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Department of Statistic, Faculty of Science and Mathematics , Universitas Diponegoro Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro Gedung F lt.3 Tembalang Semarang 50275
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Jurnal Gaussian
Published by Universitas Diponegoro
ISSN : -     EISSN : 23392541     DOI : -
Core Subject : Education,
Other
Jurnal Gaussian terbit 4 (empat) kali dalam setahun setiap kali periode wisuda. Jurnal ini memuat tulisan ilmiah tentang hasil-hasil penelitian, kajian ilmiah, analisis dan pemecahan permasalahan yang berkaitan dengan Statistika yang berasal dari skripsi mahasiswa S1 Departemen Statistika FSM UNDIP.
Arjuna Subject : -
Articles 733 Documents
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SEGMENTASI PELANGGAN E-MONEY DENGAN MENGGUNAKAN ALGORITMA DBSCAN (DENSITY BASED SPATIAL CLUSTERING APPLICATIONS WITH NOISE) DI PROVINSI DKI JAKARTA Windy Rohalidyawati; Rita Rahmawati; Mustafid Mustafid
Jurnal Gaussian Vol 9, No 2 (2020): 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 (516.233 KB) | DOI: 10.14710/j.gauss.v9i2.27818

Abstract

Customer segmentation is one effective way of marketing to determine the most potential target market. Increasing of E-money usage in DKI Jakarta and more banks are providing E-money products. One way to be able to compete in the global market, banks can segment customers. Determining potential customers of E-money users in DKI Jakarta can form segments by applying the DBSCAN (Density Based Spatial Clustering Application with Noise) algorithm. The quality of segments was measured by using the Silhouette Coefficient. In this study, E-money customers were grouped by reason of using the bank used, transaction activities, number of transactions, nominal balance, and frequency of top-up. The results of this study were using the density radius of 2 and  minimum 3 objects that enter the density radius forming 2 segments and 17 noises. The segment quality value of 0.26. The most potential segment was the segment that has an average greater than the average of all data. 
KETEPATAN KLASIFIKASI PEMBERIAN KARTU KELUARGA SEJAHTERA DI KOTA SEMARANG MENGGUNAKAN METODE REGRESI LOGISTIK BINER DAN METODE CHAID Suhendra, Muhammad Arif; Ispriyanti, Dwi; Sudarno, Sudarno
Jurnal Gaussian Vol 9, No 1 (2020): 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 (674.181 KB) | DOI: 10.14710/j.gauss.v9i1.27524

Abstract

Menurut BPS, jumlah penduduk miskin di Kota Semarang pada Maret 2018 adalah sebesar 73,65 ribu orang. Salah satu program pemerintah dalam percepatan penanggulangan kemiskinan adalah dengan mengeluarkan Kartu Keluarga Sejahtera (KKS) yang diberikan kepada masyarakat yang kurang mampu. Penelitian ini bertujuan untuk mengetahui besarnya ukuran ketepatan klasifikasi pemberian KKS di Kota Semarang. Metode klasifikasi statistik yang digunakan adalah metode Regresi Logistik Biner dan metode Chi-Squared Automatic Interaction Detection (CHAID). Pemberian KKS dipengaruhi oleh banyak faktor, diantaranya jumlah anggota keluarga, status perkawinan, jenis kelamin kepala keluarga, usia kepala keluarga, jenjang pendidikan kepala keluarga dan kepemilikan/penguasaan HP. Pada penelitian ini, data yang digunakan adalah data sekunder hasil Survey Sosial Ekonomi Nasional (SUSENAS) tahun 2018 yang diperoleh dari Badan Pusat Statistik (BPS) Provinsi Jawa Tengah. Perbandingan data training dan testing yang digunakan adalah 60:40. Hasil analisisnya menunjukkan bahwa dengan menggunakan Regresi Logistik Biner, faktor-faktor yang berpengaruh adalah jumlah anggota keluarga dan jenjang pendidikan kepala keluarga dengan ketepatan klasifikasi sebesar 88% dan kesalahan 12%, sedangkan dengan menggunakan CHAID, faktor-faktor yang berpengaruh adalah jumlah anggota keluarga, status perkawinan, usia kepala keluarga, jenjang pendidikan kepala keluarga dan kepemilikan/penguasaan HP dengan ketepatan klasifikasi sebesar 90,2% dan kesalahan 9,8%.Kata kunci: Kartu Keluarga Sejahtera, Klasifikasi, Regresi Logistik Biner, CHAID
PEMODELAN WAVELET NEURAL NETWORK UNTUK PREDIKSI NILAI TUKAR RUPIAH TERHADAP DOLAR AS Tri Yani Elisabeth Nababan; Budi Warsito; Agus Rusgiyono
Jurnal Gaussian Vol 9, No 2 (2020): 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 (686.591 KB) | DOI: 10.14710/j.gauss.v9i2.27823

