Articles

UJI LINEARITAS DATA TIME SERIES DENGAN RESET TEST Budi, Warsito; Ispriyanti, Dwi
MATEMATIKA Vol 7, No 3 (2004): JURNAL MATEMATIKA
Publisher : MATEMATIKA

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Abstract

  Tulisan ini membahas prosedur pengujian linearitas data time series menggunakan uji RESET test versi Ramsey dan Lagrange Multiplier. Uji yang digunakan adalah uji yang telah diperbaiki dengan pembentukan komponen utama dari bentuk polinomial pada persamaan uji. Prosedur uji kemudian diterapkan pada data simulasi untuk model linear AR(2), AR(2) dengan outlier dan model nonlinear  LSTAR(2) dengan n = 200. Pengujian menunjukkan hasil yang mirip diantara kedua uji dimana data simulasi dari model linear tidak menjamin kelinearan, sedangkan data simulasi model nonlinear secara signifikan berbentuk nonlinear pada taraf 5%.  
PERAMALAN BEBAN PEMAKAIAN LISTRIK JAWA TENGAH DAN DAERAH ISTIMEWA YOGYAKARTA DENGAN MENGGUNAKAN HYBRID AUTOREGRESIVE INTEGRATED MOVING AVERAGE – NEURAL NETWORK Fitriani, Berta Elvionita; Ispriyanti, Dwi; Prahutama, Alan
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Departemen Statistika FSM Undip

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Excessive use of electronic devices in household and industry has made the demand of nation?s electrical power increase significantly these days. As a corporation that aim to provide national electrical power,  Perusahaan Listrik Negara (PLN) that distributes electrical power to Central Java and Yogyakarta has to be able to provide an economical and reliable system of electrical power provider. This study aimed to forecast data of electrical power usage in Central Java and Yogyakarta for the next 30 days. There were three forecasting methods used in this study; Neural Networks and Hybrid ARIMA-NN.  The data used in this study was electrical power usage data in January 2014 - November 2014 in Central Java and Yogyakarta. The accuracy of the study was measured based on MSE criteria where the best model chosen was the model that has lowest MSE value. According to the result of the analysis, using Neural Networks model to forecast electrical power usage for the next 30 days has better forecasting result than Hybrid ARIMA-NN model.Key Word : electrical power usage, forecasting of electrical power usage, ARIMA, NN, hybrid ARIMA-NN
KLASIFIKASI KELOMPOK RUMAH TANGGA DI KABUPATEN BLORA MENGGUNAKAN MULTIVARIATE ADAPTIVE REGRESSION SPLINE (MARS) DAN FUZZY K-NEAREST NEIGHBOR (FK-NN) Kristiani, Yani Puspita; Safitri, Diah; Ispriyanti, Dwi
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Departemen Statistika FSM Undip

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Good classification method will result on less classification error. Classification method developed rapidly. Two of the existing classification methods are Multivariate Adaptive Regression Spline (MARS) and Fuzzy K-Nearest Neighbor (FK-NN). This research aims to compare the classification of poor household and prosperous household based on per capita income which has been converted according to the poverty line between MARS and FK-NN method. This research used secondary data in the form of result of National Economy and Social Survey (SUSENAS) in Blora subdistrict in 2014. The result of the classification was evaluated using APER. The best classification result using MARS method is by using the combination of BF= 76, MI= 3, MO= 1 because it will result on the smallest Generalized Cross Validation (GCV) and the APER is 10,119 %. The best classification result using FK-NN method is by using K=9 because it will result on the smallest error and the APER is 9,523 %. The APER calculation shows that the classification of household in Blora subdistrict using FK-NN method is better than using MARS method. Keywords: Classification, MARS, FK-NN, APER, SUSENAS, Blora
OPTIMALISASI PORTOFOLIO SAHAM MENGGUNAKAN METODE MEAN ABSOLUTE DEVIATION DAN SINGLE INDEX MODEL PADA SAHAM INDEKS LQ-45 Wulandari, Diah; Ispriyanti, Dwi; Hoyyi, Abdul
Jurnal Gaussian Vol 7, No 2 (2018): Jurnal Gaussian
Publisher : Departemen Statistika FSM Undip

