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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 18 Documents
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Search results for , issue "Vol. 20 No. 3 (2024): May 2024" : 18 Documents clear
Study Of Fuzzy Groups In Z_p-{0 ̅ } Group Imelda Bo’bo’ Batunna; Harina O.L Monim; Junianto Sesa
Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 3 (2024): May 2024
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v20i3.31827

Abstract

Group theory is a field of abstract algebra that studies the structure of sets. Some concepts that are developments of group theory are fuzzy subgroups. Suppose that G is a group, a fuzzy subset μ of G is called a fuzzy subgroup of G if it satisfies  and  for each . However, not all groups have fuzzy subgroups. The aim of this research is to show that  is a classical group with multiplication operations in the group and determine fuzzy subgroups in the group . From the research results, it is found that the subset  with prime modulo integers  and  is a classical group with group multiplication operations and the fuzzy subset in the group  is a fuzzy subgroup and in general The properties of the classical group apply to the fuzzy subgroup, namely the singularity of the identity and the singularity of the inverse. However, there are properties of classical groups that do not apply to fuzzy subgroups, namely the law of cancellation
Agglomerative Nesting Cluster Analyst in Mapping District/City Health Facilities in West Java Province Nadira Nisa Alwani; Megawati Megawati; Anwar Fitrianto; Erfiani Erfiani; Alfa Nugraha Pradana
Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 3 (2024): May 2024
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v20i3.32043

Abstract

The use of Hierarchical Clustering is used to group districts or cities in West Java according to the number of health facilities, distance to health facilities and population density using Agglomerative Nesting (AGNES). Clustering in this study utilizes complete linkage clustering. The elbow method produces two optimal clusters which are then validated with the sillhoute coefficient and Calinski-Harabasz. In this study, there are 27 variables in the form of health facilities spread across 27 regencies/cities in West Java in 2021. The results of the cluster analysis formed in this study are 18 districts/cities in cluster  one and 9 districts/cities in cluster two
Modeling Determinants of Composite Stock Price Index Based on Multivariable Nonparametric Penalized Spline Regression Model alized Spline Dhita Hartanti Octavia; Asma Auliarani; Siswanto Siswanto; Anisa Kalondeng
Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 3 (2024): May 2024
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v20i3.32145

Abstract

The Composite Stock Price Index (IHSG) is a critical indicator in the Indonesian capital market, playing a central role as one of the key instruments influencing the dynamics of a country's economy. Modeling IHSG can provide a substantial contribution to stakeholders in the capital market, facilitating investment decision-making. Therefore, it is essential to obtain accurate and responsive estimates for IHSG data. The IHSG data used covers the period from January 2020 to December 2022 and tends to be fluctuating. Hence, a spline regression analysis with effective penalized spline estimation is applied to overcome the limitations of assumptions in the relationship between variables. The variables used in the modeling include inflation, exchange rates, interest rates, and IDJ. From the analysis results, optimal values based on the minimum GCV for each variable are sequentially 0.278, 0.904, 0.751, and 0.665. It is also known that these four variables collectively have a 92.1% influence, with inflation having varied impacts, exchange rates exhibiting a stronger negative effect at certain levels, interest rates showing opposite effects depending on their levels, and IDJ having a positive effect on IHSG movements. The significant variability of these impacts indicates that these variables make important contributions. In other words, IHSG fluctuations can be explained by variations in the values of inflation, exchange rates, interest rates, and IDJ.
Classification Of Country Status In 2022 Based On Social Indicators With Ordinal Logistic Regression Sugha Faiz Al Maula; Alfi Nur Nitasari; Mochamad Rasyid Aditya Putra; Maelcardino Christopher Justin; Salma Bethari Andjani Sumarto; Suliyanto Suliyanto; Toha Saifudin
Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 3 (2024): May 2024
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v20i3.32356

