Pub Date : 2023-05-04DOI: 10.31605/jomta.v5i1.1762
Wahyudin Nur, Darmawati
The Blumberg model is one of the logistic models. The advantage of the Blumberg model is the flexibility of the inflection point. The Blumberg model is believed to be suitable for modeling the growth of living organs. In this article, we estimate the parameters of the Blumberg model using simulated annealing algorithm. The simulated annealing algorithm is a heuristic optimization method based on the metal annealing process. The data used is Broiler daily weight data. The model obtained fits the daily weight data of Broiler. Our results show that the closer the cooling schedule factor to 1, the smaller the error. In addition, we must carefully select the initial temperature. The selection of the initial temperature that is not suitable drives the error to enlarge.
{"title":"Parameter Estimation of The Blumberg Model Using Simulated Annealing Algorithm: Case Study of Broiler Body Weight","authors":"Wahyudin Nur, Darmawati","doi":"10.31605/jomta.v5i1.1762","DOIUrl":"https://doi.org/10.31605/jomta.v5i1.1762","url":null,"abstract":"The Blumberg model is one of the logistic models. The advantage of the Blumberg model is the flexibility of the inflection point. The Blumberg model is believed to be suitable for modeling the growth of living organs. In this article, we estimate the parameters of the Blumberg model using simulated annealing algorithm. The simulated annealing algorithm is a heuristic optimization method based on the metal annealing process. The data used is Broiler daily weight data. The model obtained fits the daily weight data of Broiler. Our results show that the closer the cooling schedule factor to 1, the smaller the error. In addition, we must carefully select the initial temperature. The selection of the initial temperature that is not suitable drives the error to enlarge.","PeriodicalId":313373,"journal":{"name":"Journal of Mathematics: Theory and Applications","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131680019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-04DOI: 10.31605/jomta.v5i1.2029
Melki Imamastri Puling Tang
Some simple polynomial equations can be solved by the remainder theorem, so there is no need for numerical methods to solve them, because the roots of equations are very easy to do using analytical methods, while there are some polynomial equations that are difficult and complex to find roots using analytical methods. In this literature review, researchers will use the bisection method and the false rule to find the roots of polynomial equations. Based on the steps or sequence of calculation of the polynomial roots of , using the bisection method, the author states that from the first step to the eleventh step, if the calculation continues then in the second step f(a)*f(c)>0 or away from zero as shown in table 1 above. The author states that if the twelfth step continues, then f(a)*f(c) will approach zero and it can be seen that there are looping process approaches resulting from f(a)*f(c). This research study concludes that the roots of the polynomial of , using the bisection method are 1.36474675. Based on the steps or sequence of calculating the roots of the polynomial of on, using the false position method (false rule), the author states that from the first step to the 366th step it turns out that f(c)=0.003195 when c=1,365423447. Thus the polynomial roots of using the false position method (regulation false) are 1.365423447. Keywords: Roots of polynomial equations.