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Stock investment is the planting of money in a securities that indicates the ownership of a company in order to provide benefits in the future. In obtaining optimal results from stock investments, investors are expected to create a series of portfolios. The portfolio will help investors in allocating some funds in different types of investments in order to achieve optimal profitability. For selection of optimal stocks representing LQ-45 Index, used 2 methods of Mean Absolute Deviation (MAD) method and Single Index Model (SIM) method. In MAD method, 5 best stocks are BBCA with weight 23%, INDF 8%, KLBF 23%, TLKM 23%, and UNVR 23%. While the SIM method of candidate portfolio obtained is AKRA with weight 15,459%, BBCA 48,193%, BBNI 5,028%,KLBF 0,258% and TLKM 31,062%. Portfolio performance meter is used by sharpe ratio. The value of sharpe ratio is 0,36754 for optimal portfolio using MAD method and 0,40782 for optimal portfolio using SIM method, this means that optimal portfolio using SIM method has better performance than MAD. Keywords: Investment, Portfolio, Index LQ-45, Mean Absolute Deviation, Single Index Model, Sharpe Ratio
PENENTUAN MODEL RETURN HARGA SAHAM DENGAN MULTI LAYER FEED FORWARD NEURAL NETWORK MENGGUNAKAN ALGORITMA RESILENT BACKPROPAGATION (STUDI KASUS : HARGA PENUTUPAN SAHAM UNILEVER INDONESIA TBK. PERIODE SEPTEMBER 2007 – MARET 2015) Priantoro, Riza Adi; Ispriyanti, Dwi; Mukid, Moch. Abdul
Jurnal Gaussian Vol 5, No 1 (2016): Jurnal Gaussian
Publisher : Departemen Statistika FSM Undip

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Determination of a return of stock price model is often associated with a process of forecasting for future periods.  A method that can be used is neural network. The use of neural network in the field of forecasting can be a good solution, but the problem is how to determine the network architecture and the selection of appropriate training methods. One possible option is to use resilent back propagation algorithm. Resilent back propagation algorithm is a supervised learning algorithm to change the weights of the layers. This algorithm uses the error in the backward direction (back propagation), but previously performed advanced stage (feed forward) to get the error. This algorithm can be used as a learning method in training model of a multi-layer feed forward neural network. From the results of the training and testing on the share return of stock price PT. Unilever Indonesia Tbk. data obtained MSE value of 0.0329. This model is good to use because it provides a fairly accurate prediction of the results shown by the proximity of the target with the output.Keywords : return, neural network, back propagation, feed forward, back propagation algorithm, weight, forecasting.
ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI UPAH MINIMUM KABUPATEN/KOTA DI PROVINSI JAWA TENGAH MENGGUNAKAN MODEL SPATIAL AUTOREGRESSIVE (SAR) Merdekawaty, Rahmah; Ispriyanti, Dwi; Sugito, Sugito
Jurnal Gaussian Vol 5, No 3 (2016): Jurnal Gaussian
Publisher : Departemen Statistika FSM Undip

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Spatial regression is the result of the development of linear regression method, wherein the location or spatial aspects of the analyzed data are also must be considered. The phenomenon that includes spatial data of which is the deployment of a minimum wage. Minimum Wages District/City is a minimum standard that is used by employers to provide wages to employees in its business environment on a district/city in any given year. Minimum Wages District/City is determined by considering the welfare of workers and the state of the local economy. Factors in worker welfare such as Worth Living Needs and the Consumer Price Index (CPI), while one important indicator to determine the economic conditions in the region within a certain time period is Gross Domestic Product (GDP). Modeling the influence of these factors can be determined by using multiple linear regression and spatial regression. Based on the data processing result, there is a spatial dependence in the Minimum Wages District/City variable in Central Java, so Spatial Autoregressive (SAR) method is used in this study. Variables that significantly affect the UMK in Central Java through multiple linear regression method and SAR is the Worth Living Needs (X1) and CPI (X2). The SAR model generates the value of R2 at 72.269% and AIC at 66.393, better than the multiple linear regression model that generates the value of R2 at 68% and AIC at 68.482.Keywords :    Minimum Wages District/City, Worth Living Needs, CPI, GDP, multiple               linear regression, spatial dependence, Spatial Autoregressive
PERBANDINGAN METODE K-MEANS DAN METODE DBSCAN PADA PENGELOMPOKAN RUMAH KOST MAHASISWA DI KELURAHAN TEMBALANG SEMARANG Budiman, Sisca Agustin Diani; Safitri, Diah; Ispriyanti, Dwi
Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian
Publisher : Departemen Statistika FSM Undip