Abstract

Each country has its own currency that is used as a tool of exchange rate valid in the transaction process. In the process of transaction between countries often experience problems in terms of payment because of the difference in the value of money prevailing in each country. The price movement of the exchange rate or the value of foreign currencies that fluctuate from time to time it encouraged predictions of the value of the rupiah exchange rate against the U.S. dollar. Wavelet Neural Network (WNN) is a combination of methods between wavelet transforms and Neural networks. WNN modeling begins with wavelet decomposition resulting in wavelet coefficients and scale coefficients. Selection of inputs is based on PACF plots and divides into training data and testing data. To determine the final output by calculating the value of MAPE in data testing. The best architecture on WNN model for prediction of the value of the rupiah exchange rate against the U.S. dollar is a model with sigmoid logistic activation function, 2 neurons in the input layer, 10 neurons in the hidden layer, and 1 neuron in the output layer. The MAPE value is obtained at 0.2221%.  
PENERAPAN STRUCTURAL EQUATION MODELLING (SEM) UNTUK MENGANALISIS FAKTOR – FAKTOR YANG MEMPENGARUHI KINERJA BISNIS (STUDI KASUS KAFE DI KECAMATAN TEMBALANG DAN KECAMATAN BANYUMANIK PADA JANUARI 2019) Ade Irma Pramudita; Tatik Widiharih; Rukun Santoso
Jurnal Gaussian Vol 9, No 2 (2020): 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 (592.269 KB) | DOI: 10.14710/j.gauss.v9i2.27814

Abstract

This research is done to examine the effect of quality of service and product attractiveness toward business strategies based on service in order to improving business performance. The sample of this study were Cafe owners in Tembalang Subdistrict and Banyumanik Subdistrict, total are 116 respondents. In this Final Project, the processing of Structural Equation Modeling (SEM) is AMOS software. The results of the analysis show that service quality has a positive effect on business strategies based on service to improving business performance. The most significant factor that affecting business performance is quality of service. Quality of service is important in the performance of a café business. Cafe owners must always pay attention to the quality of café service to customers, because the quality of service is the main consideration for customers to visit cafes.
PERBANDINGAN METODE NAÏVE BAYES DAN BAYESIAN REGULARIZATION NEURAL NETWORK (BRNN) UNTUK KLASIFIKASI SINYAL PALSU PADA INDIKATOR STOCHASTIC OSCILLATOR (Studi Kasus: Saham PT Bank Rakyat Indonesia (Persero) Tbk Periode Januari 2017 – Agustus 2019) Fredy Yoseph Marianto; Tarno Tarno; Di Asih I Maruddani
Jurnal Gaussian Vol 9, No 1 (2020): 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 (762.553 KB) | DOI: 10.14710/j.gauss.v9i1.27520

Abstract

Keputusan untuk membeli atau menjual saham merupakan kunci utama untuk memperoleh keuntungan dalam trading dan investasi. Salah satu indikator yang dapat digunakan dalam menentukan momentum untuk membeli atau menjual saham adalah Stochastic Oscillator. Sebagai indikator yang sensitif terhadap pergerakan harga saham, Stochastic Oscillator sering mengeluarkan sinyal palsu yang mengakibatkan kerugian dalam transaksi. Terdapat 9 atribut yang diduga dapat mengidentifikasi apakah suatu sinyal yang keluar dari indikator Stochastic Oscillator merupakan sinyal palsu atau tidak. Tujuan dari penelitian ini adalah melakukan klasifikasi atau deteksi sinyal dengan metode Naïve Bayes dan Bayesian Regularization Neural Network (BRNN), dan kemudian membandingkan tingkat akurasi hasil klasifikasi antara kedua metode. Hasil dari penelitian ini menunjukkan bahwa hanya terdapat 6 atribut yang dapat digunakan untuk mengidentifikasi apakah suatu sinyal yang keluar merupakan sinyal palsu atau tidak, yaitu kondisi IHSG, kondisi high price, kondisi low price, kondisi close price, posisi %K, dan posisi %D, serta tingkat akurasi dari metode Naïve Bayes adalah sebesar 76,92%, sedangkan akurasi dari metode BRNN adalah sebesar 80,77%. Dapat disimpulkan bahwa dalam penelitian ini, metode BRNN lebih baik dibandingkan dengan metode Naïve Bayes untuk mendeteksi sinyal palsu yang keluar dari indikator Stochastic Oscillator.Kata kunci: Stochastic Oscillator, Sinyal Palsu, Klasifikasi, Naïve Bayes, BRNN, Akurasi
PERAMALAN HARGA CABAI MERAH MENGGUNAKAN MODEL VARIASI KALENDER REGARIMA DENGAN MOVING HOLIDAY EFFECT (STUDI KASUS: HARGA CABAI MERAH PERIODE JANUARI 2012 SAMPAI DENGAN DESEMBER 2019 DI PROVINSI JAWA BARAT) Aulia Rahmatun Nisa; Tarno Tarno; Agus Rusgiyono
Jurnal Gaussian Vol 9, No 2 (2020): 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 (417.881 KB) | DOI: 10.14710/j.gauss.v9i2.27819

