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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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ALGORITMA ITERATIVE DICHOTOMISER 3 (ID3) UNTUK MENGIDENTIFIKASI DATA REKAM MEDIS (Studi Kasus Penyakit Diabetes Mellitus Di Balai Kesehatan Kementerian Perindustrian, Jakarta) Avia Enggar Tyasti; Dwi Ispriyanti; Abdul Hoyyi
Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Iterative Dichotomiser 3 (ID3) Algorithm is a basic decision tree learning algorithm. These algorithms perform a thorough search (greedy) in all possible decision tree. ID3 algorithm can be implemented using a recursive function, (function that calls itself). One of the problems that can be solved using the ID3 algorithm is a classification of diabetic patients. Diabetic is a disease because of the body is not able to control the amount of sugar or glucose in the bloodstream. Classification using ID3 in the case of diabetics produce trees with many vertices to 32 knot where 21 of them is a leaf node and attribute two-hour postprandial glucose fasting elected as the root node in the decision-making tree. Based on the classification performance measurements show that the classification accuracy or measurement accuracy reaches 89,75%. While the measurement accuracy of the classification algorithm ID3 using test samples totaling 84 samples showed an accuracy of 72,619%. Keywords: ID3 Algortihm, Decision Tree, DiabetesALGORITMA ITERATIVE DICHOTOMISER 3 (ID3) UNTUK MENGIDENTIFIKASI DATA REKAM MEDIS(Studi Kasus Penyakit Diabetes Mellitus Di Balai Kesehatan Kementerian Perindustrian, Jakarta)

PERAMALAN INDEKS HARGA SAHAM GABUNGAN DENGAN METODE LOGISTIC SMOOTH TRANSITION AUTOREGRESSIVE (LSTAR) Gayuh Kresnawati; Budi Warsito; Abdul Hoyyi
Jurnal Gaussian Vol 7, No 1 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Smooth Transition Autoregressive (STAR) Model is one of time series model used in case of data that has nonlinear tendency. STAR is an expansion of Autoregressive (AR) Model and can be used if the nonlinear test is accepted. If the transition function G(st,γ,c) is logistic, the method used is Logistic Smooth Transition Autoregressive (LSTAR). Weekly IHSG data in period of 3 January 2010 until 24 December 2017 has nonlinier tend and logistic transition function so it can be modeled with LSTAR . The result of this research with significance level of 5% is the LSTAR(1,1) model. The forecast of IHSG data for the next 15 period has Mean Absolute Percentage Error (MAPE) 2,932612%. Keywords : autoregressive, LSTAR, nonlinier, time series

PEMODELAN KECEPATAN ANGIN DI KOTA SEMARANG MENGGUNAKAN ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS) Alifah Zahlevi; Alan Prahutama; Abdul Hoyyi
Jurnal Gaussian Vol 8, No 3 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Semarang city is the one of the strategic areas located in the middle of the north coast of Java that has a tropical climate with the high humidity and temperature, so it often causes a high rainfall and strong wind. So that is way Semarang city is ever sustained the extreme weather like a Tropical Storm. Since January 2016 until 2017 there are 34 cases of Tornado and 24 incidents of fallen trees because of the gale. For helping the people to be allert the effect of the strong winds can be done by predicting the average of wind velocity by using Adaptive Neuro-Fuzzy Inference System (ANFIS) method which can predict the climate change that do not require the assumption of white noise and normal residual distribution. In addition ANFIS is a group of neural network with input that has been fuzzied on the first or second layer, but the weight of the artificial neural is not fuzzied. The identification result of stationaries obtained the plot of PACF on the first and second lag, with the result that these lag which will be a input variable on ANFIS model. The result of ANFIS by using cluster FCM, the third total membership show the smallest percentage of RMSE in-sample is 0,0048 on the first lag, and the smallest percentage of RMSE out-sample is 0,008 on the ANFIS model with the input lag 1 and three cluster. Keywords : the average of wind velocity, ANFIS, RMSE

