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All Journal PROSIDING SEMINAR NASIONAL Media Statistika SAINS DAN MATEMATIKA Jurnal Gaussian Jurnal Statistika Universitas Muhammadiyah Semarang E-Dimas: Jurnal Pengabdian kepada Masyarakat Seminar Nasional Variansi (Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika) EKO-REGIONAL JURNAL PENGEMBANGAN EKONOMI WILAYAH

Abdul Hoyyi

Departemen Statistika, Fakultas Sains Dan Matematika, Universitas Diponegoro

Author-ID : 3131506

Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Mathematics Social Sciences Other

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PREDIKSI JUMLAH KEBERANGKATAN PENUMPANG PESAWAT TERBANG MENGGUNAKAN MODEL VARIASI KALENDER DAN DETEKSI OUTLIER (Studi Kasus di Bandara Soekarno-Hatta) Alvi Waldira; Abdul Hoyyi; Dwi Ispriyanti
Jurnal Gaussian Vol 9, No 3 (2020): 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.v9i3.28914

Transportation has a strategic role, even becoming one of the main needs of the community, especially air transportation services. A large number of passengers in air transportation always experiences a difference every month. One of the differences occurred when approaching Eid al-Fitr, which changes every year based on an Islamic calendar that is different from Masehi calendar. The lunar shift in the occurrence of Eid al-Fitr forms a pattern called calendar variation. The effects of calendar variations can be overcome by using an additional variable, such as a dummy variable, this variable which will be used in the ARIMAX model. Observation of time series is often influenced by several unexpected events such as outliers. This outlier causes the results of data analysis to be less valid. So the researchers added the detection of outliers in this study. Based on the analysis results, the ARIMA calendar variation model is obtained (1.0, [12]), with time variable t, dummy variable , and the addition of one outlier. This model has a MAPE value of 0.07079609 which means this model is very good for forecasting. Forecasting results showed an increase in the number of passengers during the two months before Eid. Keywords: Passenger, calendar variation, outlier detection

PENGUKURAN PROBABILITAS KEBANGKRUTAN DAN VALUASI OBLIGASI KORPORASI DENGAN METODE CREDITRISK+ Yudia Yustine; Abdul Hoyyi; Di Asih I Maruddani
Jurnal Gaussian Vol 1, No 1 (2012): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

In capital market investment particularly the bonds, an investor must consider the credit risk and valuation of bonds. Credit risk refers to the risk due to unexpected changes in the credit quality of a counterparty or issuer. Valuation is amount that investor will receive on future. CreditRisk+ is from Reduced-Form Model which is used to calculate the probability of default and valuation of bonds. This method assumes that default occurs without warning and is therefore unpredictable. Default arrival is described by a Poisson process. Default intensity can expected by rate of corporate. An empirical example use a data set of bond from PT Berlian Laju Tanker, Tbk between 2007 and 2012. Probability of default from Berlian Laju Tanker III Bond is 0,6321206 and its valuation is Rp 153.481.545.500,00.

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

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.

METODE MODIFIED JACKKNIFE RIDGE REGRESSION DALAM PENANGANAN MULTIKOLINIERITAS (STUDI KASUS INDEKS PEMBANGUNAN MANUSIA DI JAWA TENGAH) Arya Huda Arrasyid; Dwi Ispriyanti; Abdul Hoyyi
Jurnal Gaussian Vol 10, No 1 (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.v10i1.29922

The human development index is a value where the value showed the measure of living standards comparison in a region. The Human Development Index is influenced by several factors, one of them is the education factor that is the average years of schooling and expected years of schooling. A statistical method to find the correlation between the independent variable and the dependent variable can be conducted using the linear regression method. Linear regression requires several assumptions, one of which is the multicollinearity assumption. If the multicollinearity assumption is not fulfilled, another alternative is needed to estimate the regression parameters. One method that can be used to estimate regression parameters is the ridge regression method with an ordinary ridge regression estimator. Ordinary ridge regression then developed more into several methods, such as generalized ridge regression, jackknife ridge regression, and modified jackknife ridge regression method. The generalized Ridge Regression method causes a reduction to variance in linear regression, while the jackknife ridge regression method is obtained by resampling jackknife process on the generalized ridge regression method. Modified jackknife ridge regression is a combination of generalized ridge regression and jackknife ridge regression method. In this journal, the three ridge regression methods will be compared based on the Mean Squared Error obtained in each method. The results of this study indicate that the jackknife ridge regression method has the smallest MSE value. Keywords: Generalized Ridge Regression, Jackknife Ridge Regression, Modified Jackknife Ridge Regression, Multicolinearity

