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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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FUZZY SUBTRACTIVE CLUSTERING BERDASARKAN KEJADIAN BENCANA ALAM PADA KABUPATEN/KOTA DI JAWA TENGAH Diah Safitri; Rita Rahmawati; Onny Kartika Hitasari
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 5, No 2 (2017): Jurnal Statistika
Publisher : Program Studi Statistika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Muham

Bencana alam adalah bencana yang diakibatkan oleh peristiwa atau serangkaianperistiwa yang disebabkan oleh alam. Provinsi Jawa Tengah terdiri dari 76kabupaten/kota, kabupaten/kota tersebut dapat dikelompokkan menjadi beberapakelompok berdasarkan frekuensi terjadinya bencana, yang mana masing-masingkelompok mempunyai karakteristik yang berbeda berdasarkan kejadian bencana alam.Metode untuk mengelompokkan yang digunakan dalam penelitian ini adalah FuzzySubtractive Clustering yang merupakan metode dalam fuzzy. Dari penelitian ini dapatdisimpulkan bahwa cluster dengan jari-jari 0,92 – 0,94 merupakan jumlah cluster yangterbaik yang digunakan dalam permasalahan ini. Pada jari-jari (r) antara 0,92 – 0,94diperoleh kesamaan kecenderungan data yang masuk pada setiap cluster, maka clusteryang terbentuk dengan r = 0,92 sampai 0,94 adalah sebagai berikut, cluster 1 terdapat14 Kabupaten/Kota, cluster 2 terdapat 7 Kabupaten/Kota, cluster 3 terdapat 7Kabupaten/Kota, cluster 4 terdapat 5 Kabupaten/Kota, dan Cluster 5 terdapat 2 Kabupaten/Kota.Kata Kunci: bencana alam, fuzzy subtractive clustering

METODE DBSCAN UNTUK PENGELOMPOKAN KABUPATEN/KOTA DI PROVINSI JAWA TENGAH BERDASARKAN PRODUKSI PADI SAWAH DAN PADI LADANG Diah Safitri; Triastuti Wuryandari; Rita Rahmawati
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 5, No 1 (2017): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Padi merupakan tanaman yang penting di Jawa Tengah karena nasi merupakan makanan pokok sebagian besar masyarakat di Jawa Tengah. Provinsi Jawa Tengah terdiri dari 35 kabupaten/kota, dalam penelitian ini berdasarkan produksi padi sawah dan padi ladang, kabupaten/kota di Provinsi Jawa Tengah akan dikelompokkan menjadi beberapa kelompok menggunakan metode Density Based Spatial Clustering Algorithm With Noise (DBSCAN)berdasarkan produksi padi sawah dan padi ladang, dimana pada masing-masing kelompok dapat dilihat karakteristiknya mengenai potensi produksi padi sawah dan padi ladang. Metode DBSCAN adalah metode yang tangguh untuk mendeteksi noise. Kelompok 1 terdiri dari Kabupaten/Kota di Provinsi Jawa Tengah selain yang sudah masuk dalam kelompok 2 dan 3, kelompok 1 adalah kelompok yang mempunyai hasil produksi padi sawah terendah dibandingkan dengan kelompok yanglain. Kelompok 2 adalah kelompok yang mempunyai hasil produksi padi ladang tertinggi dibandingkan dengan kelompok yang lain. Kelompok 2 terdiri dari KabupatenKebumen dan Kabupaten Blora. Kelompok 3 terdiri dari Kabupaten Sragen, Kabupaten Grobogan, Kabupaten Pati, Kabupaten Demak, dan Kabupaten Brebes, adalahkelompok yang mempunyai hasil produksi padi sawah tertinggi dibandingkan dengan kelompok yang lain. Pada penelitian ini ditemukan 2 noise, yaitu Kabupaten Cilacapdan Kabupaten Wonogiri.Kata Kunci: DBSCAN, Padi Sawah, Padi Ladang

PENDEKATAN REGRESI LINIER MULTIVARIAT UNTUK PEMODELAN INDEKS PEMBANGUNAN MANUSIA (IPM) DAN PERSENTASE PENDUDUK MISKIN DI JAWA TENGAH Abdul Hoyyi; Diah Safitri; Sugito Sugito; Alan Prahutama
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 6, No 2 (2018): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Model regresi multivariat merupakan model regresi yang dibangun dari beberapa variabel independen dan mempunyai variabel dependen lebih dari satu dengan setiap variabel dependen saling berkorelasi. Pada penelitian ini variabel dependennya adalah Indeks Pembangunan Manusia (IPM) dan jumlah penduduk miskin, sedangkan variabel independennya adalah upah minimum regional dan kepadatan penduduk. Data yangdigunakan adalah data sekunder yang diperoleh dari Badan Pusat Statistika (BPS) Propinsi Jawa Tengah. Parameter pada model diestimasi dengan metode kuadrat terkecil. Berdasarkan hasil dan pembahasan, pada taraf signifikansi 5 % diperoleh hasil bahwa variabel IPM dan persentase penduduk miskin berdistribusi normal multivariat.Pengujian parameter model diperoleh bahwa koefisien variabel upah minimum regional dan kepadatan penduduk signifikan terhadap model. Pengujian asumsi normalitas, homoskedastisitas dan nonautokorelasi memberikan kesimpulan eror berdistribusi normal multivariate, tidak terjadi autokorelasi dan varian dari eror homogen. Hasil akhir memberikan kesimpulan bahwa variabel upah minimum regional dan kepadatanpenduduk dapat menjelaskan Indeks Pembangunan Manusia dan persentase penduduk miskin sebesar 70,11 %. Kata kunci : Regresi multivariat, IPM, BPS.

