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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

Author-ID : 3131513

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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VALUE AT RISK (VAR) METODE DELTA-NORMAL BERDASARKAN DURASI UNTUK UKURAN RISIKO OBLIGASI PEMERINTAH Setiani Setiani; Di Asih I Maruddani; Dwi Ispriyanti
Jurnal Gaussian Vol 10, No 3 (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.v10i3.32806

A bond is one of invesment instrument that is basically a debt instrument. In investing, beside getting profit there is also the risk of loss. The risk of loss is unavoidable but it can be manageable. The concept of a portfolio in investing is to minimize risk. Value at Risk (VaR) is a method used to measure risk where VaR states the estimated amount of the maximum loss that will be obtained at a certain level of confidence during a certain period in normal market conditions. In this article the risk of bonds FR0053, FR0056, FR0059, FR0061 and portfolio combinations calculated with VaR value of the Delta-Normal method are calculated based on the duration of the bonds. Normality test of the bond market price return is required before calculating VaR. The results obtained if it is assumed that the bonds are purchased at a price of 100 and with a confidence level of 95%, then the portfolio that has the smallest risk is the Bond portfolio of FR0059 and FR0061 with a VaR value Rp 21,436 (Trillions).

KLASIFIKASI TINGKAT KELANCARAN NASABAH DALAM MEMBAYAR PREMI DENGAN MENGGUNAKAN METODE REGRESI LOGISTIK ORDINAL DAN NAÏVE BAYES (Studi Kasus pada Asuransi AJB Bumiputera Tanjung Karang Lampung) Ria Sutitis; Suparti Suparti; Dwi Ispriyanti
Jurnal Gaussian Vol 4, No 3 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

In the insurance companies a problem that often arises is the amount of customer debt in paying premiums, so it needs a system that can classify customers in the group not well, less smoothly, and smooth in paying premiums. Used two methods to perform the classification of payment premium status which is Regression Logistics Ordinal and Naïve Bayes. Variables used in determining whether a payment premium status are gender, marital status, age, work, income, insurance period, and the payment of premium. In Regression Logistics Ordinal, significant variables to the model are gender, marital status, age, insurance period, and the payment of premium. For significant variables used in the classification. Payment premium status of the data processing methods of Regression Logistics Ordinal with accuracy obtained is equal to 50.90% and the Naïve Bayes method obtained is equal to 55.41%. Based on the level of accuracy, the classification of data payment premium status of insurance AJB Bumiputera Tanjung Karang Lampung using the Naïve Bayes method has a greater degree of accuracy than the Regression Logistics Ordinal method. Keywords: Payment Premium Status, Classification, Naïve Bayes, Regression Logistics Ordinal

PEMBENTUKAN POHON KLASIFIKASI BINER DENGAN ALGORITMA QUEST (QUICK, UNBIASED, AND EFFICIENT STATISTICAL TREE) PADA DATA PASIEN LIVER Muhammad Rosyid Abdurrahman; Dwi Ispriyanti; Alan Prahutama
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

In this modern era of fast food commonly found that sometimes have chemical substances and the increasing number of motor vehicles that cause the uncontrolled circulation of air pollution that can affect the health of the human liver. To assist in analyzing the presence of liver disorders in humans can be used QUEST (Quick, Unbiased, and Efficient Statistical Tree) algorithm to classify the characteristics of the patient's liver by liver function tests performed in clinical laboratories. QUEST construct rules to predict the class of an object from the values of predictor variables. The tree is constructed by partitioning the data by recuresively, where class and the values of the predictor variables of each observation in the data sample is known. Each partition is represented by a node in the tree. QUEST is one of the binary classification tree method. The results of the classification tree is formed, an important variable in classifying a person affected by liver disease or not, that is the variable Direct Bilirubin, Alkaline Phosphatase, Serum Glutamic Oxaloacetic Transaminase (SGOT), and age of the patient. Accuracy of the QUEST algorithm classifying liver patient data by 73,4 %. Keywords: binary classification trees, QUEST algorithm, liver patient data.

