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PENGARUH KEDISIPLINAN DAN METODE MENGAJAR GURU TERHADAP HASIL BELAJAR PESERTA DIDIK PADA MATA PELAJARAN SOSIOLOGI DI MAN 1 SITUBONDO Mohammad Sudarwanto; Nur salam; Arif Sholihin
JURNAL PENDIDIKAN DAN KEWIRAUSAHAAN Vol 7 No 2 (2020)
Publisher : STKIP PGRI SITUBONDO

Penelitian ini bertujuan untuk mengetahui adanya pengaruh kedisiplinan guru dan metode mengajar guru terhadap hasil belajar peserta didik.Kedisiplinan guru mencakup berbagai macam pengaruh yang ditujukan untuk mendidik peserta didik dalam disiplin belajar sekaligus memperlancar kerjanya.Sementara metode mengajar membantu mempermudah guru dan peserta didik untuk mendalami materi pelajaran dalam pembelajran.Penelitian ini menggunakan rancangan ex post facto.Subjek penelitian di MAN 1 Situbondo pada kelas XI IPS dengan 70 responden.Metode pengumpulan data pada peneletian ini menggunakan metode observasi, kuesioner, dokumen.Analisis data diperoleh menggunakan regrasi linier berganda dan uji t. Hasil penelitian ini adalah (1) terdapat pengaruh kedisiplinan guru terhadap hasil belajar peserta didik.Dikarenakan nilai tingkat signifikansi sebesar 0.000 < 0.05.Hal ini berarti bahwa terdapat pengaruh yang signifikan antara kedisiplinan guru terhadap hasil belajar peserta didik. (2) terdapat pengaruh metode mengajar guru terhadap hasil belajar peserta didik.Dikarenakan nilai tingkat signifikansi sebesar 0.001 < 0.05.Hal ini berarti bahwa terdapat pengaruh yang signifikan antara metode mengajar guru terhadap hasil belajar peserta didik.Sehingga dapat disimpulkan bahwa kedisiplinan dan metode mengajar guru secara simultan memiliki pengaruh yang signifikan terhadap hasil belajar peserta didik.

TEKNIK PERAMALAN MENGGUNAKAN METODE PEMULUSAN EKSPONENSIAL HOLT-WINTERS Siti Nur Hamidah; Nur Salam; Dewi Sri Susanti
EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN Vol 7, No 2 (2013): JURNAL EPSILON VOLUME 7 NOMOR 2
Publisher : Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat

Time series forecasting is a method used to determine what might happen in the future based on information obtained in the past. One method used in time series forecasting is a Holt-Winters exponential smoothing method. This method can be used for time series data with trend and seasonality components. This method is based on three smoothing equations: overall smoothing, trend, and seasonal components. Holt-Winters exponential smoothing method consists of multiplicative and additive seasonality models. The method of this research is literature study by collecting and studying references that are relevant to the idea of this research, and then applying the Holt-Winters exponential smoothing method into data. The results of this research show that the multiplicative seasonality model of Holt-Winters exponential smoothing method can be used if data represent an increase in long-term and seasonal fluctuations which is the increasingly bigger with the increasing of observation time periods. These patterns identify the non-stationary of mean and variance. While, the additive seasonality model can be used if data show an increase in long-term and seasonal fluctuations that are relatively constant with the increasing of observation time. Time series forecasting is a method used to determine what might happen in the future based on information obtained in the past. One method used in time series forecasting is a Holt-Winters exponential smoothing method. This method can be used for time series data with trend and seasonality components. This method is based on three smoothing equations: overall smoothing, trend, and seasonal components. Holt-Winters exponential smoothing method consists of multiplicative and additive seasonality models. The method of this research is literature study by collecting and studying references that are relevant to the idea of this research, and then applying the Holt-Winters exponential smoothing method into data. The results of this research show that the multiplicative seasonality model of Holt-Winters exponential smoothing method can be used if data represent an increase in long-term and seasonal fluctuations which is the increasingly bigger with the increasing of observation time periods. These patterns identify the non-stationary of mean and variance. While, the additive seasonality model can be used if data show an increase in long-term and seasonal fluctuations that are relatively constant with the increasing of observation time

PERKIRAAN SELANG KEPERCAYAAN UNTUK NILAI RATA-RATA PADA DISTRIBUSI POISSON Randy Toleka Ririhena; Nur Salam; Dewi Sri Susanti
EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN Vol 10, No 1 (2016): JURNAL EPSILON VOLUME 10 NOMOR 1
Publisher : Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat

Confidence interval is an interval between two value, where we believe that the parameter value lay within those interval. To express it, approximate interval were conducted. If the parameter value is unknown, probability will be use rather than exact value. Approximation that conduct express probability that an interval contain parameter value that we estimate. One of parameter value to compute is mean. One of well-known distribution is Poisson ditribution. The purpose of this study is to find approximate interval for the mean of random variable with Poisson distribution. The result of research is confidence interval for poisson distribution by using pivotal quantity method. Based on pivotal quantity method, approximate interval for the mean of poisson distribution with the size of a large sample is???????? ???? ????????????− ????????????????/2 ???????????????? ???????? < ???????? < ???????????? + ????????????????/2 ???????????????? ???????? ???? = 1− ????????