ANALISIS ANTREAN DENGAN SISTEM JUMLAH KEDATANGAN BERDISTRIBUSI BETA, WEIBULL, NORMAL, DAN ERLANG (STUDI KASUS GERBANG TOL MUKTIHARJO) Sugito Sugito; Erna Musri Arlita; Diah Safitri; Abdul Hoyyi; Rita Rachmawati
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 6, No 1 (2018): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Semarang is the capital city of Central Java Province so that development in Semarang City progressed very rapidly, this requires transportation facilities and infrastructure that good, smoothly and efficiently. One of the important transport infrastructure for Semarang City is a toll road. Muktiharjo toll gate is one of the exiting toll gate in Semarang City. Muktiharjo toll gate provides two types of service namely booths toll booths, a regular and automated toll booths. The existances two types of toll booths that provided the analysis needs to be done, then the line to find out how the system line that is in Muktiharjo toll gate. The research result obtained the model line at the Muktiharjo toll gate (BETA / G / 3) :( GD / ¥ / ¥) for regular toll road of Surabaya-Semarang direction, (WEIB / G / 1) :( GD / ¥ / ¥) for the Surabaya-Semarang automatic toll booth, (NORM / G / 2) :( GD / ¥ / ¥) for the regular toll road of Semarang-Surabaya and (ERLA / G / 2) :( GD / ¥ / ¥) Semarang-Surabaya automatic toll road. Based on simulation using Arena software, the addition of substations can reduce the waiting time of vehicles in line, while substation reduction can extend the waiting time of vehicles in line.Keyword : Queue theory, Queue simulation, Regular toll gate, Automatic toll gate, Arrival, Service.

PERAMALAN PENUMPANG PELAYARAN DALAM NEGERI DI PELABUHAN TANJUNG PRIOK DENGAN METODE ARIMA BOX-JENKINS DAN METODE VARIASI KALENDER ARIMAX Annisa Pratiwi; Diah Safitri; Budi Warsito
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 6, No 1 (2018): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Sea transportation is an inseparable and indispensable part of society in everyday lifefor Indonesian people especially during special moment such as Eid al-Fitr. This can be shown by the increasing of the number of sea transport passengers duringEid al-Fitr every year. The month shift during Eid al-Fitr shows the effect of calendar variation.The calendar variation method is a method that combines the dummy regression model with the ARIMA model. The purpose of this research is to obtain the best model by using time series analysis approach on ARIMA Box-Jenkins method and calendar variationARIMAX method to predict the number of domestic sea passenger at Tanjung Priok Port for 12 periods in the future. Based on the analysison the data of the number of domestic sea passenger at the Port of Tanjung Priok, it is concluded that the method of calendar variationARIMAX as the best method with ARIMA model (0, 0, [3]), V2,t,S1,t, S2,t, S3,t, S4,t, S5,t, S6,t, S7,t, S8,t, S9,t, S10,t, S11,t,t, V1,tt, V2,tt, S7,tt,S8,tt, S9,ttbecause it has the smallest MAPE value that is 14.3782% which indicates that the result of forecasting is good. Keywords : Sea Passengers, ARIMA Box-Jenkins, Calendar Variation, ARIMAX

PREDIKSI HARGA MINYAK DUNIA DENGAN METODE AUTOREGRESSIVE FRACTIONALLY INTEGRATED MOVING AVERAGE (ARFIMA) Dimas Kevin Natanael; Diah Safitri; Suparti Suparti
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 6, No 1 (2018): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Autoregressive Fractionally Integrated Moving Average (ARFIMA) model is a development of the ARIMA model. The advantage of the ARFIMA method is the non-integer differentiation value so that it can overcome long memory effect that cannot be solve with the usual ARIMA method. Non-integer differential values can be estimated with a binomialexpansion approach which is an infinite weighted sum of past values to solve the long memory effect that arises. Some of the advantages of using the ARFIMA model iscapable of modeling high changes in the long term (long term persistence), be able to explain longterm and short-termcorrelation structures at the same time, to provide models with simple parameters (parsimony) for data with memory long term and short term. Data of world oil price contain long memory effect, then used ARFIMA method to get the best model.The best model obtained is the ARMA([1,7]; 0) model with the differentialvalue is 0,48937, then the model can be written into ARFIMA ([1,7]; d;1).The best model chosen has an MSE value of 0,44 and a MAPE value of 3,32%. Keywords : Sea Passengers, ARIMA Box-Jenkins, Calendar Variation, ARIMAX

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

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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