PERBANDINGAN METODE MOORA DAN TOPSIS DALAM PENENTUAN PENERIMAAN SISWA BARU DENGAN PEMBOBOTAN ROC MENGGUNAKAN GUI MATLAB Rafida Zahro Hasibuan; Alan Prahutama; Dwi Ispriyanti
Jurnal Gaussian Vol 8, No 4 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

MAN Asahan is an educational institution that selects new students every year. MAN Asahan sets certain criteria in choosing new students so that selected students are of high quality. The criteria determined are the Al-Qur'an test scores, national exam scores, Academic Potential Test scores and achievement certificates. In selecting new students who were accepted as many as 271 of the 530 registrants the school still used the manual process so that it needed accuracy and a long time. In this study a decision support system was created that could be a solution to assist the selection process according to school criteria. The system will applied is MOORA (Multi-Objective Optimization on the Base of Ratio Analysis) method and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) with the weighting method of ROC (Rank Order Centroid). Then the sensitivity analysis is done to determine the appropriate method to be chosen to obtain optimal results. This research was conducted with the help of the MATLAB GUI as a computing tool. The GUI that is built can simplify and speed up the selection process. Based on the results of the study, the average percentage value of sensitivity for the MOORA method is -1.61% while the TOPSIS method is -7.96%. With the existence of sensitivity analysis it can be known the most appropriate method for this case is the MOORA method.Keywords: Students, MOORA, TOPSIS, ROC, Sensitivity, GUI Matlab

REGRESI ROBUST MM-ESTIMATOR UNTUK PENANGANAN PENCILAN PADA REGRESI LINIER BERGANDA Sherly Candraningtyas; Diah Safitri; Dwi Ispriyanti
Jurnal Gaussian Vol 2, No 4 (2013): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The multiple linear regression model is used to study the relationship between a dependent variable and more than one independent variables. Estimation method which is the most frequently be used to analyze regression is Ordinary Least Squares (OLS). OLS for linear regression models is known to be very sensitive to outliers. Robust regression is an important method for analyzing data contaminated by outliers. This paper will discuss the robust regression MM-estimator. This estimation is a combined estimation method which has a high breakdown value (LTS-estimator or S-estimator) and M-estimator. Generally, there are three steps for MM-estimator: estimation of regression parameters initial using LTS-estimators, residual and robust scale using M-estimator, and the final estimation parameter using M-estimator. The purpose of writing this paper are to detect outliers using DFFITS and determine the multiple linear regression equations containing outliers using robust regression MM-estimator. The data used is the generated data from software Minitab 14.0. Based on the analysis results can be concluded that data 21st, 27th, 34th are outliers and equation of multiple linear regression using robust regression MM-estimators is .

PENDEKATAN METODE MARKOWITZ UNTUK OPTIMALISASI PORTOFOLIO DENGAN RISIKO EXPECTED SHORTFALL (ES) PADA SAHAM SYARIAH DILENGKAPI GUI MATLAB Umiyatun Muthohiroh; Rita Rahmawati; Dwi Ispriyanti
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.v10i4.33098

A portfolio is a combination of two or more securities as investment targets for a certain period of time with certain conditions. The Markowitz method is a method that emphasizes efforts to maximize return expectations and can minimize stock risk. One method that can be used to measure risk is Expected Shortfall (ES). ES is an expected measure of risk whose value is above Value-at-Risk (VaR). To make it easier to calculate optimal portfolios with the Markowitz method and risk analysis with ES, an application was made using the Matlab GUI. The data used in this study consisted of three JII stocks including CPIN, CTRA, and BSDE stocks. The results of the portfolio formation with the Markowitz method obtained an optimal portfolio, namely the combination of CPIN = 34.7% and BSDE = 65.3% stocks. At the 95% confidence level, the ES value of 0.206727 is greater than the VaR value (0.15512).

PENDUGAAN ANGKA PUTUS SEKOLAH DI KABUPATEN SEMARANG DENGAN METODE PREDIKSI TAK BIAS LINIER TERBAIK EMPIRIK PADA MODEL PENDUGAAN AREA KECIL Nandang Fahmi Jalaludin Malik; Abdul Hoyyi; Dwi Ispriyanti
Jurnal Gaussian Vol 3, No 1 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Nowadays, small area information that has a small sample size is needed. A direct estimation in the small area will produce a large variance of values. In order of that, another alternative is needed that can be used is the indirect estimation. Small area estimation is an indirect estimation method that can be used to estimate parameters in a small area by utilizing information from outside the area, from the area itself, and from outside the survey. One of the methods that can be used is the empirical best linear unbiased prediction (EBLUP). EBLUP will be used to estimate the dropout rate for each village in the district of Semarang. Additional information used in this EBLUP method are the number of educational facilities, population, average expenditure per capita and distance from village to district. The results of EBLUP estimation showed that the lowest dropout rate village is Beji village and the highest is Pledokan village. Indirect estimation with EBLUP methods for the case of dropout rate in the district of Semarang has a coefficient variance 0,598% smaller than the coefficient variance that obtained from direct estimation

