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<![CDATA[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) ]]>
<![CDATA[Dewi Anggraini]]>;
<![CDATA[Dewi Sri Susanti]]>;
<![CDATA[Nur Salam]]>
<![CDATA[ EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN Vol 5, No 1 (2011): JURNAL EPSILON VOLUME 5 NOMOR 1 ]]>
Publisher : <![CDATA[Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat ]]>
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DOI: 10.20527/epsilon.v5i1.66
<![CDATA[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.]]>
<![CDATA[SPLIT-SPLIT PLOT DESIGN (SSPD) ]]>
<![CDATA[Rizki Fatriasi]]>;
<![CDATA[Dewi Anggraini]]>;
<![CDATA[Dewi Sri Susanti]]>
<![CDATA[ EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN Vol 7, No 2 (2013): JURNAL EPSILON VOLUME 7 NOMOR 2 ]]>
Publisher : <![CDATA[Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat ]]>
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DOI: 10.20527/epsilon.v7i2.98
<![CDATA[Split-Split Plot Design (SSPD) is an extension of the Split Plot Design (SPD) where in SSPD has an additional sub-subplot. SSPD requires the same principles with SPD so the placement of factor is set to be the main plot for a less important factor, the subplot for a more important factor, and the sub-subplot for the most important factor. The purpose of the research is to explain the properties of usage and estimate the parameters of SSPD’s model.]]>
<![CDATA[PENGGUNAAN METODE BRANCH AND BOUND UNTUK MENYELESAIKAN MASALAH PENUGASAN PADA KASUS PENYUSUNAN JARINGAN KOMUNIKASI ]]>
<![CDATA[Fitriadi Fitriadi]]>;
<![CDATA[Dewi Sri Susanti]]>;
<![CDATA[Nur Salam]]>
<![CDATA[ EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN Vol 4, No 1 (2010): JURNAL EPSILON VOLUME 4 NOMOR 1 ]]>
Publisher : <![CDATA[Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat ]]>
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DOI: 10.20527/epsilon.v4i1.47
<![CDATA[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 AsHalo Matrix Mentari starOne Im3Flexi Fren XL Simpati with minimum costRp 1.298.]]>
<![CDATA[PEMBENTUKAN FUNGSI PELUANG BIRTH-DEATH PROCESS MELALUI SISTEM PERSAMAAN DIFERENSIAL LINIER HOMOGEN ]]>
<![CDATA[Etza Budiarti]]>;
<![CDATA[Karim Karim]]>;
<![CDATA[Dewi Sri Susanti]]>
<![CDATA[ EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN Vol 8, No 2 (2014): JURNAL EPSILON VOLUME 8 NOMOR 2 ]]>
Publisher : <![CDATA[Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat ]]>
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DOI: 10.20527/epsilon.v8i2.108
<![CDATA[PEMBENTUKAN FUNGSI PELUANG BIRTH-DEATH PROCESS MELALUI SISTEM PERSAMAAN DIFERENSIAL LINIER HOMOGEN]]>
<![CDATA[KOMPUTASI METODE SIMPLEKS PADA PENYELESAIAN PROGRAM LINIER ]]>
<![CDATA[Ahmad Yusuf]]>;
<![CDATA[Dewi Sri Susanti]]>
<![CDATA[ EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN Vol 4, No 1 (2010): JURNAL EPSILON VOLUME 4 NOMOR 1 ]]>
Publisher : <![CDATA[Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat ]]>
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DOI: 10.20527/epsilon.v4i1.44
<![CDATA[One of the methods that can be used to solve linear program problem is a simplex method. In this study, researchers will try to make a computational algorithm the simplex method that can be used for the completion of the linear program, so obtained a series of efficient completion processes and optimal end results. This research generate program code Computing using Borland Delphi 6.0 programming language which can be used to solve linear program problems by using simplex method.]]>
<![CDATA[PELUANG TRANSISI PADA PENENTUAN PREMI TUNGGAL BERSIH ASURANSI JIWA BERJANGKA ]]>
<![CDATA[Muhammad Meidy Maulana]]>;
<![CDATA[Dewi Sri Susanti]]>;
<![CDATA[Aprida Siska Lestia]]>
<![CDATA[ EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN Vol. 16(1), 2022 ]]>
Publisher : <![CDATA[Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat ]]>
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DOI: 10.20527/epsilon.v16i1.5174
