Garuda - Garba Rujukan Digital

p-Index From 2021 - 2026

2.757

P-Index

This Author published in this journals

All Journal Media Statistika MATEMATIKA SAINS DAN MATEMATIKA Jurnal Gaussian Jurnal Ilmiah Bening : Belajar Bimbingan Dan Konseling GUIDENA: Jurnal Ilmu Pendidikan, Psikologi, Bimbingan dan Konseling JURNAL PENDIDIKAN TAMBUSAI JURNAL KAJIAN TEKNIK SIPIL Abdi Laksana : Jurnal Pengabdian Kepada Masyarakat MURHUM : JURNAL PENDIDIKAN ANAK USIA DINI Journal Of Human And Education (JAHE) EJOIN : Jurnal Pengabdian Masyarakat Innovative: Journal Of Social Science Research Jurnal Pengabdian Masyarakat Indonesia Sejahtera Alamtana

Tarno Tarno

Jurusan Statistika FSM Undip

Author-ID : 401717

Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Public Health Social Sciences Transportation Other

Published : 34 Documents Claim Missing Document

Claim Missing Document

Articles

Title

1 2

MODEL KOMBINASI ARIMA DALAM PERAMALAN HARGA MINYAK MENTAH DUNIA Setiyowati, Eka; Rusgiyono, Agus; Tarno, Tarno
Jurnal Gaussian Vol 7, No 1 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Oil is the most important commodity in everyday life, because oil is one of the main sources of energy that is needed for other people. Changes in crude oil prices greatly affect the economic conditions of a country. Therefore, the aim of this study is develop an appropriate model for forecasting crude oil price based on the ARIMA and its ensembles. In this study, ensemble method uses some ARIMA models to create ensemble members which are then combined with averaging and stacking techniques. The data used are the price of world crude oil period 2003-2017. The results showed that ARIMA (1,1,0) model produces the smallest RMSE values for forecasting the next thirty six months. Keywords: Ensemble, ARIMA, Averaging, Stacking, Crude Oil Price

PERAMALAN JUMLAH WISATAWAN YANG BERKUNJUNG KE OBJEK WISATA DI JAWA TENGAH MENGGUNAKAN VARIASI KALENDER ISLAM REGARIMA Jesica, Haniela Puja; Ispriyanti, Dwi; Tarno, Tarno
Jurnal Gaussian Vol 8, No 3 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Tourism is one of the most strategically controlled areas that have been developed.The number of tourists in Central Java is constantly rising in the month of Eid Al-Fitr caused by holiday and mudik to hometown. The shift of the Eid Al-Fitr month on the data will form a seasonal pattern with an unequal period, then called moving holiday effect.One of the calendar variationsare often used to remove the moving holiday effect is RegARIMA model. RegARIMA is a combination of the linier regression and ARIMA, which a weight was used as a regression variable and error of regression model was used a variable in the ARIMA process. Based on the analysis carried out on the monthly number of tourists visiting tourist attractions in Central Java data for the period January 2011 to December 2017, the RegARIMA (1,1,1) (0,0,1)12model as the best model because it have the lowest AIC value than other model. The forecasting results in 2018 shows an increase on number of tourists data on June 2018 which coincided with the Eid Al-Fitr holiday on 15 June 2018. sMAPE value is 23,298%.Keyowrds:Time Series, Tourists, RegARIMA, Moving Holiday Effect

PEMILIHAN INPUT MODEL REGRESSION ADAPTIVE NEURO FUZZY INFERENCE SYSTEM (RANFIS) UNTUK KAJIAN DATA IHSG Sari, Sasmita Kartika; Tarno, Tarno; Safitri, Diah
Jurnal Gaussian Vol 6, No 3 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The Jakarta Composite Index (JCI) is one of indexes issued by the Indonesia Stock Exchange (IDX) with its calculation component using all the registered emiten. Several factors affecting the JCI are Dow Jones Index, inflation, and USD/IDR exchange rate. The study used Regression Adaptive Neuro Fuzzy Inference System (RANFIS) to analyze the affect of predictor variables on the JCI. The role of regression in RANFIS is a preprocessing in the determination of input in ANFIS. The optimum ANFIS model in RANFIS is strongly influenced by three things, they are input determination, membership functions, and rule. The technique of defining rules followed the rule of genfis1 and genfis3. The model accuracy was measured using the smallest RMSE and MAPE. Based on the empirical studies which implemented Dow Jones Index, inflation, and USD/IDR exchange rate as the predictors and JCI as the response, it was obtained that optimum RANFIS model with gauss membership function, the number of cluster 2 with 2 rules generated by genfis3 produced RMSE in-sample 233.0 and out-sample 301.9, as well as MAPE in-sample 6.5% and out-sample 4.8%. While in regression analysis, it obtained RMSE in-sample 351.27 and out-sample 590.99, as well as MAPE in-sample 9.6% and out-sample 10.2% with violation of assumption. This shows that the result of RANFIS method is better than regression analysis. Keywords: JCI, regression analysis, neuro fuzzy, RANFIS, genfis