PEMILIHAN HELM TERFAVORIT DENGAN MADM BERBASIS GUI MATLAB (Studi Kasus : Pemilihan Helm Terfavorit oleh Mahasiswa FSM Undip, Semarang) Nadya Kiki Aulia; Tatik Widiharih; Abdul Hoyyi
Jurnal Gaussian Vol 6, No 3 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Safety is an important factor that need to be considered in driving safety. One of important factor that need to be considered is the use of Indonesian National Standard (SNI) helmets. The large number of SNI helmets existance, make consumers especially students, have their own preferences in choosing a helmet. The methods that can be used to choose the most favorite helmet is ELECTRE and TOPSIS. Both of these methods are the methods used to solve MADM problems. There are 8 brands of helmets namely INK, KYT, BMC, HIU, VOG, BOGO, NHK, dan GM. For helmet selection criteria are safety helmets (helmet safety straps when worn), affordable helmet prices, easy found helmet, variety of helmet colors, various sizes available, helmets cover the entire face, comfortable helmet glass when worn, clear helmet glass, quality of the outside of the helmets, helmet foam quality, and resistance to impact. By using ELECTRE method, this research got result that the most favorite helm is INK helmet brand which has the number of row element as much 5. For TOPSIS method, the most favorite helmet is KYT helmet brand with preference value equal to 0.7146. Keywords: ELECTRE, TOPSIS, Helmet, favorite, GUI

PEMODELAN RETURN HARGA SAHAM MENGGUNAKAN MODEL INTERVENSI–ARCH/GARCH (Studi Kasus : Return Harga Saham PT Bayan Resources Tbk) Dea Manuella Widodo; Sudarno Sudarno; Abdul Hoyyi
Jurnal Gaussian Vol 7, No 2 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The intervention method is a time series model which could be used to model data with extreme fluctuation whether up or down. Stock price return tend to have extreme fluctuation which is caused by internal or external factors. There are two kinds of intervention function; a step function and a pulse function. A step function is used for a long-term intervention, while a pulse function is used for a short-term intervention. Modelling a time series data needs to satisfy the homoscedasticity assumptions (variance of residual is homogeneous). In reality, stock price return has a high volatility, in other words it has a non-constant variance of residuals (heteroscedasticity). ARCH (Autoregressive Conditional Heteroscedasticity) or GARCH (Generalized Autoregressive Conditional Heteroscedasticity) can be used to model data with heteroscedasticity. The data used is stock price return from August 2008 until September 2018. From the stock price return data plot is found an extreme fluctuation in September 2017 (T=110) that is suspected as a pulse function. The best model uses the intervention pulse function is ARMA([1,4],0) (b=0, s=1, r=1). The intervention model has a non-constant variance or there is an ARCH effect. The best variance model obtained is ARMA([1,4],0)(b=0, s=1, r=1)–GARCH(1,1) with the AIC value is -205,75088. Keywords: Stock Return, Intervention, Heteroscedasticity, ARCH/GARCH

ANALISIS JALUR TERHADAP FAKTOR-FAKTOR YANG MEMPENGARUHI INDEKS PRESTASI KUMULATIF (IPK) MAHASISWA STATISTIKA UNDIP Malik Hakam; Sudarno Sudarno; Abdul Hoyyi
Jurnal Gaussian Vol 4, No 1 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Education is a priority thing everyone today. Education is implemented in learning, by learning humans can develop all the potential there is in him. Learning is always related to the achievement of learning, because learning is a process while learning achievement is the result of the learning process. In the course of learning achievement levels measured by GPA (Grade Point Average). Factors that influence GPA among allowance, age, value of the UN Senior High School, many organizations, the internet long, long time to learn. Path analysis is the development of multiple regression which the independent variables affect the dependent variable not only directly but also indirectly affect. Based on the results of the discussion of the factors that affect the GPA is concluded that the allowance has indirect effect of -0,211, age has direct effect of age at 0,1901, the UN has direct effect of 0,258, many organizations have a direct effect of -0,3582 and has indirect effect of -0,132, the internet long direct effect of -0,2376 and has indirect effect of -0,038, long learning has a direct effect of 0,2344. Keywords: Education, GPA, Path analysis, Direct effect, Indirect effect

