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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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Klasifikasi Kelayakan Penerima Bantuan Program Keluarga Harapan Di Desa Cinta Rakyat Menggunakan Metode Weighted Naive Bayes Dengan Laplace Smoothing Nurjannah Nurjannah; Hendra Cipta; Rima Aprilia
Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 2 (2024): JANUARY 2024
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v20i2.32069

Abstract

The Indonesian government sometimes faces difficulties in dealing with poverty problems.  The Indonesian government utilizes a number of programs and stimulants to overcome the problem of poverty.  The government's PKH program offers conditional assistance to low-income families who have been designated as PKH recipient households.  PKH provision is still below optimal standards, this may be because the data used is not updated frequently.  To assist village officials in determining which residents are eligible to receive PKH assistance, this research tries to classify the eligibility of recipient residents in Cinta Rakyat Village.  With the Weighted Naive Bayes method, classification calculations are not only based on probability distributions but also by adding weights to each attribute to the class.  Assisted with Laplace Smoothing to avoid a probability value of 0. As a result, there are eight factors that determine a person's eligibility to receive PKH assistance, including age, occupation, income, number of family members, number of dependent school children, quality of house, type of floor, and type of walls. As well as classification into eligible and non-eligible groups.  And obtained test results using the Confusion Matrix with an accuracy value of 95.65%, error rate of 4.34%, sensitivity of 100% and specificity of 94.74%.  To identify village communities who deserve PKH assistance, Cinta Rakyat Village administrators can use the findings of this research.  
Prediksi persediaan air bersih dengan metode fuzzy time series cheng pada PDAM Tirta Silau Piasa Deva Rezky Ramadhani; Fibri Rakhmawati; Rima Aprilia
Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 2 (2024): JANUARY 2024
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v20i2.32071

Abstract

This research aims to determine predictions of clean water supply at PDAM Tirta Silau Piasa in 2023 using the Fuzzy Time Series Cheng method. This type of research is quantitative research using data sources, namely secondary data. This research data was taken from clean water supply data at PDAM Tirta Silau Piasa, namely data on the volume of clean water for the period January 2021 to May 2023. From the calculation results of the prediction analysis of clean water supply at PDAM Tirta Silau Piasa using the Fuzzy Time Series Cheng method, for the amount of water supply clean water in June 2023 is 443,620, with a total predicted clean water supply from 2021 to June 2023 of 12,031,703. With a MAPE value of 3%, if we look at the MAPE which is less than 10%, the results of predicting clean water supply using the Fuzzy Time Series Cheng method produce the best prediction value.  
Analisis Klasifikasi K-Means Terhadap Pemahaman Konsep dan Self-Efficacy: Menjelajahi Hubungan dalam Konteks Pendidikan Magdalena Wangge; Dadan Dasari; Turmudi Turmudi
Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 2 (2024): JANUARY 2024
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v20i2.32099

Abstract

Concept understanding and self-efficacy are two important aspects of mathematics learning that are interrelated. However, there is still debate about the relationship between these two aspects in the context of mathematics learning. Therefore, this study was conducted to analyze the classification of concept understanding and self-efficacy using K-means clustering with a sample of grade VIII students from three selected schools in Wolowaru District and Kelimutu District, Ende Regency, NTT. Data were collected through concept understanding test and self-efficacy questionnaire. The results showed that students' concept understanding and self-efficacy belonged to the medium and low classes. However, there was a practically insignificant correlation between the two aspects. The implication is the importance of developing learning strategies that can improve students' concept understanding and self-efficacy in the context of mathematics education.
Analisis Regresi Ordinal Faktor-Faktor yang Mempengaruhi Capaian Literasi Matematika SMP dan SMA di Kabupaten Manggarai Barat Patrisius Afrisno Udil; Dadan Dasari; Elah Nurlaelah
Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 2 (2024): JANUARY 2024
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v20i2.32118

