Abdul Hoyyi
Departemen Statistika, Fakultas Sains Dan Matematika, Universitas Diponegoro

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ANALISA FAKTOR-FAKTOR YANG MEMPENGARUHI KINERJA PERUSAHAAN MENGGUNAKAN PENDEKATAN PARTIAL LEAST SQUARE (Studi Kasus pada PT. Telkom Indonesia Divisi Regional Jawa Tengah-DIY dan Wilayah Telekomunikasi Semarang) Endah Cahyaningrum; Abdul Hoyyi; Moch. Abdul Mukid
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (562.264 KB) | DOI: 10.14710/j.gauss.v4i4.10135

Abstract

Persaingan dalam pasar global membawa banyak perubahan yang cukup dinamis pada semua aspek di suatu perusahaan. Hal ini menimbulkan trend baru dimana perusahaan yang berkelanjutan bergantung pada kemampuan perusahaan dalam merespon perubahan-perubahan yang ada secara efektif. Adanya sejumlah keunikan yang menjadi karakteristik sebuah perusahaan dan tidak dimiliki perusahaan lain dapat menciptakan faktor-faktor yang dapat meningkatkan suatu kinerja perusahaan. Faktor-faktor yang mempengaruhi kinerja perusahaan pada PT. Telkom Indonesia diungkapkan secara komprehensif dengan persamaan struktural berbasis komponen, Partial Least Square (PLS). PLS merupakan metode analisis yang tidak didasarkan pada banyak asumsi. Pada PLS tidak diperlukan asumsi normal multivariat, dapat menggunakan skala pengukuran nominal, ordinal, interval dan rasio serta ukuran sampel tidak harus besar. PLS mengestimasi model hubungan antar variabel laten dan variabel laten dengan indikatornya. Berdasarkan hasil analisis diperoleh kesimpulan bahwa kinerja perusahaan dipengaruhi oleh kinerja manajerial, keunggulan bersaing, Total Quality Management, kompensasi, sistem pengukuran kinerja dan budaya kualitas namun angkanya relatif kecil. Kata kunci : Partial Least Square, kinerja perusahaan
PENGUKURAN KINERJA PORTOFOLIO OPTIMAL SAHAM LQ45 MENGGUNAKAN METODE CAPITAL ASSET PRICING MODEL (CAPM) DAN LIQUIDITY ADJUSTED CAPITAL ASSET PRICING MODEL (LCAPM) Kristika Safitri; Tarno Tarno; Abdul Hoyyi
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.v10i2.29414

Abstract

Investment is planting some funds to get profit and the stock is one of the type of investment in fincancial that the most interested for investors. To avoid the risk of investing, investors try to diversify their invesments by using portfolio. Stock portfolio is investment which comprised of various stocks from different companies, with the expect when the price of one stock decreases, while the other increases, then the investments do not suffer losses. Models that can be used to make a portfolio, one of them is Capital Asset Pricing Model (CAPM)  and Liquidity Adjusted Capital Asset Pricing Model (LCAPM). CAPM is a model that connects expected return with the risk of  an asset under market equilibrium condition. LCAPM is a method of new development of the CAPM model which is influenced by liquidity risk. To  analyze whether the formed portfolio have a good performance or not, so portfolio perfomance assessment will be done by using The Sharpe Index. This research uses data from closing prices, transaction volume and volume total of LQ45 Index stock on period March 2016-February 2020 and then data of JCI and interest rate of central bank of the Republic of Indonesia. Based on The Sharpe Index, optimal portfolio is LCAPM model portfolio with 3 stock composition and the proportion investment are 32,39% for LPPF, 49,86% for SRIL and  17,75% for TLKM. Keywords: LQ45 Index, Portfolio, Capital Asset Pricing Model (CAPM), Liquidity Adjusted Capital Asset Pricing Model (LCAPM), The Sharpe Index.
PEMODELAN FUNGSI TRANSFER DENGAN DETEKSI OUTLIER UNTUK MEMPREDIKSI NILAI INFLASI BERDASARKAN BI RATE (Studi Kasus BI Rate dan Inflasi Periode Januari 2006 sampai Juli 2016) Firda Dinny Islami; Abdul Hoyyi; Dwi Ispriyanti
Jurnal Gaussian Vol 6, No 3 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (484.317 KB) | DOI: 10.14710/j.gauss.v6i3.19305