ANALISIS REGRESI LINEAR BERGANDA DENGAN SATU VARIABEL BONEKA (DUMMY VARIABLE) Tanti Krisnawardhani; Nur Salam; Dewi Anggraini
EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN Vol 4, No 2 (2010): JURNAL EPSILON VOLUME 4 NOMOR 2
Publisher : Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat

To accommodate the existence of free qualitative variables into the regression model then used variable dummy (variable dummy) in the regression equation that can be written as follows: i i i i Y     X   D  0 1 2. This study aims to determine parameter estimation of the multiple linear regression model with one variable the puppet uses the least squares method, determining the model match test statistic using the method likelihood ratio and apply the model in the case example. The results showed that the parameter estimation of the regression model is: β  X'X (X'Y) 1  . Application of usage this regression model is in the example about the calculation of tuition fees Y based on the average Scholastic Aptitude Test (X nilai) and university type D so obtained the following model: Y  7263,56 19,52X  8732,418D 

ESTIMASI PARAMETER PADA DISTRIBUSI EKSPONENSIAL Renny Aulia; Noor Fajriah; Nur Salam
EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN Vol 5, No 2 (2011): JURNAL EPSILON VOLUME 5 NOMOR 2
Publisher : Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat

Point estimation of a population parameter is a value obtained from relatedsample and used as an estimator of the parameter whose value is unknown. Pointestimator can be determined by using two methods: classical method (momentmethod and maximum likelihood) and Bayes method. The purpose of this researchis to determine the point estimation of an exponential distribution with oneparameter using Moment method, Maximum Likelihood method and Bayesmethod and determine the point estimation of an exponential distribution with twoparameters Moment method and Maximum Likelihood method.The method of this research is a literature study from various sources thatsupport and relevant to the topic.The result shows that the point estimation of Exponential distribution forone parameter by using Moment method and Maximum Likelihood Method isx , while the Bayes estimator of Exponential distribution for one parameter withprior konjugate Gamma distribution is   x  n pn pnii      11 and Chi Square is      2 21121kx nknnii. The point Estimation of exponential distribution for twoparameters by using the Moment method is 1 22ˆ Xnxnii  and1 22ˆ XnxXnii   , whereas by using the Maximum Likelihood method is nx xnin i  11:ˆand n x 1: ˆ  .

PENGARUH PEMBERIAN VITAMIN C DAN SULFAS FERROSES (SF) PADA IBU HAMIL UNTUK MENGURANGI RISIKO ANEMIA PADA SAAT PERSALINAN MENGGUNAKAN ANALISIS DATA BERPASANGAN (STUDI KASUS SEBUAH KLINIK BERSALIN DI BANJARMASIN) Dewi Anggraini; Dewi Sri Susanti; Nur Salam
EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN Vol 5, No 1 (2011): JURNAL EPSILON VOLUME 5 NOMOR 1
Publisher : Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat

Paired data analysis (analysis of paired data) is a statistical analysis that is used for a studywhere there is only one sample or group of individuals or objects of observation are used andsubjected to two treatments or measurements resulting in a valuepairs. Two pieces of data said tobe in pairs when any value in the first data in accordance and is associated with a single value toboth the data. In other words, two pairs of data which can be interpreted as a sample of thesubject/object of the same observations are given two treatments (treatment)/differentmeasurements. This study aims to clarify the effect of Vitamin C and Sulfas Ferroses (SF) inpregnant women towards the increasing level of their hemoglobin.The method of this research is a study literature and case study, by collecting and studyng therelevant references on the analysis of data pairs, then applying to data in a maternity clinic inBanjarmasin.The results shows that Vitamin C and Sulfas Ferroses (SF) have influenced to the increase ofhemoglobin level in pregnant women.

PENGGUNAAN METODE BRANCH AND BOUND UNTUK MENYELESAIKAN MASALAH PENUGASAN PADA KASUS PENYUSUNAN JARINGAN KOMUNIKASI Fitriadi Fitriadi; Dewi Sri Susanti; Nur Salam
EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN Vol 4, No 1 (2010): JURNAL EPSILON VOLUME 4 NOMOR 1
Publisher : Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat

