JURNAL GAUSSIAN (e-ISSN: 2339-2541) is an open access journal (e-journal) published since October 2012. Jurnal Gaussian is published by Department of Statistics, Faculty of Science and Mathematics, Diponegoro University. Jurnal Gaussian publishes scientific articles on various aspects related to statistics, probability, and its application. Articles can be in the form of research results, case studies, or literature reviews. This journal has been accredited by the National Journal Accreditation (ARJUNA) managed by The Ministry of Education, Culture, Research, and Technology, Republic of Indonesia with Third Rank (SINTA 3) from 2023 to 2027 according to SK Nomor: 74/E/KPT/2024. https://sinta.kemdikbud.go.id/journals/profile/8421 Publication Schedule: Starting in 2024 it is published every month of the year, with two numbers. The first number starts from February and the second number starts from August. Indexing: Dimension, SINTA, Google Scholar, BASE, GARUDA, and One Search Document at Scopus: Citation Tracker OAI address: https://ejournal3.undip.ac.id/index.php/gaussian/oai
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
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OPTIMASI XGBOOST DALAM PREDIKSI KECEPATAN KENDARAAN SECARA REAL-TIME : PERBANDINGAN METODE TUNING HYPERPARAMETER
Real-time vehicle speed prediction plays a vital role in the development of intelligent transportation systems aimed at improving traffic flow and safety. This study investigates the performance of the XGBoost algorithm enhanced with three hyperparameter tuning techniques: Grid Search, Bayesian Optimization, and Genetic Algorithm. A simulated dataset was constructed reflect diverse urban traffic scenarios, incorporating environmental variables such as weather, road conditions, and traffic density. The models were assessed using 5 and 10-fold cross-validation based on prediction metrics (MSE, RMSE, MAE and R²) as well as computational efficiency in terms of training and inference time. The findings reveal that Bayesian Optimization achieves the highest prediction accuracy, while Grid Search offers the fastest training time. Genetic Algorithm demonstrates a balanced trade-off between accuracy and computational efficiency, making it a competitive and practical choice. These results highlight the importance of selecting hyperparameter tuning strategies based on specific system needs in real-time traffic prediction using XGBoost.
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MODELING CHILD MALNUTRITION IN EAST JAVA USING THE INTEGRATION OF PARTIAL LEAST SQUARE WITH IMPORTANCE-PERFORMANCE ANALYSIS
Child malnutrition remains a persistent public health concern in Indonesia, particularly in East Java, where stunting and undernutrition rates remain high despite national progress. Malnutrition in children is a multidimensional issue, influenced not only by dietary intake and disease but also by socio-economic conditions, caregiving practices, and environmental health factors such as access to clean water, sanitation, and health services. Addressing this complexity requires robust analytical methods. This study applies Partial Least Squares–Importance Performance Analysis (PLS-IPA) to model the pathways influencing child malnutrition and identify priority interventions. Results indicate that socio-economic factors act as primary predictors influencing food security, healthcare access, and parenting practices. Among direct determinants, environmental health services, including sanitation, clean water access, and maternal healthcare, show the strongest direct effect in reducing malnutrition. IPA results highlight food security as the most critical intervention priority due to its high importance but moderate field performance. Strengthening socio-economic development, improving food security, and sustaining environmental health interventions are recommended to support long-term reductions in child malnutrition in East Java.
ANALISIS KINERJA PORTOFOLIO SAHAM PADA INDEKS IDX30 DENGAN MEAN-SEMIVARIANCE MODEL
Investment involves allocating funds to gain future profits, one way being the purchase of stocks representing company ownership. Investors seek high returns with low risk, but stock price fluctuations introduce risk. Diversification through a stock portfolio helps minimize this risk. The Mean Variance method by Markowitz in 1952 optimizes portfolios based on risk and return, but it assumes data must be normally distributed, often misaligned with financial data. This study adopts the Mean-Semivariance optimization method, which does not require normality assumptions and is more suitable for non-normal data. The study uses 6 stocks from the IDX30 index, to form 2 portfolios with 3 stocks each. The results show an optimal portfolio composed of BMRI stocks with a weight of 48,69%, PGEO stocks with a weight of 17,01%, and INKP stocks with a weight of 34,31%. This portfolio has a Sharpe index of 0,03985, indicating better risk optimization using the Mean-Semivariance method.
