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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 803 Documents
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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.
ANALISIS AUTOKORELASI SPASIAL DAN STATISTIK GETIS-ORD GI* TERHADAP DISTRIBUSI PERSENTASE PENDUDUK MISKIN DI PROVINSI JAWA TENGAH Fajar Dwi Cahyoko; Nanda Oktarina Aditya; Muhammad Riefky
Jurnal Gaussian Vol 15, No 2 (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.15.2.344-355

Abstract

Central Java Province is one of the provinces with a higher percentage of poor population compared to the national average. Although the poverty rate has declined in recent years, poverty issues in Central Java still require comprehensive solutions, particularly through evidence-based, area-based policy formulation. This study aims to identify the existence of spatial dependence in the percentage of poor population in Central Java Province, to map local poverty clusters, and to detect statistically significant concentrations of high and low poverty values in order to reveal structural poverty pockets. Global spatial autocorrelation testing using Moran's I yielded a value of 0.181 (Z = 1.802; p = 0.036), indicating a statistically significant clustered spatial pattern in the percentage of poor population across regencies/cities. Local spatial autocorrelation analysis (LISA) further identified three types of local association patterns at the 10% significance level: High–High, Low–High, and High–Low. Complementing these local patterns, hotspot and coldspot analysis using the Getis–Ord statistic—which captures the concentration of high or low values within a neighborhood rather than deviation-based association—identified seven regencies as statistically significant hotspots (one at the 99%, three at the 95%, and three at the 90% confidence level) and one regency as a significant coldspot (95% confidence level). This dual approach enables a more specific and operational identification of priority regions for poverty reduction policy.
IMPLEMENTASI ALGORITMA FUZZY K-NEAREST NEIGHBOR UNTUK KLASIFIKASI PENYAKIT DIARE Nur Dihyah; Budi Warsito; Iut Tri Utami
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.15.1.255-263

Abstract

Diarrhea is digestive disruption retrieved by defecation that become more fluid and occur over three times a day. The prevalence of diarrhea in Indonesia is public health problem with high cases. Diarrhea management is carried out with rehydration efforts by administering oral rehydration salts at puskesmas. Diarrhea is classified into 2 types, namely acute diarrhea (mild) and chronic diarrhea (severe). Puskesmas only handles mild diarrhea so that a method is needed to classify diagnosis of diarrhea that occurs at puskesmas appropriately so that diagnosis of acute diarrhea is not misclassified into chronic diarrhea. This research implements Fuzzy K-Nearest Neighbor procedure for Diarrhea Classification. Fuzzy K-Nearest Neighbor incorporates fuzzy logic and K-Nearest Neighbor in the classification practice. The advantage of Fuzzy K-Nearest Neighbor is data will have membership value in each data class so that it further strengthens reason for data to enter predicted class. Data is processed by applying Shiny Package in Rstudio to create Graphical User Interface (GUI-R) so that it makes it easier for researchers to process data. The results obtained highest classification accuracy at K = 3 with accuracy of 80.19% and specificity of 85.45% so that Fuzzy K-Nearest Neighbor was able to classify diarrhea well.

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