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All Journal Jurnal Matematika Media Statistika BAREKENG: Jurnal Ilmu Matematika dan Terapan Beta: Jurnal Tadris Matematika Jurnal Pengabdian Magister Pendidikan IPA Jurnal Aplikasi Statistika & Komputasi Statistik EIGEN MATHEMATICS JOURNAL Jurnal Matematika UNAND Jurnal Gema Ngabdi Variance : Journal of Statistics and Its Applications Jurnal Varian Jurnal Pengabdian Masyarakat Sains Indonesia (Indonesian Journal Of Science Community Services) Prosiding Konferensi Nasional PKM-CSR Jurnal Karya Pengabdian Teknik Mesin Jurnal Pepadu

Mustika Hadijati

Program Studi Matematika, FMIPA, Universitas Mataram

Author-ID : 495628

Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Mathematics Mechanical Engineering Nursing Physics Public Health Social Sciences Transportation Other

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Model Regresi Nonparametrik Deret Fourier pada Pola Data Curah Hujan di Kota Mataram Widiya Tri Astuti; Mustika Hadijati; Irwansyah -
Eigen Mathematics Journal In Press Desember 2018
Publisher : University of Mataram

Rainfall is one of the elements of the climate that has influence on people's lives in West Nusa Tenggara Province. Thecapital city of West Nusa Tenggara Province (NTB), namely the City of Mataram, in December 2016 was affected byflood disaster due the rainfall increation. This causes all activities in the City of Mataram paralyzed. This study aimed tomodelling the rainfall and to determine the rainfall grade prediction in the City of Mataram in 2017. The method usedwas nonparametric regression of Fourier series. Based on the results of the analysis that has been committed, the bestFourier series of nonparametric regression model obtained at the Selaparang station was a model with 101 number ofknots and 0.959116 value of R2 . For the Ampenan station, the best model obtained with 101 knots and 0.966992 valueof R2 . As well as for the Cakranegara station, the best model obtained with 106 number of knots and 0.987778 value ofR2 .

Estimasi Parameter Distribusi Mixture Eksponensial dan Weibull dengan Metode Bayesian Markov Chain Monte Carlo Ulfa Destiarina; Mustika Hadijati; Desy Komalasari; Nurul Fitriyani
Eigen Mathematics Journal Vol. 2 No. 1 Juni 2019
Publisher : University of Mataram

Dalam estimasi parameter, kadangkala terdapat beberapa permasalahan yang menuntut penyelesaian dengan suatu distribusi mixture atau distribusi campuran. Penelitian ini bertujuan untuk menerapkan estimasi parameter distribusi mixture eksponensial dan Weibull pada data simulasi dengan metode estimasi Bayesian Markov Chain Monte Carlo (MCMC). Hasil yang diperoleh menunjukkan bahwa perhitungan analitik estimasi parameter lebih akurat dibandingkan perhitungan dengan bantuan perangkat lunak, apabila dipandang dari segi kesesuaian teori serta proses integrasinya

Model Regresi Semiparametrik Spline Hasil Produksi Padi di Kabupaten Lombok Timur Bidayani Bidayani; Mustika Hadijati; Nurul Fitriyani
Eigen Mathematics Journal Vol. 2 No. 1 Juni 2019
Publisher : University of Mataram

Beras merupakan suatu sumber bahan makanan pokok penting yang harus tetap terjaga ketersediannya sepanjang tahun. Namun untuk tahun-tahun terakhir ini Indonesia yang dikenal dengan kekayaan alamnya, menjadi salah satu negara pengimpor beras. Hal ini dikarenakan konsumsi beras di indonesia terus meningkat setiap tahunnya, sedangkan produksi beras yang dihasilkan kurang mencukupi konsumsi masyarakat Indonesia. Penelitian ini dilakukan dengan tujuan untuk menentukan model regresi semiparametrik spline pada analisis faktor-faktor yang mempengaruhi hasil produksi padi di Kabupaten Lombok Timur tahun 2014, serta mengetahui faktor-faktor apa saja yang mempengaruhi hasil produksi padi tersebut. Metode yang digunakan adalah regresi semiparametrik spline dengan pemilihan titik knot optimum menggunakan Generalized Cross Validation. Hasil yang diperoleh menunjukkan bahwa variabel yang secara signifikan mempengaruhi hasil produksi padi adalah ketinggian wilayah dari permukaan laut, dengan nilai koefisien determinasi sebesar 99,71% dan nilai Root Mean Square Error of Prediction sebesar 41,65.

