Garuda - Garba Rujukan Digital

Analisis Regresi Data Panel Dengan Pendekatan Common Effect Model (CEM), Fixed Effect Model (FEM) dan Random Effect Model (REM) (Studi Kasus : IPM Sumatera Utara Periode 2014 – 2020) Ide Prasanti Hutagalung; Open Darnius
FARABI: Jurnal Matematika dan Pendidikan Matematika Vol 5 No 2 (2022): FARABI
Publisher : Program Studi Pendidikan Matematika FKIP UNIVA Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47662/farabi.v5i2.422

Analisis regresi data panel merupakan perpaduan diantara informasi cross section serta time series. Pemanfaatan data panel dapat memaknai dua macam data, yaitu data antar unit dan antar waktu. IPM merupakan penanda yang terpenting adalah menetapkan hasil pada usaha menciptakan hakikat kehidupan manusia, IPM pun mampu menetapkan posisi dan tingkat kemajuan sebuah negara. Studi ini berencana untuk memutuskan model regresi data panel terbaik dan elemen-elemen yang pada dasarnya mempengaruhi IPM di Sumatera Utara. Dalam regresi data panel, ada tiga model penilaian, khususnya CEM, FEM dan REM. Strategi CEM merupakan teknik yang mengharapkan jika intercept dan slope setiap subjek dan setiap kali adalah sesuatu yang serupa, teknik FEM menerima bahwa blok berbeda di antara subjek dan kemiringan adalah sesuatu yang sangat mirip di antara subjek, sedangkan strategi REM menerima bahwa faktor pengganggu mempunyai korelasi antar waktu dan antar subjek. Dari analisis yang telah dilakukan, model regresi data panel terbaik adalah dengan menggunakan Common Effect Model (CEM). Angka harapan hidup, Harapan Lama Sekolah, Rata-rata Lama Sekolah, konsumsi per kapita, dan Persentase penduduk miskin mempengaruhi IPM di Sumatera Utara sebesar 92,71% dan model kondisi penilaian sebagai berikut:

Penaksiran Parameter Pada Distribusi Erlang Berdasarkan Metode Maksimum Likelihood Dengan Menggunakan Algoritma Newton Raphson Dan Fisher Scoring Meili Yanti; Open Darnius
Jurnal Riset Rumpun Matematika dan Ilmu Pengetahuan Alam (JURRIMIPA) Vol. 2 No. 1 (2023): April : Jurnal Riset Rumpun Matematika dan Ilmu Pengetahuan Alam
Publisher : Pusat riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jurrimipa.v2i1.720

The Erlang distribution is a special case of the Gamma distribution with the k shape parameter and the λ rate parameter. In this study, the parameter estimation of the Erlang distribution was carried out using the Maximum Likelihood method. In maximizing the function, an implicit and non-linear form is obtained, then it is solved using the Newton Raphson algorithm. Apart from Newton Raphon, the estimation of parameters was also carried out using the Fisher Scoring algorithm. The Fisher Scoring algorithm is similar to the Newton Raphson algorithm, the difference is that Fisher Scoring uses an matrix information. The result of parameter estimation in Erlang distribution using Newton Raphson algorithm which is applied to outgoing telephone call data that generated by Matlab R2010a software cannot be done simultaneously. Therefore, the parameter assessment is carried out on the k parameter first, then followed by the λ parameter estimation and the parameter and = 0.6886812 are obtained. Meanwhile, the parameter estimation using the Fisher Scoring algorithm produces an equation that is not different from the Newton Raphson algorithm

Binary Logistic Regression Analysis Using Stepwise Method on Tuberculosis Events Rifan halomoan tua sinaga; open darnius
JMEA : Journal of Mathematics Education and Application Vol 2, No 1 (2023): Februari
Publisher : JMEA : Journal of Mathematics Education and Application

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jmea.v2i1.12164

Tuberculosis is an infectious disease caused by the bacteria Mycobacterium tuberculosis. Among all the districts/cities of North Sumatra province, Medan has the highest cases of tuberculosis sufferers with a total of 12,105 cases in 2019. This study aims to determine the factors that significantly influence tuberculosis. The factors analyzed were age, gender, occupation, education, BCG immunization, history of diabetes mellitus and HIV infection. This study uses secondary data for the period January 2019 to December 2020 obtained from the Sentosa Baru Health Center. With the help of SPSS, this study uses a stepwise method with forward selection and backward elimination as the method for analysis. Akaike Information Criterion (AIC) is used to select the best model in the stepwise method. With the AIC criteria obtained, the best model is forward selection because the AIC value is lower at 28,527 compared to backward elimination at 41,664. Of the 7 variables studied, there are 3 factors that have a significant effect, namely age, history of diabetes mellitus, and HIV infection so that the model g(x) = 2.802 1.056 X1 0.614 X6 2.477 X7.

