DES model is employed to estimate the increase in the poverty line, demonstrating good accuracy with a MAPE value of 2.968%. Meanwhile, the ARIMA(0,2,1) model is applied to forecast a reduction in the number of impoverished individuals, yielding a MAPE of 3.543% through 2028. The findings of this study indicate that government policies have had a positive impact on reducing poverty, although challenges remain. The results of this analysis are expected to guide policymakers in crafting more effective and targeted poverty alleviation strategies in the DIY region. These findings suggest that government policies have had a positive impact on reducing poverty, despite ongoing challenges.
Simulasi Pemodelan Dampak Pengobatan yang Tidak Lengkap pada Penyebaran Tuberkulosis Muna Afdi Muniroh
Jurnal Matematika, Statistika dan Komputasi Vol. 21 No. 2 (2025): JANUARY 2025
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v21i2.36825

Abstract

Among the most common diseases globally is tuberculosis (TB). The spread dynamics of TB are formulated in the form of a mathematical model with five subpopulation densities, namely, susceptible individuals, latent individuals, TB active individuals, treated individuals, and recovered individuals. The existence of an equilibrium point is contingent upon the value of the basic reproduction number Ro. Ro  is a key metric for understanding the potential for disease transmission and is obtained from the next generation matrix. Stability analysis for TB models is investigated by determining the criteria for the local stability of equilibrium points. After that, a sensitivity analysis is conducted to identify TB model parameters that most affect Ro  value. The solution behavior of the TB model is shown by graphs generated numerically with the Runge-Kutta fourth-order method and Matlab software
Komparasi Metode Extreme Learning Machine (ELM) dan Multi-Support Vector Machine (Multi-SVM) pada Identifikasi Tanaman Herbal Luluk Sarifah; Lailiyatus Sa’adah; Iis Setiana
Jurnal Matematika, Statistika dan Komputasi Vol. 21 No. 2 (2025): JANUARY 2025
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v21i2.37107

Abstract

In Indonesia, there are more than 2.039 species of herbal medicinal plants, which sometimes have similarities and make it difficult to identify the type of herbal plant. The purpose of this study is to facilitate the identification of herbal plant species by comparing the performance of the Extreme Learning Machine (ELM) and Multi-Support Vector Machine (Multi-SVM) methods. The ELM method was created to overcome the weaknesses of feedforward artificial neural networks, especially in terms of learning speed, while the Multi-SVM method is an advanced development of the SVM method. The stages of this research begin with image input which is through previous data acquisition, data preprocessing, and then the identification with ELM and Multi-SVM methods. Based on the simulations that have been carried out, the average accuracy on training data for the ELM method is 93%, while the Multi-SVM method is 44%. Also, the average accuracy on testing data for the ELM method is 85%, while the Multi-SVM method is 40%.
Forecasting Dry Rubber Production in Indonesia for the Year 2022 Using Pegel's Exponential Smoothing Method with Modified Golden Section Optimization Rahmi Nurul Ainun Fitrah; Sitti Sahriman
Jurnal Matematika, Statistika dan Komputasi Vol. 21 No. 2 (2025): JANUARY 2025
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v21i2.37158