ANALISIS DATA RUNTUN WAKTU DENGAN METODE ADAPTIVE NEURO FUZZY INFERENCE SYSTEM (ANFIS) Saputra, Arsyil Hendra; Tarno, Tarno; Warsito, Budi
Jurnal Gaussian Vol 1, No 1 (2012): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

One popular method of time series analysis is ARIMA. The ARIMA method requires some assumptions; residual of model must be white noise, normal distribution and constant variance. The ARIMA model tends to be better for time series data which is linear. Whereas for the nonlinear time series data have been widely studied by nonlinear methods, one of that is Adaptive Neuro Fuzzy Inference System or ANFIS. The ANFIS method is a method that combines techniques Neural Network and Fuzzy Logic. In this thesis discussed the ANFIS method specifically for the analysis of time series data that have characteristics such as stationary, stationary with outlier, non stationary and non stationary with outlier, and the data of Indonesian palm oil prices is used as a case study. The ANFIS results which were obtained are compared with the results of ARIMA method by the value of RMSE. Based on the analysis and discussion, it is obtained that the results of ANFIS method are better than the results of ARIMA method.

PERAMALAN VOLATILITAS MENGGUNAKAN MODEL GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY IN MEAN (GARCH-M) (Studi Kasus pada Return Harga Saham PT. Wijaya Karya) Ratnasari, Dwi Hasti; Tarno, Tarno; Yasin, Hasbi
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Stock return volatility in the markets of developing countries (emerging markets) is generally much higher than the markets of developed countries. High volatility illustrates the level of high risk faced by investors due to reflect fluctuations in stock price movement. Therefore, it is probable, stock investments that are carried in Indonesia have a high risk opportunity. Important properties are often owned by time series data in the financial sector in particular to return data that the probability distribution of returns is fat tails and volatility clustering or often referred to as a case of heteroscedasticity.Time series models that can be used to model this condition are ARCH and GARCH. One form of ARCH/GARCH is Generalized Autoregressive Conditional Heteroscedasticity In Mean (GARCH-M). The purpose of this study is to predict volatility by using GARCH-M model in the return data analysis of daily stock price closing of Wijaya Karya (Persero) Tbk from October 18, 2012 until March 14, 2014 by using the active days (Monday to Friday). The best model is used for forecasting the volatility case in the stock price return of PT. Wijaya Karya is ARIMA (0,0, [35]) GARCH (1,1)-M. Keywords: Stocks, Volatility, Generalized Autoregressive Conditional Heteroscedasticity in Mean (GARCH-M)

OPTIMASI VALUE AT RISK RETURN ASET TUNGGAL DAN PORTOFOLIO MENGGUNAKAN SIMULASI MONTE CARLO DILENGKAPI GUI MATLAB Astuti, Nur Indah Yuli; Tarno, Tarno; Yasin, Hasbi
Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Value at Risk (VaR) is a scale that can measure the maximum loss that may happen for a specified period of time in the normal market conditions at a certain level of confidence. The most important thing in the VaR is to determine the type of methodology and assuming appropriate with the distribution of the return. One of the methods in calculating the VaR is Monte Carlo simulation. VaR with Monte Carlo simulation method assumes that the return value is normal distribution simulated using the appropriate parameters and portfolio return is linier towards its single asset return. From the results and analysis research conducted use GUI Matlab, VaR single asset of value risk on the stock of United Tractors Tbk (UNTR) is greater than Bank Rakyat Indonesia (Persero) Tbk (BBRI), Astra International Tbk (ASII), and Bank Negara Indonesia Tbk ( BBNI), VaR value of portfolio consisting of two assets, the three assets, and four assets have lower value than the sum of its single asset of the value of VaR. Matlab (Matrix Laboratory) is an interactive programming system with the basic elements of array database which dimensions do not need to be stated in particular, while the GUI is the submenu of Matlab. In this research, determining the level of trust and specified time period is very important to count of VaR value because it can describe how much investors bear the risk. Keywords: Value at Risk, time period, confidence level, Monte Carlo simulation

