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Iut Tri Utami
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
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ANALISIS KLASIFIKASI MENGGUNAKAN METODE REGRESI LOGISTIK BINER DAN BOOTSTRAP AGGREGATING CLASSIFICATION AND REGRESSION TREES (BAGGING CART) (Studi Kasus: Nasabah Koperasi Simpan Pinjam Dan Pembiayaan Syariah (KSPPS)) Salma Innassuraiya; Tatik Widiharih; Iut Tri Utami
Jurnal Gaussian Vol 11, No 2 (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.v11i2.35458

Abstract

The Save Loan and Sharia Financing Cooperatives (KSPPS) is a financial institution that offers deposits, loans, and financing to its members while adhering to Islamic sharia rules. Customers payment behaviour is influenced by their background differences, such as age, gender, occupation, and so on. The classification method is used to determine the characteristics of members who are currently in arears or are stuck in arears. Binary Logistic Regression and Bootstrap Aggregating Classification and Regression Trees were utilized as classification methods (BAGGING CART). A Logistic Regression with binary response variables is known as a Binary Logistic Regression. By resampling 50 times, the technique with the BAGGING process is used to improve the performance of the classification using CART. Customer data from one of the KSPPS in Central Java in 2021 was used in this investigation. Gender, age, marital status, employment, education level, time period, and income were the independent variables in this study, whereas payment status was the dependent variable (not stuck and stuck). The Binary Logistic Regression approach had an accuracy of 78.67 percent with an APER 21.33 percent, a Press's Q of 24.65, and a specificity of 98.30 percent, according to the classification accuracy statistics. The accuracy of the classification produced by CART with an accuracy value of 77.33 percent with an APER 22.67 percent, the value of Press's Q is 22,413, and specificity is 94.91 percent, then approached by BAGGING process the accuracy of the resulting classification by predicting data testing accuracy value of 78.67 percent with an APER 21.33 percent, press's Q value of 24.65, and specificity of 96.61 percent. Based on these findings, it can be inferred that using the BAGGING process can increase the CART method's performance to the point where it is nearly as good as Binary Logistic Regression, which has a slightly higher classification accuracy
IMPLEMENTASI ALGORITMA K-MEDOIDS DAN K-ERROR UNTUK PENGELOMPOKAN KABUPATEN/KOTA DI PROVINSI JAWA TENGAH BERDASARKAN JUMLAH PRODUKSI PETERNAKAN TAHUN 2020 Fahrur Rozzi Iskak; Iut Tri Utami; Triastuti Wuryandari
Jurnal Gaussian Vol 11, No 3 (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.11.3.366-376

Abstract

The livestock sub-sector is one of the sub-sectors that contribute to the national economy and can significantly absorb labour so that it can be relied upon in efforts to improve the national economy. One of the steps used to increase livestock production in each region in Central Java Province is regional mapping. Cluster analysis is one of the regional mapping methods that can increase livestock production by grouping regencies/cities with characteristics of the same level of livestock production based on the type of livestock production. The k-error and k-medoids method is a non-hierarchical cluster analysis method, where the k-error is a method developed to overcome the problem of data measurement errors in classical cluster analysis, while the k-medoids is a method used to overcome the problem of outliers contained in the data. The validity test of the standard deviation ratio and the WB Index was used to determine the quality of the clustering results. The small validity value of the standard deviation ratio and the WB Index shows the best results of clustering and selecting method. Based on the results of the clustering, the optimal cluster was obtained at k=7 using the k-medoids algorithm, where the validation value of the standard deviation ratio=0.773 and WB Index=0.531.
ANALISIS PENGARUH KUALITAS PELAYANAN TERHADAP KEPUASAN PENUMPANG BRT TRANS SEMARANG MENGGUNAKAN PARTIAL LEAST SQUARE (PLS) Irma Dwi Tyana; Tatik Widiharih; Iut Tri Utami
Jurnal Gaussian Vol 11, No 4 (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.11.4.591-604

Abstract

BRT Trans Semarang is an integrated bus transportation system that operates in Semarang City and parts of Semarang Regency. This transportation provides service facilities such as the availability of bus stops, air-conditioned rooms to travel route information. The facility is expected to be able to provide service satisfaction for its passengers. This study was conducted to determine the effect of service quality on the satisfaction of Trans Semarang BRT passengers using Partial Least Square (PLS), with a case study of Diponegoro University students. PLS is an alternative approach from covariance-based SEM to variance-based. The advantage of PLS is that it is able to handle covariance-based SEM problems such as small sample numbers, abnormal data and the presence of multicholinearity. The quality of this service is measured through the variables of Direct Evidence, Reliability, Responsiveness, Empathy and Guarantee. Passenger satisfaction is measured through a sense of pleasure, a positive impression and the absence of complaints. The results showed that the variables that had a significant effect on the satisfaction of Trans Semarang BRT passengers were the variables of Direct Evidence, Reliability and Responsiveness. Variables that do not have a significant effect on the satisfaction of Trans Semarang BRT passengers are the empathy and guarantee variables. The Adjusted R-Square value is included in the medium category with a value of 0.414, means that the variables of Direct Evidence, Reliability and Responsiveness affect the satisfaction of Trans Semarang BRT passengers by 41.4%. 
PENERAPAN TEXT MINING DAN FUZZY C-MEANS CLUSTERING UNTUK IDENTIFIKASI KELUHAN UTAMA PELANGGAN PDAM TIRTA MOEDAL KOTA SEMARANG Genisia Pramestiloka Aulia; Tatik Widiharih; Iut Tri Utami
Jurnal Gaussian Vol 12, No 1 (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.12.1.126-135