Abstract

Pegel’s Exponential Smoothing is a forecasting method that considers separating trend and seasonal aspects, with additive and multiplicative models. Pegel’s Exponential Smoothing has three parameters, α, β, and γ. Many possible parameter combinations may yield an optimal solution, so a modified Golden Section method is used. The principle of this method is to iteratively reduce the boundary area of x that may produce an optimal objective function value, systematically decreasing the number of search steps to minimize the number of trials. Data obtained from the Central Bureau of Statistics regarding the amount of dry rubber production in Indonesian plantations from January 2017 to December 2022 is assumed to contain a multiplicative seasonal effect due to the relatively unstable seasonal pattern heights. This study compares three trend models: no trend, additive trend, and multiplicative trend in the multiplicative seasonal Pegel’s Exponential Smoothing method. This study aims to predict the amount of dry rubber production in Indonesian plantations from January 2022 to December 2022. Forecast validation results show that the multiplicative trend in the multiplicative seasonal Pegel’s Exponential Smoothing method, with a MAPE of 3.389001% and an RMSE of 8,839.965080, has the best forecasting accuracy for this data compared to the other three trend models.
Pendekatan Minimum Variance Quadratic Unbiased Estimation dalam Analisis Regresi Data Panel dengan Pendugaan Komponen Galat Dua Arah Menggunakan Metode Biggers Andi Atirah Arumtiwi; Raupong Raupong; Siswanto Siswanto
Jurnal Matematika, Statistika dan Komputasi Vol. 21 No. 2 (2025): JANUARY 2025
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v21i2.37325

Abstract

Panel data that have missing observations can be known as incomplete panel data. The model used is a two-way error component. The missing data estimation used is the Biggers method. This study aims to model the incomplete panel data regression of two-way error components on Manufacturing Company Stock Return data. The method used for estimating the error variance component is Minimum Variance Quadratic Unbiased Estimation (MIVQUE) with parameter estimation using Maximum Likelihood (ML). The method was applied to IDX data for 10 companies from 2014-2021. The results obtained using the MIVQUE method are σ ̂_v^2= 0.1142, σ ̂_μ^2=-0.0107, and σ ̂_λ^2=0.0068, for the ML method produces β ̂_0=0.0304719 〖 β ̂〗_1= -0.021107, and β ̂_2=0.0087936. Based on these methods, if there is an increase in the Debt to Equity Ratio, there is a decrease in the value of stock returns, and vice versa for Net Profit Margin.
Bilangan Kromatik Graf Hasil Operasi Korona Sisi Graf Siklus dan Graf Bintang Alivia Alivia; Kartika Yulianti; Yaya S. Kusumah
Jurnal Matematika, Statistika dan Komputasi Vol. 21 No. 2 (2025): JANUARY 2025
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v21i2.37361

Abstract

One of the concepts in graph theory that can be analyzed is chromatic numbers of a graph and operation of two graphs. There are various kinds of operations of two graphs, one of which is the corona edge operation. This research aims to determine the chromatic number of the edge corona operation of graph Cn*K1,m  and K1,m*Cn, where Cn is a cycle graph and K1,m is a star graph. The chromatic number is determined based on the pattern formed from several n and m values. The results of this research show that the chromatic number of the edge corona operation of graph Cn*K1,m  is 4 for n= 3, 4, ... k   and m=1, 2, 3, ..., l. The chromatic number of the edge corona operation of graph K1,m*Cn is 5 if n is odd number. and is 4 if n is even number.
Pemodelan Peluang Pencemaran Air Sungai Menggunakan Model Geographically Weighted Logistic Regression (Studi Kasus: Data DO Air Sungai di Kalimantan Timur) Adelia Miranda; Suyitno Suyitno; Meirinda Fauziyah
Jurnal Matematika, Statistika dan Komputasi Vol. 21 No. 2 (2025): JANUARY 2025
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v21i2.40346