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Abstract

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
PENERAPAN METODE KLASIFIKASI SUPPORT VECTOR MACHINE (SVM) PADA DATA AKREDITASI SEKOLAH DASAR (SD) DI KABUPATEN MAGELANG Octaviani, Pusphita Anna; Wilandari, Yuciana; Ispriyanti, Dwi
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Departemen Statistika FSM Undip

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Accreditation is the recognition of an educational institution given by a competent authority, that is Badan Akreditasi Nasional Sekolah/Madrasah (BAN - S/M) after it is assessed that the institution has met the eight components of the accreditation assessment. An elementary school, as one of the compulsory basic education, should have the status of accreditation to ensure the quality of education. This study aimed to apply the classification method Support Vector Machine (SVM) on the data accreditation SD in Magelang. Support Vector Machine (SVM) is a method that can be used as a predictive classification by using the concept of searching hyperplane (separator functions) that can separate the data according to the class. SVM using the kernel trick for non-linear problems which can transform data into a high dimensional space using a kernel function, so that the data can be classified linearly. The results of this study indicate that the prediction accuracy of SVM classification using Gaussian kernel function RBF is 93.902%. It is calculated from 77 of 82 elementary schools that are classified correctly with the original classes. Keywords : Accreditation, Classification, Support Vector Machine (SVM), hyperplane, Gaussian RBF Kernel, Accuracy 
PENERAPAN METODE ORDINARY KRIGING PADA PENDUGAAN KADAR NO2 DI UDARA (STUDI KASUS: PENCEMARAN UDARA DI KOTA SEMARANG) Rozalia, Gera; Yasin, Hasbi; Ispriyanti, Dwi
Jurnal Gaussian Vol 5, No 1 (2016): Jurnal Gaussian
Publisher : Departemen Statistika FSM Undip

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Air pollution must be addressed. Nitrogen Dioxide is one of the important factors in air pollution. To determine concentration level of the pollutant ?Badan Lingkungan Hidup Kota Semarang? already take measurements  at several  points.  However,  because of  blocked  considerable cost, is  not  much  point to do measurements. In this study, will be used Ordinary Kriging method to estimate at some points in Semarang. In  this  methode will compare the value of  the eksperimental semivariogram  with  some theoretical semivariogram models (spherical, eksponensial, and gaussian) to get the best model that will be used in the estimation. In this study, estimate the concentration of Nitrogen Dioxide in the air in a number of village in Semarang. Based on analysis we found the best model is spherical model with Nitrogen Dioxide produces estimates is the highest in Sub Gebangsari and Nitrogen Dioxide lowest in Sub Patemon. Keywords: Ordinary Kriging, Semivariogram, Nitrogen Dioxide.
PERAMALAN HARGA SAHAM DENGAN METODE EXPONENTIAL SMOOTH TRANSITION AUTOREGRESSIVE (ESTAR) (STUDI KASUS PADA HARGA SAHAM MINGGUAN PT UNITED TRACTORS) Rahmayani, Dwi; Ispriyanti, Dwi; Mukid, Moch. Abdul
Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian
Publisher : Departemen Statistika FSM Undip