Abstract

Chili is one of the vegetable commodities that has high economic value, because of it’s role is large enough to supply domestic needs as an export commodity in the food industry. The price of red chilliesalways increase in the month of Eid al-Fitr. This is due to the large number of people who use Red Chili as food they consume. Shifting the moon during the Eid al-Fitr will form a seasonal system with different periods, which became known as the Moving Holiday Effect. One of the calendar variation models used to eliminate the Moving Holiday Effect and has a simple processing flow is the RegARIMA model. The RegARIMA model is a combination of linear regression with ARIMA. In the regression model the weighting matrix is used as an independent variable and the price of red chili as the dependent variable. The weight value is obtained based on the number of days that affect Eid, which is 14 days. Based on the analysis the red chili price data in West Java Province with the period of January 2012 to December 2018, the RegARIMA model (1.0,0)(0,1,1) 12 is the best model because it has the smallest AIC. Forecasting results in 2020 showed an increase in the price of red chili in West Java  occurred in May to coincide with the Eid al-Fitr holiday which fell on May 24, 2020, the sMAPE value obtained by 24.96%. It means, the forecast still in the level of reasonableness. 
PENGUKURAN RISIKO GLUE-VALUE-AT-RISK PADA DATA DISTRIBUSI ELLIPTICAL (Studi Kasus: Data Saham PT Indocement Tunggal Prakarsa Tbk, PT Unilever Indonesia Tbk, PT United Tractors Tbk, Periode 1 Juni 2018 – 29 November 2019) Dede Andrianto; Di Asih I Maruddani; Tarno Tarno
Jurnal Gaussian Vol 9, No 1 (2020): 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 (762.944 KB) | DOI: 10.14710/j.gauss.v9i1.27525

Abstract

Risk measurement is carried out to determine the risk. Popular methods that can be used to measure risk at a confidence level are Value-at-Risk (VaR) and Tail-Value-at-Risk (TVaR). A Risk measurement should satisfy: translation invariance, positive homogenicity, monocity and subadditivity. VaR does not satisfy one of coherent axioms, namely subadditivity. TVaR is considered capable of overcoming VaR problems, but it’s too large for a risk measure. Glue-Value-at-Risk (GlueVaR) is a method that can overcome these problems because it can be valued between VaR and TVaR and fulfills four coherent axioms. In this paper GlueVaR used in the elliptical distribution for normal distribution to measure the risk of the stock of PT Indocement Tunggal Prakarsa Tbk (INTP), PT Unilever Indonesia Tbk (UNVR), and PT United Tractors Tbk (UNTR) for the period June 1st 2018 – 29th November 2019. After knowing the stock return is normally distributed and used confidence levels of α = 95% and β = 98%, a high selection of distortion ℎ1=0,3≤1−????1−???? and ℎ2=0,4≥ℎ1. The high distortion selected makes GlueVaR worth between VaR and TVaR. GlueVaR for INTP, UNVR, and UNTR respectively are 4.886%; 2.999%; and 4.083%. Thus the lowest risk level is PT Unilever Indonesia Tbk.Keywords : Value-at-Risk, Tail-Value-at-Risk, Glue-Value-at-Risk
ANALISIS KEPUASAN DAN LOYALITAS PELANGGAN DALAM PEMESANAN TIKET PESAWAT SECARA ONLINE MENGGUNAKAN PENDEKATAN PARTIAL LEAST SQUARE (PLS) Trisnawati Gusnawita Berutu; Abdul Hoyyi; Sugito Sugito
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.v7i4.28863

