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<![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).]]>
<![CDATA[ANALISIS KRIGING UNTUK MENDETEKSI POLA SPASIAL KASUS DBD DI KABUPATEN TANAH LAUT ]]>
<![CDATA[Sri Mulyanie Hardiyanthy]]>;
<![CDATA[Dewi Sri Susanti]]>;
<![CDATA[Thresye Thresye]]>
<![CDATA[ EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN Vol 13, No 2 (2019): JURNAL EPSILON VOLUME 13 NOMOR 2 ]]>
Publisher : <![CDATA[Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat ]]>
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DOI: 10.20527/epsilon.v13i2.1646
<![CDATA[Geostatistics is a data processing in geological field that contains spatial information in it. Spatial information is information that identifies geographical location, characteristics of natural conditions and boundaries of the earth. Geostatistics is used to handle regionalized variables. One of the method that used to handle regionalized variables is the kriging method. The kriging method has a lot of expansion in its development, including the Simple Kriging method and the Cokriging method. Both of these methods will be applied in case studies of spatial patterns of dengue in Tanah Laut District. The purpose of this study was to estimate the distribution pattern of DHF in Tanah Laut District and compare the results of the RMSE method of Simple Kriging and Cokriging. The smallest RMSE value was compared and selected, followed by estimation using the Cokriging and Simple Kriging methods. From the two methods used the smallest RMSE value is in the Simple Kriging method. But when you looked from the thematic map of the distribution of dengue patients with the Cokriging and Simple Kriging method, it can be seen that the Cokriging method has a more diverse pattern. Keywords: geostatisticts , Cokriging , Simple Kriging , DHF]]>
<![CDATA[TEOREMA TITIK TETAP BANACH PADA RUANG METRIK-D ]]>
<![CDATA[Muhammad Ahsar Karim]]>;
<![CDATA[Dewi Sri Susanti]]>;
<![CDATA[Nurul Huda]]>
<![CDATA[ EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN Vol 4, No 2 (2010): JURNAL EPSILON VOLUME 4 NOMOR 2 ]]>
Publisher : <![CDATA[Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat ]]>
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DOI: 10.20527/epsilon.v4i2.58
<![CDATA[In the space of metrics known the fixed point theorem of Banach. In this paper, the theorem will be constructed in the D-metric space. This study begins with construction concepts: open ball, open set, convergent lines, and Cauchy rows respectively in the D-metric space. Then given the concept of continuous mapping and mapping continuous uniform in the D-metric space. Further constructed Banach's fixed point theorem at in the D-metric space.]]>
<![CDATA[ESTIMASI PARAMETER UNTUK DISTRIBUSI HALF LOGISTIK ]]>
<![CDATA[Rizqi Elmuna Hidayah]]>;
<![CDATA[Nur Salam]]>;
<![CDATA[Dewi Sri Susanti]]>
<![CDATA[ EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN Vol 7, No 1 (2013): JURNAL EPSILON VOLUME 7 NOMOR 1 ]]>
Publisher : <![CDATA[Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat ]]>
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DOI: 10.20527/epsilon.v7i1.92
<![CDATA[Point estimation is a value obtained from the samples and used as anestimator of the parameter whose value is unknown. To determine the point estimatorcan be used several methods, such as Maximum Likelihood Estimator (MLE) and Methodof Moments Estimator (MME). Half logistic distribution is used for lifetime data such asthe survival data of a unit or individual of a particular situation in terms of failure time.In this journal, several methods for estimating the location and scale parameters of thehalf-logistic distribution.]]>
<![CDATA[THE CONSTRUCTION OF SOFT SETS FROM FUZZY SUBSETS ]]>
<![CDATA[Na'imah Hijriati]]>;
<![CDATA[Irma Sari Yulianti]]>;
<![CDATA[Dewi Sri Susanti]]>;
<![CDATA[Dewi Anggraini]]>
<![CDATA[ BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications ]]>
Publisher : <![CDATA[PATTIMURA UNIVERSITY ]]>
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DOI: 10.30598/barekengvol17iss3pp1473-1482
