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QUERY EXPANSION RANKING PADA ANALISIS SENTIMEN MENGGUNAKAN KLASIFIKASI MULTINOMIAL NAÏVE BAYES (Studi Kasus : Ulasan Aplikasi Shopee pada Hari Belanja Online Nasional 2020) Lutfiah Maharani Siniwi; Alan Prahutama; Arief Rachman Hakim
Jurnal Gaussian Vol 10, No 3 (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.v10i3.32795

Shopee is one of the e-commerce sites that has many users in Indonesia. Shopee provides various attractive promos on special days such as National Online Shopping Day on December 12. Shopee site was a complete error on December 12, 2020. Complaints and opinions of Shopee users were also shared through various media, one of them was Google Play Store. Sentiment analysis was used to see the user's response to the Shopee’s incident. Sentiment analysis results can be extracted to obtain information regarding positive or negative reviews from Shopee users. Sentiment analysis was performed using the Multinomial Naïve Bayes classification. the simplest method of probability classification, but it is sensitive to feature selection so that the amount of data is determined by the results of feature selection Query Expansion Ranking. The algorithm that has the highest accuracy and kappa statistic is the best algorithm in classifying Shopee’s users sentiment. The results showed that the classification performance using Multinomial Naïve Bayes with 80% of the features (terms) which have the highest Query Expansion Ranking value was obtained at the accuracy and kappa statistics values are 89% and 77.62%. This means that Multinomial Nave Bayes has a good performance in classifying reviews and the number of features used affects the performance results obtained.

PEMODELAN MIXED GEOGRAPHICALLY WEIGHTED REGRESSION DENGAN ADAPTIVE BANDWIDTH UNTUK ANGKA HARAPAN HIDUP (Studi Kasus : Angka Harapan Hidup di Jawa Tengah) Rizki Faizatun Nisa; Sugito Sugito; Arief Rachman Hakim
Jurnal Gaussian Vol 11, No 1 (2022): 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.v11i1.33998

Life expectancy at birth (AHH) is an estimate of the years a person will take from birth. AHH is used as an indicator of public health and welfare. These two indicators are of concern to the government in relation to human development. It is hoped that the AHH value will continue to increase so that the quality of human development will also increase. Modeling of the factors that influence AHH needs to be done so that efforts to increase AHH become more effective.The AHH value for Central Java (Central Java) in 2020 is 74.37. Factors thought to influence AHH in Central Java are the percentage of poor people (X1), the percentage of households with proper sanitation (X2), the percentage of children under five who are fully immunized (X3) and the open unemployment rate (X4). The assumption of homoscedasticity in AHH modeling in Central Java using linear regression was not fulfilled, meaning that there was spatial heterogeneity between districts/cities, so the Geographically Weighted Regression (GWR) method was used. The weighting function used is the Bisquare and Tricube kernels with adaptive bandwidth. The GWR method will encounter problems if not all independent variables are local, so the Mixed Geographically Weighted Regression (MGWR) method is used. The results of the GWR analysis for the two weighting functions are that the X1 variable is not local, so the MGWR method is used. The results of MGWR modeling for the two weighting functions are that local variables and global variables have a significant effect. The best model is the MGWR model with Kernel Tricube weighting because it has the smallest AICc value. Keyword : AHH, GWR, MGWR, Adaptive Kernel Bisquare, Adaptive Kernel Tricube, AICc

PENERAPAN PENGENDALIAN KUALITAS DENGAN MEWMA DAN FUNGSI DENSITAS KERNEL MULTIVARIAT (Studi Kasus: PT Sukorejo Indah Textile Kab. Batang) Mifta Fara Sany; Rukun Santoso; Arief Rachman Hakim
Jurnal Gaussian Vol 8, No 1 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

In an era of industrial revolution 4.0, technology is increasingly sophisticated, requiring companies to be more creative. Product quality control is an effort to minimize the defective products produced by the company. The production of weaving sarongs at PT SUKORINTEX pays attention to the accuracy of the length and width of the sarong to conform to the standards set by the company. To find out the quality of woven sarong products at PT SUKORINTEX, analysis was performed using Multivariate Exponentially Weighted Moving Average (MEWMA) control charts and multivariate kernel control charts. The research variable was the characteristics of the X sarongs which is reflected in 2 variates, namely the average length and average width. Based on the results and discussion that has been done, the MEWMA control chart used a weighting λ which is determined using trial and error. MEWMA control charts can be said to be stable and controlled by λ = 0.1, Upper Control Limit (UCL) of 14.62943, and Lower Control Limit (LCL) of 0. Multivariate kernel control chart were declared uncontrolled with α = 0.1 and level = 0.06130611 because there were data that was outside the contour. Chart improvement was done by trial and error and obtained a controlled chart results at α = 0.01 and a level value of 0.03125701. Based on this case study, the quality control of the average length and width of WADIMOR woven sarong types 30 STR with MEWMA is better than the multivariate kernel density, because MEWMA is controlled and stable in controlling product quality. The results of the MEWMA control chart show a capable process because more than 1 process capability index value is obtained. Keywords: Multivariate Exponentially Weighted Moving Average (MEWMA) control chart, multivariate kernel control chart, process capability.

