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<![CDATA[PERKIRAAN SELANG KEPERCAYAAN UNTUK PARAMETER PROPORSI PADA DISTRIBUSI BINOMIAL ]]>
<![CDATA[Jainal Jainal]]>;
<![CDATA[Nur Salam]]>;
<![CDATA[Dewi Sri Susanti]]>
<![CDATA[ EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN Vol 10, No 2 (2016): JURNAL EPSILON VOLUME 10 NOMOR 2 ]]>
Publisher : <![CDATA[Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat ]]>
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DOI: 10.20527/epsilon.v10i2.32
<![CDATA[Selang kepercayaan adalah sebuah selang antara dua angka yang diperoleh dari perkiraan titik sebuah parameter. Karena besar nilai parameter tidak diketahui, sehingga yang dipakai dalam perkiraan adalah sebuah peluang. Nilai parameter yang diperkirakan adalah proporsi. Tujuan penelitian ini adalah menentukan perkiraan selang kepercayaan untuk parameter proporsi pada distribusi Binomial. Hasil dari penelitian ini adalah perkiraan selang kepercayaan untuk parameter proporsi pada distribusi Binomial dengan menggunakan metode besaran pivot dengan ukuran sampel ????????≥30 dan ????????<30.Kata Kunci: Selang Kepercayaan (1−????????), Distribusi Binomial, Proporsi, Metode Kemungkinan Maksimum, Metode Besaran Pivot]]>
<![CDATA[REGRESI POISSON TERGENERALISASI I DALAM MENGATASI OVERDISPERSI PADA REGRESI POISSON ]]>
<![CDATA[Zakiah Zakiah]]>;
<![CDATA[Nur Salam]]>;
<![CDATA[Dewi Anggraini]]>
<![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.v9i1.8
<![CDATA[Regression analysis is one method to determine and test the causality relationship (cause-effect) between the dependent variable (Y) with the independent variables (X). In general, regression analysis is used to analyze non-free variable data in the form of continuous data and normal distribution. However, in some applications, non-free variable data to be analyzed in the form of discrete data and not normally distributed. One of the regression models that can be used to analyze the relationship between the dependent variable (Y) in the form of discrete data is Poisson regression model whose dependent variable is Poisson distributed. Poisson regression has the assumption of equidispersion that is the condition in which the mean and variance values of the dependent variable are equal, but sometimes there is an assumption violation, where the value of variance is greater than the so-called overdispersion value, so to overcome it can be used one of the extensions of the regression model Poisson is Poisson regression model generalized, this is because the assumption does not require the same mean value with the value of variance. The purpose of this study is how to estimate the Poisson regression model and Poisson regression model generalized I and explain how the generalized Poisson regression model I in overcoming the overdispersion in Poisson regression.]]>
<![CDATA[METODE TAGUCHI UNTUK PENINGKATAN KUALITAS MUTU PRODUK ]]>
<![CDATA[Akhriyandi Wijanarta]]>;
<![CDATA[Nur Salam]]>;
<![CDATA[Dewi Anggraini]]>
<![CDATA[ EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN Vol 8, No 1 (2014): JURNAL EPSILON VOLUME 8 NOMOR 1 ]]>
Publisher : <![CDATA[Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat ]]>
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DOI: 10.20527/epsilon.v8i1.103
<![CDATA[Costumers tend to choose a better product so that the quality improvement of a product is crucial. Quality control is a continuous process to ensure the quality of the products. The Taguchi method that was introduced by Dr. Genichi Taguchi in 1940 used to improve the quality of product and process as well as to reduce the production cost incurred by the company to minimize damage or defect in the products. The purpose of this research is to explain the procedures of Taguchi method to improve product quality. The results of the research show that the procedures using Taguchi method, are: the first step is counting the number of experiments and choosing the form of orthogonal arrays from the number of factors and levels that will be tested. The second step is conducting experiment and obtains data than calculating the mean value, and determining signal to noise rasio that is consistent to the quality characteristics of the experiment. The third step is analyzing experiment data using analysis of variance to determine factors that have a significant influence, then calculating the contribution value of each factor. If the contribution value of factor is smaller than the contribution value of error value then the factor will be pooling up. After getting the optimal alternative factors the fourth step is confirming experiment to examine the conclusion of the obtained data experiment. Furthermore, the five step is calculating the confidence intervals of response mean value betwen the prediction result of Taguchi method and the result of confirming experiment. After that, the sixth step is calculating Taguchi loss function to determine the amount of damage cost spent to improve the quality of product.]]>