Abstract

Customer complaints can be handled effectively by identifying the main complaints that cause customers to be dissatisfied. Many customer complaints cause difficulty for PDAM Tirta Moedal Semarang to identify problems, which are frequently the primary complaints of customers. Grouping complaints that have similarities using Fuzzy C-Means Clustering will make the identification of the main customer complaints easier. Fuzzy C-Means uses fuzzy models, allows data to be a member of all formed clusters with membership level between 0-1. Fuzzy C-Means Clustering can also introduce more flexible patterns and show results in more accurate cluster placement. Text mining is used to convert textual data into numerical data. Customer complaints received through all contacts in PDAM Tirta Moedal Semarang from October–December 2021 were used as data. The clustering process forms 6 clusters,with the number of clusters tried being 3, 4, 5, and 6, which are seen by the smallest Xie-Beni Index. The main complaints from PDAM Tirta Moedal Semarang customer that seen through Word cloud in each cluster are that the water stops running in clusters 1 and 6 and the pipes leak in clusters 4 and 5. Complaints in clusters 2 and 3 are complaints related to water meters and water flow.
PERAMALAN JUMLAH PENUMPANG KERETA API DI PULAU JAWA MENGGUNAKAN METODE HOLT WINTERS EXPONENTIAL SMOOTHING DAN FUZZY TIME SERIES MARKOV CHAIN Santa Agata Mendila; Iut Tri Utami; Puspita Kartikasari
Jurnal Gaussian Vol 12, No 1 (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.12.1.104-115

Abstract

One of the public transportation choices by the public is the train. The number of train passengers on the island of Java often increases and decreases in certain months. PT.KAI can monitor the number of train passengers by forecasting. Forecasting aims to predict the number of train passengers so that PT.KAI is ready to provide the best service. This study uses monthly data on the number of train passengers on Java Island from January 2015 to February 2020. This study uses multiplicative holt winters exponential smoothing and fuzzy time series markov chain. The multiplicative Holt Winters exponential smoothing method is used on data that contains trend and seasonal elements that experience data fluctuations simultaneously. The fuzzy time series markov chain method is a combination of the fuzzy time series with the markov chain which aims to obtain the greatest probability using the transition probability matrix. Based on the analysis results, it can be concluded that the multiplicative holt winters exponential smoothing method is better at predicting the number of train passengers on Java Island because the value of sMAPE multiplicative holt winters exponential smoothing is smaller, it is 3,0643% and the sMAPE fuzzy time series markov chain value is 5,2955%.
PERAMALAN PENDAPATAN BULANAN MENGGUNAKAN FUZZY TIME SERIES CHEN ORDE TINGGI Muhammad Rizky Yuliyanto; Triastuti Wuryandari; Iut Tri Utami
Jurnal Gaussian Vol 12, No 1 (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.12.1.61-70

Abstract

Cooperatives need consideration in the making of business strategy decisions. Forecasting can assist cooperatives in deciding on their business strategy. This study used n-orde Fuzzy Time Series Chen. n-orde Fuzzy Time Series Chen captures data patterns formed by two or more historical data in each period called fuzzy logic relation (FLR). The pattern of FLR is used to be projected in forecasting future conditions. This study used 2-orde, 3-orde, and 4-orde with 1-orde as the comparison. This study used data on the monthly revenue of the Employee Cooperative of PT. Telekomunikasi Indonesia Semarang Region for the period of January 2019 to May 2022 to predict revenue for the period of June and July 2022. This study used symmetric Mean Absolute Percentage Error (sMAPE) in calculating the forecasting error rate. 1-orde, 2-orde, 3-orde, and 4-orde of Fuzzy Time Series Chen produced different forecasting results for the period of June and July 2022. 1-orde has sMAPE value of 23.15% (good enough forecasting), 2-orde and 3-orde have sMAPE value of 10.06% (good forecasting), and 4-orde has sMAPE value of 4.52% (very good forecasting). This study showed that the larger orde used in Fuzzy Time Series Chen, the lower forecasting error rate.
Co-Authors Fahrur Rozzi Iskak Genisia Pramestiloka Aulia Irma Dwi Tyana Muhammad Rizky Yuliyanto Puspita Kartikasari Salma Innassuraiya Santa Agata Mendila Tatik Widiharih Triastuti Wuryandari