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All Journal COPING (Community of Publishing in Nursing) Jurnal Gaussian Jurnal Statistika Universitas Muhammadiyah Semarang

Diah Safitri

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Author-ID : 2977048

Decision Sciences, Operations Research & Management Education Health Professions Nursing Public Health Other

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ANALISIS SPASIAL PENYEBARAN PENYAKIT DEMAM BERDARAH DENGUE DENGAN INDEKS MORAN DAN GEARY’S C (STUDI KASUS DI KOTA SEMARANG TAHUN 2011) Nuril Faiz; Rita Rahmawati; Diah Safitri
Jurnal Gaussian Vol 2, No 1 (2013): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Dengue Haemorrhagic Fever (DHF) is an infectious disease transmitted by the mosquito Aedes aegypti through its the virus dengue virus from patient to another via the bite. Rate dependence dengue in an area estimated to be affected by dengue fever in other neighboring areas. The statement was supported by the First Law of Geography expressed Tobler that all things related to everything else, but near things are more related than distant things. Therefore, if a dengue endemic area, the suspected region make the area immediately adjacent to endemic dengue with a new one. The purpose of this study was to determine whether there is spatial autocorrelation in the spread of dengue fever in the city of Semarang. Limited to methods index and Geary's C Moran and mapping the spread of dengue fever in the city of Semarang with respect to the location (district) in 2011. Of the two methods used showed a pattern of spread of Dengue Hemorrhagic Fever (DHF) are spatially in Semarang and show positive spatial autocorrelation, indicating a nearby location to have similar values, and tend to cluster. Keyword: Dengue Hemorrhagic Fever (DHF), Spatial, Moran Index, Geary’s c.

KETEPATAN KLASIFIKASI KEIKUTSERTAAN KELUARGA BERENCANA (KB) MENGGUNAKAN ANALISIS REGRESI LOGISTIK BINER DAN FUZZY K-NEAREST NEIGHBOR IN EVERY CLASS DI KABUPATEN KLATEN Dhinda Amalia Timur; Yuciana Wilandari; Diah Safitri
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Fertility is one of the factors that affect population growth. High population growth resulted in the emergence of a variety of problems for a country including Indonesia. This requires a treatment that population growth can be controlled, one attempts to handle by using a Keluarga Berencana program. Therefore conducted a study to determine the factors that affect that participation of Keluarga Berencana (KB) by using Binary Logistic Regression analysis in which the participation of KB divided into two, namely join KB and KB did not participate. Based on the results obtained Binary logistic regression analysis predictor variables that significantly affect participation KB is the number of children, father's education, and mother's education. The resulting classification accuracy with training data comparison testing was 90:10 at 84.375%. Furthermore, the data were analyzed by using Fuzzy K-Nearest Neighbor in every Class (FK-NNC) to determine the accuracy of the classification results comparison with FK-NNC Binary Logistic Regression. From the analysis of the classification accuracy using the FK-NNC with a 90:10 ratio of training data and testing the value of K = 7 values obtained tersebesar ie 87.5%. The comparison of classification accuracy of this value indicates if the FK-NNC is better classify participation in Keluarga Berencana in Klaten district 2012. Keywords: Keluarga Berencana, Binary Logistic Regression, Fuzzy K-Nearest Neighbor in every Class (FK-NNC)

PEMODELAN INDEKS HARGA SAHAM GABUNGAN (IHSG) MENGGUNAKAN MULTIVARIATE ADAPTIVE REGRESSION SPLINES (MARS) Ndaru Dian Darmawanti; Suparti Suparti; Diah Safitri
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Composite Stock Price Index (CSPI) is a historical information about the movement of joint-stock until a certain date. CSPI is often used by inventors to see a representation of the overall stock price, it can analyze the possibility of increase or decrease in stock price. Following old examination, some economy macro variables affecting CSPI is inflation, interest rate,and exchange rate the Rupiah againts the u.s.dollar. MARS method is particularly suitable to analyze a CSPI because many variables that affected. Furthermore, in the real world is very difficult to find a spesific data pattern. The analysis is MARS analysis. The purpose is an obtained a MARS model to be used to analyze the CSPI movement’s. Selection MARS model can be used CV method. The MARS model is an obtained from combination of BF, MI, dan MO. In this case, happens the best models with BF=9, MI=2, dan MO=1. Accuracy for MARS model can see MAPE values is 14,32588% it means the model can be used.Keyword: CSPI, economy macro, MARS, CV, MAPE.

PERBANDINGAN METODE K-MEANS DAN METODE DBSCAN PADA PENGELOMPOKAN RUMAH KOST MAHASISWA DI KELURAHAN TEMBALANG SEMARANG Sisca Agustin Diani Budiman; Diah Safitri; Dwi Ispriyanti
Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Students as well as community or household, as well as economic activities daily, including consumption. The student needs to choose a place to stay is also one form of consumption activities. There are many factors that affect student preferences in the selection of boarding houses, including price, amenities, location, income, lifestyle, and others. The rental price boarding and facilities offered significant positive effect on student preferences in choosing a boarding house. Based on rental rates and facilities it offered to do the grouping in order to know the condition of the student boarding house in the Village Tembalang. Grouping is one of the main tasks in data mining and have been widely applied in various fields. The method used to classify is K-Means and DBSCAN with a number of groups of three. Furthermore, the results of both methods were compared using the Silhouette index values to determine which method is better to classify the student boarding house. Based on the research that has been conducted found that the K-Means method works better than DBSCAN to classify the student boarding house as evidenced by the value of the Silhouette index on K-Means of 0.463 is higher than the value at DBSCAN Silhouette index is equal to 0.281. Keywords: student boarding houses, data mining, clustering, K-Means, DBSCAN

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

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

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

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

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

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

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

Co-Authors Abdul Hoyyi Afianti Sonya Kurniasari Agus Rusgiyono Alan Prahutama Amelia Crystine 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 Erna Musri Arlita FANDI AHMAD Galuh Ayu Prameshti Hasbi Yasin Indri Puspitasari Lailly Rahmatika Mekar Sekar Sari Moch. Abdul Mukid Muhammad Abid Muhyidin Muhammad Sunu Widianugraha Ndaru Dian Darmawanti 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 Yufitriana Amir Yulia Irvani Dewi

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