PENGGUNAAN METODE PERAMALAN KOMBINASI TREND DETERMINISTIK DAN STOKASTIK PADA DATA JUMLAH PENUMPANG KERETA API (Studi Kasus : KA Argo Muria) Titis Nur Utami; Abdul Hoyyi; Agus Rusgiyono
Jurnal Gaussian Vol 6, No 1 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The amount of the data of KA Argo Muria indicates the improve in every year during Ied mubarak day. Ied Mubarak day follows the Hijriyah calender, this is inditates that there is case effect of variation on the calender. The aims of this research is to predict the amount of the KA Argo Mulia passanger of destination of Semarang – Jakarta for 12 periodes in the future by using forecasting time series model of variation calender. The data used mounthly amount data KA Argo Mulia at PT KAI DAOP IV Semarang in the periode of January 2014 until Desember 2015. The result of the data analysis shows significant variable toward the model is and the model of Autoregressive Integrated Moving Average (ARIMA) (1,0,0). Based on the result of forecasting out-sample data, is gained Mean Absolute Percentage Error (MAPE) is 1,8089 % which indicates that the result of forecasting is very good.Keywords: deterministic trend, calender variation, time series, stochastic model, dummy regression.

ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI KEPUTUSAN PEMBELIAN DAN KEPUASAN KONSUMEN PADA NOTEBOOK MEREK ACER (Studi Kasus Mahasiswa Universitas Diponegoro) Koko Arie Bowo; Abdul Hoyyi; Moch. Abdul Hoyyi
Jurnal Gaussian Vol 2, No 1 (2013): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Consumer perception about notebook product is a variated, this condition based on consumer need is referred that will exploit existing facility at a notebook. Generally, consumer buys a notebook product based on some considerations for example price, brand and product quality. If the product that the of exceed its expectation, consumer will satisfied and possibility will submit the good things about the products to others people. This research aims to analyze the factors that have an effect on purchasing decisions and consumer satisfaction on Acer notebook. Data collecting in this research use questionnaire , that was distributed to 110 students from Diponegoro University that have a Acer notebook.Technique sample uses accidental sampling method. The data obtained are then analyzed using Structural Equation Modeling (SEM). Based on research result is obtained that brand image not has an effect on to purchasing decision Acer notebook, while the product quality and price have an effect on purchasing decision Acer notebook. Despitefully also, the product quality and purchasing decision Acer notebook have an effect on consumer satisfaction. Keywords: brand image, price, quality product, purchasing decision, consumer satisfaction.

PEMODELAN REGRESI LINIER MULTIVARIAT DENGAN METODE PEMILIHAN MODEL FORWARD SELECTION DAN ALL POSSIBLE SUBSET SELECTION PADA JUMLAH KEMATIAN BAYI DAN INDEKS PEMBANGUNAN MANUSIA (IPM) ( Studi Kasus di Provinsi Jawa Tengah Tahun 2013 ) Indri Puspitasari; Abdul Hoyyi; Diah Safitri
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Regression analysis is a statistical analysis that aims to measure the effect of the independent variables to the dependent variable. Multivariate Linear Regression is a regression model that consists of more than one dependent variables and the dependent variables are correlated. The Number of Infant Mortality and Human Development Index (HDI) of Central Java Province in 2013 was influenced by several variables, such as: mean years of schooling and the number of health centers. To analyze the effects of mean years of schooling and the number of health centers to The Number of Infant Mortality and Human Development Index (HDI) can use multivariate linear regression analysis becuase the dependent variables are correlated. Model selection is determined by using the Forward Selection and All Possible Subset Selection. Selection the model by using Forward Selection, first variables that is included in the model is based of independent variable that have the greatest correlation with the dependent variables. For All Possible Subset Selection, model selection is done by modeling all the models that may have formed. AIC criteria is used for determining the model for All Possible Subset Selection. The model which is selected by using Forward Selection and All Possible Subset Selection has the same independent variables, the model with independent variables mean years of schooling and the number of health centers. The error of the model fulfill all of the error assumptions. Based on the model, the value of AIC is 247.8142 and Eta Squared Lambda is 92.22%. Keywords : Multivariate Linear Regression, Forward Selection, All Possible Subset Selection, AIC