Analisis Dependensi Faktor Makroekonomi terhadap Tingkat Harga Emas Dunia dengan Pendekatan Copula Sri Wati Agustini; Mustika Hadijati; Nurul Fitriyani
Eigen Mathematics Journal Vol. 2 No. 2 Desember 2019
Publisher : University of Mataram

Gold is a precious metal that used many times as an alternative investment. Before investing, every investor requires relevant information to make profitable investment decisions. Relevant information can be obtained by looking at the dependency relationship between variables. In identifying the relationship between variables, a Copula approach could be used, since it is not tight against the assumption of normality, which is common in macroeconomic variables. Copula used were Archimedean Copula family, such as Clayton, Frank, and Gumbel. The results of this study indicated that the Archimedean Copula of the Frank family is the best Copula models to explain the structure of dependencies between gold and each composite stock price index and exchange rate, with each parameter obtained were 2.286 and -2.2390, respectively, while Clayton Copula family was the best Copula models to explain the structure of dependencies between gold and oil, with parameter obtained was 3.4090.

Mengatasi Error Berkorelasi Menggunakan Metode Transformasi Prewhitening pada Regresi Nonparametrik Kernel Bivariat Nurasiah Amini; Mustika Hadijati; Qurratul Aini
Eigen Mathematics Journal Vol. 2 No. 2 Desember 2019
Publisher : University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/emj.v1i2.42

Suppose that given ???? data {(????1????, ????2????, ????????)}???? with nonparametric regression model :????=1???????? = ????(????1????, ????2????) + ???????? ; ???? = 1,2, ⋯ , ????with ????(????????) is a regression function and ???????? is a random errors. In nonparametric regression often found correlated errors, i.e. the error value does not meet the identical and independent assumptions. Correlated errors will adversely affect the estimation model. Correlated errors can be resolved by prewhitening transformation method, a method where the error is assumed to follow the model ARMA (????, ????). Applied on data is shown that regression model was obtained with correlated errors. The error obtained from the conventional Kernel regression model follows the AR(1) model with the value ∅1= 0.932. After the prewhitening transformation, the kernel regression model results from the prewhitening transformation with uncorrelated errors. The MSE value of the conventional Kernel estimation modal is 639203.308 smaller than the MSE value of the estimated Kernel prewhitening transformation model that is 290303.832, so the Kernel estimator resulting from prewhitening transformation is more efficient than conventional Kernel estimator.

Analisis Masalah Heteroskedastisitas Menggunakan Generalized Least Square dalam Analisis Regresi Aditya Setyawan R; Mustika Hadijati; Ni Wayan Switrayni
Eigen Mathematics Journal Vol. 2 No. 2 Desember 2019
Publisher : University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/emj.v1i2.43

Regression analysis is one statistical method that allows users to analyze the influence of one or more independent variables (X) on a dependent variable (Y).The most commonly used method for estimating linear regression parameters is Ordinary Least Square (OLS). But in reality, there is often a problem with heteroscedasticity, namely the variance of the error is not constant or variable for all values of the independent variable X. This results in the OLS method being less effective. To overcome this, a parameter estimation method can be used by adding weight to each parameter, namely the Generalized Least Square (GLS) method. This study aims to examine the use of the GLS method in overcoming heteroscedasticity in regression analysis and examine the comparison of estimation results using the OLS method with the GLS method in the case of heteroscedasticity.The results show that the GLS method was able to maintain the nature of the estimator that is not biased and consistent and able to overcome the problem of heteroscedasticity, so that the GLS method is more effective than the OLS method.

