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All Journal Jurnal Presipitasi : Media Komunikasi dan Pengembangan Teknik Lingkungan Media Statistika MATEMATIKA SAINS DAN MATEMATIKA Jurnal Ilmu Dasar Jurnal Gaussian Jurnal Statistika Universitas Muhammadiyah Semarang Indonesian Journal of Applied Statistics Seminar Nasional Variansi (Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika) BAREKENG: Jurnal Ilmu Matematika dan Terapan
Dwi Ispriyanti
Departemen Statistika, Fakultas Sains Dan Matematika, Universitas Diponegoro
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Environmental Science Mathematics Mechanical Engineering Physics Transportation Other

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PREDIKSI TINGGI PASANG AIR LAUT DI KOTA SEMARANG DENGAN MENGGUNAKAN METODE SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (SARIMA) DAN DETEKSI OUTLIER Sa'adah, Alfi Faridatus; Ispriyanti, Dwi; Suparti, Suparti
Jurnal Gaussian Vol 3, No 3 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (581.532 KB) | DOI: 10.14710/j.gauss.v3i3.6437

Abstract

Semarang as the capital of the province of Central Java is a central transportation  that has a high intensity and strategic activities. However, this area has a tidal disaster threat level is high enough. Tidal flood is a phenomenon where sea water entered the land area when the sea level has getting tides. In the future impact of tidal inundation in Semarang city is predicted to be greaterso that has needed the forecasting of high tide. The data pairs tend to experience seasonal monthly and contained outliers that may affect the suitability of the model so that Seasonal Autoregressive Integrated Moving Average (SARIMA) and outlier detection is used for forecasting method. For outlier detection, there are four types of outliers are additive outlier (AO), innovational outlier (IO), level shift (LS) and temporary change (TC). The study was conducted on the data of tide in Semarang period January 2004 - December 2012 based on the average high tide occurs when the maximum. The results of research showed that the model SARIMA with 7 outliers result predictions with high accuracy because it has a smaller AIC value is 649,1083 compared to the SARIMA models without outlier is 705,6404.
KLASIFIKASI NASABAH KREDIT BANK “X” DI PROVINSI LAMPUNG MENGGUNAKAN ANALISIS DISKRIMINAN KERNEL Azkiya, Maulida; Mukid, Moch. Abdul; Ispriyanti, Dwi
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (432.755 KB) | DOI: 10.14710/j.gauss.v4i4.10229

Abstract

Credit is the biggest asset carried out by a bank and become the most dominant contributor to the bank income. However, the activity to distribute the credit takes a risk which can influence health and continuance of bank business. The credit risk which potentially occurs can be measured and controlled by analyzing directly the credit client which belongs to current credit or bad credit based on the character in credit assessment, such as age, and amount of loan, how long the relationship between company and bank, the period of company, total income, and debt risk of company to the income. Discriminant analysis is a multivariate statistical technique which can be used to classify the new observation into a specific group. Kernel discriminant analysis is a non-parametric method which is flexible because it does not have to concern about assumption from certain distribution and equal variance matrices as in parametric discriminant analysis. The classification using the kernel discriminant analysis with the normal kernel function with optimum bandwidth 0,1 in data of credit client from bank “X” in Lampung Province gives accurate classififcation 92% whereas kernel discriminant analysis with the epanechnikov function with the optimum bandwidth 4,6 gives the accurate classification 79%. Keywords: credit, classification, kernel discriminant analysis
KETEPATAN KLASIFIKASI PEMBERIAN KARTU KELUARGA SEJAHTERA DI KOTA SEMARANG MENGGUNAKAN METODE REGRESI LOGISTIK BINER DAN METODE CHAID Suhendra, Muhammad Arif; Ispriyanti, Dwi; Sudarno, Sudarno
Jurnal Gaussian Vol 9, No 1 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (674.181 KB) | DOI: 10.14710/j.gauss.v9i1.27524