IDENTIFIKASI VARIABEL YANG MEMPENGARUHI BESAR PINJAMAN DENGAN METODE POHON REGRESI (Studi Kasus di Unit Pengelola Kegiatan PNPM Mandiri) Shaumal Luqman; Moch. Abdul Mukid; Abdul Hoyyi
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Most people need a loan to fullfil their daily needs, such as a loan of goods or money. Loan can be obtained from financial institutions or individuals. In order to the loan granted by a financial institutions is not wrong target, financial institutions usually apply precaution principle. In making decisions related to how much a decent loan granted to a customer, the financial institutions often use the help of statistical methods. One methods often used is the Classification and Regression Trees (CART). Classification and Regression Trees (CART) is a nonparametric method that can be used to identify the variable that affect the amount of the loan at a financial institutions and estimate how much worth of loans granted. Because of the loan is a continous variable so the form of the tree is a Regression Tree. In this thesis, the financial institutions is UPK PNPM Mandiri Mekar Sejati in Kecamatan Bawang Kabupaten Batang. Variables that may be affected for large loans are age, occupation, type of warranty, the number family members, and the average income per month. The analysis showed that the variables that most influence on the income of the loans. Mean Absolute Percentage Error (MAPE) value from this method is 36%.Keyword : Regression tree, CART, Large loans.

PENENTUAN VALUE AT RISK SAHAM KIMIA FARMA PUSAT MELALUI PENDEKATAN DISTRIBUSI PARETO TERAMPAT Dede Zumrohtuliyosi; Abdul Hoyyi; Agus Rusgiyono
Jurnal Gaussian Vol 4, No 3 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Each investment object being traded in the stock market will get return that it has risk potential. Return and risk has mutual correlation that equilibrium. If the risk is high, then it obtains high return and vice versa. Risk management is the desain and implementation procedure for controlling risk. Value at Risk (VaR) is instrument to analyze risk management. Financial time series data for return data is assumed that it has heavy tail distribution and heteroscedasticity case (volatility clustering). Time series model that used to modelling this condition are Autoregressive Conditional Heteroscedasticity (ARCH) and Generalized Autoregresive Conditional Heteroscedasticity (GARCH), while Value at Risk calculation is used Generalized Pareto Distribution (GPD) approach. This research uses return data from stock closing prices of Kimia Farma Pusat period October 2009 until September 2014. The best ARCH-GARCH model is ARIMA(0,1,1) GARCH(1,2) model because the parameters are significant and it has the smallest AIC value. Risk calculation that is gotten with GPD approach if invest in Kimia Farma Pusat with interval confidence 95% is 13.6928% rupiah from current asset. Keywords: Stock, Risk, Generalized Autoregressive Conditional Heteroscedasticity (GARCH), Generalized Pareto Distribution (GPD), Value at Risk (VaR)

PEMODELAN FUNGSI TRANSFER DAN BACKPROPAGATION NEURAL NETWORK UNTUK PERAMALAN HARGA EMAS (Studi Kasus Harga Emas Bulan Juli 2007 sampai Februari 2019) Silvia Nur Rinjani; Abdul Hoyyi; Suparti Suparti
Jurnal Gaussian Vol 8, No 4 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The prestige of investment is increasingly rising as the people educates in managing finances. Gold is an alternative that most people tend to choose to invest. One of the important knowledge in gold investing is to predict the price in the future with factors that influence the price of gold. Therefore, in this research we made a model of gold prices based on crude oil prices. One method to forecast gold prices based on crude oil prices is the transfer function and backpropagation neural network. The results of transfer function model will be used as input for the backpropagation neural network method. The purpose of this research is to get the right forecasting method through the transfer function and backpropagation neural network model that can be used to predict gold prices. The results showed that the transfer function model with b = 0, r = [2], s = 0 and the ARMA noise model (0, [6]) is the best model to forecast the price of gold with the MAPE value of data out sample as 3,3507%. Keywords : Gold Price, Crude Oil Prices, Transfer Function,Backpropagation Neural Network, Forecasting

PENGELOMPOKAN PROVINSI-PROVINSI DI INDONESIA MENGGUNAKAN METODE WARD (StudiKasus: Produksi Tanaman Pangan di Indonesia Tahun 2018) Besya Salsabilla Azani Arif; Agus Rusgiyono; Abdul Hoyyi
Jurnal Gaussian Vol 9, No 1 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Cluster analysis is a technique for grouping objects or observations into homogeneous groups. Cluster analysis is divided into two methods, namely hierarchy and non-hierarchy. The hierarchy method generally involves a series of n-1 decisions (n is the number of observations) that combine observations into a tree-like structure or dendogram. Hierarchy is divided into two methods, namely agglomerative (concentration) and splitting (distribution). For non-hierarchical methods, the number of clusters can be determined by the researcher. Ward method is a hierarchical cluster analysis method that can maximize homogeneity in the cluster. The Sum-of-Square (SSE) formula is used in this method to minimize variations in the clusters that are formed. In this research, squared euclid distance is used to measure the similarity between object pairs. The data used in this study are secondary data on food crop production, namely rice, corn, soybeans, peanuts, green beans, sweet potatoes, and cassava in Indonesia 2018. To determine the cluster, the elbow method is used to form optimal clusters using WSS formula. Based on the analysis results, it was found that the optimal cluster is four clusters. The first cluster consists of 9 Province, the second cluster consists of 20 Province, the third cluster consists of 1 Province, the fourth cluster consists of 2 Province, and the fifth cluster consists of 2 Province.Keywords: Food Crop, Cluster Analysis, Ward Method, Squared Euclid, Elbow Method