PENERAPAN METODE RANDOM FOREST UNTUK ANALISIS SENTIMEN PENGGUNA APLIKASI BANK DIGITAL SEABANK
Sentiment analysis on reviews of an application becomes an option to see users responses to the service of that particular application. Random Forest is one of the classification modeling techniques that originate from a combination of Decision Trees, providing the final result based on majority voting. This research aims to improve the performance of sentiment classification on customer reviews of Seabank, one of the most widely used digital banking services in Indonesia, by utilizing the Random Forest algorithm. The study involves sentiment analysis of user reviews on the Seabank application, collected from 15,000 reviews on Google Playstore. The review features available on Google Playstore are used as a means to convey opinions as user feedback for an application. Random Forest is trained to classify reviews into 3 sentiment classes: positive, neutral, and negative. Based on the research conducted with model evaluation using Confusion Matrix, an accuracy value of 94.1% was obtained, indicating that Random Forest's accuracy in classifying Seabank customer reviews is 94.1%. This demonstrates the effectiveness of using Random Forest in text review classification due to its high accuracy value.
KLASIFIKASI SENTIMEN KASUS ONLINE TRADING BINOMO PADA TWITTER MENGGUNAKAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK (CNN)
Investment in futures trading and technology is growing in Indonesia, making many domain sites of online trading companies appear that are easy for everyone to access. The Binomo app is so viral among the public because of an ad that displays professional traders who can earn 1000 USD a day without leaving their homes. Binary options trading ultimately causes losses for some people, allegedly caused by affiliates. The Binomo case is widely discussed on social media, especially Twitter. Tweets displaying public opinion on Twitter can be used for sentiment analysis and categorizing public opinion on the Binomo case. 2.686 tweets were collected by Twitter scraping between January 1 and April 1, 2022. 1.519 tweets were left after pre-processing. The data were processed using the Convolutional Neural Network algorithm with the Word2Vec method to determine their accuracy and identify topics often discussed by the public on Twitter. A CNN model with 70% training data, 3, 4, 5, kernel sizes, 4 batch sizes, 30 epochs, and Adam optimizers was used to build the classification in this research. The accuracy value obtained from the performance evaluation of the Convolutional Neural Networ model research was 89%.
LATENT DIRICHLET ALLOCATION DALAM IDENTIFIKASI RESPON MASYARAKAT INDONESIA TERHADAP PROFESI PEGAWAI NEGERI SIPIL
Data on social media comments can be extracted to produce hidden information that is useful as a guide for evaluation and decision making. YouTube has a comments feature as a forum for expressing opinions, experiences and questions. Civil Servants are known as one of the job choices of Indonesian people, the government announced that there were resignations of Civil Servant Candidates in 2022. Responses written in the comment column are difficult to understand, topic modeling can be applied as a text analysis process to find descriptions from unstructured data. Latent Dirichlet Allocation method is able to find out hidden topics in a document as well as the words that make up a topic so that the application of this method will help in identifying responses discussed by the audience. The data used is textual data in the form of comments from YouTube scrapping during 2022. The results of topic modeling form eight topics, namely retirement life, parents hopes, dream jobs, civil servants, job differences, characteristics of generation Z, salary and benefits, and reasons for resignation. The RStudio GUI program can make it easier for users to analyze topic modeling with similar methods.
PEMODELAN CLUSTERWISE LINEAR REGRESSION UNTUK IDENTIFIKASI FAKTOR YANG MEMENGARUHI PREVALENSI STUNTING DI JAWA TENGAH
Sustainable development in the Sustainable Development Goals (SDGs) emphasizes health as a key pillar, including in overcoming malnutrition that causes stunting. Central Java Province recorded a stunting prevalence rate of 20.7% in 2023, so it is necessary to analyze the factors that influence this condition. This study uses the Clusterwise Linear Regression (CLR) method to identify factors that contribute to the prevalence of stunting based on regional characteristics. The variables analyzed include the percentage of low birth weight babies (LBW), mothers who exclusively breastfeed less than six months, women who marry at an early age, households with proper sanitation, households with clean water sources, and households that have a Prosperous Family Card (KKS). The results showed that there were 3 optimal clusters. The coefficient of determination for each cluster was 99.52% for cluster 1, 99.76% for cluster 2, and 98.26% for cluster 3.
