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<![CDATA[PERANCANGAN MODEL PREDIKSI CURAH HUJAN BULANAN BERDASARKAN SUHU PERMUKAAN LAUT DI KALIMANTAN SELATAN ]]>
<![CDATA[Handiana, Dian]]>;
<![CDATA[Wahyono, Sri Cahyo]]>;
<![CDATA[Susanti, Dewi Sri]]>
<![CDATA[ Jurnal Fisika FLUX Vol 10, No 1 (2013): Jurnal Fisika FLUX Edisi Februari 2013 ]]>
Publisher : <![CDATA[Lambung Mangkurat University Press ]]>
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DOI: 10.20527/flux.v10i1.1956
<![CDATA[Abstrak : Kebutuhan akan adanya informasi prediksi curah hujan yang baik sangat diperlukan dalam berbagai sektor. Penelitian ini menentuan keterkaitan Suhu Permukaan Laut (SPL) terhadap curah hujan dan perancangan model prediksi curah hujan bulanan di Kalimantan Selatan. Data masukan yang digunakan adalah data SPL terpilih di sembilan luasan (Samudera Pasifik, Samudera Hindia, Laut Cina Selatan, Perairan Kalimantan Selatan) dan data curah hujan pada sepuluh titik di Kalimantan Selatan. Metode yang digunakan adalah koefisien korelasi dalam menentukan keeratan hubungan antara variabel curah hujan dan SPL dan metode regresi stepwise untuk mendapatkan model prediksi terbaik. Hasil penelitian menunjukkan bahwa terdapat pengaruh yang signifikan SPL di 9 luasan yang dipilih terhadap curah hujan di Kalimantan Selatan. SPL yang banyak berperan pada pembentukan model prediksi curah hujan adalah SPL Samudera Pasifik equator timur yaitu SPL Nino 3,4 dan SPLNino 4. Model prediksi curah hujan terbaik terjadi pada bulan â bulan musim kemarau dan masa transisi kemarau menuju hujan yaitu Juli, Agustus, September dan Oktober, Model prediksi curah hujan yang dihasilkan mampu membaca perilaku hujan pada kondisi ekstrim, sehingga dapat dijadikan peringatan dini terhadap kekeringan atau hujan sepanjang tahun. Nilai korelasi signifikan curah hujan dengan SPL terbaik pada bulan Agustus di Banjarbaru mencapai 0,802, dan model prediksi terbaik bulan Agustus di Banjarbaru yaitu = 335.553 â 72.701 ( ) + 63.863 ( ) dengan hasil pengujian yang signifikan dan model tersebut mampu menjelaskan variasi data sebesar 71,8%.Kata kunci : Prediksi curah hujan, Suhu Permukaan laut, koefisien korelasi, regresi stepwise]]>
<![CDATA[PERANCANGAN MODEL PREDIKSI CURAH HUJAN BULANAN BERDASARKAN SUHU PERMUKAAN LAUT DI KALIMANTAN SELATAN ]]>
<![CDATA[Dian Handiana]]>;
<![CDATA[Sri Cahyo Wahyono]]>;
<![CDATA[Dewi Sri Susanti]]>
<![CDATA[ Jurnal Fisika FLUX Vol 10, No 1 (2013): Jurnal Fisika FLUX Edisi Februari 2013 ]]>
Publisher : <![CDATA[Lambung Mangkurat University Press ]]>
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DOI: 10.20527/flux.v10i1.1956
<![CDATA[Abstrak : Kebutuhan akan adanya informasi prediksi curah hujan yang baik sangat diperlukan dalam berbagai sektor. Penelitian ini menentuan keterkaitan Suhu Permukaan Laut (SPL) terhadap curah hujan dan perancangan model prediksi curah hujan bulanan di Kalimantan Selatan. Data masukan yang digunakan adalah data SPL terpilih di sembilan luasan (Samudera Pasifik, Samudera Hindia, Laut Cina Selatan, Perairan Kalimantan Selatan) dan data curah hujan pada sepuluh titik di Kalimantan Selatan. Metode yang digunakan adalah koefisien korelasi dalam menentukan keeratan hubungan antara variabel curah hujan dan SPL dan metode regresi stepwise untuk mendapatkan model prediksi terbaik. Hasil penelitian menunjukkan bahwa terdapat pengaruh yang signifikan SPL di 9 luasan yang dipilih terhadap curah hujan di Kalimantan Selatan. SPL yang banyak berperan pada pembentukan model prediksi curah hujan adalah SPL Samudera Pasifik equator timur yaitu SPL Nino 3,4 dan SPLNino 4. Model prediksi