Abstract

This research examines the classification of country status in 2022 by applying ordinal logistic regression on various social indicators including education, health and economic. The urgency of the research is to know the country determine factors with specific factors in the form of research variables that can be useful for policy makers, unlike the existing classification which is only divided based on GDP per capita or HDI score only. By dividing 3 country status classes, namely not developed, developing and developed countries using the world bank classification baseline, the accuracy results were obtained at 72,5% but there were several variables that were not significant. After re-modelling, the accuracy was found increased to 76.4% with the odds ratio results for the minimum wage variable being 42,32 in the high class compared to the middle class and 11,66 for the middle class compared to the lower class, which means that the higher the minimum wage tends to be classify countries as developed countries. Another variable that has significance level is the birth rate with an odds ratio of 0,71 in the high and middle classes and 0.89 in the middle and lower classes comparison, which shows that this variable has a negative effect because the odds ratio is <1, which means that the higher the birth rate tends to make the country will be classified as a non-developed country.
Model Machine Learning Stacking untuk Prediksi Pembatalan Pemesanan Hotel Jus Prasetya; Sefri Imanuel Fallo; Moch Anjas Aprihartha
Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 3 (2024): May 2024
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v20i3.32619

Abstract

The hotel prepares rooms and resources according to the room booking. Advance booking from customers is a relationship between customers and hotels that ensures price stability for customers to enjoy services. Cancellation of hotel bookings and inability to satisfy potential customers is a widespread and alarming problem that can increase hotel operating costs and affect customer satisfaction. Given that the impact on the hospitality industry can be very bad, predicting hotel cancellations can be a solution to help build an appropriate operational strategy. Method used in this research is stacking machine learning model. Stacking consists of two levels, where in this study level 0 (base learner) uses the Naive Bayes, Logistic Regression, and Gradient Boosting Machine algorithms while at level 1 (meta learner) uses the Random Forest algorithm. Accuracy value of the stacking model classification and the gradient boosting machine has the highest accuracy value of 0.87. Sensitivity value of the stacking model is 0.86 and is the highest sensitivity value which means that the stacking model classification is very precise in predicting consumers in canceling hotel reservations. Specificity value of the gradient boosting machine is 0.88 and is the highest specificity value, which means that the gradient boosting machine classification is very precise in predicting consumers who do not cancel hotel reservations. Naive bayes and logistic regression classifications have accuracy, sensitivity, specificity, precision values that are not high.  
Pengelompokan Kecamatan di Provinsi Bali Berdasarkan Indeks Desa Membangun (IDM) Tahun 2022 dengan Analisis Diskriminan Azzah Nazhifa Wina Ramadhani; Aulia Ramadhanti; Aini Divayanti Arrofah; M. Nabil Saputra; Dita Amelia; M. Fariz Fadillah Mardianto; Elly Ana
Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 3 (2024): May 2024
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v20i3.32662

Abstract

One of the key achievements in national development is the success in building villages as the smallest administrative units because that is starting point for the development of an economy within the community. Therefore, it is crucial for the government to conduct mapping for development to enhance the quality of the population and the respective regions. The purpose of this research is to categorize several districts in Province of Bali into specific statues based on Village Development Index using discriminant analysis method. The result of this research indicates a high classification rate of 92.857% for the discriminant model formed. This suggests that almost all districts in both categories have been classified into groups that align with the original data.  
Estimasi Selang Dana Tabarru’ Pada Asuransi Jiwa Syariah dengan Menggunakan Perhitungan Cost of Insurance Vito Cahyadi; Nurul Azizah; Achmad Zanbar Soleh
Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 3 (2024): May 2024
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v20i3.32822

Abstract

Contributions is an amount of funds paid by the insured at the beginning of the period of a sharia life insurance contract. Contribution also constitutes the sum of net contributions with expenses. Net contributions are further categorized as Tabarru' funds obtained based on the Cost of Insurance (COI) method. This research incorporates the influence of interest rate in estimating Tabarru' funds. Assuming a Normal Distribution of interest rate and the Central Limit Theorem for a confidence level, a confidence interval is obtained from the interest rate mean. The research findings indicate that the larger the management costs and the older the insurance participants, the greater the COI value will be. Furthermore, the larger the interest rate value, the smaller the COI value. Consequently, as the interest rate value increases, the Tabarru' funds will decrease, while the management costs increase and the age of the insurance participants rises, the Tabarru’ funds will increase.
Integer Linear Programming In Production Profit Optimization Problems Using Branch And Bound Methods & Gomory Cutting Plane Nurweni putri; Maya Sari Syahrul; Rosi Ramayanti
Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 3 (2024): May 2024
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v20i3.32888