PEMODELAN ANGKA HARAPAN HIDUP PROVINSI JAWA TENGAH MENGGUNAKAN ROBUST SPATIAL DURBIN MODEL Maghfiroh Hadadiah Mukrom; Hasbi Yasin; Arief Rachman Hakim
Jurnal Gaussian Vol 10, No 1 (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.v10i1.30935

Spatial regression is a model used to determine relationship between response variables and predictor variables that gets spatial influence. If there are spatial influences on both variables, the model that will be formed is Spatial Durbin Model. One reason for the inaccuracy of the spatial regression model in predicting is the existence of outlier observations. Removing outliers in spatial analysis can change the composition of spatial effects on data. One way to overcome of outliers in the spatial regression model is by using robust spatial regression. The application of M-estimator is carried out in estimating the spatial regression parameter coefficients that are robust against outliers. The aim of this research is obtaining model of number of life expectancy in Central Java Province in 2017 that contain outliers. The results by applying M-estimator to estimating robust spatial durbin model regression parameters can accommodate the existence of outliers in the spatial regression model. This is indicated by the change in the estimating coefficient value of the robust spatial durbin model regression parameter which can increase adjusted R2 value becomes 93,69% and decrease MSE value becomes 0,12551.Keywords: Outliers, M-estimator, Spatial Durbin Model, Number of Life Expectancy.

PENERAPAN FUZZY C-MEANS KLUSTER UNTUK SEGMENTASI PELANGGAN E-COMMERCE DENGAN METODE RECENCY FREQUENCY MONETARY (RFM) Stevanus Sandy Prasetyo; Mustafid Mustafid; Arief Rachman Hakim
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.v9i4.29445

E-commerce has become a medium for online shopping which is growing and popular among the public. Due to the ease of access for all internet users and the completeness of the products offered, e-commerce has become a new alternative in meeting people's needs. Currently, the competition in the business world is very fierce, any e-commerce company needs to be able to carry out the right marketing strategy to compete in acquiring, retaining, and partnering with customers. In this research, the segmentation of e-commerce customers was carried out using the Fuzzy C-Means cluster and the RFM method. The clustering process is carried out six times with the number of clusters starts from two to seven clusters. The results showed that the optimum number of clusters formed according to the Xie-Beni validity index was four clusters. The cluster becomes customer segments that have the characteristics of each customer based on their recency, frequency, and monetary value. The best segment is segment 4 which has very loyal customers in shopping on tumbas.in e-commerce. From the segments that have been formed, they can be used as a consideration in implementing the right marketing strategy for each customer. Keywords : E-commerce, customer segmentation, Fuzzy C-Means Cluster, RFM, Xie-Beni Index

KLASIFIKASI PEMBERIAN KREDIT SEPEDA MOTOR MENGGUNAKAN METODE REGRESI LOGISTIK BINER DAN CHI-SQUARED AUTOMATIC INTERACTION DETECTION (CHAID) DENGAN GUI R (Studi Kasus: Kredit Sepeda Motor di PT X) Chalimatus Sa'diah; Tatik Widiharih; Arief Rachman Hakim
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.v10i2.29923

One of the factors causing the bankruptcy of a company is bad credit. Therefore, prospective customers need to be selected so that bad credit cases can be minimized. This study aims to determine the classification of credit granting to prospective customers of company X in order to reduce the risk of bad credit. The method used is the binary logistic regression method and the Chi-Squared Automatic Interaction Detection (CHAID) method. In this study, data used in November 2019 were 690 motorcycle credit data for company X in Gresik. The independent variables in this study are the factors that affect bad credit such as gender, marital status, education, employment, income, expenses, home ownership status and the dependent variable is credit status (bad and current). The analysis results show that the binary logistic regression has an accuracy value of 76.38% with an APER of 23.62%, while CHAID has an accuracy value of 93.19% with an APER of 6.81%. The accuracy value of the CHAID method is greater than the binary logistic regression method, while the APER value of the CHAID method is smaller than the binary logistic regression method. So it can be concluded that the CHAID method is better than the binary logistic regression method in classifying bad credit at company X. Keywords: Credit, Classification, Binary Logistic Regression, CHAID.

