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All Journal COPING (Community of Publishing in Nursing) Jurnal Gaussian Jurnal Statistika Universitas Muhammadiyah Semarang Pendas : Jurnah Ilmiah Pendidikan Dasar Review of Accounting and Taxation Jurnal Riset Ilmu Kesehatan Umum dan Farmasi
Diah Safitri
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Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Health Professions Nursing Public Health Social Sciences Other

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METODE BOOTSTRAP AGGREGATING REGRESI LOGISTIK BINER UNTUK KETEPATAN KLASIFIKASI KESEJAHTERAAN RUMAH TANGGA DI KOTA PATI Ridha Ramandhani; Sudarno Sudarno; Diah Safitri
Jurnal Gaussian Vol 6, No 1 (2017): 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 (480.525 KB) | DOI: 10.14710/j.gauss.v6i1.14775

Abstract

Kesejahteraan merupakan salah satu aspek yang cukup penting untuk menjaga dan membina terjadinya stabilitas sosial dan ekonomi. Berbagai penelitian yang telah dilakukan mengenai kesejahteraan mengindikasikan bahwa banyak faktor yang mempengaruhi kesejahteraan rumah tangga. Faktor-faktor yang mempengaruhi kesejahteraan rumah tangga antara lain jenis kelamin kepala rumah tangga, usia kepala rumah tangga, lapangan usaha kepala rumah tangga, jumlah anggota rumah tangga, bahan bakar utama untuk memasak, pengalaman membeli raskin dan ada atau tidaknya anggota keluarga yang menguasai penggunaan telepon seluler/HP. Dalam penelitian ini dilakukan kajian tentang klasifikasi kesejahteraan rumah tangga di Kota Pati dengan tujuan untuk mengidentifikasi faktor-faktor apa saja yang mempengaruhi kesejahteraan rumah tangga di Kota Pati. Dari hasil kajian dengan menggunakan metode Bootstrap Aggregating (Bagging) regresi logistik biner diperoleh tiga variabel prediktor yang berpengaruh signifikan terhadap kesejahteraan rumah tangga di Kota Pati, yaitu jenis kelamin kepala keluarga, jumlah anggota rumah tangga, dan penguasaan telepon seluler dengan tingkat akurasi sebesar 79,87%. Hasil analisis bagging regresi logistik biner dengan replikasi bootstrap sebesar 50, 60, 70, 80, 90, 100, 150, 200, 626, dan 1000 kali menunjukkan bahwa terdapat konsistensi pada setiap pengulangan. Kata Kunci : Klasifikasi, Regresi Logistik Biner, Bootstrap Aggregating
PENDUGAAN DATA HILANG PADA RANCANGAN ACAK KELOMPOK LENGKAP DENGAN ANALISIS KOVARIAN Vina Riyana Fitri; Triastuti Wuryandari; Diah Safitri
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 (371.804 KB) | DOI: 10.14710/j.gauss.v3i3.6485

Abstract

Analysis of Covariance (ANCOVA) is mostly used in the analysis of research or experimental design. ANCOVA is the combination between regression analysis and Analysis of Variance (ANOVA). ANCOVA were used because there are some concomitant variable, which is variable that difficult to control by the researchers but an impact on observed the response variable. The purpose from concomitant variable is reduces variability in the experiment. If there is missing data on Randomized Complete Block Design (RCBD) the first must be done estimating the missing data before ANCOVA done. ANCOVA on RCBD with complete data or missing data isn’t much different, if there are missing data, the degrees of freedom is reduced by the total amount of missing data and the sum of square treatment reduced by the value of the bias. Application of tensile strength of the glue experiment to the case ANCOVA on RCBD with one missing data show no effect of treatment and group by the tensile strength of the glue. For Fe toxicity experiment with two missing data are found only treatment effect to Fe texicity. Based on value from the coefficient of variance for one missing data and two missing data showed that ANCOVA is more appropriately used than ANOVA.
ANALISIS PREFERENSI KONSUMEN PENGGUNA JASA MASKAPAI PENERBANGAN UNTUK RUTE SEMARANG-JAKARTA DENGAN METODE CHOICE-BASED CONJOINT (FULL PROFILE) Vierga Dea Margaretha Sinaga; Diah Safitri; Agus Rusgiyono
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 (401.185 KB) | DOI: 10.14710/j.gauss.v4i4.10241