Penerapan Model Vector Autoregressive Integrate Moving Average dalam Peramalan Laju Inflasi dan Suku Bunga di Indonesia Jusmawati Jusmawati; Mustika Hadijati; Nurul Fitriyani
Eigen Mathematics Journal VOL. 3 NO. 2 DESEMBER 2020
Publisher : University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/emj.v3i2.62

The inflation and interest rates in Indonesia have a significant impact on the country's economic development. Indonesian inflation and interest rates data are multivariate time series data that show activity over a certain period of time. Vector Autoregressive Integrated Moving Average (VARIMA) is a method for analyzing multivariate time series data. This method is a simultaneous equation modeling that has several endogenous variables simultaneously. This study aimed to model the inflation and interest rates data, from January 2009 to December 2016 and predict inflation and interest rates by using VARIMA method. The model obtained was the VARIMA(0,2,2) model, with estimated parameters using the maximum likelihood method. The choice of the VARIMA(0,2,2) model was based on the smallest AIC value of -4,2891, with a MAPE value for the inflation and interest rates forecasting were 6,04% and 1,84%, respectively, which indicates a very good forecast results.

Perbandingan Metode Classification and Regression Trees (CART) dengan Naïve Bayes Classification (NBC) dalam Klasifikasi Status Gizi Balita di Kelurahan Pagesangan Barat Nurul - Insan; Mustika Hadijati; Irwansyah Irwansyah
Eigen Mathematics Journal Vol. 3 No. 1 Juni 2020
Publisher : University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/emj.v3i1.68

This study aims to compare the Classification and Regression Trees (CART) and Naïve Bayes Classification (NBC) methods in classifying the nutritional status of toddlers in West Pagesangan by looking at their accuracy and also knowing the variables that influence the classification of toddler nutritional status. The data used in this study were toddlers who come to the posyandu in May 2019, with predictor variables used namely gender, ages, weight, mother’s employment status, mother’s education level, number of children and parents income. The result showed that Naïve Bayes Classification (NBC) is better in classifying the nutritional status of toddlers in West Pagesangan than Classification and Regression Trees (CART). This can be seen from the accuracy values obtained with three comparisons of training data and testing data. In the comparison of 90% of training data: 10% of testing data, obtained an accuracy value of 90% for NBC and 85% for CART, in the comparison of 80% of training data: 20% of testing data, obtained an accuracy value 0f 82.5% for NBC and 80% for CART, while in comparison 70% traing data : 30% testing data, obtained an accuracy value 72% for NBC and 70%for CART. This study also showed that significant variables the classification of nutritional status of toddlers in West Pagesangan village are age, gender, weight and parents income.

Penggunaan Edmodo Dan Statistika Dalam Menghadapi Tantangan Era Revolusi Industri 4.0 Pada MA Darunnajah Duman Agus Kurnia; Mustika Hadijati; Desy Komalasari; Nurul Fitriyani
Prosiding Konferensi Nasional Pengabdian Kepada Masyarakat dan Corporate Social Responsibility (PKM-CSR) Vol 1 (2018): Prosiding PKM-CSR Konferensi Nasional Pengabdian kepada Masyarakat dan Corporate Socia
Publisher : Asosiasi Sinergi Pengabdi dan Pemberdaya Indonesia (ASPPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (448.086 KB)