ANALISIS PREFERENSI MERK LAPTOP MAHASISWA UNIVERSITAS DIPONEGORO MENGGUNAKAN MODEL LOGIT TERSARANG Ain Hafidita; A Rusgiyono; Dwi Ispriyanti
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Today’s rapidly evolving information technology affects mostly the change of people’s choice in electronic devices (gadget) usage, especially laptops. Addressing this phenomenon, laptop manufacturers are competing to create innovative products to reach various elements of the consumer. Nested logit, which sorts the alternatives based on common properties into smaller groups (nests) and has a level so as to form a tree structure, is a method that can be used to model consumer preferences. Alternatives may have either unique or common characteristics that describe properties or components so called attributes. In this study, laptop brands are treated as alternatives and classified by the operating system. This research concluded that the most favorite brand is Asus (25.35 %), followed by Toshiba (22.81%), Lenovo (14.27%), HP (13.90%), Acer (12.40%) and the least is Macbook (11.27%). Attributes that significantly affect the brand preferences are laptop classification and warranty, while color is considered insignificant.

PERAMALAN INDEKS HARGA KONSUMEN MENGGUNAKAN MODEL INTERVENSI FUNGSI STEP Dita Ruliana; Sugito Sugito; Dwi Ispriyanti
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Intervention model is a model for time series data in which practically there is an extreme fluctuation, whether it’s anupward or downward fluctuation. Consumer price index is one of economic data which plot has a fluctuation, the data that will being used for analyze is consumer price index of Indonesia in January 2009 until March 2015, on data detectable downward fluctuation significantly on January 2014 (T=61). Intervention in data was occurred in long time period (T=61 until T=75), so the model of intervention’s assumption is step function. Based on the result and analysis, the obtaining best model of intervention is ARIMA (2,1,3) with intervention order b=0 s=15 and r=0 which later on being used for predicting Indonesian consumer price index in six periods ahead. Keywords : consumer price index, stationery, ARIMA, step function intervention analysis, forecasting

KLASIFIKASI LAMA STUDI MAHASISWA FSM UNIVERSITAS DIPONEGORO MENGGUNAKAN REGRESI LOGISTIK BINER DAN SUPPORT VECTOR MACHINE (SVM) Sri Maya Sari Damanik; Dwi Ispriyanti; Sugito Sugito
Jurnal Gaussian Vol 4, No 1 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Wisuda adalah hasil akhir dari proses kegiatan belajar mengajar selama mengikuti perkuliahan di perguruan tinggi. Dalam mencapai gelar S1 membutuhkan waktu normal yaitu selama empat tahun, tetapi ada banyak mahasiswa yang menyelesaikan studinya melebihi batas normal (lebih dari empat tahun) dan ada juga yang kurang dari empat tahun. Lama studi mahasiswa dapat dipengaruhi oleh banyak faktor antara lain Indeks Prestasi Kelulusan (IPK), jenis kelamin, jurusan, lama studi yang ditempuh, beasiswa, part time, organisasi, dan jalur masuk universitas. Pada penelitian ini, akan dilakukan klasifikasi berdasarkan status lama studi mahasiswa lebih dari empat tahun dan kurang dari sama dengan empat tahun. Metode yang digunakan untuk klasifikasi lama studi mahasiswa dengan jenis data nominal adalah Metode Support Vector Machine (SVM) dan akan dibandingkan dengan metode Regresi Logistik Biner. Berdasarkan hasil penelitian dengan metode regresi logistik biner, menunjukkan variabel yang berpengaruh terhadap lama studi mahasiswa adalah Jurusan dan IPK dengan ketepatan klasifikasi 70%. Sedangkan ketepatan klasifikasi dengan menggunakan SVM ketepatan klasifikasi tertinggi dengan menggunakan kernel linear, Polynomial dan RBF mencapai 90%.Kata kunci : Lama studi, Regresi Logistik Biner, Support Vector Machine (SVM), Ketepatan Klasifikasi.

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

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