Assignment problem is how to match exactly only one agent for one task, the aim is to getmaximum advantage or minimum cost. One of assignment problem case is arrangingcommunication network, that how to make order formation of sending Short Massage Service(SMS) from someone to another one in a group. Each one in that group has to send one SMS to theother member. The last stage in this process is the SMS was sent by the first sender will comebackto the first sender again as indication that all of member of a group was received SMS.Arranging communication network is done by using branch and bound method by distributingthe big scale problem to the small until it can be solved. Distributing is done recursive until formedtree structure. The objective of this research is how to find optimal solution for solving assignmentproblem in arranging communication network by using branch and bound method.This research is done with literature study method that is by collecting and studying fromsupporter reference related to assignment problem and branch and bound method. Procedure inthis research that is forming cost matrix, reducing rows and columns, counting up all rows andcolumns reducer as bound of 0 node. 0 node have branch that called first level, node’s bound offirst level finding by using formula Cs = Cr + Ci,j + r. optimum bound of first level make as E-nodethat will branched and resulting second level, etc. The result of research is finding one of communication network that below: Simpati  AsHalo Matrix Mentari starOne Im3Flexi Fren XL Simpati with minimum costRp 1.298.

PERBANDINGAN METODE ROBUST MCD-LMS, MCD-LTS, MVE-LMS, DAN MVE-LTS DALAM ANALISIS REGRESI KOMPONEN UTAMA Sekar Wulandari; Nur Salam; Dewi Anggraini
EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN Vol 4, No 1 (2010): JURNAL EPSILON VOLUME 4 NOMOR 1
Publisher : Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat

Principal Component Regression (PCR) is one of the widely used statistical techniques forregression analysis with colinearity. A robust technique on CR required is when data containsoutlier is urgently needed.In this research we consider combination between Robust Principal Ccomponent Analysis(PCA): Minimum Covariance Determinant (MCD) and Minimum Volume Ellipsoid (MVE) withRobust Regression methods: Least Median Square (LMS), and Least Trimmed Square (LTS), thencompare resistance level of MCD-LMS, MCD-LTS, MVE-LMS and MVE-LTS through the biasand the mean square error on some samples size and outlier’s percentage.The result shows that the MCD-LMS perform better than MCD-LTS, MVE-LMS, and MCDLTS.

PENERAPAN TEORI KENDALI PADA MASALAH PROGRAM DINAMIK Pardi Affandi; Dewi Anggraini; Nur Salam
EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN Vol 6, No 1 (2012): JURNAL EPSILON VOLUME 6 NOMOR 1
Publisher : Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat

This study examines the application of Control Theory to the problem of Dynamic Program. Dynamic program is a design analysis in math to determine a series decisions relating to the decision-making process are gradually double to optimize problem solving effectively. Classic problems in dynamic programming is the concept of phase, state and acquisition. Problem solving will use Control Theory.

ESTIMASI PARAMETER MODEL REGRESI TERBOBOTI GEOGRAFIS (STUDI KASUS TINGKAT KESEJAHTERAAN PENDUDUK DI KABUPATEN BANJAR) Nurul Qomariyah; Dewi Sri Susanti; Nur Salam
EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN Vol 12, No 1 (2018): JURNAL EPSILON VOLUME 12 NOMOR 1
Publisher : Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat

Analisis regresi adalah suatu metode analisis statistik yang digunakan untuk mengetahui pengaruh antara dua atau lebih variabel. Model regresi yang sering digunakan dalam penelitian adalah model regresi berganda, yaitu model regresi dengan lebih dari satu variabel penjelas. Ada beberapa asumsi yang harus dipenuhi dalam regresi berganda, salah satunya adalah variansi dari error konstan (homoskedastisitas). Apabila variansi error tidak konstan (heterokedastisitas) maka menggunakan metode regresi terboboti. Model regresi yang melibatkan pengaruh heterogenitas spasial ke dalam model adalah model Regresi Terboboti secara Geografis (RTG). Jika data yang akan digunakan pada analisis regresi diperoleh dari lokasi-lokasi yang berbeda maka data tersebut disebut data spasial. Tujuan dari penelitian ini adalah untuk mengaplikasikan model RTG yang diterapkan pada kasus tingkat kesejahteraan penduduk di Kabupaten Banjar. Penelitian ini bersifat studi kasus dengan variabel respon banyaknya penduduk miskin yang terkategori PMKS dan variabel penjelas yaitu kepadatan penduduk, jumlah fasilitas pendidikan untuk SDN, SMP dan SMA, serta jumlah potensi desa untuk pekerja sosial masyarakat, organisasi sosial dan karang taruna. Hasil penelitian ini menunjukkan bahwa tidak semua variabel penjelas memberikan pengaruh terhadap banyaknya penduduk miskin yang terkategori PMKS. Sebanyak 74% kecamatan di Kabupaten Banjar menyatakan banyaknya penduduk miskin yang terkategori PMKS tidak dipengaruhi oleh variabel penjelas yang diduga dan sebanyak 21% kecamatan dipengaruhi oleh satu variabel penjelas. Sedangkan 5% kecamatan dipengaruhi oleh lima variabel bebas yang diduga.

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