Analisis Statistik Faktor-Faktor yang Mempengaruhi Rendahnya Minat Masyarakat dalam Menggunakan Layanan PT Pos Indonesia (PERSERO) Elvin Juliani Gulo; Asima Manurung; Parapat Gultom; Open Darnius
FARABI: Jurnal Matematika dan Pendidikan Matematika Vol 6 No 1 (2023): FARABI
Publisher : Program Studi Pendidikan Matematika FKIP UNIVA Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47662/farabi.v6i1.433

Analisis faktor adalah salah satu metode multivariat yang digunakan untuk menemukan faktor-faktor yang menjelaskan hubungan atau korelasi antara berbagai indikator independen dengan menganalisis variabel-variabel yang diduga memiliki keterkaitan satu sama lain. Pada penelitian ini, analisis faktor digunakan untuk mengetahui faktor-faktor yang mempengaruhi rendahnya minat masyarakat Kota Gunungsitoli dalam menggunakan layanan PT Pos Indonesia (Persero) berdasarkan konsep Service Marketing Mix (Bauran Pemasaran Jasa) 7P. Berdasarkan hasil penelitian diperoleh 6 faktor yang mempengaruhi rendahnya minat masyarakat dalam menggunakan layanan PT Pos Indonesia (Persero) yaitu Faktor Promosi (19,935%), Faktor Harga (13,544%), Faktor Produk (11,493%), Faktor Kurlog (8,214%), Faktor Lokasi (7,102%) dan Faktor Proses (6,410%). Keenam faktor tersebut memberikan proporsi keragaman kumulatif sebesar 66,968% artinya keenam faktor tersebut dapat mempengaruhi minat masyarakat Kota Guunungsitoli sebesar 66,968% dan sisanya dapat dipengaruhi faktor-faktor lainnya yang tidak teridentifikasi dalam model ini.

Zero-Inflated Poisson Regression Testing In Handling Overdispersion On Poisson Regression Mutia Sari; Open Darnius
JMEA : Journal of Mathematics Education and Application Vol 2, No 2 (2023): Juni
Publisher : JMEA : Journal of Mathematics Education and Application

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jmea.v2i2.13591

The classical linear regression analysis is an analysis aimed at knowing the relationship between the response variables and the explanatory variables assuming the normal distribution data, but in the applied data is often not the case. Generalized Linear Model (GLM) was developed for data in the form of categorical and discrete distribution. In this study the data was raised which has a poisson distribution by as much as n, with average λ and the odds appearing zero p. Poisson regression is GLM for Poisson-distributed data assuming that Var(X ) = E(X ), but asusumption is rare in applied data. For rare occurrences of a specified interval X variables are often zero-valued, thus causing overdispersion (Var(X ) E(X )). Lambert (1992) introduced a method for overcoming overdispersion in poisson regression i.e. the Zero-Inflated Poisson regression (ZIP). In this research conducted a ZIP regression test in overcoming overdispersion to see the opportunity limit p appears zero- valued as the value that causes overdispersion. Testing is done with RStudio ver. 1.1.463.0 software. Based on the simulated data obtained that Regression ZIP stopped overcoming overdis persion at the condition n = 500, λ = 0.7 with the odds p = 0.2 with a dispersion ratio of τ = 1.010.

Modelling of Subject Scheduling Systems Using Hybrid Artificial Bee Colony Algorithm Sri Wahyuni Lingga; Sutarman; Open Darnius
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2023): Article Research Volume 8 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar

A common schedule problem found in colleges is the positioning of courses in a certain space and time. This placement process often encounters barriers that must be met so that there is no imbalance in the school schedule. One of the problems that often arise is the placement of class capacity that does not match the course requirements. In this study, the researchers used the Artificial Bee Colony Hybrid Algorithm (HABC) to construct course schedules efficiently at the college. The objective of the research was to develop a course scheduling system using the HABC algorithm by combining the Engineering of Artificial Bee Colony (ABC) and genetic algoritms, especially on the crossover process to better address the schedule problems. The research procedure used is to design and implement a course scheduling system using the Hybrid ABC algorithm. The results of the research demonstrate that the Hybrid ABC algorithm is effective in generating optimal course schedule schedules, in line with time limits, room needs, and lecturer requirements and can automate course schedule processes, saving time and resources, while ensuring optimal schedules.