Abstract

Inflation control is one of the important things in managing a country besides economic growth. Inflation received special attention in the economy of Indonesia. Every time there is a distortion in the society, politic or economic development, people always relate it to inflation. Low and stable inflation is a stimulator of economic growth. Inflation is also the final target in the monetary policy framework so the need for a central bank role to determine the policy direction. The BI Rate is one of the variables capable of controlling inflation. This study aims to forecast inflation based on the BI Rate using the transfer function model with outlier detection. The transfer function model depends on the parameters b, r, and s. The result of the analysis has been obtained the transfer function model with the value of b = 1, r = 0, s = 1 and the noise series ARMA (2,0). The addition of 16 outliers on the model yielded the best model with the AIC value is -868,56. The forecasting results show that the value of inflation has fluctuated, where in September 2016 it has decreased and then increased until December 2016.Keywords : Inflation, BI Rate, transfer function, outlier detection, AIC
PEMODELAN INDEKS HARGA KONSUMEN DI JAWA TENGAH DENGAN METODE GENERALIZED SPACE TIME AUTOREGRESSIVE SEEMINGLY UNRELATED REGRESSION (GSTAR-SUR) Mega Fitria Andriyani; Abdul Hoyyi; Hasbi Yasin
Jurnal Gaussian Vol 7, No 4 (2018): 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.v7i4.28859

Abstract

The Generalized Space Time Autoregressive (GSTAR) model with Seemingly Unrelated Regression (SUR) estimation method or often called GSTAR-SUR is more efficient to be used for residual correlation than Ordinary Least Square (OLS) estimation method. The SUR estimation method utilizes residual correlation information to improve the estimated efficiency resulting in a smaller standard error. The purpose of this research is to get the GSTAR-SUR model according to Consumer Price Index (CPI) data in four regencies or cities in Central Java namely Purwokerto, Surakarta, Semarang, and Tegal. Based on the assumed white noise assumption, the smallest MAPE and RMSE averages, the best model chosen in this research is the GSTAR-SUR(11)I(1) model with the heavy of normalized cross-correlation with the average MAPE value of 0.4455% and RMSE value of 0.80582. The best model obtained explains that the CPI data in Purwokerto, Semarang, and Tegal not only influenced by the previous time but also influenced by the locations. Meanwhile, the CPI data in Surakarta is only influenced by the previous time, but it is not affected by other locations. Keywords: SUR, OLS, Consumer Price Index
PERAMALAN PASANG SURUT AIR LAUT DI PULAU JAWA MENGGUNAKAN MODEL GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) (Studi Kasus : Ketinggian Pasang Surut Air Laut di Stasiun Pasang Surut Jakarta, Cirebon, Semarang dan Surabaya) Chyntia Arum Widyastusti; Abdul Hoyyi; Rita Rahmawati
Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (433.925 KB) | DOI: 10.14710/j.gauss.v5i4.14719

Abstract

In daily life is often found time series data contains not only connection among  the events in previous times, but also has a relationship between one location to another. Data with time series and location linkage is called space-time data. Generalized Space Time Autoregressive (GSTAR) model is one of the commonest used to make model and forecast space-time data. The purposes of this research are to get the best GSTAR model and the forecasting results for the data ocean tide heights at four stations of Java island, those are Stations of Jakarta, Cirebon, Semarang and Surabaya. The best model obtained is GSTAR(1;1)-I(1) which is using cross correlation normalization weight because its residuals fulfill white noise assumption with the smallest value of MAPE and RMSE. The best GSTAR model explains that the elevation ocean tide data in Stations of Cirebon and Semarang is only influenced by the earlier times, and not influenced by other locations but can affect the height of the tide at other locations. As for the elevation ocean tide data stations of Jakarta and Surabaya are influence each other. Keywords: GSTAR, Space-Time, Ocean Tide, MAPE and RMSE.
ANALISIS SISTEM ANTRIAN PADA LAYANAN PENGURUSAN PASPOR DI KANTOR IMIGRASI KELAS I SEMARANG Purina Pakurnia Artiguna; Sugito Sugito; Abdul Hoyyi
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (463.608 KB) | DOI: 10.14710/j.gauss.v3i4.8091