Abstract

Menurut BPS, jumlah penduduk miskin di Kota Semarang pada Maret 2018 adalah sebesar 73,65 ribu orang. Salah satu program pemerintah dalam percepatan penanggulangan kemiskinan adalah dengan mengeluarkan Kartu Keluarga Sejahtera (KKS) yang diberikan kepada masyarakat yang kurang mampu. Penelitian ini bertujuan untuk mengetahui besarnya ukuran ketepatan klasifikasi pemberian KKS di Kota Semarang. Metode klasifikasi statistik yang digunakan adalah metode Regresi Logistik Biner dan metode Chi-Squared Automatic Interaction Detection (CHAID). Pemberian KKS dipengaruhi oleh banyak faktor, diantaranya jumlah anggota keluarga, status perkawinan, jenis kelamin kepala keluarga, usia kepala keluarga, jenjang pendidikan kepala keluarga dan kepemilikan/penguasaan HP. Pada penelitian ini, data yang digunakan adalah data sekunder hasil Survey Sosial Ekonomi Nasional (SUSENAS) tahun 2018 yang diperoleh dari Badan Pusat Statistik (BPS) Provinsi Jawa Tengah. Perbandingan data training dan testing yang digunakan adalah 60:40. Hasil analisisnya menunjukkan bahwa dengan menggunakan Regresi Logistik Biner, faktor-faktor yang berpengaruh adalah jumlah anggota keluarga dan jenjang pendidikan kepala keluarga dengan ketepatan klasifikasi sebesar 88% dan kesalahan 12%, sedangkan dengan menggunakan CHAID, faktor-faktor yang berpengaruh adalah jumlah anggota keluarga, status perkawinan, usia kepala keluarga, jenjang pendidikan kepala keluarga dan kepemilikan/penguasaan HP dengan ketepatan klasifikasi sebesar 90,2% dan kesalahan 9,8%.Kata kunci: Kartu Keluarga Sejahtera, Klasifikasi, Regresi Logistik Biner, CHAID
PEMODELAN REGRESI ROBUST M-ESTIMATOR DALAM MENANGANI PENCILAN (STUDI KASUS PEMODELAN JUMLAH KEMATIAN IBU NIFAS DI JAWA TENGAH Alan Prahutama; Agus Rusgiyono; Dwi Ispriyanti; Tiani Wahyu Utami
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 9, No 1 (2021): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.9.1.2021.35-39

Abstract

Regression analysis is statistical method that used to model between predictor variables and response variables. In the regression model, the residual assumed normal distribution, non-autocorrelation, and homoscedasticity. When the assumptions doesn’t fulfilled, then the measurement of goodness not well enough. One of the causes may be outlier of data. Handling the outlier can be used robust regression, which one of method is robust M-estimator.   In this article, we purposed modelling the number of maternal postpartum in Central Java province with predictor variables are the percentage of pregnant who consumed Fe tablet (X1), the percentage of household whom applied clean and health lifestyle(X2), and the percentage of pregnant who First visited to midwife of doctor (K1) (X3).  In the multiple regression only X3 was significantly with R-square was 14.25209%, and Mean Square Error (MSE) was 20.4177. Moreover, in outlier detection, there were two outlier in the data, then modelled with Robust M-estimator. The measurement of goodness used R-square of regression robust M-estimator was 21.74% with MSE was 15.02766. Robust M-estimator regression resulted better model than multiple regression to model the number of maternal postpartum in Central Java Province.
PERBANDINGAN MODEL JARINGAN SYARAF TIRUAN DENGAN ALGORITMA LEVENBERG-MARQUADT DAN POWELL-BEALE CONJUGATE GRADIENTPADA KECEPATAN ANGIN RATA-RATA DI KOTA SEMARANG Dwi Ispriyanti; Alan Prahutama; Tarno Tarno; Budi Warsito; Hasbi Yasin; Pandu Anggara
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 8, No 2 (2020): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.8.2.2020.127-133

Abstract

Wind is one of the most important weather components. Wind is defined as the dynamics of horizontal air mass displacement measured in two parameters, namely speed and direction. Wind speed and direction depend on the air pressure conditions around the place. High wind speed intensity can cause high sea water waves. To estimate wind speed intensity required a study of wind speed prediction. One of method that can be used is Artificial Neural Network (ANN). In ANN there are several models, one of which is backpropagation. Thepurpose of this researchis to compare between backpropagation model with Levenberg-Marquadt and Powell-Beale Conjugate Gradient algorithms. The results of this researchshowing that Powell-Beale Conjugate Gradient better than Levenberg-Marquadtalgorithms. The best model architecture obtained is a network with two input layer neurons, six hidden layer neurons, and one output layer neuron. The activation function used are the logistic sigmoid in the hidden layer and linear in the output layer. MAPE value based on the chosen model is 0,0136% in training process and 0,0088% in testing process.
PENERAPAN METODE FUZZY WEIGHTED PRODUCT (WP) DENGAN PEMBOBOTAN ENTROPY Dwi Ispriyanti; Azizah Mulia Mawarni; Alan Prahutama; Tarno Tarno
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 8, No 1 (2020): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (316.425 KB) | DOI: 10.26714/jsunimus.8.1.2020.%p