Abstract

Airline services nowadays become one of the highly coveted options by many consumers for long-distance transportation. The increasing numbers of users makes airlines tightly compete each other to attract consumers’ interest. Thus, analysis to consumer preference has always been the starting point in market research as reference in creating new innovation. This research uses the choice-based conjoint analysis with the full profile as method of presentation. Conjoint analysis is a multivariate analysis method that can be used as a measurement for the level of preference. In the instrument, consumers were asked to choose one among three attribute combination of each choice set within 9 choice sets. Utility values were obtained by conditional logic model. The results show that for each attribute the order of preference is Price-Airport tax-Class-Facility. Judging from the value of its usefulness, the most preferred attribute by consumer is Airport tax and that Include is preferably from Exclude.  For Price attribute, lower than 500 thousand rupiahs is the most preferred categories among others. In Class attribute, Business is more preferable than other categories. And for Facility attribute, entertainment is the most preferred one of other categories. Keywords: preferences, airlines, choice-based conjoint
PEMODELAN FAKTOR-FAKTOR YANG MEMPENGARUHI INDEKS PEMBANGUNAN MANUSIA KABUPATEN/ KOTA DI JAWA TIMUR MENGGUNAKAN GEOGRAPHICALLY WEIGHTED ORDINAL LOGISTIC REGRESSION Rahma Nurfiani Pradita; Hasbi Yasin; Diah Safitri
Jurnal Gaussian Vol 4, No 3 (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 (472.263 KB) | DOI: 10.14710/j.gauss.v4i3.9488

Abstract

Human Development Index (HDI) is a measurement used for measuring human developmental achievement in certain area. Although, it does not measure all dimensions of human development, HDI seems able to measure principal dimension of human development that include longevity and health, knowledge and a good life. Geographically Weighted Ordinal Logistic Regression (GWOLR) Model is used to model a relationship between categorical response variable that have ordinal scale toward predictor variable that depend on geographical location where the data are observed. This research aims to know the factors that influence HDI of Regency/ City in East Java Province 2013 using ordinal logistic regression model and GWOLR with exponential kernel function weighting. Factors that are influencing HDI of Regency/ City in East Java are percentage of population that finish Junior High School (X2), the number of health facility (X4), and population density (X5). Based on HDI of Regency/ City in East Java’s accuracy classification result, between observations and prediction counted based on Apparent Error Rate (APER) value, it is known that GWOLR model with exponential kernel function weighting has better classification’s accuracy (86,84%) than ordinal logistic regression model (81,58%). Keywords:      HDI, Ordinal Logistic Regression Model, GWOLR, Exponential         Kernel Function,                     Classification’s Accuracy, APER
ANALISIS KECENDERUNGAN PEMILIHAN KOSMETIK WANITA DI KALANGAN MAHASISWI JURUSAN STATISTIKA UNIVERSITAS DIPONEGORO MENGGUNAKAN BIPLOT KOMPONEN UTAMA Rizka Asri Brilliani; Diah Safitri; Sudarno Sudarno
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 (437.762 KB) | DOI: 10.14710/j.gauss.v5i3.14711

Abstract

This study aims to reviews trend of using the cosmetics brand among the students of Department of Statistics at Diponegoro University. The observed cosmetics brand are Wardah, Sariayu Martha Tilaar, Pixy, Pond's, and Garnier. The data used in the form of primary data with total samples drawn 180 students, then it's been analyzed using principal component biplot. The result showed that Wardah has advantages in safety of product composition, and its benefit as a skin care. Wardah also more attractive to students. Sariayu Martha Tilaar, Pixy, and Pond's have the same profit, they are safety of product composition, the variations according the skin type, and their use as a skin care and make up. The diversity is 73,01% which means that principal component analysis biplot is able to explained 73,01% of the total diversity of the actual data. Keywords: principal component biplot analysis, cosmetics brand, perceptions
PEMODELAN DAN PERAMALAN VOLATILITAS PADA RETURN SAHAM BANK BUKOPIN MENGGUNAKAN MODEL ASYMMETRIC POWER AUTOREGRESSIVE CONDITIONAL HETEROSKEDASTICITY (APARCH) Nur Musrifah Rohmaningsih; Sudarno Sudarno; Diah Safitri
Jurnal Gaussian Vol 5, No 4 (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 (550.139 KB) | DOI: 10.14710/j.gauss.v5i4.14727