Abstract

Queue is something that can not be separated in everyday life. Almost all services will form a queue, including passport treatment services at the Immigration Office Class I Semarang.To solve the problems associated with the queue, queuing system model needs to be determined in accordance with the conditions and characteristics queue of the service facility at the Immigration Office Class I Semarang appropriately. So it can be known the measure of system performance to create an effective and efficient service. Based on the data analysis of the six (6) counters work, obtained queuing system model that occurs at the Immigration Office Class I Semarang is, (M/M/2)   queuing model for Passports Taking Counter and Customer Service Counter,  queuing model for file transfer counter and payment transfer counter, and  queuing model for photos counter and interview counter. The effectiveness of the applicant’s passport service process can be determined by calculating the average number of applicants in the system and queue, calculates the average time spent in the system and queue, and calculates the probability of a server that is not serving an applicant. Keywords : Queuing system model, Passport’s services, Size of system performanceANALISIS SISTEM ANTRIAN PADA LAYANAN PENGURUSAN PASPOR  DI KANTOR IMIGRASI KELAS I SEMARANG
FAKTOR-FAKTOR YANG MEMPENGARUHI KRIMINALITAS DI KABUPATEN BATANG TAHUN 2013 DENGAN ANALISIS JALUR Dermawanti Dermawanti; Abdul Hoyyi; Agus Rusgiyono
Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (404.857 KB) | DOI: 10.14710/j.gauss.v4i2.8423

Abstract

Crime or criminality in Indonesia is rampant both in print or television can be seen almost every day news about crime. Basically, each individual will be influenced by several factors, both internal and external causes a person to commit a criminal act, including population, education, morality, poverty, and unemployment. In this case will be studied in a statistical analysis that can detect the magnitude of these factors, either directly or indirectly to the level of criminality. One of the statistical analysis that can be used to analyze the causal relationship of the variables is the path analysis (path analysis) which is a direct development of multiple regression form with the aim to provide estimates of the level of interest (magnitude) and significance (significance) in a hypothetical causal link set variable. In this study showed that the factor that has the greatest positive effect on crime is unemployment factor of 0.395 with immediate effect. A factor which has the second largest positive effect of education is a factor of 0.222 to the direct effects and the indirect effect of 0.0818. Meanwhile, a factor that has a positive influence smallest is the moral factor to the effect of 0.180.Keywords : Criminality, Path Analysis
METODE PERAMALAN DENGAN MENGGUNAKAN MODEL VOLATILITAS ASYMMETRIC POWER ARCH (APARCH) Cindy Wahyu Elvitra; Budi Warsito; Abdul Hoyyi
Jurnal Gaussian Vol 2, No 4 (2013): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (583.385 KB) | DOI: 10.14710/j.gauss.v2i4.3786

Abstract

Exchange rate can be defined as a ratio the value of currency. The exchange rate shows a currency price, if it exchanged with another currency. Exchange rates of a currency fluctuate all the time. Rise and fall exchange rates of a currency in the money market shows the magnitude of volatility occurred in a country currency to other's. To estimate the volatility behavior of the data gave rise to volatility clustering or heteroscedasticity problems, can’t be modeled using ARMA model and asymmetric effects that can‘t be modeled by ARCH or GARCH, can be modeled by Asymmetric Power ARCH (APARCH). In determining the estimated parameter values of APARCH model, used the maximum likelihood method, followed by using the iteration method is Berndt, Hall, Hall and Hausman (BHHH). The APARCH model used to the data return of exchange rate against dollar is APARCH(2,1) or in the form as follows :  = 0,00000268 + 0,830902 + 0,130516  + 0,074784  + 0,151157
PEMODELAN REGRESI ROBUST S-ESTIMATOR UNTUK PENANGANAN PENCILAN MENGGUNAKAN GUI MATLAB (Studi Kasus : Faktor-Faktor yang Mempengaruhi Produksi Ikan Tangkap di Jawa Tengah) Dhea Kurnia Mubyarjati; Abdul Hoyyi; Hasbi Yasin
Jurnal Gaussian Vol 8, No 1 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (285.704 KB) | DOI: 10.14710/j.gauss.v8i1.26616