ESTIMATOR RIDGE-DERET FOURIER PADA REGRESI NONPARAMETRIK
Nonparametric regression is frequently applied to describe the connection between variables when the functional form is not defined. The Fourier series estimator is especially effective in capturing periodic patterns in regression curves. In multivariable cases, however, strong correlations among predictor variables often lead to multicollinearity, which results in unstable parameter estimation due to the near-singularity of the design matrix. While the Fourier approach has been widely developed for curve estimation, its theoretical framework has not explicitly accommodated this issue. This research introduces a ridge–Fourier series estimator for nonparametric regression to achieve stable parameter estimation in the presence of multicollinearity. The estimator is derived under a penalized likelihood framework by incorporating a ridge penalty into the Fourier series model and optimizing it using Maximum Likelihood Estimation (MLE). This approach yields a closed-form estimator with reduced variance and improved numerical stability while retaining the flexibility of the nonparametric structure. The oscillation parameter and ridge penalty parameter are determined through the Generalized Cross Validation (GCV) criterion to achieve an optimal smoothing level. This research's major contribution involves the theoretical formulation and derivation of the ridge–Fourier series estimator within the additive nonparametric regression model.
PERBANDINGAN PERFORMA DISTANCE MEASURES PADA ALGORITMA NEAREST CENTROID NEIGHBOR DAN K-NEAREST NEIGHBOR DALAM PROSES KLASIFIKASI BINTANG
Stars are celestial bodies that can be classified based on several characteristics, including temperature, luminosity, radius, magnitude, stellar color, and spectral class. Stars are generally grouped into six categories: brown dwarfs, red dwarfs, white dwarfs, main sequence stars, supergiants, and hypergiants. Stellar classification is important to astronomers because it can help identify new types of stars and improve our understanding of their composition, temperature, and evolutionary stages. This classification process can be done using the Nearest Centroid Neighbor (NCN) and k-Nearest Neighbor (k-NN) algorithms, by applying various distance measures such as Euclidean, Manhattan, Minkowski, Chebyshev, Cosine, Jaccard, and Hamming. This study aims to compare the performance between NCN and k-NN using these seven distance measures. The results show that Euclidean, Manhattan, and Minkowski distances produce a perfect performance of 100% in both algorithms. Chebyshev distance yielded perfect performance in k-NN but slightly lower in NCN with a performance of 92%. Thus, the k-NN algorithm provides superior performance compared to the NCN algorithm in the stellar classification process.
PREDIKSI KEKERINGAN DENGAN MENGGUNAKAN RANDOM FOREST DAN XGBOOST SEBAGAI STRATEGI MITIGASI BENCANA DI KABUPATEN MAJENE
The growing incidence of drought attributable to climate change poses serious challenges to food systems and agricultural livelihoods in Majene Regency, West Sulawesi. To address this, the present study compares the predictive accuracy of Random Forest and XGBoost in classifying drought occurrences from meteorological variables, using 300 monthly observations recorded by BMKG between 2000 and 2024. Exploratory data analysis revealed that 60% of drought events are concentrated in August and the target variable is severely imbalanced (16.3% drought vs. 83.7% non-drought). At the default threshold of 0.50, Random Forest outperforms XGBoost in all metrics. However, because imbalanced data causes both models to underdetect drought at the default threshold (sensitivity = 0.267), threshold optimization was applied by lowering the decision threshold to τ = 0.15. The result of threshold optimalization was significantly improved Random Forest sensitivity from 0.267 to 0.867, detecting 13 out of 15 drought events in the test set while maintaining an AUC-ROC of 0.916. Feature importance analysis consistently identifies X3 as the dominant predictor in both models. Random Forest with threshold τ = 0.15 is recommended as the primary model for drought early warning systems in Majene.
PENGENDALIAN KUALITAS PUPUK NITROGEN, PHOSPAT, KALIUM (NPK) PELANGI FUSION DI PT PUPUK KALIMANTAN TIMUR MENGGUNAKAN PETA KENDALI MEWMA & MEWMV
The NPK Pelangi Fusion fertilizer is one of the main products manufactured by PT Pupuk Kalimantan Timur. NPK fertilizer is a blend of various plant nutrients, primarily Nitrogen, Phospat, and Kalium, aimed at enhancing crop yields. However, deviations from the NPK fertilizer specifications can lead to inconsistent plant growth and low harvest yields. Statistical Process Control (SPC) is a method used to process data and monitor production processes using statistical techniques, with the goal of detecting changes in process performance through the use of control charts. In this study, the Multivariate Exponentially Weighted Moving Variance (MEWMV) control chart is used to monitor the variance of the production process due to its optimal performance in detecting small variance shifts. Additionally, the Multivariate Exponentially Weighted Moving Average (MEWMA) control chart is used to monitor the mean of the NPK Pelangi Fusion production process, as it quickly detects subtle shifts in variance. The analysis results indicate that the optimal weighting for the MEWMV control chart is ω=0.2 and λ=0.4, resulting in an Average Run Length (ARL) of 370. Similarly, the optimal weighting for the MEWMA control chart is λ=0.06, with an upper control limit of H=9.80 and an ARL of 200. This study concludes that the mean and variance of the multivariate production process have been effectively controlled.