Abstract

The government, through the Directorate General of Higher Education, Ministry of National Education seeks to allocate funds to provide scholarships to students who are economically unable to finance their education, and provide scholarships to students who have achievements. The provision of learning assistance in the form of scholarships was given to students in various universities including Diponegoro University. Scholarships awarded include Academic Achievement Achievement scholarships (PPAs) awarded to outstanding students and scholarships Student Learning Assistance (BBM) given to underprivileged students. In recruiting prospective PPA scholarship recipients, the selection committee applies several assessment criteria. The required assessment criteria are the GPA value, the parent's income, the championship achievement, the semester, the number of dependents, and the electric power. The PPA scholarship selection system has not been effective even though it has been with the help of a computer. So there is a need for decision-making methods in assisting selection. The method applied in selecting scholarship recipients is WP, with Entropy weighting method. Previously, the criteria value was changed to fuzzy numbers. Fuzzy Weighted Product (WP) method successfully selected PPA scholarship recipients with optimal results to help screening committee.
ANALISIS KLASIFIKASI KEMISKINAN DI KOTA SEMARANG MENGGUNAKAN ALGORITMA QUEST Dwi Ispriyanti; Alan Prahutama; Mustafid Mustafid
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 7, No 1 (2019): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (371.335 KB) | DOI: 10.26714/jsunimus.7.1.2019.%p

Abstract

Kemiskinan menjadi suatu permasalahan bagi negara-negara berkembang termasuk Indonesia.  Kemiskinan merupakan keadaan kondisi perekonomian suatu orang atau sekelompok orang yang tidak bisa memenuhi kebutuhan dasar seperti pendidikan, kesehatan, pangan, perumahan dan lainnya. Analisis klasifikasi kemiskinan merupaka bagian dari analisis kemiskinan yang yang mengkategorikan rumah tangga atau kelompok kedalam kategori miskin dan tidak miskin. Pengkategorian tersebut didasarkan pada pengeluaran perkapita yang dibandingkan dengan nilai garis kemiskinan. Metode klasifikasi didalam statistika salah satunya pohon klasifikasi, yang meliputi  antara lain Algoritma CART, QUEST, ID3, C45 dan lainnya. Algoritma QUEST merupakan pohon klasifikasi biner dengan prosedur pemilihan variabel penyekat/pemisah, penentuan titik sekat/pemisah serta proses pemberhentian. Pada penelitian ini hasil klasifikasi menggunakan algoritma QUEST dengan semua variabel prediktor diasumsikan skala rasio maka hasil klasifikasi yang didapat mempunyai akurasi 94.9%. Variabel prediktor yang mempengaruhi antara lain penerimaan beras miskin, jenis bahan bakar utama untuk emasak, jenin dinding rumah tinggal yang digunakan dan sumber utama air minum. Sedangkan hasil klasifikasi menggunakan algoritma QUEST dengan variabel prediktornya diasumsikan skala nominal juga menghasilkan akurasi 94.9%. Variabel prediktor yang mempengaruhi antara lain penerimaan beras miskin, jenis bahan bakar utama yang digunakan untuk memasak, bahan utaam dinding rumah serta jumlah angota rumah tangga.
PENERAPAN REGRESI ZERO-INFLATED GENERALIZED POISSON DAN PENGUJIAN AUTOKORELASI SPASIAL PADA KASUS PENYAKIT FILARIASIS DI JAWA TENGAH Sylvi Natalia P P; Dwi Ispriyanti; Sugito Sugito
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 6, No 1 (2018): Jurnal Statistika
Publisher : Program Studi Statistika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Muham

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (400.316 KB) | DOI: 10.26714/jsunimus.6.1.2018.%p