Abstract

Stock is a sign of ownership of an individual or entity within a corporation or limited liability company. While the stock price index is a reflection of the movement of the stock price. Stock investments can not avoid the risk, so we need a model that can predict stock returns and volatility. Models are often used is ARCH/GARCH models. On the stock market also shows asymmetric effect(leverage), which is a negative relationship between the change in the value of returns with volatility movement. So, the model can be used is Asymmetric Power Autoregressive Conditional Heteroscedasticity (APARCH) model. APARCH model chosen to modeling and forecasting the volatility of Bukopin return stock is APARCH (1,2) model Keywords: Stock, volatility, asymmetric, return, APARCH
ANALISIS BIPLOT ROW METRIC PRESERVING UNTUK MENGETAHUI KARAKTERISTIK PROVIDER TELEPON SELULER PADA MAHASISWA S1 FSM UNIVERSITAS DIPONEGORO Artha Ida Sri Anggriyani; Diah Safitri; Triastuti Wuryandari
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 (588.102 KB) | DOI: 10.14710/j.gauss.v5i3.14689

Abstract

Communication is the basis of human interaction. One of the progression in telecommunications is telecommunication tools, e.g. a mobile phone. Usually on every communication tools such as mobile phones are equipped with a provider. The methods used to analyze mobile phone provider is the biplot analysis. Biplot analysis is an analysis which gives a demonstration of the matrix data graphically X into a plot with vector in row matrix X as describing an object, with a vector in column matrix X describing variables. If α = 1 then it is called analysis biplot Row Metric Preserving (RMP). The predictor variable used in this final project is the product, price, promotion and distribution. After analysis biplot, can be known that a two-dimensional graph biplot was able to explain 97,7% of actual data. The nearest competitor for Indosat provider is XL Axiata provider. Indosat provider winning in terms of promotions and distribution, Telkomsel provider winning in terms of products and Hutchison provider winning in terms of price. Keywords: telephone provider, Row Metric Preserving biplot, marketing mix
MODEL REGRESI MENGGUNAKAN LEAST ABSOLUTE SHRINKAGE AND SELECTION OPERATOR (LASSO) PADA DATA BANYAKNYA GIZI BURUK KABUPATEN/KOTA DI JAWA TENGAH Aulia Putri Andana; Diah Safitri; Agus Rusgiyono
Jurnal Gaussian Vol 6, No 1 (2017): 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 (764.966 KB) | DOI: 10.14710/j.gauss.v6i1.14760

Abstract

Gizi buruk adalah bentuk terparah dari proses terjadinya kekurangan gizi yang menahun. Gizi  buruk dipengaruhi oleh banyak faktor yang saling terkait. Dalam penelitian ini, dilakukan pemodelan dari faktor-faktor yang mempengaruhi gizi buruk menggunakan metode Least Absolute Shrinkage Selection and Operator (LASSO) dengan algoritma Least Angle Regression (LARS) karena pada faktor-faktor yang mempengaruhi gizi buruk terdeteksi multikolinearitas. LASSO menyusutkan koefisien regresi dari variabel bebas yang memiliki korelasi tinggi menjadi tepat pada nol atau mendekati nol. Koefisien LASSO dicari dengan menggunakan pemrograman kuadratik sehingga digunakan algoritma LARS yang lebih efisien dalam komputasi LASSO. Berdasarkan analisis yang telah dilakukan, model LASSO pada data gizi buruk Kabupaten/Kota di Jawa Tengah tahun 2014 diperoleh pada tahap kedua saat nilai s=0.02 dengan nilai MSE sebesar 0,82977. Disimpulkan bahwa variabel bayi (0-6 Bulan) yang diberi ASI Eksklusif, rumah tangga berperilaku hidup bersih dan sehat, bayi yang mendapat imunisasi Hepatitis B, bayi yang mendapat imunisasi DPT-HB3, rumah dengan sanitasi yang layak, dan rumah dengan air minum sesuai dengan syarat kesehatan berpengaruh terhadap bayi gizi buruk di Jawa Tengah tahun 2014. Kata Kunci: gizi buruk, multikolinearitas, LASSO, LARS
PERBANDINGAN ANALISIS KLASIFIKASI MENGGUNAKAN METODE K-NEAREST NEIGHBOR (K-NN) DAN MULTIVARIATE ADAPTIVE REGRESSION SPLINE (MARS) PADA DATA AKREDITASI SEKOLAH DASAR NEGERI DI KOTA SEMARANG Bisri Merluarini; Diah Safitri; Abdul Hoyyi
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 (325.515 KB) | DOI: 10.14710/j.gauss.v3i3.6441