Co-Authors Abdurakhman Abdurakhman Afifah Alrizqi Agus Rusgiyono Agus Somantri Ahmat Dhani Riau Bahtiyar Alan Prahutama Alan Prahutama Alifah Zahlevi Allima Stefiana Insani Alvi Waldira Alwi Assegaf Amelia Crystine Anggit Ratnakusuma Anggita, Esta Dewi Anik Nurul Aini Annisa Intan Mayasari ANNISA RAHMAWATI Ari Fakhrus Sanny Arief Rachman Hakim Arya Huda Arrasyid Aulia Desy Deria Avia Enggar Tyasti Bella Cynthia Devi Besya Salsabilla Azani Arif Bisri Merluarini Bitoria Rosa Niashinta Budi Warsito Budi Warsito Candra Silvia Chyntia Arum Widyastusti Cindy Wahyu Elvitra Darwanto Darwanto Dea Manuella Widodo Deby Fakhriyana, Deby Dede Zumrohtuliyosi Deden Aditya Nanda, Deden Aditya Dedi Rosadi Dermawanti Dermawanti Desriwendi Desriwendi Dewi Erliana Dewi Setya Kusumawardani Dhea Kurnia Mubyarjati Di Asih I Maruddani Di Asih I Maruddani Di Asih I Maruddani Diah Safitri Diah Safitri Diah Wulandari Dilla Retno Deswita Dwi Ispriyanti DWI RAHMAWATI Emyria Natalia br Sembiring Endah Cahyaningrum Erna Musri Arlita Esti Pratiwi Faiqotul Himmah Fiki Farkhati Firda Dinny Islami Fitra Ramdhani Gayuh Kresnawati Hasbi Yasin Hasbi Yasin Henny Setyowati Herwindhito Dwi Putranto Ikha Rizky Ramadani Indri Puspitasari Irfan Afifi Isowedha Widya Dewi Issabella Marsasella Christy Jeffri Nelwin J. O. Siburian Juli Sekar Sari, Juli Sekar Kartikaningtiyas Hanunggraheni Saputri Khotimatus Sholihah Khusnul Umi Fatimah Kiki Febri Azriati Koko Arie Bowo Kristika Safitri Kumo Ratih Leni Pamularsih Maidiah Dwi Naruri Saida Malik Hakam Mega Fitria Andriyani Mega Fitria Andriyani Mia Anastasia Sinulingga Moch. Abdul Hoyyi Moch. Abdul Mukid Moch. Abdul Mukid MUHAMMAD HARIS Mustafid Mustafid Mustafid Mustafid Mutiara Ardin Rifkiani Nadya Kiki Aulia Nandang Fahmi Jalaludin Malik Novika Pratnyaningrum Nurissalma Alivia Putri Nurul Fauziah Ovie Auliya’atul Faizah Priska Rialita Hardani Purina Pakurnia Artiguna Rita Rachmawati Rita Rahmawati Rita Rahmawati Rizki Pradipto Widyantomo Rizky Oky Ari Satrio Rukun Santoso Saputri, Ani Funtika Saraswati, Mei Sita Shaumal Luqman Silvia Nur Rinjani SITI NURLATIFAH Sudarno Sudarno Sudarno Sudarno Sugito - Sugito Sugito Sugito Sugito Suparti Suparti Suparti Suparti Tarno Tarno Tarno Tarno Tatik Widiharih Tatik Widiharih Tatik Widiharih Titis Nur Utami Tresno Sayekti Nuryanto Triastuti Wuryandari Triastuti Wuryandari Trisnawati Gusnawita Berutu Ubudia Hiliaily Chairunnnisa Ulfah Sulistyowati Yosi Dhyas Monica Yuciana Wilandari Yuciana Wilandari Yudia Yustine Yunisa Ratna Resti Yustian Dwi Saputra

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