Abstract

Technology advances are bring rapid changes, thus bringing the world to the information society. From this technological progress thus e-commerce emerged, as a means to meet the needs of goods and services through internet access (online). This is what the airlines utilized by cooperating with various internet service providers (online), to provide convenience and comfort of airplane passengers in buying tickets without having to come directly to the place and through intermediaries. To provide the best service, need to know what factors that influence customer satisfaction in ordering airline tickets online. Appropriate modeling for this problem using structural equation modeling, with Partial Least Square (PLS) approach. The PLS approach is chosen because it is not based on several assumptions, one of these is the normal multivariate assumption. In this research, the exogenous latent variables used are performance, access, security, sensation, information, and web design, while the endogenous latent variables are satisfaction and loyalty. Based on the results of the analysis it can be concluded that the latent variables of access, security, sensation, information, and web design are able to explain the latent satisfaction variable of 70.32% while the satisfaction latent variable is able to explain the latent variable of loyalty by 36.02%. 
VALUE at RISK (VaR) DAN CONDITIONAL VALUE at RISK (CVaR) DALAM PEMBENTUKAN PORTOFOLIO BIVARIAT MENGGUNAKAN COPULA GUMBEL Dina Rahma Prihatiningsih; Di Asih I Maruddani; Rita Rahmawati
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.v9i3.28913

Abstract

One way to minimize risk in investing is to form of portfolio by combining several stocks.Value at Risk (VaR) is a method for estimating risk but has a weakness that is VaR is incoherent because it does not have the subadditivity. To overcome the weakness of VaR, Conditional Value at Risk (CVaR) can use. Stock data is generally volatile, so ARIMA-GARCH is used to model it. The selection of ARIMA models on R software can be automatically using the auto.arima() function. Then Copula Gumbel is a method for modeling joint distribution and flexible because it does not require the assumption of normality and has the best sensitivity to high risk so that it is suitable for use in stock data.The first step in this research is to modeling Copula Gumbel-GARCH with the aim to calculate VaR and CVaR on the portfolio of PT Bank Mandiri Tbk (BMRI) and PT Indo Tambangraya Megah Tbk (ITMG). At the confidence level 99%, 95%, and 90% obtained the VaR results sequentially amounted to 3.977073%; 2.546167%; and 1.837288% and the CVaR results sequentially amounted to 4.761437%; 3.457014%; and 2.779182%. The worst condition is a loss with VaR and it is still possible if a worse condition occurs is a loss with CVaR so that investors can be more aware of the biggest loss that will be suffered.Keywords: Value at Risk, Conditional Value at Risk, Auto ARIMA, Copula Gumbel.
ANALISIS SISTEM PELAYANAN KERETA API DI STASIUN SEMARANG TAWANG MENGGUNAKAN PROSES BAYESIAN Lifana Nugraeni; Sugito Sugito; Dwi Ispriyanti
Jurnal Gaussian Vol 9, No 4 (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.v9i4.29407

Abstract

Along with the times, transportation has progressed. Regarding the means of transportation, one of the phenomenon that is easily encountered in everyday life is the queue at public transportation facilities. One of the queues that occurred at public transportation facilities is  the train queue at Semarang Tawang Station. The number of trains that passes the station can cause the train service at the station busy. This study aims to see whether the train service system of Semarang Tawang Station is good or not. This can be consider by the queues method, determining the distribution of arrival patterns and service patterns to obtain a queues system model and a system performance standard. In this study, the distribution of arrival patterns and service patterns are determined by calculating the posterior distribution using the Bayesian method. The bayesian method was chosen because it is able to combine the sample distribution in the current study with the previous information for the same cases. The prior distribution and the likelihood function are the elements needed to obtain the posterior distribution. The distribution of arrival patterns and service patterns obtained from previous information follows the Poisson distribution. Based on the calculation of the posterior distribution, the result shows that the distribution of the arrival pattern is a discrete uniform distribution and the distribution of the service pattern is a Poisson distribution. The result shows that the train service system at Semarang Tawang Station has a model (Uniform Discrete / Gamma / 7: GD / ~ / ~) and has good service based on the performance values obtained.

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