PERBANDINGAN METODE K-NEAREST NEIGHBOR DAN ADAPTIVE BOOSTING PADA KASUS KLASIFIKASI MULTI KELAS Ade Irma Prianti; Rukun Santoso; Arief Rachman Hakim
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.v9i3.28924

The company's financial health provides an indication of company’s performance that is useful for knowing the company's position in industrial area. The company's performance needs to be predicted to knowing the company's progress. K-Nearest Neighbor (KNN) and Adaptive Boosting (AdaBoost) are classification methods that can be used to predict company's performance. KNN classifies data based on the proximity of the data distance while AdaBoost works with the concept of giving more weight to observations that include weak learners. The purpose of this study is to compare the KNN and AdaBoost methods to find out better methods for predicting company’s performance in Indonesia. The dependent variable used in this study is the company's performance which is classified into four classes, namely unhealthy, less healthy, healthy, and very healthy. The independent variables used consist of seven financial ratios namely ROA, ROE, WCTA, TATO, DER, LDAR, and ROI. The data used are financial ratio data from 575 companies listed on the Indonesia Stock Exchange in 2019. The results of this study indicate that the prediction of company’s performance in Indonesia should use the AdaBoost method because it has a classification accuracy of 0,84522 which is greater than the KNN method’s accuracy of 0,82087. Keywords: company’s performance, classification, KNN and AdaBoost, classification accuracy.

KLASIFIKASI STATUS KEMISKINAN RUMAH TANGGA DENGAN METODE SUPPORT VECTOR MACHINES (SVM) DAN CLASSIFICATION AND REGRESSION TREES (CART) MENGGUNAKAN GUI R (Studi Kasus di Kabupaten Wonosobo Tahun 2018) Lutfia Nuzula; Alan Prahutama; Arief Rachman Hakim
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.v9i4.29449

The poor are people who have average monthly expenditures per capita below the poverty line. Wonosobo District became the poorest district in Central Java in 2011-2018, although the percentage of poor people has decreased every year. It cannot be separated from the efforts of the Wonosobo District Government to overcome poverty through various programs. This study classified households in Wonosobo District in 2018 as poor and non-poor based on influencing factors. This study used the Support Vector Machines (SVM) method to be compared with the Classification and Regression Trees (CART) method. It used the data from the 2018 National Socio-Economic Survey of Central Java with a total of 795 observations. Result of the research using the SVM method and the RBF kernel, the classification accuracy reaches 89.82% then the classification accuracy using the CART method reaches 87.08%. GUI designed by RShiny package can make easier for users to analyze the SVM and CART with the valid output.

ANALISIS KLASTER METODE WARD DAN AVERAGE LINKAGE DENGAN VALIDASI DUNN INDEX DAN KOEFISIEN KORELASI COPHENETIC (Studi Kasus: Kecelakaan Lalu Lintas Berdasarkan Jenis Kendaraan Tiap Kabupaten/Kota di Jawa Tengah Tahun 2018) Sisca Indah Pratiwi; Tatik Widiharih; Arief Rachman Hakim
Jurnal Gaussian Vol 8, No 4 (2019): 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.8.4.486-495

Based on Central Java Regional Police data, traffic accidents from 2017 to 2018 increased from 17.522 to 19.016 or 8,54 percent. To reduce the number of traffic accidents in Central Java, the initial step was carried out by grouping districts/cities that had the same accident level characteristics based on vehicle type with cluster analysis. The ward and average linkage method is a hierarchical cluster analysis method. ward method can maximize cluster homogeneity. While the average linkage method can generate clusters with small cluster variants. In this study using a measure of squared euclidean distance to measure the similarity between pairs of objects. To determine the quality of clustering results, the validation dunn index and cophenetic coefficients corelation are used. Based on the results of the clustering, the optimal number of clusters is obtained at q = 5 for the average linkage method with the results of validation dunn index = 0,08571196 and the rcoph = 0,687458. Keywords: Accidents, Cluster Analysis, Ward Method, Average linkage, Squared Euclidean Distance, Dunn Index, Cophenetic Correlation Coefficient

PERAMALAN JUMLAH PENUMPANG PESAWAT DI BANDARA INTERNASIONAL AHMAD YANI DENGAN METODE HOLT WINTER’S EXPONENTIAL SMOOTHING DAN METODE EXPONENTIAL SMOOTHING EVENT BASED Sofiana Sofiana; Suparti Suparti; Arief Rachman Hakim; Iut Triutami
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.v9i4.29448

Forecasting the number of airplane passengers can be a consideration for the airline at Ahmad Yani International Airport related with addition of extra flight. The number of airplane passengers can be influenced by certain seasonal or special events. The seasonal influences can be known through historical data patterns and if there is a seasonal pattern, the Holt Winter’s Exponential Smoothing method can be used. Exponential Smoothing Event Based (ESEB) forecasting method can be use to see the special events that effect the number of airplane passengers at Ahmad Yani International Airport. After compared, the Holt Winter’s Exponential Smoothing method is a better method of forecasting the number of airplane passengers at Ahmad Yani International Airport because it has a smaller error value, namely the MSE value and the MAPE value than the Exponential Smoothing Event Based (ESEB)method. The MAPE and MSE values be produced from the best method each of 5,644139% and 619,998,718 .Keywords : Airplane Passengers, Seasonal Pattern, Special Event, Exponential Smoothing Event Based , Holt Winter’s Exponential Smoothing.

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