Dalam menghadapi tantangan era revolusi industri 4.0, kegiatan pembelajaran dituntut untuk berubah, termasuk dalam menghasilkan lulusan berkualitas bagi generasi masa depan. Kegiatan pembelajaran dapat dilaksanakan dimana saja, seperti dengan memanfaatkan teknologi digital, big data, dimana bahan pembelajaran telah masuk ke dalam big data. Kegiatan pengabdian kepada masyarakat ini dilakukan dalam rangka menguatkan keberadaan 3 komponen dasar pendidikan, mindset atau pola pikir guru dan siswa serta pemanfaataan dan pengelolaan big data di Madrasah Aliyah Darunnajah, Duman, Lingsar dalam menghadapi tantangan era revolusi industri 4.0, melalui penguatan karakter dan mindset atau pola pikir guru dan siswa mengenai fenomena yang tengah terjadi di Indonesia, serta mengoptimalkan pemanfaatan dan penggunaan Edmodo dan Statistika big data dalam kegiatan pembelajaran. Tahapan yang dilakukan dalam pengabdian ini dengan memberikan Pretest kepada Guru dan Siswa mengenai pemahaman terhadap Pengembangan Karakter dalam menghadapi Era Revolusi Indusri 4.0, Pemanfaatan Edmodo dan SPSS, dan penggunaan Big Data, penggunaan Edmodo dalam pembelajaran, penyampaian materi pengembangan karakter 3 komponen dasar Pendidikan, penggunaan, penyampaian materi tentang pemanfaatan dan pengelolaan big data dalam dalm kegiatan pembelajaran, dan evaluasi tingkat penyerapan materi oleh peserta didik dengan mengajukan beberapa pertanyaan (Post Test). Hasil dari pengbadian ini adalah meningkatnya mindset tentang pentingnya karakter dalam menghadapi Era Revolusi Industri 4.0, meningkatnya kemampuan mereka dalam penggunaan aplikasi Edmodo dan SPSS dalam pembelajaran, dan meningkatnya kemampuan para siswa dalam melakukan self assesment dalam pembelajaran.

MODEL DEBIT DAERAH ALIRAN SUNGAI JANGKOK BERDASARKAN HASIL PREDIKSI MODEL STATISTICAL DOWNSCALING NONPARAMETRIK KERNEL CURAH HUJAN DAN TEMPERATUR Mustika Hadijati; Irwansyah Irwansyah
MEDIA STATISTIKA Vol 12, No 2 (2019): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

River water discharge is influenced by climatic conditions. River water discharge is important information for water resources management planning, so it is necessary to develop river water discharge model as basis of its predictions. In order to get the result of predictions of river water discharge with high accuracy, it is developed a model of river water discharge based on the predictions of local climate (local rainfall and temperature) that are influenced by global climate conditions..Prediction of local climate is based on the Kernel nonparametric statistical downscaling model by utilizing GCM data. GCM data is a high dimensional global data, so data pre-processing is needed to reduce data dimension. It is done by CART algoritm. Statistical downscaling model is used to predict local rainfall and temperature. The prediction results are quite good with relatively small RMSE value. They are used to develop model of river water discharge. Modeling river water discharge is carried out using the Kernel nonparametric approach. The model of river water discharge produced is quite good because it can be used to predict river water discharge with relatively small RMSE.

Co-Authors Adis Tia Juli Agil Asri Aditya Setyawan R Agus Kurnia Attina Ulansari Baskara, Zulhan Bidayani Bidayani Dara Puspita Anggraeni Desy Komalasari Desy Komalasari Dina Eka Putri Fara Fid Graha, Syifa Salsabila Satya Hafizah Ilma Hairunnisa Hairunnisa Harsyiah, Lisa Helmina Andriani Irwansyah - Irwansyah Irwansyah Irwansyah Irwansyah Jihan Melani Jurnal Pepadu Jusmawati Jusmawati Kamal Faisal Hikam Kartini, Ni Nyoman Dewi Lilik Hidayati, Lilik Luh Putu Ratna Sundari M. Naoval Husni M. Syahrul Mamika Ujianita Romdhini Marwan Marwan Meliyana, Hesti Muhammad Sopian Sauri Ni Wayan Switrayni Nirwana, Nirwana Nur Asmita Purnamasari Nurasiah Amini Nurul - Insan Nurul Fitriyani Nurul Fitriyani PURNAMASARI, NUR ASMITA Qomaria Sinta Sari Qudsi, Jihadil Qurratul Aini Rifdah Fadhilah Ristiandi, Ristiandi Saputra, Dede Sri Wati Agustini Ulfa Destiarina Usman, Siti Dwi Khairun Rahmatin Widiya Tri Astuti Wirajaya Kusuma Yuliana Lestari Zulhan Widya Baskara

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