PENGUKURAN PROBABILITAS KEBANGKRUTAN OBLIGASI KORPORASI DENGAN SUKU BUNGA VASICEK MODEL MERTON (Studi Kasus Obligasi PT Bank Lampung, Tbk) Kumo Ratih; Di Asih I Maruddani; Abdul Hoyyi
Jurnal Gaussian Vol 1, No 1 (2012): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Bond is one of financial instrument that have lower investment risk than stock. One of investment risk is credit risk. Its refers to the risk due to unexpected changes in the credit quality of a counterparty or issuer on maturity date. There are two ways in the modelling of credit risk, structural model and reduced models. The structural model introduced by Black-Scholes (1973) and Merton (1974). On the Merton model assume that default occurs when the firm can not pay the coupon or face value at the maturity date. The interest rate on this model asssumed following Vasicek rate. An empirical study using corporate bond of PT Bank Lampung, Tbk with 300 billion face value. Value of Probability of Default 0,0000007910811% provethat PT Bank Lampung still can full their obigation at November 2012.

PENERAPAN METODE KORESPONDENSI BERSAMA UNTUK ANALISIS PERUBAHAN PERILAKU PENGGUNA SMARTPHONE Isowedha Widya Dewi; Mustafid Mustafid; Abdul Hoyyi
Jurnal Gaussian Vol 3, No 3 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Competition is extremely tight in the technology sector including smartphones , the manufacturers compete to satisfy the desire of consumers with a variety of innovations. This study aims is form joint correspondence plot to determine whether consumers switch from one type to the other types of smartphones, as well as knowing what respondents consider when buying a smartphone . By adding a time variable data on methods of joint correspondence, changes in consumer behavior can be determined within a certain time . Time variables used was from 2011 to 2013 and smartphones that will be compared is Blackberry, Android and iOS. From the resulting graph can be seen that many kinds of smartphones used in each time variable and variables that affect the time of purchase . After doing research , showed that smartphone users in 2011, mostly used a Blackberry switched to Android in 2012 and 2013. Blackberry users at the time of puchase paid attention to the brand , color , design , and camera , but did not pay attention to prestige . Android users paid attention to completeness of the application , RAM , data capacity , color , resale price and network coverage . While iOS is not widely used by respondents from 2011 to 2013. iOS users considered the prestige , but did not consider the brand , design and battery life.

ANALISIS INTERVENSI FUNGSI STEP (Studi Kasus Pada Jumlah Pengiriman Benda Pos Ke Semarang Pada Tahun 2006 – 2011) Amelia Crystine; Abdul Hoyyi; Diah Safitri
Jurnal Gaussian Vol 3, No 3 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Data time series yang dipengaruhi oleh beberapa kejadian yang disebut intervensi akan mengakibatkan perubahan pola data pada satu waktu t. Analisis intervensi terdiri dari dua fungsi yaitu fungsi step dan fungsi pulse.Time series data that are influenced by several events called the intervention will lead to changes in the pattern of data at a t time. Analysis of intervention consists of two functions, that is the step function and pulse function. Intervention of step function represents an intervention that have long-term effects, whereas pulse function represents an intervention that takes place at a particular time. Step function intervention model was created based on the delay time of the intervention (b), the length of the intervention effect (s), and the pattern of intervention effects that was occured after b + s period (r). Intervention modeling was done after ARIMA (Autoregressive Integrated Moving Average) model was acquired. ARIMA model was used to determine the b, s, and r order of intervention. In this study, the step function intervention analysis was used to assess the amount of postage on the period January 2006 to February 2011. Based on the analysis, the ARIMA model produced was ARIMA (0,1,1). Based on intervention response obtained residual value b = 4, s = 0, r = 2 is used to form a model of intervention using the least squares method.

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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