<![CDATA[Molodtsov introduced the concept of soft sets formed from fuzzy subsets in 1999. The soft set formed from a fuzzy subset is a particular form of a soft set on its parameter set. On a soft set formed from a fuzzy subset, the parameter used is the image of a fuzzy subset which is then mapped to the collection of all subsets of a universal set. This research explains the construction of soft sets formed from fuzzy subsets. We provide the sufficient condition that a soft set formed from a fuzzy subset is a subset of another soft set. Also, give some properties of the soft sets formed from a fuzzy subset related to complement and operations concepts in soft sets]]>
<![CDATA[Peramalan Produksi Kelapa Sawit dan Karet di Provinsi Kalimantan Selatan ]]>
<![CDATA[Anis Huzaimanor Izafera]]>;
<![CDATA[Nur Salam]]>;
<![CDATA[Dewi Sri Susanti]]>
<![CDATA[ Jurnal Agro Industri Perkebunan Volume 11 Nomor 2 Tahun 2023 ]]>
Publisher : <![CDATA[Politeknik Negeri Lampung ]]>
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DOI: 10.25181/jaip.v11i2.2870
<![CDATA[In Indonesia, the plantation sub-sector has an important role in increasing state revenue through the exports of its products, besides the mining and gas sector. The most widely produced plantation crops in Indonesia are oil palm and rubber and South Kalimantan is one of the top 10 provinces in Indonesia with oil palm plantations. This study aims to detect the correct forecasting model for data on oil palm crops and rubber production in South Kalimantan Province and to analyse the forecasting results for oil palm crops and rubber in South Kalimantan Province using the double exponential smoothing method.  This research was conducted for 8 months (March 2022 to December 2022), using observational data from 2001 to 2021. Double Exponential Smoothing Holt was used in this study by looking at the error value obtained with the smallest Mean Absolute Percentage Error (MAPE). For palm oil production, the parameters α=0.8 and β=0.6 were the best parameters with a MAPE value of 8.05% and resulted in the forecasting of oil palm crops production in 2022 not increasing, in 2023 and 2024 experiencing an increase of 1%. As for forecasting rubber production, the parameters α=0.9 and β=0.9 are the best parameters with a MAPE value of 5.45% and forecasting rubber production in 2022 will increase by 1%, in 2023 and 2024 by 2%.]]>
<![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]]>
<![CDATA[Pendampingan Ujian Kompetensi Peminatan Sains Bagi Peserta Didik SMP Muhammadiyah 1 Banjarbaru ]]>
<![CDATA[Dewi Sri Susanti]]>;
<![CDATA[Abdul Gafur]]>;
<![CDATA[Mariza Uthami]]>;
<![CDATA[Erna Noordyanti]]>;
<![CDATA[Muhammad Reza Faisal]]>;
<![CDATA[Fuad Muhajirin Farid]]>;
<![CDATA[Maisarah Maisarah]]>;
<![CDATA[Yeni Rahkmawati]]>;
<![CDATA[Yusi Riza]]>
<![CDATA[ Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 4 No. 4 (2023): Jurnal Pengabdian kepada Masyarakat Nusantara (JPkMN) ]]>
Publisher : <![CDATA[Sistem Informasi dan Teknologi (Sisfokomtek) ]]>
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<![CDATA[Kemampuan literasi dan komunikasi peserta didik pada jenjang pendidikan dasar perlu ditingkatkan agar lebih siap mengikuti pembelajaran pada kurikulum merdeka. Salah satu cara untuk meningkatkan potensi ini adalah dengan melaksanakan Ujian Kompetensi Peminatan bagi peserta didik menjelang kelulusan. Kegiatan pengabdian pada masyarakat yang dilakukan ini bertujuan untuk meningkatkan kemampuan literasi dan komunikasi peserta didik kelas IX SMP Muhammadiyah 1 Banjarbaru. Melalui kegiatan ini dapat diukur sekaligus mengembangkan kemampuan literasi dan komunikasi sains pada peserta didik agar nantinya lulusan lebih siap untuk memberikan unjuk kerja pada pembelajaran sains di jenjang pendidikan berikutnya. Dalam kegiatan ini, secara bersama-sama dosen dan guru-guru melaksanakan proses pembimbingan dan memberikan penilaian terhadap kegiatan ujian kompetensi peminatan sains. Kegiatan diawali dengan proses penentuan topik yang akan dipilih oleh peserta didik untuk disajikan sebagai presentasi ilmiah dan penyusunan kerangka acuan kerja. Setelah peserta didik menyelesaikan penyusunan presentasi ilmiah, dilakukan proses ujian lisan. Kerangka penilaian disusun dengan memperhatikan aspek kognitif, afektif dan psikomotorik. Peserta didik yang mengikuti ujian sebanyak 24 (duapuluh empat) peserta didik, dimana 3 (tiga) diantaranya adalah peserta didik berkebutuhan khusus. Setelah dilakukan ujian, diperoleh nilai peserta didik dari ketiga aspek tersebut berada dalam rentang 87.25 ± 5.59k. Hal ini menunjukkan bahwa kemampuan peserta didik kelas 9 SMP Muhammadiyah 1 Banjarbaru dalam literasi dan komunikasi sains sangat memuaskan.]]>