Abstract

Filariasis merupakan penyakit yang disebabkan oleh cacing filaria yang dapat menular melalui gigitan nyamuk. Jumlah kasus filariasis di Provinsi Jawa Tengah tahun 2016 berupa data diskrit berdistribusi poisson dengan proporsi data bernilai nol sebesar 60 persen. Banyaknya data yang bernilai nol mengindikasikan adanya overdispersi. Untuk mengatasinya digunakan model regresi Zero-Inflated Generalized Poisson (ZIGP). Penaksiran parameter dilakukan menggunakan metode Maximum Likelihood Estimation dan dalam penyelesaiannya digunakan iterasiNewton Raphson. Regresi Zero-Inflated Generalized Poisson (ZIGP) menghasilkan 3 variabel prediktor yang berpengaruh terhadap jumlah kasus filariasis di Provinsi Jawa Tengah tahun 2016 dengan nilai R2 sebesar 5,37%. Tiga variabel prediktor tersebut yaitu banyaknya penduduk dengan akses terhadap fasilitas sanitasi yang layak, kepadatan penduduk, dan jumlah sarana kesehatan. Untuk mengetahui keterkaitanantar wilayah berdasarkan jumlah kasus filariasis dilakukan pengujian menggunakan Indeks Moran. Hasil pengujian signifikansi terhadap nilai Indeks Moran menyatakan tidak terdapat autokorelasi spasial terhadap jumlah kasus filariasis di Provinsi Jawa Tengah tahun 2016.  Kata kunci : Filariasis, regresi Zero-Inflated Generalized Poisson (ZIGP), Indeks Moran
PERBANDINGAN MODEL REGRESI BINOMIAL NEGATIF BIVARIAT DENGAN MODEL GEOGRAPHICALLY WEIGHTED NEGATIVE BINOMIAL BIVARIAT REGRESSION (GWNBBR) PADA KASUS ANGKA KEMATIAN BAYI DAN KEMATIAN IBU DI JAWA TENGAH Yashmine Noor Islami; Dwi Ispriyanti; Puspita Kartikasari
Jurnal Gaussian Vol 10, No 4 (2021): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v10i4.33096

Abstract

Infant mortality (0-11 months) and maternal mortality (during pregnancy, childbirth, and postpartum) are significant indicators in determining the level of public health. Central Java Province which has 35 regencies/cities is included in the top five regions with the highest number of infant and maternal mortality in Indonesia. The data characteristics of the number of infants and maternal mortality are count data. Therefore, the Poisson Regression method can be used to analyze the factors that influence the number of infants and maternal mortality. In Poisson regression analysis, there must be a fulfilled assumption, called equidispersion. Frequently, the variance of count data is greater than the mean, which is known as the overdispersion. The research, binomial negative bivariate regression is used as a solutions to overcome the problem of overdispersion in poisson regression. This method produce a global model. In reality, the geographical, socio-cultural, and economic conditions of each region will be different. This illustrates the effect of spatial heterogeneity, so it needs to be developed into Geographically Weighted Negative Binomial Bivariate Regression (GWNBBR). The model of GWNBBR provides weighting based on the position or distance from one observation area to another. Significant variables for modeling infant mortality cases included the percentage of obstetric complications treated (X1), the percentage of infants who were exclusively breastfed (X3), and the percentage of poor people (X5). Significant variable for modeling maternal mortality cases is the percentage of poor people (X5). Based on the AIC value, GWNBBR model is better than binomial negatif bivariat regression model because it has a smaller AIC value. 
METODE ROBUST KRIGING UNTUK MENGESTIMASI DATA SPASIAL BERPENCILAN (Studi Kasus: Pencemaran Udara Gas NO2 di Kota Semarang) Anjan Setyo Wahyudi; Sugito Sugito; Dwi Ispriyanti
Jurnal Gaussian Vol 5, No 3 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (493.281 KB) | DOI: 10.14710/j.gauss.v5i3.14688