curah hujan terbaik terjadi pada bulan – bulan musim kemarau dan masa transisi kemarau menuju hujan yaitu Juli, Agustus, September dan Oktober, Model prediksi curah hujan yang dihasilkan mampu membaca perilaku hujan pada kondisi ekstrim, sehingga dapat dijadikan peringatan dini terhadap kekeringan atau hujan sepanjang tahun. Nilai korelasi signifikan curah hujan dengan SPL terbaik pada bulan Agustus di Banjarbaru mencapai 0,802, dan model prediksi terbaik bulan Agustus di Banjarbaru yaitu = 335.553 − 72.701 ( ) + 63.863 ( ) dengan hasil pengujian yang signifikan dan model tersebut mampu menjelaskan variasi data sebesar 71,8%.Kata kunci : Prediksi curah hujan, Suhu Permukaan laut, koefisien korelasi, regresi stepwise]]>
<![CDATA[Analisa Drainase Sumur Resapan Pada Kampus UNLAM Banjarbaru ]]>
<![CDATA[Chairil Fachrurazie]]>;
<![CDATA[Yulian Firmana Arifin]]>;
<![CDATA[Dewi Sri Susanti]]>
<![CDATA[ INFO-TEKNIK Vol 3, No 1 (2002): INFOTEKNIK VOL. 3 NO. 1 2002 ]]>
Publisher : <![CDATA[Universitas Lambung Mangkurat ]]>
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DOI: 10.20527/infotek.v3i1.454
<![CDATA[One of the enviromental drainage that consider water conservation aspect isdrainage by infiltration rehargr system. Banjarbaru has a high coeffiesient permeability soil and gate on laboratory measurement of soil mechanic, for study area gaied k = 1,23.10 cm/sec dan k = 1,62.10 cm/sec, f = 0,046 m/hr, f = 0,054 m/hr dan f = 0,113 m/hr and ground water level from -7 to -8 meters. According to rainfall intencity measurement, the rainfall intencity is 71,65 m/hr for 5 year return priode with Q = 0,068 A m3/det. Result for infiltration recharge system the dimension is get for H = depth of well, R = radius of well, n = number of wells is drawing in graph.]]>
<![CDATA[Application of a permutation group on sasirangan pattern ]]>
<![CDATA[Na'imah Hijriati]]>;
<![CDATA[Dewi Sri Susanti]]>;
<![CDATA[Raihan Nooriman]]>;
<![CDATA[Geofani Setiawan]]>
<![CDATA[ Desimal: Jurnal Matematika Vol 4, No 3 (2021): Desimal: Jurnal Matematika ]]>
Publisher : <![CDATA[Universitas Islam Negeri Raden Intan Lampung ]]>
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DOI: 10.24042/djm.v4i3.10338
<![CDATA[A permutation group is a group of all permutations of some set. If the set that forms a permutation group is the n-first of natural number, then a permutation group is called a symmetry group. There is another type of group, i.e., a cyclic group and a dihedral group, and they are a subgroup of a symmetry group by numbering the vertices of the polygon. Sasirangan is the traditional batik from the South Kalimantan. There are 18 traditional patterns. All the patterns make some polygon. Because of this, the purpose of this research is to investigate the type of group that forms the patterns of Sasirangan. First, the authors give the procedure to investigate the patterns of Sasirangan, then use that procedure to the patterns of Sasirangan. The result of this research is the patterns of Sasirangan form cyclic groups C_1 and C_2, and dihedral groups D_2, D_4, D_5 and D_8.]]>
<![CDATA[PEMODELAN TINGKAT KERAWANAN DEMAM BERDARAH DI KABUPATEN BANJAR DENGAN METODE ANALISIS REGRESI LOGISTIK YANG TERBOBOTI GEOGRAFIS ]]>
<![CDATA[Dewi Sri Susanti]]>;
<![CDATA[Pamona Dwi Rahayu]]>;
<![CDATA[Oni Soesanto]]>
<![CDATA[ MEDIA BINA ILMIAH Vol 14, No 4: Nopember 2019 ]]>
Publisher : <![CDATA[BINA PATRIA ]]>
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DOI: 10.33758/mbi.v14i4.348