{"title":"Bisection Method and Falsi Regulation Method to Determine The Roots of Polynomial Equations","authors":"Melki Imamastri Puling Tang","doi":"10.31605/jomta.v5i1.2029","DOIUrl":"https://doi.org/10.31605/jomta.v5i1.2029","url":null,"abstract":"Some simple polynomial equations can be solved by the remainder theorem, so there is no need for numerical methods to solve them, because the roots of equations are very easy to do using analytical methods, while there are some polynomial equations that are difficult and complex to find roots using analytical methods. \u0000In this literature review, researchers will use the bisection method and the false rule to find the roots of polynomial equations. Based on the steps or sequence of calculation of the polynomial roots of , using the bisection method, the author states that from the first step to the eleventh step, if the calculation continues then in the second step f(a)*f(c)>0 or away from zero as shown in table 1 above. The author states that if the twelfth step continues, then f(a)*f(c) will approach zero and it can be seen that there are looping process approaches resulting from f(a)*f(c). This research study concludes that the roots of the polynomial of , using the bisection method are 1.36474675. Based on the steps or sequence of calculating the roots of the polynomial of on, using the false position method (false rule), the author states that from the first step to the 366th step it turns out that f(c)=0.003195 when c=1,365423447. Thus the polynomial roots of using the false position method (regulation false) are 1.365423447. \u0000Keywords: Roots of polynomial equations.","PeriodicalId":313373,"journal":{"name":"Journal of Mathematics: Theory and Applications","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123647874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-04DOI: 10.31605/jomta.v5i1.2401
Dewi Anugrawati, Nurhikma, Iyut Wahyu Saputri, Khalilah Nurfadilah
This research is an application/applied research, namely by taking or collecting data and analyzing it using a binary logistic regression model to determine the factors that influence the accuracy of graduating students at UIN Alauddin Makassar. The type of data used in this research is secondary data. These data originally from undergraduate students data 0f 8 faculties obtained from the PUSTIPAD Information System of UIN Alauddin Makassar Rector Class of 2016. Undergraduate/D-IV program students are declared to graduate on time if they complete their studies at tertiary institutions for less than or equal to 8 semesters or you could say 4 years, with a minimum number of credits of 144 credits. To determine the binary logistic regression model, parameter significance tests were carried out simultaneously using the G test and partially using the Wald test. Then test the fit of the model by measuring the chi-square value and the Hosmer and Lowshow test at a significant level of 5%. The results showed that there were three factors that influenced the timeliness of graduation accuracy, namely gender (X1), IPK (X3) and educational background (X4)
本研究是一项应用/应用研究，即通过采集或收集数据，并使用二元逻辑回归模型进行分析，以确定影响望加锡大学毕业生准确性的因素。本研究中使用的数据类型是二手数据。这些数据来源于2016届望加锡大学校长班的PUSTIPAD信息系统中8个学院的本科生数据。本科/D-IV课程的学生如果在高等教育机构完成的学习少于或等于8个学期，或者你可以说是4年，最低学分为144学分，则宣布按时毕业。为了确定二元logistic回归模型，同时使用G检验和部分使用Wald检验进行参数显著性检验。然后在5%显著水平下，通过测量卡方值和Hosmer and Lowshow检验来检验模型的拟合性。结果表明，影响毕业准确性及时性的因素有性别(X1)、IPK (X3)和学历(X4)三个方面。
{"title":"Analisis Regresi Logistik Biner dalam Penentuan Faktor-Faktor yang Mempengaruhi Ketepatan Waktu Lulus Mahasiswa UIN Alauddin Makassar","authors":"Dewi Anugrawati, Nurhikma, Iyut Wahyu Saputri, Khalilah Nurfadilah","doi":"10.31605/jomta.v5i1.2401","DOIUrl":"https://doi.org/10.31605/jomta.v5i1.2401","url":null,"abstract":"This research is an application/applied research, namely by taking or collecting data and analyzing it using a binary logistic regression model to determine the factors that influence the accuracy of graduating students at UIN Alauddin Makassar. The type of data used in this research is secondary data. These data originally from undergraduate students data 0f 8 faculties obtained from the PUSTIPAD Information System of UIN Alauddin Makassar Rector Class of 2016. Undergraduate/D-IV program students