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All Journal EKSAKTA: Journal of Sciences and Data Analysis Jurnal Matematika Sains dan Teknologi
Muhammad Hasan Sidiq Kurniawan
Department Of Statistics, Faculty Of Mathematics And Natural Sciences, Universitas Islam Indonesia
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Chemistry Earth & Planetary Sciences Materials Science & Nanotechnology Mathematics Other

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 1 
Bootstrapping Residuals to Estimate the Standard Error of Simple Linear Regression Coefficients Muhammad Hasan Sidiq Kurniawan
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 16, ISSUE 2, August 2016
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/eksakta.vol16.iss2.art1

Abstract

Regression models are the statistical methods that widely used in many fields. The models allow relatively simple analysis of complicated situations. The aim of the regression models is to analyze the relationship between the predictor and response. In order to do that, we have to estimate the regression coefficient. In case of simple linear regression, the method to estimate the regression coefficient is either least square method or maximum likelihood estimation. Also, the standard error of the regression coefficient is being estimated. In this paper, we apply the bootstrap method to estimate the standard error of the regression coefficient. We compare the result of the bootstrapping method with the least square method. From this study, we know that the standard error estimation value of regression model using the bootstrap method is close to the value if we use the least square method. So we can say that the bootstrap method can be used to estimate the standard error of another regression models coefficient which does not have the closed-form formula
Study of Student Satisfaction Level in the Faculty Based on Performance Assessment and Interest Level Achmad Fauzan; Muhammad Hasan Sidiq Kurniawan; Jaka Nugraha
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 19, ISSUE 1, February 2019
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/eksakta.vol19.iss1.art8

Abstract

One way to evaluate various services at the university is seen from the level of student satisfaction. The purpose of this study is to measure how much the level of student satisfaction in the university environment, especially in the Faculty of Mathematics and Natural Sciences, Universitas Islam Indonesia (FMIPA UII) and future expectations of students focusing on their respective study programs. 6 aspects were used to find out how much satisfaction the students had, namely: (1) Tangible, (2) Reliability, (3) Responsiveness, (4) Assurance, (5) Empathy, and (6) Information. The research method used is descriptive analysis method related to satisfaction represented by the Cartesian diagram. The study was conducted in a period of 3 months with the sample used being active students in the 2016 and 2017 FMIPA classes proportionally in each study program (study program). The data used are primary data consisting of 2 main assessments, namely performance assessment and importance assessment. The results of the level of satisfaction / suitability are classified into the Cartesian diagram which consists of 4 priorities, namely: top priority, achievement priority, low priority, and excessive. The results of the study obtained overall levels of satisfaction in Mathematics as much as 90% of students were satisfied with the level of performance provided. However, there are still 2 indicators that are included in the priority, namely problems in the key-in process and ease of communication for parents of students to consult. In addition to the contents of each indicator, an analysis of suggestions for improvement in the FMIPA environment using text mining based on barplot and wordcloud is associated with the dominant words appearing to describe the general expectations of students
Analysis of Factors Influencing The Decision to Choose The Department in The Natural Science Campus Muhammad Hasan Sidiq Kurniawan; Achmad Fauzan; Jaka Nugraha
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 1, ISSUE 1, February 2020
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol1.iss1.art12

Abstract

Education is very important thing for everyone. Parents tend to choose high-quality school or campus, to ensure their children’s education. One thing which determined parents to choose the campus for their children is the prospect for work. Faculty of Natural Science UII have high-quality department. Some of them already had highest accreditation level and even Internationally accredited. But some peoples in Indonesian often asked about what become of their children after graduated from the faculty of natural science or what is job that suit for their chlidren. The department of that faculty often not become the first choice when choosing campus. Therefore, the research to study about factors which determine people to choose their college department is needed. In this paper, the study is focused on factors which influence people’s decision score to choose the department in the faculty of natural science. We are using correlation and regression analysis. The result show that factors which influence the people’s decision are different between one department with another. Those factor consist of: product, promotion, and the price/cost.
Implementasi Teori Antrian dan Simulasi Monte Carlo pada Jumlah pasien Covid-19 di Kota Batam Dwi Septiandini Putri; Muhammad Hasan Sidiq Kurniawan
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 4, ISSUE 2, August 2023
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol4.iss2.art4

Abstract

The number of Covid-19 patients is the main thing to be concerned about in the time of pandemic, because Covid-19 can directly affect the sustainability of human life. Thus, adequate health services are needed in treating Covid-19 patients. One of the health services is home referral hospital. For example, in Batam City, there were 3 referral hospitals and one of them is a special hospital to hospitalized the Covid-19 patients. In this research, we were using queuing theory and Monte Carlo simulation to predict the queuing system of the patients. Queue conditions have levels density more than 100%, so that a hospital simulation is carried out using a minimum number of hospitals. It is obtained that at least 5 referral hospitals are required so that the density level becomes 83%. Based on the Monte Carlo simulation, it is suggested that the minimum number of hospitals is about three to four hospitals. When the minimum number is fulfilled, the average number of patients waiting to be treated and currently under treatment reduced to 3-4 people from the original, that is 4-5 people. The average waiting time to get treatment is about 5 minutes, originally 18 minutes and average waiting time to get treatment until it was stated that the condition had improved enough to be 5 hours which was originally 8 hours.
Estimating The Probability of Depression in Adolescent Based on Internal Family Factors using Binary Logistic Regression and Naïve Bayes Muhammad Hasan Sidiq Kurniawan; Achmad Fauzan
Jurnal Matematika Sains dan Teknologi Vol. 25 No. 2 (2024)
Publisher : LPPM Universitas Terbuka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33830/jmst.v25i2.10312.2024

Abstract

Depression is a common mental health issue affecting approximately 300 million people globally, with severe cases potentially leading to death. Adolescents with depression are reported to have a 30-fold increased risk of suicide compared to other age groups, making early identification and intervention in this age group essential. Internal family factors play a crucial role in influencing adolescent mental health, including variables such as family type, parenting style, residence status, birth order, and parental occupation. This study aims to identify which internal family factors significantly impact the likelihood of depression and to estimate the probability of depressive episodes in adolescents. By understanding these factors, preventive measures can be better tailored to reduce adolescent depression in the future. The data utilized in this study is primary data obtained through a sampling process employing the simple random sampling method applied to first-year university students. A binary logistic regression model was employed to analyze the significance of each family-related factor. Findings indicate that parenting style and parental occupation are among the most significant factors associated with adolescent depression. The novelty of this study lies in the exploration of internal family factors that are rarely examined comprehensively in previous research, such as the effects of family type, birth order, and parental occupation. Additionally, the study adopts a dual-method approach, combining logistic regression and Naive Bayes, to provide a robust and comparative analysis of predictive accuracy. Probability estimates were conducted using both binary logistic regression and Naive Bayes methods. Results from these analyses suggest that a democratic parenting style tends to foster more stable mental health in adolescents, while adolescents with parents employed in the private sector or similar occupations face a higher likelihood of depression. Both methods demonstrated high predictive accuracy, with 96.31% for logistic regression and 96.93% for Naive Bayes.
Co-Authors ACHMAD FAUZAN Dwi Septiandini Putri Jaka Nugraha