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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 805 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.
PERBANDINGAN PREDIKSI INDEKS HARGA SAHAM GABUNGAN (IHSG) MENGGUNAKAN METODE REGRESI RANDOM FOREST DAN EXTREME GRADIENT BOOSTING Agnes Monika Simalango; Muklas Rivai; Fuji Lestari
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.15.2.356-367

Abstract

Indeks Harga Saham Gabungan (IHSG) serves as the benchmark of the Indonesian stock market and is characterized by nonlinear and volatile movements driven by macroeconomic conditions, trading activities, and market sentiment. This study aimed to compare the forecasting performance of Random Forest and Extreme Gradient Boosting (XGBoost) for the IHSG using historical data from January 2019 to July 2025 with an 80:20 chronological train-test split. Model performance was evaluated using RMSE and MAPE, followed by variable importance analysis and Value at Risk (VaR) estimation to asses potential investment losses. The results showed that Random Forest outperformed XGBoost, achieving an RMSE of 117.54 and a MAPE of 0.90%, whereas XGBoost produced an RMSE of 136.45 and a MAPE of 1.03%. Variable importance analysis identified the highest index value as the most influential predictor, followed by the lowest index value, the opening index value, and trading volume. Furthermore, the estimated expected return was 0.00014, with VaR values of IDR 470,000.00, IDR 610,000.00, and IDR 960,000.00 at the 90%, 95%, and 99% confidence levels, respectively. Based on the evaluation results, Random Forest achieved better forecasting accuracy than XGBoost for the IHSG.
ANALISIS PERBANDINGAN MODEL SARIMAX, RANDOM FOREST, DAN HYBRID SARIMAX–RF PADA PERAMALAN CURAH HUJAN BULANAN DI KOTA MAJENE TAHUN 2025 Muhammad Rifandi; Mohammad Zahid; Bintang Guntur
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.15.2.368-378

Abstract

This study compares the performance of three forecasting models: SARIMAX, Random Forest (RF), and a Hybrid SARIMAX–RF approach in predicting monthly rainfall in Majene City for the year 2025. The dataset consists of five years of monthly rainfall and relative humidity data (2020-2024), sourced from BMKG and national meteorological records. The SARIMAX model captures seasonal trends and includes humidity as an exogenous variable, while the RF model addresses nonlinear relationships using lag-based predictors. A Hybrid model is then developed by combining SARIMAX outputs with RF-learned residual corrections. The forecasting performance is evaluated using standard error metrics such as MAE, RMSE, and MAPE on 2024 test data before forecasting 2025. Results show that the Hybrid model significantly outperforms the individual models, achieving the lowest MAPE (23.31%) compared to SARIMAX (42.21%) and RF (41.23%), and successfully capturing both seasonal patterns and irregular variations. The study demonstrates the importance of model integration for improving rainfall prediction accuracy, especially in tropical coastal regions with complex weather dynamics. Future studies are encouraged to explore additional Hybrid architectures and incorporate other climatic variables to further enhance predictive performance.

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