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All Journal Jurnal Gaussian
Bagus Arya Saputra
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
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ANALISIS KLASIFIKASI MENGGUNAKAN REGRESI LOGISTIK BINER DAN K-NEAREST NEIGHBOR PADA DATA IMBALANCE Eva Fitriyani; Tatik Widiharih; Bagus Arya Saputra
Jurnal Gaussian Vol 15, No 1 (2026): 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.15.1.154-165

Abstract

Savings and Loan Cooperative or (KSP) is a cooperative that conducts its business activities only saving and borrowing. KSP members come from various different backgrounds so that they can affect their behavior in carrying out their obligations. To find out the status of current or bad customer payments, a classification process is carried out. The division of KSP customer data is carried out in the classification process into two, namely training data and test data. In the classification process, there are often cases of data imbalance, so it is necessary to handle data imbalance in training data with SMOTE and ADASYN. SMOTE and ADASYN were chosen because these methods handle imbalance data by generating data from minor classes so as not to eliminate important parts of the data. Classification was performed with Binary Logistic Regression and K-Nearest Neighbor. Binary Logistic Regression is a regression where the dependent variable is binary. While K-Nearest Neighbor is a grouping method based on the closeness of the distance of a data with other data as many as k nearest neighbors. The results of this study indicate that the ADASYN Binary Logistic Regression method is the best method that can classify and predict the payment status of KSP customers because it produces the highest accuracy and G-mean, namely the accuracy value of 70.67% and G-Mean 67.63%.
ANALISIS SISTEM ANTREAN DENGAN METODE BAYESIAN Sanjaya Pamungkas; Sugito Sugito; Bagus Arya Saputra
Jurnal Gaussian Vol 14, No 2 (2025): 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.14.2.599-607

Abstract

Along with the times, the transportation sector has progressed quite rapidly. In connection with the transportation sector, a phenomenon that is easily found in everyday life is the queue at public transportation facilities. One of them is at the transportation facility at the airport. At the airport the queue that occurs is due to the large number of aircraft that come to get service from airport service facilities. However, the queue can be minimized with a good system. The purpose of this research is to find out changes or additional information from aircraft services, get a queue system model, and find out whether the service at the airport is good or not. The Bayesian method is used to combine prior information from previous research data (Widiawati, 2010) and current observed data (samples) to obtain updated information. The sample distribution (Weibull and inverse Gaussian) of the current observed data and the prior distribution (inverse Gaussian and Weibull) obtained from the prior information in the previous research data (Widiawati, 2010). The prior distribution and the likelihood function of the sample distribution are combined to obtain the posterior distribution. After calculating the posterior distribution, it is found that the model of the aircraft queue at Adi Soemarmo International Airport - Surakarta is (GAMMA/GAMMA/3): (GD/∞/∞) with steady state conditions already met (ρ<1) and based on the results of the performance measure of the aircraft queue system at Adi Soemarmo International Airport has a good condition.
KLASIFIKASI MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBOR DAN C5.0 PADA IMBALANCE CLASS DATA DENGAN SMOTE Salsabilla Rizka Ardhana; Tatik Widiharih; Bagus Arya Saputra
Jurnal Gaussian Vol 15, No 1 (2026): 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.15.1.280-288

Abstract

Rural Banks (BPR) provide financial services to micro-businesses and low repayment communities, especially in rural areas. The main activity of the bank is lending. Customer credit classification is expected to assist BPR in anticipating potentially bad loans. K-Nearest Neighbor and C5.0 classify current and potentially bad credit status based on customer data from BPR “X” in Central Java in October 2022. K-Nearest Neighbor is effective against a large amount of training data and works based on the nearest neighbor. C5.0 can improve classification accuracy and work by calculating entropy, gain, split info, and gain ratio to form a decision tree. There is an imbalance class data which causes the classification process to focus more on the majority class. Imbalance class data is handled using SMOTE as an oversampling approach. Classification with the addition of SMOTE can improve the evaluation of classification accuracy, especially G-Mean. G-mean is the most comprehensive measurement compared to accuracy, sensitivity and specificity in evaluating classification performance on imbalance class data. Result show an increased G-Mean to 58.55% on KNN and 64.05% on C5.0. Based on the classification results, it is concluded that C5.0 with SMOTE is a more appropriate classification model for customer credit status.
Co-Authors Eva Fitriyani Salsabilla Rizka Ardhana Sanjaya Pamungkas Sugito Sugito Tatik Widiharih