PEMODELAN KURS MATA UANG RUPIAH TERHADAP DOLLAR AMERIKA MENGGUNAKAN METODE GARCH ASIMETRIS Sulistyowati, Ulfah; Tarno, Tarno; Hoyyi, Abdul
Jurnal Gaussian Vol 4, No 1 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

One factor causing to slowing economic growth in Indonesia is the currency exchange rate. In Indonesia,the exchange rate of the rupiah against the dollar is always become an attention of society. To monitor the movement needed a mathematical model that can be used to forecast the rupiah exchange rate to the dollar. Data rupiah exchange rate against the dollar is a financial time series data has a non-constant volatility. One model that is often used for the prediction of these data is ARIMA-GARCH. In this study discussed about modeling the data rate of the rupiah against the dollar using asymmetric GARCH, such as exponential GARCH (EGARCH), Threshold GARCH (TGARCH) and Autoregressive Power ARCH (APARCH). Modeling the exchange rate against the dollar using all three types of the Asymmetric GARCH models produce the best models, the ARIMA ([4.5], 1, [4,5]) - APARCH (2,1). With the results obtained using the model for volatility forecasting that volatility decreased from the previous forecast but still be at its high volatility.Keywords : Exchange rate, ARIMA, GARCH, Asymmetric GARCH, volatilty

PERBANDINGAN FUZZY TIME SERIES DENGAN METODE CHEN DAN METODE S. R. SINGH (Studi Kasus : Nilai Impor di Jawa Tengah Periode Januari 2014 â€“ Desember 2019) Rachim, Febyani; Tarno, Tarno; Sugito, Sugito
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.v9i3.28912

Import is one of the efforts of an area to meet the needs of its population in order to stabilize prices and maintain stock availability. The value of imports in Central Java throughout 2016 amounted to 8811.05 Million US Dollars. The value of imports in Central Java is the top 10 in all provinces in Indonesia with a percentage of 6.50%. Import data in Central Java is included in the time series data category. To maintain the stability of imports in Central Java, it is deemed necessary to make a plan based on a statistical model. One of the time series models that can be applied is the fuzzy time series model with the Chen method approach and the S. R. Singh method because the method is suitable for cyclical patterned data with monthly time periods such as Import data in Central Java. Important concepts in the preparation of the model are fuzzy sets, membership functions, set basic operators, fuzzy variables, universe sets and domains. The fuzzy time series modeling procedure is carried out through several stages, namely the determination of universe discourse which is divided into several intervals, then defines the fuzzy set so that it can be performed fuzzification. After that the fuzzy logical relations and fuzzy logical group relations are determined. The accuracy calculation in both methods uses symmetric Mean Absolute Percentage Error (sMAPE). In this study the sMAPE value obtained in the Fuzzy Time Series Chen method of 10.95% means that it shows good forecasting ability. While the sMAPE value on the Fuzzy Time Series method of S. R. Singh method by 5.50% shows very good forecasting ability. It can be concluded that the sMAPE value in the S. R. Singh fuzzy time series method is better than the Chen method.Keywords:Â Import value,Â fuzzy time seriesÂ , Chen, S. R. Singh,Â sMAPE

ANALISIS INTEGRASI SPASIAL PASAR CABAI MERAH KERITING DI JAWA TENGAH DENGAN METODE VECTOR ERROR CORRECTION MODEL Samantha, Kenia; Tarno, Tarno; Rahmawati, Rita
Jurnal Gaussian Vol 10, No 2 (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.v10i2.29007