Abstract

Integer Linear Programming is a mathematical model that allows the results of solving cases in linear programming in the form of integers. Methods to solve Integer Programming problems include the Branch and Bound Method and the Gomory Cutting Plane Method. Both of these methods have certain rules for adding new constraint functions until an optimal solution to an integer is obtained. The purpose of this study is to optimize the profits of the production of UMKM Capal Classic Shoes Kab. Agam  by using the Branch and Bound method and the Gomory Cutting Plane method and analyzing the comparison of optimal results resulting from the two methods. The data used in the study are data on raw materials for making classic sandals and profit data. The results obtained by these two methods produce the same maximum profit, namely RP. 664,000 with each producing 15 pairs of men's sandals and 13 pairs of women's sandals. But in its completion, the Branch and Bound method requires many iterations and a longer time compared to the Gumory Cutting plane method.
Performance Evaluation of Classification Methods on Big Data: Decision Trees, Naive Bayes, K-Nearest Neighbors, and Support Vector Machines Justin Eduardo Simarmata; Gerhard-Wilhelm Weber; Debora Chrisinta
Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 3 (2024): May 2024
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v20i3.32970

Abstract

Performance evaluation of classification methods on big data is becoming increasingly important in addressing the challenges of data analysis at scale. This study aims to conduct a comparative evaluation of the classification method, namely Decision Trees (DT), Naive Bayes (NB), k-Nearest Neighbors (KNN), and Support Vector Machines (SVM), in analysis on big data evaluated from data simulation and application of real data available in the Rstudio package, namely ISLR. The simulation data used consisted of 2 types of datasets generated based on predictor variables that were normally distributed with different averages and variants and response variables generated in classes adjusted to the characteristics of predictor variables with different proportions. Real data are taken from two types of numeric variables and predictor variables available in the package. The number of sample sizes to be evaluated in each method is n = 500, n = 1000 and n = 5000. In real data, sample division is done randomly to maintain data representativeness. At the evaluation stage, the performance of the method is measured using accuracy metrics. The results of the evaluation of the simulation of Dataset 1 show that the methods that have an influence on the quality of the classification produced if applied to Big Data are the DT and KNN methods. However, in Dataset 2 there is a change in the results of the DT method, because of the influence on the number of classes and the proportion of class distribution in the data. The results obtained from data simulation, proven by applying to real data by showing that similar methods provide a quality influence if applied to Big Data, while the NB and SVM methods do not show a consistent influence when applied to Big Data. The results of observations in this study show that the DT and KNN methods have several advantages that make them suitable for application to Big Data.
Perbandingan Metode Klasifikasi dalam Memprediksi Penjualan Produk Ban Terlaris moch anjas aprihartha; Fitri Astutik; Nani Sulistianingsih
Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 3 (2024): May 2024
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v20i3.33187

Abstract

Data mining is a term to describe the process of moving through large databases in search of certain previously unknown patterns. In finding certain patterns, you need a supporting technique, called machine learning. Machine learning involves learning hidden patterns in data and further using patterns to classify or predict an event related to a problem. One of the problems can be solved with machine learning such as predicting the sales rate of tire products. This can help companies predict tire products that are selling well in the market. In producing an accurate prediction model, it will be compared with decision tree classification methods of CART, CART + Discrete Adaboost, and Naive Bayes applied to tire sales data by PT. Mitra Mekar Mandiri. The results of the study based on successive model performance evaluations are model Naive Bayes < model CART < model CART+Discrete Adaboost. The Discrete Adaboost model with a data proportion of 90:10 is the best model for predicting tire sales. The accuracy, sensitivity and specificity values for the model were 79.17%; 89.47%; and 68.84%. The AUC value is 0.8 which indicates the model is good

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