PENERAPAN KLASIFIKASI REGRESI LOGISTIK BINER DAN ADAPTIVE BOOSTING MENGGUNAKAN CLASSIFICATION AND REGRESSION TREES PADA PREDIKSI PENYAKIT HEPATITIS C
Chronic liver disease is primarily attributed to the hepatitis C virus. Disorders of liver function can inhibit metabolism and threaten health. Hepatitis C disease must be detected earlier to reduce the risk of spreading it. Data processing using the Binary Logistic Regression and Adaptive Boosting classification methods to predict the category of patients with positive or negative hepatitis C status. Problems with unbalanced data are found in the classification process. Data imbalance can be overcome with the Synthetic Minority Over-Sampling Technique (SMOTE). Data retrieval was obtained from the 2020 UCI (University of California Irvine) Machine Learning Repository regarding data on predictions of hepatitis C patients which were downloaded on October 25, 2022. The results for the accuracy of the classification show that the Binary Logistic Regression method produces an accuracy value of 97,44%, the value sensitivity of 100%, and specificity of 97,17%. The accuracy of the classification produced by the Adaptive Boosting method with an accuracy value of 92,31%, a sensitivity value of 63,64%, and specificity of 100%. Binary Logistic Regression is the best method that can classify hepatitis C status of patients with the highest sensitivity of 100%.
METODE GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) DENGAN ESTIMASI GENERALIZED LEAST SQUARE (GLS)
A model that combines both time and location factors in a multivariate time series called space time model. Generalized Space Time Autoregressive (GSTAR) is one of the space-time models that can be utilized in data forecasting. The GSTAR model is a development method of Space Time Autoregressive (STAR) that can be used for heterogeneous locations. The GSTAR model is used to forecast data in multiple locations at once. Generalized Least Squares (GLS) is one of the estimation methods that can be used in the GSTAR model. The GLS method is used on data that has residuals that are correlated across equations. This study applies GSTAR model to forecast farmer exchange rates of horticultural subsector in West Java, Yogyakarta, East Java, and Banten using GSTAR-GLS (11)I(1) model with uniform, invers distance, and cross-correlation normalization weights. The analysis result for model GSTAR-GLS (11)I(1) with three weighted methods shows that the best forecast result is using uniform weights with SMAPE for West Java, Yogyakarta, East Java, and Banten are 2.85%; 3.63%; and 1.92% or the forecasting result is highly accurate.
KOMBINASI LEXICON-BASED DAN MULTINOMIAL NAЇVE BAYES CLASSIFIER DALAM ANALISIS SENTIMEN ARTIS SONG JOONG KI SEBAGAI BRAND AMBASSADOR SCARLETT WHITENING
Twitter as a social media platform can be utilized as a means of exchanging information about current events. The topic of K-Drama is often discussed by the public on Twitter and reaches trending topics, especially during the pandemic in Indonesia. The popularity of K-Drama in Indonesia has led to marketing strategies where actors are chosen as brand ambassadors. Song Joong Ki is one of the actors who has been chosen by Scarlett Whitening products to become their brand ambassador. The public expresses their responses on Twitter, and sentiment analysis is necessary to classify these responses as positive, neutral, or negative. The sentiment analysis combines the Lexicon-Based method and Multinomial Naїve Bayes Classifier. SentiWordNet is used in the Lexicon-Based classification method. The data preprocessing stage of this research includes cleansing, case folding, word normalization, tokenizing, filtering, and stemming. The combination of the Lexicon-Based method and Multinomial Naїve Bayes Classifier yielded an accuracy score of 81.50%. The words “jadi”, “brand”, and “ambassador” dominate the word cloud, indicating that the public extensively discusses the appointment of Song Joong Ki as the brand ambassador for Scarlett Whitening.