Curly red chili (Capsicum annuum L.) is one of commodity which has a big influence to the national economy. To maintain the price stability of curly red chili, an integrated market is needed. Spatial market integration is the level of closeness of relations between regional markets and other regional markets. Spatial market integration will be modeled by the Vector Error Correction Model (VECM) method to see the closeness of both short and long term relationships. The object of this study is the price of curly red chili for several regions in Central Java, such as Kota Semarang, Kab. Demak, Kab. Pati, and Kab. Pekalongan in the period January 2016 to December 2019 where the data has met the stationarity test at first level of difference. In Johansen's cointegration test, it was obtained 3 cointegrations, which means that in each short-term period all variables tend to adjust to each other to achieve long-term balance. Granger causality test shows that there is a two-way relationship and the relationship affects one variable to another for all variables. The VECM model obtained has the MAPE accuracy value for HCMK Semarang 15.93%, Kab. Demak 17.61%, Kab. Pati 15.88%, and Kab. Pekalongan 14.49% which can be interpreted that the performance of the model is good.Â Keywords: Curly Red Chili, Spatial Market Integration, VECM, Johansen's Cointegration, Granger Causality

PREDIKSI HARGA DAGING SAPI DI KABUPATEN BREBES MENGGUNAKAN PEMODELAN ARFIMA DENGAN EFEK GARCH Imani, Nanda Diva Lingkar; Tarno, Tarno; Saputra, Bagus Arya
Jurnal Gaussian Vol 12, No 4 (2023): 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.12.4.570-580

Beef is a source of animal protein which is rich in nutrients and much-loved by the people of Indonesia. Brebes Regency is an area in Indonesia that has local livestock assets, namely Java Brebes cattle or also known as Jabres cattle. The existence of this jabres cattle is one of the guardians of beef price stability in Brebes in particular and in Central Java in general. The price of beef often fluctuates, to minimize losses, it is necessary to predict the market price. The model for predicting research data is the ARFIMA-GARCH model which is a model that can explain long memory patterns in time series data and experience heteroscedasticity problems. This study aims to obtain the best model with time series analysis and predict the selling price of beef in Brebes Regency for the next 52 weeks using ARFIMA modeling which is enhanced using the addition of the GARCH model. The results of the analysis that has been carried out on beef price data in Brebes Regency can be concluded that the best model obtained is the ARFIMA model ([9], 0.5461747, 0) – GARCH (1, 1). Based on the predictions that have been made using the best model, the resulting MAPE value is 1.56375%, so the model is very good for predicting beef prices in Brebes Regency in the next several periods.

Co-Authors Abdul Hoyyi Abdul Rahim Acoci, Acoci Adnan Adnan Agus Rusgiyono Agusalim, Agusalim Agustien, Stevani Akbar, Azaz Aris Sugiharto Aris Susanto Arsyil Hendra Saputra Budi Warsito Dadang, Rahmat Deby Fakhriyana, Deby Desti, Desti Di Asih I Maruddani Diah Safitri Dwi Hasti Ratnasari Dwi Ispriyanti Edison Edison Ervina, Evi Fadillah Yamin, Kurnia Farisatma, Farisatma Faslia, Faslia Febriani, Karina Gawise, Gawise Halim, Rossa Fitria Hartati Hartati Hasbi Yasin Imani, Nanda Diva Lingkar Irman Matje Irwan Irwan Jesica, Haniela Puja Jeti, La Junaidin, Junaidin Maimuna Maimuna, Maimuna Manan, Manan Matje, Irman Nur Indah Yuli Astuti, Nur Indah Yuli Nurihsan, Achmad Juntika Paula Meilina Dwi Hapsari Payitno, Isnu Harjo Purnamasari, Wa Ode Dian Rachim, Febyani Rita Rahmawati Rokhma Al Anisah Rosilawati, Elsa Dwi Saefudin, Encep Saleh, Rachman Samantha, Kenia Saputra, Bagus Arya Sari, Sasmita Kartika Setiyowati, Eka Suardin Suardin, Suardin Suarti, Suarti Subanar . Suciyanti, Suciyanti Sudarno Sudarno Sugito - Sukmawati, Dina Sulasmi, Andi Suparti Suparti Syamsu Yusuf, Syamsu TITI RACHMI, TITI Tri Wahyu Kuningsih, Tri Wahyu Triastuti Wuryandari Trimono, Trimono Ulfah Sulistyowati Utami, Rianti Siswi Wati, Isna Wijaya, Fatma Pandu Wijaya, Rasman Sastra Yamin, Kurnia Fadillah Yon Haryono Yuciana Wilandari Yusi Riksa Yustiana, Yusi Riksa Yusnan, Muhammad Zubair, Zubair

Title Search

Found 13 Documents Search Journal : Jurnal Gaussian

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 13 Documents
Search
Journal : Jurnal Gaussian