Abstract

Classification methods have been developed and two of the existing are K-Nearest Neighbor (K-NN) and Multivariate Adaptive Regression Spline (MARS). The purpose of this research is comparing the classification of public elementary school accreditation in Semarang city with K-NN and MARS methods. This research using accreditation data with the result of eight accreditation components in public elementary school that has A accreditation (group 1) and B accreditation (group 2) in Semarang city. To evaluate the classification method used test statistic  Press’s Q, APER, specificity, and sensitivity. The best classification results of the K-NN method is when using K=5 because it produces the smallest error rate and obtained information that the correct classification data are 159 and the misclassification data are 9. The best classification result of the MARS method is when using combination BF=32, MI=2, MO=1 because it produces the smallest Generalized Cross Validation (GCV) and obtained information that the correct classification data are 164 and the misclassification data are 4. Based on analyze result, Press’s Q showed that both methods are good as classification or statistically significant to classify the public elementary school in Semarang city based of the accreditation. APER, specificity, and sensitivity showed that classify of public elementary school accreditation in Semarang city using MARS method is better than K-NN method.
PERAMALAN JUMLAH TAMU HOTEL DI KABUPATEN DEMAK MENGGUNAKAN METODE SUPPORT VECTOR REGRESSION Desy Trishardiyanti Adiningtyas; Diah Safitri; Moch. Abdul Mukid
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 (450.569 KB) | DOI: 10.14710/j.gauss.v4i4.10133

Abstract

The purpose of this research is to forecast the number of hotel’s guests in Demak using Support Vector Regression. Support Vector Regression (SVR) is method used for forecasting. Forecasting the number of hotel’s guests in Demak using SVR produce good accuracy for forecasting the training and testing data. Forecasting for the training data generate MAPE value of 10.2806% and forecasting of testing data generate MAPE value of 11.622%.Keywords: Support Vector Regression, hotel, Demak, accuracy, forecasting, training, testing
Co-Authors Abdul Hoyyi Afianti Sonya Kurniasari Agus Rusgiyono Alan Prahutama Amelia Crystine Amir, Yufitriana Annisa Pratiwi Artha Ida Sri Anggriyani Aryono Rahmad Hakim Aulia Putri Andana Bisri Merluarini Budi Warsito Desy Trishardiyanti Adiningtyas Dhinda Amalia Timur Di Asih I Maruddani Dimas Kevin Natanael Dwi Ispriyanti Erin Padilla Siregar Erna Musri Arlita FANDI AHMAD Galuh Ayu Prameshti Hasbi Yasin Indri Puspitasari Lailly Rahmatika Mekar Sekar Sari Mertika Moch. Abdul Mukid Muhammad Abid Muhyidin Muhammad Sunu Widianugraha Mulyasari, Windu Nana Nofianti, Nana Ndaru Dian Darmawanti Nindy Citroresmi Nur Musrifah Rohmaningsih Nuril Faiz Onny Kartika Hitasari Puti Cresti Ekacitta Rahma Nurfiani Pradita Revaldo Mario Ridha Ramandhani Rita Rachmawati Rita Rahmawati Rizal Yunianto Ghofar Rizka Asri Brilliani Sherly Candraningtyas Sisca Agustin Diani Budiman Sudarno Sudarno Sugito Sugito Suparti Suparti Syilfi Syilfi Trianita Resmawati Triastuti Wuryandari Vierga Dea Margaretha Sinaga Vina Riyana Fitri Wella Rumaenda Yani Puspita Kristiani Yogi Setiyo Pamuji Yuciana Wilandari Yulia Irvani Dewi