<![CDATA[A life insurance contract contains the amount of funds that must be paid by insured as a responsibility for a received compensation. There funds are called as premium. Payment of the premium which paid with one payment at the beginning of the contract time called as net single premium. One factor that influenced the calculation of life insurance premiums is a life probability. In general, a life probability constructed by the assumption that death only involves two conditions, life and death. Yet, there are another condition for the insured that also affect a person’s death condition which is sick. The objecktive of this research is to determine net single premium of term life insurance formula using transition probability as a life probability. The first will constructed transition from three condition which are health, sick, and death as stochastic process. Transition probability will be determined by solving Chapman Kolmogorov system differential equation. Then the probability transition that determined will be used for calculate net single premium from term life insurance. Net single premium will be determined by using expectation value of present value of benefit random variables. From this research get formula of net single premium of term life insurance contains discount function, transition probability, and force of mortality of someone.]]>
<![CDATA[KLASIFIKASI PEMILIHAN PROGRAM STUDI DI FAKULTAS MIPA UNIVERSITAS LAMBUNG MANGKURAT MENGGUNAKAN REGRESI LOGISTIK MULTINOMIAL ]]>
<![CDATA[Silvi Risaria Dewi]]>;
<![CDATA[Nur Salam]]>;
<![CDATA[Dewi Sri Susanti]]>
<![CDATA[ EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN Vol 12, No 2 (2018): JURNAL EPSILON VOLUME 12 NOMOR 2 ]]>
Publisher : <![CDATA[Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat ]]>
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DOI: 10.20527/epsilon.v12i2.315
<![CDATA[Pengklasifikasian merupakan salah satu metode statistik dalam mengelompokkan suatu data yang disusun secara sistematis. Pengklasifikasian sering dijumpai dalam kehidupan sehari-hari, contohnya pengklasifikasian data pada bidang akademik, pada bidang sosial, pada bidang ekonomi dan pada bidang lainnya. Salah satu alat statistika untuk klasifikasi adalah model Regresi Logistik Multinomial. Tujuan dari penelitian ini adalah menerapkan metode Regresi Logistik multinomial untuk mengetahui kesesuaian pemilihan program studi pada mahasiswa FMIPA Universitas Lambung Mangkurat dengan variabel yang berpengaruh adalah Nilai UN Mahasiswa pada saat di Sekolah Menengah, Nilai Semester 1 Mahasiswa, Asal Sekolah dan Asal Daerah Mahasiswa. Metode penelitian yang digunakan bersifat studi literatur dan menguji data Mahasiswa Fakultas MIPA Universitas Lambung Mangkurat angkatan 2011-2014. Hasil dari penelitian ini adalah metode Regresi Logistik Multinomial dapat digunakan untuk klasifikasi kesesuaian dalam memilih program studi. Pada tingkat kepercayaan 90% dari 10 (sepuluh) variabel bebas yang digunakan terdapat 5 (lima) variabel yang mampu menjadi faktor yang berpengaruh yaitu Nilai Kalkulus 1, Nilai Biologi Umum, Nilai Fisika Dasar, Nilai Kimia Dasar dan Asal Sekolah Mahasiswa dan Pada tingkat kepercayaan 95% terdapat 3 (tiga) variabel yang mampu menjadi faktor yang berpengaruh yaitu Nilai Kalkulus 1, Nilai Biologi Umum dan Nilai Kimia Dasar. Kesesuaian Pemilihan Program Studi yang tertinggi terdapat pada Program Studi Fisika yaitu sebanyak 70% dan yang terendah terdapat pada Program Studi Biologi yaitu sebanyak 34,4%.Kata kunci: Klasifikasi, Regresi Logistik Multinomial, Program Studi]]>
<![CDATA[ESTIMASI PARAMETER MODEL REGRESI TERBOBOTI GEOGRAFIS (STUDI KASUS TINGKAT KESEJAHTERAAN PENDUDUK DI KABUPATEN BANJAR) ]]>
<![CDATA[Nurul Qomariyah]]>;
<![CDATA[Dewi Sri Susanti]]>;
<![CDATA[Nur Salam]]>
<![CDATA[ EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN Vol 12, No 1 (2018): JURNAL EPSILON VOLUME 12 NOMOR 1 ]]>
Publisher : <![CDATA[Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat ]]>
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DOI: 10.20527/epsilon.v12i1.200
<![CDATA[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.]]>
<![CDATA[JOINT LIFE DALAM ASURANSI JIWA BERJANGKA ]]>
<![CDATA[Dini Hidayati]]>;
<![CDATA[Dewi Anggraini]]>;