Inventory Model for Order Quantity Optimization with Partial Backlogging on Greater Demand at The Beginning Reanty Teresa Aritonang; Open Darnius; Sutarman Sutarman
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2023): Article Research Volume 8 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12600

This article discusses the model of inventory with greater demand at the beginning which allows shortages. During the shortage period, it is assumed that there is a backlogged demand, and the remainder is considered lost sales. This research is completed by using the deterministic inventory model method, namely the EOQ model. The result of using the EOQ method is to determine the inventory lot size and length, with the goal of minimizing the total cost of inventory and generating maximum profits related to the inventory model. An numerical example is given to show the use of this model.

MODEL HIBRIDA AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) DAN FUZZY TIME SERIES (FTS) UNTUK PERAMALAN PRODUKSI KELAPA SAWIT PT. PERKEBUNAN NUSANTARA II Utari Sri Mayanti; Open Darnius; Israil Sitepu
CARTESIUS : Jurnal Pendidikan Matematika Vol 6 No.1 Tahun 2023
Publisher : Unika Santo Thomas Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar

Model autoregressive integrated moving average (ARIMA) merupakan model yang secara penuh mengabaikan independen varibel dalam pembuatan peramalan. Untuk menghasilkan peramalan dalam jangka pendek yang akurat metode ARIMA menggunakan nilai masa lalu dan sekarang dari variabel dependen. Peramalan dengan menggunakan model ARIMA masih memiliki kekurangan dengan nilai kesalahan pengukuran yang cukup besar selain itu pada beberapa data time series terkadang mengandung pola linier maupun nonlinier sekaligus di dalamnya, maka diperlukan penggabungan model lain untuk meramalkan data non linier yang efektif seperti model fuzzy. Fuzzy Time Series (FTS) merupakan konsep yang digunakan untuk meramalkan masalah, dimana data aktual diubah menjadi nilai-nilai linguistik. Dengan Hibrida ARIMA dan FTS ditemukan model terbaik dengan Pemodelan Hibrida ARIMA (6,1,4) dan FTS pembobot Cheng dengan nilai RMSE terkecil yaitu sebesar 0,10615.

MODEL HIBRIDA AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) DAN FUZZY TIME SERIES (FTS) UNTUK PERAMALAN PRODUKSI KELAPA SAWIT PT. PERKEBUNAN NUSANTARA II Utari Sri Mayanti; Open Darnius; Israil Sitepu
CARTESIUS : Jurnal Pendidikan Matematika Vol 6 No.1 Tahun 2023
Publisher : Unika Santo Thomas Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar

Model autoregressive integrated moving average (ARIMA) merupakan model yang secara penuh mengabaikan independen varibel dalam pembuatan peramalan. Untuk menghasilkan peramalan dalam jangka pendek yang akurat metode ARIMA menggunakan nilai masa lalu dan sekarang dari variabel dependen. Peramalan dengan menggunakan model ARIMA masih memiliki kekurangan dengan nilai kesalahan pengukuran yang cukup besar selain itu pada beberapa data time series terkadang mengandung pola linier maupun nonlinier sekaligus di dalamnya, maka diperlukan penggabungan model lain untuk meramalkan data non linier yang efektif seperti model fuzzy. Fuzzy Time Series (FTS) merupakan konsep yang digunakan untuk meramalkan masalah, dimana data aktual diubah menjadi nilai-nilai linguistik. Dengan Hibrida ARIMA dan FTS ditemukan model terbaik dengan Pemodelan Hibrida ARIMA (6,1,4) dan FTS pembobot Cheng dengan nilai RMSE terkecil yaitu sebesar 0,10615.

Simplifying Complexity: Linearization Method for Partial Least Squares Regression Herlin Simanullang; Sutarman Sutarman; Open Darnius
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2023): Article Research Volume 8 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12754

This research investigates Romera’s local linearization approach as a variance prediction method in partial least squares (PLS) regression. By addressing limitations in the original PLS regression formula, the local linearization approach aims to improve accuracy and stability in variance predictions. Extensive simulations are conducted to assess the method's performance, demonstrating its superiority over traditional algebraic methods and showcasing its computational advantages, particularly with a large number of predictors. Additionally, the study introduces a novel computational technique utilizing bootstrap parameters, enhancing computational stability and robustness. Overall, the research provides valuable insights into the local linearization approach's effectiveness, guiding researchers and practitioners in selecting more reliable and efficient regression modeling techniques.

Title

Found 21 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 21 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 21 Documents
Search