<![CDATA[PERHITUNGAN UKURAN RISIKO UNTUK MODEL KERUGIAN AGREGAT ]]>
<![CDATA[Nadya Pratiwi]]>;
<![CDATA[Aprida Siska Lestia]]>;
<![CDATA[Nur Salam]]>
<![CDATA[ EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN Vol 14, No 1 (2020): JURNAL EPSILON VOLUME 14 NOMOR 1 ]]>
Publisher : <![CDATA[Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat ]]>
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DOI: 10.20527/epsilon.v14i1.2200
<![CDATA[In the case of nonlife insurance, insurance companies are very potential to get losses if claims submitted by customers (policyholders) exceeds the reserves of budgeted claims. It is the risk that have to managed properly by insurance companies . One possible disadvantage is the aggregate loss model. The aggregate loss model is a random variable that states the total of all losses incurred in an insurance policy block. This kind of loss can be modeled using a collective risk approach where the number of claims is a discrete random variable and the size of claim is a continuous random variable. The purpose of this study is to determine risk measure of standard deviation premium principle, value at risk (VaR), and conditional tail expectation (CTE) of the aggregate loss model. Standard deviation premium principle risk measure of aggregate loss model is determined analytically by substituted it expected value and varians. Meanwhile, VaR risk measure is determined using numerical method by Monte Carlo method, then the quantile value and it confidence interval for the actual value will estimate. In the CTE calculation, based on the loss data obtained in the Monte Carlo method, the CTE value is estimated by calculating the average loss that exceeds the VaR value. If the data size is large enough, the CTE value estimation will converge to the actual value.Keywords: Aggregate Loss Model, Standard Deviation Premium Principle, Value at Risk (VaR), Conditional Tail Expectation (CTE).]]>
<![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[PERAMALAN CURAH HUJAN DI KALIMATAN SELATAN DENGAN JARINGAN SYARAF TIRUAN ]]>
<![CDATA[Gt.Khiruddin Indra Permana]]>;
<![CDATA[Ahmad Yusuf]]>;
<![CDATA[Nur Salam]]>
<![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.v9i1.7
<![CDATA[South Kalimantan is in the area of high rainfall so it is included in the criteria of the rainy season. Artificial Neural Network (ANN) is one method that can identify patterns of data from rainfall forecasting system by conducting training method. One of the model ANN used is Backpropagation (BP). The training of a network using BP consists of 3 steps, namely: feedforward input pattern training, calculation and BP from the set of error and weight adjustment. The purpose of this research is to predict rainfall in South Kalimantan in 2015 using JST BP. The research method used in this research is literature study and case study related to rainfall forecasting, JST and BP. This research procedure will begin by collecting data, analyzing data and training data then predicting the data to be achieved. The results of this research is the highest rainfall in South Kalimantan in 2015 occurred in the area of Martapura Kota Kab. Banjar in January. In this period of the month there is a possibility that the area will experience an increase in water level or flood. While the lowest rainfall occurred in the region Pelaihari Kab. Land of the Sea around August and September. In this period the rainfall is so low that the area is likely to be in dry conditions.]]>
<![CDATA[ESTIMASI MODEL REGRESI NONPARAMETRIK DENGAN METODE B-SPLINE ]]>
<![CDATA[Nur Salam]]>;
<![CDATA[Yuana Sukmawaty]]>;
<![CDATA[Annisa Halida]]>
<![CDATA[ Media Bina Ilmiah Vol. 16 No. 10: Mei 2022 ]]>
Publisher : <![CDATA[LPSDI Bina Patria ]]>