<![CDATA[Regression analysis is a metodh for investigating the relationship between the dependent variable (Y) and independent variables (X). Logistic regression is a regression model that used related to the qualitative Dependent variable. If the Logistic regression influenced by factors of the location of each point from observation where the data is collected, it will be a Geographically Weighted Logistic Regression (GWLR). In the case of insecurity rate model of dengue fever has two or more categories, so that this case can be resolved by GWLR. This research aims to clarify the procedure of testing the parameters GWLR model and form insecurity rate model of dengue fever with GWLR method in Banjar Regency. Dependent variable with catagoric is Insecurity rate of dengue fever ( ) and independent variables is the population density ( ), the distance from the capital of the subdistrict to capital of regency ( ), fogging per subdistrict ( ), the percentage of households living clean and healthy ( ), pesentase healthy homes ( ), the percentage of access to decent sanitation ( ). The results from this research are estimate parameters using Maximum Likelihood Estimation method and presented in the form of thematic map that shows not all dependent variables give influence on Insecurity rate dengue fever]]>
<![CDATA[ANALISIS RESPON MAHASISWA TERHADAP PENERAPAN PENDEKATAN ETNOMATEMATIKA (POLA KAIN SASIRANGAN) PADA PEMBELAJARAN STRUKTUR ALJABAR ]]>
<![CDATA[Dewi Sri Susanti]]>;
<![CDATA[Na'imah Hijriati]]>;
<![CDATA[Rahmi Hidayati]]>;
<![CDATA[Raihan Nooriman]]>;
<![CDATA[Geofani Setiawan]]>
<![CDATA[ AKSIOMA: Jurnal Program Studi Pendidikan Matematika Vol 11, No 1 (2022) ]]>
Publisher : <![CDATA[UNIVERSITAS MUHAMMADIYAH METRO ]]>
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DOI: 10.24127/ajpm.v11i1.4486
<![CDATA[Penelitian ini bertujuan untuk mengukur efektivitas pembelajaran aljabar dengan menerapkan model discovery learning dengan pendekatan etnomatematika. Konsep etnomatematika yang dimaksud adalah dengan mengaitkan materi tentang grup dengan pola kain sasirangan yang merupakan kain khas dari Kalimantan Selatan. Respon mahasiswa atas proses pembelajaran tersebut diamati dari persepsi selama pembelajaran berlangsung dan hasil penilaian yang diperoleh setelah pembelajaran. Persepsi mahasiswa dirangkum melalui kuesioner yang didalamnya memuat komponen penilaian untuk dosen pengajar yaitu aspek pedagogis dan profesional, sedangkan tingkat pemahaman mahasiswa diukur melalui butir-butir pertanyaan yang memuat aspek afektif dan kognitif. Aspek psikomotorik dievaluasi melalui penilaian video pembelajaran yang dihasilkan mahasiswa. Efektivitas pembelajaran terukur melalui signifikasi peningkatan nilai ujian sebelum dan setelah metode pengajaran diterapkan. Subyek penelitian ini adalah mahasiswa peserta pembelajaran mata kuliah Struktur Aljabar. Dari hasil penelitian menunjukkan bahwa pelaksanaan pembelajaran mata kuliah Struktur Aljabar dengan pendekatan etnomatematika telah memberikan peningkatan kemampuan mahasiswa yang signifikan baik dari sisi kognitif, afektif dan psikomotorik. Hal ini terukur dari respon mahasiswa dalam angket pembelajaran, bukti penyelesaian tugas video pembelajaran dan hasil nilai yang diperoleh mahasiswa. Penilaian untuk dosen pengajar juga memberikan hasil yang positif dari sisi pedagogis dan sisi profesionalitas.Improving the ability of mathematical understanding can be conduted by building different learning nuances. If so far the learning process has focused more on the teacher/lecturer (teacher center), a solution is needed to improve student understanding, one of which is by applying discovery learning learning techniques, where students can find their own formulas in learning & reasoning. Along with the desire to raise cultural values in the learning process, the ethnomathematical approach to learning is one of the best solutions to motivate students. This method is a collaborative discovery learning model with an ethnomathematical approach. namely the sasirangan cloth. After taking an ethnomathematical approach in the learning process, especially on topic of special groups, students are asked to provide an assessment of the learning process. The implementation of the Algebraic Structure learning course with an ethnomathematical approach has provided a significant increase in student abilities in terms of cognitive, affective and psychomotor. This is measured from student responses in learning questionnaires, evidence of completion of learning video assignments and the test results. Assessment for teaching lecturers also gave positive results from the pedagogical and professional side.]]>
<![CDATA[STATISTICAL CONTROL ANALYSIS OF THE STUDENT’S FINAL ASSIGNMENT COMPLETION PERIOD AT THE MATHEMATICS AND NATURAL SCIENCES FACULTY ]]>
<![CDATA[Arika Febriani]]>;
<![CDATA[Dewi Sri Susanti]]>;
<![CDATA[Na'imah Hijriati]]>
<![CDATA[ BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 2 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan ]]>
Publisher : <![CDATA[PATTIMURA UNIVERSITY ]]>
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DOI: 10.30598/barekengvol16iss2pp385-392
<![CDATA[The final assignment is one of the requirements to get a bachelor’s degree for college students at the Faculty of Mathematics and Natural Sciences (FMIPA) University of Lambung Mangkurat (ULM). The average period of completion of the final assignment in the year 2015 until 2019 is 8 months, while the determined specification by the guideline is 6 months. The aim of this research is to identify the quality control of the final assignment completion process and whether satisfy the determined specification using statistical quality control. The used data in this research is the student’s final assignment completion period (variable data) and the nonconforming proportion of data (attribute data). The and control charts are used for variable data and control chart for attribute data and process capability analysis. The result of variable data is that the average period of final assignment completion is statistically in control with a control limit of months. For attribute data concluded that final assignment completion is statistically in control with a big average proportion that is . For the capability analysis process by index and value sequentially is and for the DPU value is . This shows that the completion period of the student’s final assignment of FMIPA ULM is not capable to fulfill the specified standard of the period.]]>
<![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[TEKNIK PERAMALAN MENGGUNAKAN METODE PEMULUSAN EKSPONENSIAL HOLT-WINTERS ]]>
<![CDATA[Siti Nur Hamidah]]>;
<![CDATA[Nur Salam]]>;
<![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.v7i2.97
<![CDATA[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]]>
<![CDATA[PERKIRAAN SELANG KEPERCAYAAN UNTUK NILAI RATA-RATA PADA DISTRIBUSI POISSON ]]>
<![CDATA[Randy Toleka Ririhena]]>;
<![CDATA[Nur Salam]]>;
<![CDATA[Dewi Sri Susanti]]>
<![CDATA[ EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN Vol 10, No 1 (2016): JURNAL EPSILON VOLUME 10 NOMOR 1 ]]>
Publisher : <![CDATA[Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat ]]>
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DOI: 10.20527/epsilon.v10i1.54
<![CDATA[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− ????????]]>