<![CDATA[Dewi Sri Susanti]]>
<![CDATA[ EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN Vol 9, No 1 (2015): JURNAL EPSILON VOLUME 9 NOMOR 1 ]]>
Publisher : <![CDATA[Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat ]]>
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DOI: 10.20527/epsilon.v9i1.6
<![CDATA[Generally in life insurance apply the condition of single life and joint life. Single life condition on life insurance is a condition when someone who wants to buy an insurance policy only for himself, meaning that can not be replaced by other people or parties. While the condition of joint life is a condition when two or more people who want to buy an insurance policy. For example husbands, wives, parents, and children, so there is dependence between policyholders either in joint opportunities, the sum insured, or premium payments. This study aims to determine the form of life and death opportunities for 3 policyholders, and joint life formulation in term and term life insurance. This research is a literature study, ie researchers collect materials or materials related to the research topic. Then study and re-explain the concept by applying it to the sample problem. The results of this study indicate that the chances of life and death for 3 people policyholders shaped mxyznp = () Σ = 3miixyznp. The term life joint annuity depends on the chance of living together and certain interest in the form of: xyzna = Σ - = ++ 1011ntxyzttpv and: xyzna = Σ- = 10ntxyzttpv. Insurance joint life futures depend on the chance of dead together and a particular interest in the form of 1: xyznA = Σ - = + 101ntxyzttqv.]]>
<![CDATA[ESTIMASI MODEL LINEAR PARSIAL DENGAN PENDEKATAN KUADRAT TERKECIL DAN SIMULASINYA MENGGUNAKAN PROGRAM S-PLUS ]]>
<![CDATA[Nur Salam]]>;
<![CDATA[Dewi Sri Susanti]]>;
<![CDATA[Dewi Anggraini]]>
<![CDATA[ EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN Vol 6, No 2 (2012): JURNAL EPSILON VOLUME 6 NOMOR 2 ]]>
Publisher : <![CDATA[Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat ]]>
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DOI: 10.20527/epsilon.v6i2.82
<![CDATA[Partial linear model (model semiparametric) is a new approach in the regressionmodels between the two regression models are already popular parametric regression andnonparametric regression. Partial linear model is a model that includes both thecombination of parametric components and nonparametric components. This study usesliterature by studying semiparametric regression analysis, finding and determining theestimated parameters. Partial linear model has the form: : ???????? = ???????????????? + g(????????)+ ???????? with???????? and ???????? are explanatory variables, g (.) is an unknown function (smooth function), β isthe parameter of unknown function, ???????? response variable and ???????? is an error with the mean(????????) = 0 and variance ????????2 = ????(????????2).The results showed that the partial linear model parameter estimation canbe performed using the least squares method in which part of the linear model usingnonparametric kernel approach and subsequent estimation results are substituted into thepartial linear model to estimate the parametric part of the model by using the linear leastsquares method. Results obtained partial linear estimation is ???? ???? (t) = ????????????????????=1 (Yi - ???????????? +???????? ) dengan ???????? = (???? ???? ???? )−???? ???? ???? ???? .Based on the simulation results obtained output values and graphs are for theparametric, graphical display and qqline qqnorm estimator beta (β) is (????) yaitu ????0, ????1and ????2 can be seen clearly, where if n is greater (n → ∞) and the greater replicationiteration r , then the points are spread around the more straight line and a straight line.This indicates the greater n and r, the beta (β) closer to the normal distribution.Nonparametric estimator simulation results in this section are taken as an example of anormal kernel function values approaching g (T). So it can be concluded briefly that if thelarger n (n → ∞), the estimator of the nonparametric part closer to the partial linearmodel g (T).]]>