Abstract

Multiple Linear Regression can be solved by using the Ordinary Least Squares (OLS). Some classic assumptions must be fulfilled namely normality, homoskedasticity, non-multicollinearity, and non-autocorrelation. However, violations of assumptions can occur due to outliers so the estimator obtained is biased and inefficient. In statistics, robust regression is one of method can be used to deal with outliers. Robust regression has several estimators, one of them is Scale estimator (S-estimator) used in this research. Case for this reasearch is fish production per district / city in Central Java in 2015-2016 which is influenced by the number of fishermen, number of vessels, number of trips, number of fishing units, and number of households / fishing companies. Approximate estimation with the Ordinary Least Squares occur in violation of the assumptions of normality, autocorrelation and homoskedasticity this occurs because there are outliers. Based on the t- test at 5% significance level can be concluded that several predictor variables there are the number of fishermen, the number of ships, the number of trips and the number of fishing units have a significant effect on the variables of fish production. The influence value of predictor variables to fish production is 88,006% and MSE value is 7109,519. GUI Matlab is program for robust regression for S-estimator to make it easier for users to do calculations. Keywords: Ordinary Least Squares (OLS), Outliers, Robust Regression, Fish Production, GUI Matlab.
PEMODELAN METODE BROWN’S DOUBLE EXPONENTIAL SMOOTHING (B-DES) DAN BROWN’S WEIGHTED EXPONENTIAL MOVING AVERAGE (B-WEMA) MENGGUNAKAN OPTIMASI LEVENBERG-MARQUARDT PADA JUMLAH WISATAWAN DI JAWA TENGAH Dilla Retno Deswita; Abdul Hoyyi; Tatik Widiharih
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.v9i3.27956

Abstract

The tourism sector is one of the national development priority sectors because it contributes to foreign exchange earnings, the development of business areas, and the absorption of investment and labor. In 2018 the tourism sector will become the second largest foreign exchange earner after oil palm. Foreign exchange contributed by the tourism sector in 2018 was US $ 19.29 billion, an increase of 15.4%. The increase in contributions was driven by an increase in the number of foreign tourist arrivals by 12.58%, domestic tourists by 12.37%, and from investment. Therefore it is necessary to study the forecasting of the number of tourists after seeing the great potential generated from the tourism sector. The data forecast is data on the number of tourists in Central Java, both foreign and domestic data. Both data shows the tendency of an upward trend pattern. So that both data can be analyzed using B-DESmethods (Brown's Double Exponential Smoothing) and B-WEMA (Brown's Weighted Exponential Moving Average)that are optimized with LM (Levenberg-Marquardt). Both methods are able to analyze trend patterned data without assumptions making it easier in the analysis process. In addition, the two methods in previous studies were able to produce a small forecasting accuracy. The MAPE (Mean Absolute Percentage Error) value out sample is used to compare the forecasting results of the two methods. The results of the implementation of LM optimization on the data of the number of domestic tourists obtained the optimal parameter value of the B-DES method is 0.21944386 with MAPE out sample 16.26516% and B-WEMA method is 0.219441 with MAPE out sample 16.26515%. While the data on the number of foreign tourists obtained the optimal parameter value of the B-DES method was 0.26213368 with the MAPE out of the sample 23.61278% and the B-WEMA method was 0.26213367 with the MAPE out the sample 23.61278%. This means that both methods have a good level of forecasting accuracy in the data on the number of domestic tourists and an adequate level of accuracy in the data on the number of foreign tourists. Keywords : B-DES, B-WEMA, Levenberg-Marquardt, Tourists in Central Java
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