Abstract

This study aims to analyze the significant factors that affect mathematics literacy achievement, especially in junior and senior high schools in West Manggarai Regency. It begins by presenting descriptive information regarding mathematics literacy achievement at junior and senior high schools in West Manggarai Regency, especially in relation to other factors measured in the national assessment. This study is a quantitative study. The data analyzed were secondary data obtained from the Education, Youth and Sports Office of West Manggarai Regency in the form of Education Report. The analysis was carried out descriptively first, then analyzed with ordinal logistic regression. The results showed that the achievement of mathematics literacy at junior and senior high schools in West Manggarai Regency is still not optimal, both in general and in terms of each factor. The results of this study also show that the factors that significantly influence the achievement of mathematical literacy at junior and senior high schools in West Manggarai Regency include the level of the education unit, increased of learning quality score, increased of instructional leadership score, and increased of the algebraic content score.
Comparison Predictions of the Demam Berdarah Dengue (DBD) using Model Exponential Smoothing: Pegel’s Classification and ChatGPT Wiwik Wiyanti; Bakti Siregar
Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 2 (2024): JANUARY 2024
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v20i2.32122

Abstract

The evolution of AI since the Covid-19 pandemic has developed very rapidly. Until 2023, AI is claimed to be a threat to several professional jobs, especially data analysts and scientists. The purpose of this research is to check the effectiveness chat-GPT to predict about demam berdarah dengue (DBD) case. Method of the analyzing the data in this research is Mixed method. Quantitative method using exponential smoothing: pegel’s classification and qualitative method using GPT-3. The aim of this research is to check whether ChatGPT can predict the demam berdarah dengue (DBD) data time series. The prediction result are check it by exponential smoothing: pegel’s classification method. The benefit of this research is it can be used to reference how far the evolution of AI can be threaten the profession of data analyst or data scientist. The result of this study conclude that the ChatGPT (GPT-3) can’t predict DBD’d data correctly.
r-Chromatic Number On r-Dynamic Vertex Coloring of Comb Graph Heryati Nur Fatimah Sari; Budi Nurwahyu; Jusmawati Massalesse
Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 2 (2024): JANUARY 2024
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v20i2.32143

Abstract

Let  be a graph with vertex set  and edge set . An r-dynamic vertex coloring of a graph  is a assigning colors to the vertices of  such that for every vertex  receives at least  colors in its neighbors. The minimum color used in r-dynamic vertex coloring of graph  is called the r-dynamic chromatic number denoted as . In this research we well determine the coloring pattern and the r-dynamic chromatic number of the comb graph , central graph of comb graph , middle graph of comb graph , line graph of comb graph , sub-division graf of comb graph , and para-line graph of comb graph Let  be a graph with vertex set  and edge set . An r-dynamic vertex coloring of a graph  is a assigning colors to the vertices of  such that for every vertex  receives at least  colors in its neighbors. The minimum color used in r-dynamic vertex coloring of graph  is called the r-dynamic chromatic number denoted as . In this research we well determine the coloring pattern and the r-dynamic chromatic number of the comb graph , central graph of comb graph , middle graph of comb graph , line graph of comb graph , sub-division graf of comb graph , and para-line graph of comb graph 
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.
Path Analysis of Influence of Economic and Social Factors on the Human Development Index in South Sulawesi in 2022 Anni Ivoni Parapa; Clarisa Eudia Chesynanda; Siswanto Siswanto; Anisa Kalondeng
Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 2 (2024): JANUARY 2024
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v20i2.32147

Abstract

The Human Development Index (HDI) serves as an indicator for assessing socio-economic development in a region. Each area strives to improve its HDI by considering the factors that influence it in that specific region. This research aims to identify the direct and indirect influences of economic and social factors, such as Life Expectancy (LE), Gross Regional Domestic Product per capita (GRDPpc), Labor Force Participation Rate (LFPR) through Average Years of Schooling (AYS) on the HDI in South Sulawesi in 2022. The data used in this study are secondary data obtained from the Central Statistics Agency (BPS) of South Sulawesi Province in 2022. The method applied in this research is a path analysis that examines the relationships between variables, both direct and indirect influences. The research results show that in the equation of sub-structure 1, LE and GRDP per capita ADHB have a direct influence on AYS, while LFPR does not have a direct impact on AYS. The magnitude of the influence of variables in sub-structure 1 is 53%. In the equation of sub-structure 2, LE, GRDP per capita ADHB, LFPR, and AYS have a significant direct impact on HDI. Additionally, LE and GRDP per capita ADHB have an indirect influence through AYS on HDI. The magnitude of the influence of variables in sub-structure 2 is 93.5%. Therefore, the variables that have both direct and indirect effects on HDI through AYS are LE and GRDP per capita ADHB.
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.  

Page 44 of 50 | Total Record : 496

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