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<![CDATA[Analisis regresi merupakan teknik statistik yang digunakan untuk menjelaskan hubungan antara variabel bebas (X) dengan variabel tak bebas (Y). Salah satu model dalam analisis regresi adalah model regresi nonparametrik dengan asumsi bentuk fungsi regresinya tidak diketahui. Untuk mengestimasi fungsi regresi yang tidak diketahui tersebut dapat dilakukan melalui metode pendekatan spline. Spline atau B-Spline adalah potongan-potongan polinomial, yang polinomial memiliki sifat tersegmen. Sifat tersegmen ini memberikan fleksibilitas lebih dari polinomial biasa, sehingga memungkinkan untuk menyesuaikan diri secara lebih efektif terhadap karakteristik dari suatu fungsi atau data. Penelitian ini bertujuan untuk mengestimasi model regresi nonparametrik dengan metode B-Spline. Metode penelitian ini menggunakan studi literatur dengan mengumpulkan semua bahan, baik itu buku, jurnal atau referensi lain yang menunjang dan relevan dengan materi yang akan dibahas dan diteliti. Hasil penelitian menunjukkan bahwa estimator dari fungsi regresi pada model regresi nonparametrik yang diperoleh dengan menggunakan metode pendekatan B-spline adalah : y ̂= S_λ y dengan S_λ =〖〖B(B〗^T B+n λK^T)〗^(-1) B^T]]>
<![CDATA[INFERENSI MODEL REGRESI LINEAR UNTUK EKSPOR DAN IMPOR PROVINSI KALIMANTAN SELATAN TAHUN 2020 ]]>
<![CDATA[Nur Salam]]>;
<![CDATA[Fuad Muhajirin Farid]]>;
<![CDATA[Zainal Zainal]]>
<![CDATA[ EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN Vol. 16(2), 2022 ]]>
Publisher : <![CDATA[Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat ]]>
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DOI: 10.20527/epsilon.v16i2.6646
<![CDATA[Regression analysis is a statistical technique used to explain the relationship between an independent variable (independent) as a predictor variable (X) and the dependent variable as a response variable (Y) which can be expressed as a form of mathematical model. In linear regression analysis there are two models, namely a simple linear regression model where the independent variable is only one and a multiple linear regression model where the independent variable is more than one. This study aims to infer the parameters of the linear regression model both estimation and hypothesis testing and to apply the inference results to the export and import case of South Kalimantan province in 2020. This research method uses a literature study by collecting all materials and data, be it books, journals, web.site or other references that support and are relevant to the material to be discussed and researched. From the results of research on exports and imports of South Kalimantan province in 2022, inference results are obtained in the form of explicit forms for each parameter for point and interval estimates from linear regression models. Furthermore, an inference is obtained about hypothesis testing for each parameter, the results of which show that both significant parameters are included in the linear regression model]]>
<![CDATA[Peningkatan Kompetensi Peneliti Yayasan Kakikota Banajrmasin Dalam Melakukan Pre-Proccesing Data Hasil Survei, Analisis Data Kategorik, Dan Pembuatan Peta Tematik ]]>
<![CDATA[Yeni Rahkmawati]]>;
<![CDATA[Selvi Annisa]]>;
<![CDATA[Dewi Anggraini]]>;
<![CDATA[Dewi Sri Susanti]]>;
<![CDATA[Nur Salam]]>;
<![CDATA[Yuana Sukmawaty]]>;
<![CDATA[Fuad Muhajirin Farid]]>
<![CDATA[ Jurnal Pengabdian ILUNG (Inovasi Lahan Basah Unggul) Vol 3, No 1 (2023) ]]>
Publisher : <![CDATA[Universitas Lambung Mangkurat ]]>
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DOI: 10.20527/ilung.v3i1.9334
<![CDATA[Banjarmasin KAKIKOTA Foundation is one of the NGOs based in South Kalimantan that works on urban issues in Banjarmasin City. Banjarmasin KAKIKOTA Foundation has conducted several social stuides on phenomena in Banjarmasin City through several surveys. However, due to the lack of knowledge about data processing, the researchers of the Banjarmasin KAKIKOTA Foundation experienced difficulties in analyzing survey results data. Therefore, Program Studi Statistika, FMIPA, Universitas Lambung Mangkurat (ULM) provides assistance in the form of statistical training to researcher of the Banjarmasin KAKIKOTA Foundation in order to improve the researcher’s competency and technical skills in analyzing research data. The method used in this community service was training. The training was divided into three subthemes, namely: 1) Data preprocessing, 2) Categorical data analysis, and 3) Thematic map making. Based on the evaluation results, this training was very useful for the researcher of the Banjarmasin KAKIKOTA Foundation and is expected to carry out further training in 2023. Keywords: Statistical Training; NGO; Banjarmasin KAKIKOTA Foundation; Research]]>