Abstract

Geographically Weighted Logistic Regression (GWLR) is a local binary logistic regression model, and it’s applied to the spatial heterogeneity data. The parameter estimation of GWLR model in this study uses Maximum Likelihood Estimation (MLE) method, and it’s conducted at each observation location with spatial weighting. The spatial weight in this study was calculated using the adaptive tricube function. The spatial weighting function depends on distance between observation location and bandwidth, where the determination of optimal bandwidth uses the Akaike Information Criterion (AIC). The aim of this research is to identify the factors influencing the probability of river water pollution in East Kalimantan Province through GWLR modelling to Dissolved Oxygen (DO) data 2022, and to interpret it based on the best model. The research data is secondary data provided by Life Environment Department of East Kalimantan Province. Research concludes that the GWLR was fit model based on the results of similarity testing of the GWLR model and global model, as well as simultaneous parameter testing, with the model fitting measure was a McFadden R-Squared value of 61,1%, and an AIC value of 29,629. Based on partial parameter testing, local factors influencing chance of river water pollution in East Kalimantan can be identified, namely nitrate concentration and water color degree. Based on the GWLR modelling to DO data 2022, it can be interpreted that increasing nitrate concentration and water colour degree respectively will increase the probability of river water pollution
Spectral Characteristics of The Antiadjacency Matrix of Kite Graph Miming Fikria Camilla; Melza Rensiana; Denny Riama Silaban
Jurnal Matematika, Statistika dan Komputasi Vol. 21 No. 2 (2025): JANUARY 2025
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v21i2.40490

Abstract

Let G=(V,E)  be a connected graph, where V is the set of vertices and E is the set of edges of G. The kite graph, denoted by Kiten,m, is a graph obtained by appending a complete graph Kn to a pendant vertex of path Pm. This research investigates the spectrum of antiadjacency matrix of kite graph. The antiadjacency matrix of a graph G of order n is a square matrix with order n where the entries of the matrix represent the nonadjacency of the vertices.  
Deteksi Komunitas, Analisis Topik, dan Sentimen Isu Palestina-Israel Fathnin Nur Azmina; Muhaza Liebenlito; Dhea Urfina Zulkifli
Jurnal Matematika, Statistika dan Komputasi Vol. 21 No. 2 (2025): JANUARY 2025
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v21i2.41308

Abstract

The combination of community detection, topic modeling, and sentiment analysis provides deep insights into conversation data on the social media platform X (formerly Twitter) regarding the Palestine-Israel issue. The data, collected in Indonesian using several keywords, resulted in 108,969 tweets. The analysis process began with community detection using the Leiden algorithm, which identified five communities. The three dominant communities identified are Community 1 comprising 37.13% of users, Community 2 with 26.95%, and Community 3 with 19.76%. Topic modeling using LDA revealed that these communities focused on various aspects of the conflict. Sentiment analysis using the IndoBERT model uncovered that the majority of users expressed negative attitudes such as disappointment and anger. This study provides insights into public opinions and social dynamics surrounding the conflict.
Penerapan ARIMA dan Residual Bootstrap untuk Peramalan Mortalitas Dinamis Model PLAT pada Penduduk Laki-Laki di Indonesia Fery Widhiatmoko; Danardono Danardono; Mila Kurniawaty; Amanda Nadhifa Maydika; Thessalonika Sandra Devina Nishi; Chasib Idris
Jurnal Matematika, Statistika dan Komputasi Vol. 21 No. 2 (2025): JANUARY 2025
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v21i2.41403

Abstract

The farmer’s terms of trade of food crops subsector (NTPP) in Nusa Tenggara Timur Province has always been below 100 in 2019-2023. Food crops are a substantial agricultural subsector in which its contribution to the PDRB is significant and concerns the food adequacy of the region. NTPP is a proxy indicator to see farmers’ welfare and its value is expected to grow periodically. Therefore, predictive modeling is required to know future NTPP values and to know the purchasing power of food crop farmers. The aim of this research is to compare the accuracy of Chen and Lee model with the high order fuzzy time series for NTPP forecasting in Nusa Tenggara Timur Province. This research uses monthly data from NTPP Nusa Tenggara Timur from January 2016 to October 2024. The research results show that additions up to the 3rd order increase forecast accuracy and the Lee model is more accurate than the Chen model seen from the smaller RMSE and MAPE values. The MAPE values ​​of the 3rd order fuzzy time series Chen and Lee model are 0.5453% and 0.5088% respectively. Based on the MAPE value, the 3rd order Lee model is the most accurate in forecasting NTPP in Nusa Tenggara Timur Province.    

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