are declared to graduate on time if they complete their studies at tertiary institutions for less than or equal to 8 semesters or you could say 4 years, with a minimum number of credits of 144 credits. To determine the binary logistic regression model, parameter significance tests were carried out simultaneously using the G test and partially using the Wald test. Then test the fit of the model by measuring the chi-square value and the Hosmer and Lowshow test at a significant level of 5%. The results showed that there were three factors that influenced the timeliness of graduation accuracy, namely gender (X1), IPK (X3) and educational background (X4)","PeriodicalId":313373,"journal":{"name":"Journal of Mathematics: Theory and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130382584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-02DOI: 10.31605/jomta.v5i1.2402
Arwini Arisandi, Syandriana Syarifuddin
Abstrak. Indeks Pembangunan Manusia (IPM) merupakan salah satu indikator yang penting dalam melihat sisi lain dari pembangunan. Setiap indikator komponen penghitungan IPM dapat dimanfaatkan untuk mengukur keberhasilan pembangunan kualitas hidup manusia seperti Umur Harapan Hidup (UHH), Harapan Lama Sekolah (HLS), Pengeluaran per Kapita Disesuaikan (PKD), dan Lama Sekolah (LS). Penelitian ini bertujuan untuk mengetahui sebaran IPM di Kawasan Timur Indonesia, kemudian melakukan pemodelan data IPM dengan menggunakan regresi logistik, decision tree, dan random forest untuk mendapatkan model terbaik dalam memprediksi IPM serta mengetahui faktor-faktor yang memiliki pengaruh terhadap perubahan nilai IPM. Hasilnya menunjukkan bahwa daerah dengan kategori IPM rendah dan IPM sedang memiliki persentase sebesar 69% yang lebih tinggi dibandingkan dengan daerah dengan kategori IPM tinggi dan IPM sangat tinggi sebesar 31% untuk kawasan Timur Indonesia. Model terbaik untuk pemodelan data IPM pada Kawasan Timur Indonesia adalah model random forest dengan nilai kebaikan model sebesar 94.03% dan nilai balanced accuracy sebesar 93.33%. Hasil prediksi diperoleh sebanyak 2 kabupaten/kota atau 4.08% yang diprediksi tidak tepat. Variabel Umur Harapan Hidup memiliki pengaruh atau kontribusi yang signifikan dalam perubahan nilai IPM kabupaten/kota di Kawasan Timur Indonesia. Kata kunci: IPM, Kawasan Timur Indonesia, Random forest
{"title":"Memprediksikan Indeks Pembangunan Manusia di Wilayah Indonesia Bagian Timur Menggunakan Random Forest Classification","authors":"Arwini Arisandi, Syandriana Syarifuddin","doi":"10.31605/jomta.v5i1.2402","DOIUrl":"https://doi.org/10.31605/jomta.v5i1.2402","url":null,"abstract":"Abstrak. Indeks Pembangunan Manusia (IPM) merupakan salah satu indikator yang penting dalam melihat sisi lain dari pembangunan. Setiap indikator komponen penghitungan IPM dapat dimanfaatkan untuk mengukur keberhasilan pembangunan kualitas hidup manusia seperti Umur Harapan Hidup (UHH), Harapan Lama Sekolah (HLS), Pengeluaran per Kapita Disesuaikan (PKD), dan Lama Sekolah (LS). Penelitian ini bertujuan untuk mengetahui sebaran IPM di Kawasan Timur Indonesia, kemudian melakukan pemodelan data IPM dengan menggunakan regresi logistik, decision tree, dan random forest untuk mendapatkan model terbaik dalam memprediksi IPM serta mengetahui faktor-faktor yang memiliki pengaruh terhadap perubahan nilai IPM. Hasilnya menunjukkan bahwa daerah dengan kategori IPM rendah dan IPM sedang memiliki persentase sebesar 69% yang lebih tinggi dibandingkan dengan daerah dengan kategori IPM tinggi dan IPM sangat tinggi sebesar 31% untuk kawasan Timur Indonesia. Model terbaik untuk pemodelan data IPM pada Kawasan Timur Indonesia adalah model random forest dengan nilai kebaikan model sebesar 94.03% dan nilai balanced accuracy sebesar 93.33%. Hasil prediksi diperoleh sebanyak 2 kabupaten/kota atau 4.08% yang diprediksi tidak tepat. Variabel Umur Harapan Hidup memiliki pengaruh atau kontribusi yang signifikan dalam perubahan nilai IPM kabupaten/kota di Kawasan Timur Indonesia. \u0000Kata kunci: IPM, Kawasan Timur Indonesia, Random forest","PeriodicalId":313373,"journal":{"name":"Journal of Mathematics: Theory and Applications","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126325372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：