Abstract

Kriging is a geostatistical analysis used to estimate the value of the function at an unsampled point by computing a spatial correlation in the neighbourhood of the sample point. Interpolation can produce less precise predictive value if there are outliers among the data. Outliers defined as extreme observation value of the other observation values. Robust kriging is development method of ordinary kriging which transform weight of classic semivariogram thus become semivariogram that robust to outlier of the data. This research aims to estimate the concentrate of Nitrogen Dioxide (NO2) in Semarang using robust kriging method. The spatial data used in this research is coordinates point and concentrate of Nitrogen Dioxide (NO2). This method compare between robust semivariogram and theoretical semivariogram (such as spherical, exponential, and gaussian models) to determine the best estimator of the theoretical semivariogram model. The analysis showed that the best theoretical semivariogram model is exponential model. The estimation of Nitrogen Dioxide concentration conducted at 177 urban communities in Semarang.Keywords: kriging, outliers, robust kriging, robust semivariogram
Co-Authors A Rusgiyono Abdul Hoyyi Agus Rusgiyono Agustinus Salomo Parsaulian Ain Hafidita Ajeng Dwi Rizkia Alan Prahutama Alan Prahutama Alvi Waldira Ana Kartikawati Anisa Septi Rahmawati Anjan Setyo Wahyudi Annisa Ayu Wulandari Arief Rachman Hakim Arkadina Prismatika Noviandini Taryono Arya Despa Ihsanuddin Arya Huda Arrasyid Atika Elsadining Tyas Aulia Ikhsan Avia Enggar Tyasti Azizah Mulia Mawarni Berta Elvionita Fitriani Bitoria Rosa Niashinta Budi Warsito Budi Warsito Cylvia Evasari Margaretha Dedi Nugraha Di Asih I Maruddani Di Asih I Maruddani Diah Safitri Diah Safitri Diah Wulandari Dita Ruliana Dwi Rahmayani, Dwi Dyan Anggun Krismala Dydaestury Jalarno Eis Kartika Dewi Endah Fauziyah Erna Sulistianingsih Erna Sulistio Evi Yulia Handaningrum Fadhilla Atansa Tamardina Firda Dinny Islami Firdha Rahmatika Pratami Fithroh Oktavi Awalullaili Gandhes Linggar Winanti Gera Rozalia Ghina Nabila Saputro Putri Hanifah Nur Aini Hasbi Yasin Hasbi Yasin Henny Widayanti, Henny Ilham Maggri Imam Desla Siena Innosensia Adella Irawati Tamara Iut Tri Utami Jesica, Haniela Puja Kishatini Kishartini Lifana Nugraeni Lingga Bayu Prasetya M. Ali Ma'sum Marlia Aide Revani Masfuhurrizqi Iman Maulida Azkiya, Maulida Maulida Najwa, Maulida Merinda Pangestikasari Moch. Abdul Mukid Moch. Abdul Mukid Muhammad Fitri Lutfi Anshari Muhammad Rosyid Abdurrahman Muhammad Zidan Eka Atmaja Mustafid Mustafid Mustafid Mustafid Nanci Rajagukguk, Nanci Nandang Fahmi Jalaludin Malik Nida Adelia Nidaul Khoir Nova Nova Noviana Nurhayati Nurwihda Safrida Umami Oka Afranda Pandu Anggara Pritha Sekar Wijayanti Puput Ramadhani Pusphita Anna Octaviani Puspita Kartikasari Putri Fajar Utami Rafida Zahro Hasibuan Rahafattri Ariya Fauzannissa Rahmah Merdekawaty Rahmaniar, Ratna Rany Wahyuningtias Ratih Nurmalasari, Ratih Ratna Pratiwi Ria Sutitis Rio Tongaril Simarmata Riszki Bella Primasari Rita Rahmawati Rita Rahmawati Riza Adi Priantoro Riza Fahlevi Sa'adah, Alfi Faridatus Sania Anisa Farah Setiani Setiani Sherly Candraningtyas Sindy Saputri Sisca Agustin Diani Budiman Sri Maya Sari Damanik Sudarno Sudarno Sudarno Sudarno Sudarno Sudarno Sudarno Sudarno Sugito - Sugito Sugito Sugito Sugito Suhendra, Muhammad Arif Suparti Suparti Suparti Suparti Suparti, S. Suryaningrum, Fahlevi Syilfi Syilfi Sylvi Natalia P P Tarno Tarno Tarno Tarno Tarno Tarno Tatik Widiharih Tatik Widiharih Tatik Widiharih Tiani Wahyu Utami Triastuti Wuryandari Triastuti Wuryandari Trimono Trimono Ulya Tsaniya Umiyatun Muthohiroh Warsito Budi Yani Puspita Kristiani Yashmine Noor Islami Yuciana Wilandari Yuciana Wilandari