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The stock price data series of PT United Tractors in the period of December 1th 2008 to December 29th 2014 is fluctuative. To model data nonlinear time series one method that can be used is Smooth Transition Autoregressive (STAR), if the function of an exponential transition then a method that can be used is Exponential Smooth Transition Autoregressive (ESTAR). In modelling ESTAR determined transition variable ( of transition function ). Of the research result obtained model ESTAR (1,1). With significance level of 5% obtainedthe value of the stock price data for pt united tractors in the next four to the original. It was also strengthened by Mean Absolute Percentage Error (MAPE) 0,768233 %  are relatively small. Keywords : Autoregressive,time series, nonlinearity, ESTAR, MAPEThe stock price data series of PT United Tractors in the period of December 1th 2008 to December 29th 2014 is fluctuative. To model data nonlinear time series one method that can be used is Smooth Transition Autoregressive (STAR), if the function of an exponential transition then a method that can be used is Exponential Smooth Transition Autoregressive (ESTAR). In modelling ESTAR determined transition variable ( of transition function ). Of the research result obtained model ESTAR (1,1). With significance level of 5% obtainedthe value of the stock price data for pt united tractors in the next four to the original. It was also strengthened by Mean Absolute Percentage Error (MAPE) 0,768233 %  are relatively small. Keywords : Autoregressive,time series, nonlinearity, ESTAR, MAPE
Co-Authors A Rusgiyono Abdul Hoyyi Agus Rusgiyono Ain Hafidita Alan Prahutama Ana Kartikawati Anisa Septi Rahmawati, Anisa Septi Anjan Setyo Wahyudi, Anjan Setyo Arief Rachman Hakim Atika Elsadining Tyas, Atika Elsadining Aulia Ikhsan Avia Enggar Tyasti, Avia Enggar Berta Elvionita Fitriani, Berta Elvionita Bitoria Rosa Niashinta, Bitoria Rosa Budi Warsito Dedi Nugraha Di Asih I Maruddani Diah Safitri Diah Wulandari Dita Ruliana, Dita Dwi Rahmayani, Dwi Dyan Anggun Krismala Dydaestury Jalarno Erna Sulistio Evi Yulia Handaningrum Farah, Sania Anisa Firdha Rahmatika Pratami, Firdha Rahmatika Gera Rozalia, Gera Hasbi Yasin Hasibuan, Rafida Zahro Henny Widayanti, Henny Ilham Maggri Irawati Tamara, Irawati Islami, Firda Dinny Jesica, Haniela Puja Kishatini Kishartini Ma'sum, M. Ali Margaretha, Cylvia Evasari Marlia Aide Revani Masfuhurrizqi Iman Maulida Azkiya, Maulida Maulida Najwa, Maulida Mawarni, Azizah Mulia Moch. Abdul Mukid Muhammad Fitri Lutfi Anshari Muhammad Rosyid Abdurrahman Mustafid Mustafid Nanci Rajagukguk, Nanci Nandang Fahmi Jalaludin Malik Natalia P P, Sylvi Natalia P P, Sylvi Nova Nova Noviana Nurhayati Nurwihda Safrida Umami Oka Afranda, Oka Pangestikasari, Merinda Pritha Sekar Wijayanti Pusphita Anna Octaviani Rahafattri Ariya Fauzannissa, Rahafattri Ariya Rahmah Merdekawaty, Rahmah Rahmaniar, Ratna Ramadhani, Puput Rany Wahyuningtias Ratih Nurmalasari, Ratih Ratna Pratiwi Ria Sutitis, Ria Rio Tongaril Simarmata Rita Rahmawati Riza Adi Priantoro, Riza Adi Sa'adah, Alfi Faridatus Sherly Candraningtyas Sindy Saputri Sisca Agustin Diani Budiman, Sisca Agustin Diani Sri Maya Sari Damanik Sudarno Sudarno Sugito Sugito Suhendra, Muhammad Arif Suparti Suparti Suparti, S. Syilfi Syilfi Tarno Tarno Taryono, Arkadina Prismatika Noviandini Tatik Widiharih Tiani Wahyu Utami Triastuti Wuryandari Trimono, Trimono Warsito Budi Wulandari, Annisa Ayu Yani Puspita Kristiani, Yani Puspita Yuciana Wilandari