ANALISIS KLASIFIKASI MENGGUNAKAN REGRESI LOGISTIK BINER DAN K-NEAREST NEIGHBOR PADA DATA IMBALANCE
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 FAKTOR-FAKTOR YANG MEMENGARUHI DAYA KONSENTRASI BELAJAR MENGGUNAKAN EXTENDED COX REGRESSION
Learning concentration plays a major role in the success of teaching and learning activities and is the main asset for students in receiving and mastering the subject matter presented. This study aims to determine the average endurance time of learning concentration power of students in grades 4-6 at SDN 02 Jenarwetan and the factors that influence it. The method used is Cox Extended because there are independent variables that do not meet the Proportional Hazard assumption. Parameter estimation uses the Maximum Partial Likelihood Estimation (MPLE) method with the Efron approach because there is data with co-occurrence. Based on the results of data analysis, the average endurance time of students' learning concentration power is 13.22 minutes. It is also known that the factors that influence the endurance of students' learning concentration power are the level of learning motivation and the level of stress experienced by students. Students with high learning motivation are able to maintain their learning concentration for a long period of time , while students with high stress levels are more at risk of losing learning concentration 4.6294 times higher than students with low stress levels.
MODEL COX PROPORTIONAL HAZARD UNTUK MENGIDENTIFIKASI FAKTOR RISIKO KEMATIAN PADA PASIEN PRIMARY BILIARY CIRRHOSIS
Primary biliary cirrhosis (PBC) is a progressive chronic liver disease associated with a high risk of mortality. This study aims to identify clinical factors influencing the survival of PBC patients using the Cox Proportional Hazard (Cox PH) model. The Cox PH model was selected because it is appropriate for censored survival data and offers the advantage of estimating the effects of multiple covariates without requiring a specific baseline hazard distribution. The study used data from 312 PBC patients treated at the Mayo Clinic. All variables were evaluated using the Log-Rank test and satisfied the proportional hazard assumption. The results indicate that ascites (HR = 3.951), high copper levels (HR = 1.964), high bilirubin levels (HR = 3.789), and advanced disease stage (HR = 9.222) significantly increase the risk of mortality, while higher cholesterol levels reduce the risk (HR = 0.392). These findings demonstrate that the Cox PH model is effective for identifying significant clinical risk factors and providing interpretable survival analysis for PBC patients, which may support clinical decision-making and patient management.
PREDIKSI PENUMPANG LRT JAKARTA MENGGUNAKAN SARIMAX DAN XGBOOST DENGAN EFEK KALENDER
The daily passenger volume of Jakarta’s LRT fluctuates significantly due to weekly seasonality and calendar variations, making accurate forecasting important for operational planning and decision-making. This study aims to determine the most effective model for forecasting daily passenger demand by comparing the SARIMAX and XGBoost methods on transportation data characterized by strong seasonal patterns and external influences. SARIMAX was selected because it models seasonal and autoregressive structures alongside exogenous variables, while XGBoost captures nonlinear relationships between temporal features and external factors. The dataset covers the period from 1 January 2024 to 31 August 2025 and includes variables such as weekends, national holidays, and special events. Model evaluation was conducted using walk-forward cross-validation and hyperparameter tuning. The results show that the SARIMAX(1,0,1)(0,1,1)7 model achieved the best performance, with a validation MAPE of 11.26% and a test MAPE of 8.64%, outperforming XGBoost. SARIMAX also reproduced weekly fluctuation patterns more consistently, indicating that it is more suitable for forecasting transportation demand with strong seasonal characteristics and relatively stable external influences.
BURR XII REGRESSION AND OPTIMIZATION FOR DIARRHEA INCIDENCE ANALYSIS IN SURAKARTA CITY
Diarrhea remains a major public health concern in Indonesia, with Surakarta recording 11,434 cases in 2024, an increase of 58% from the previous year. This study applies the Burr XII regression model to identify environmental factors influencing diarrhea incidence and accommodate heavy-tailed variability across urban villages. The Burr XII model was chosen for its robustness in handling skewed data and extreme incidence patterns commonly found in epidemiological studies. Parameter estimation was carried out through the Maximum Likelihood Estimation (MLE) approach, with the BFGS algorithm as the primary optimization method, compared against Genetic Algorithm (GA) and Simulated Annealing (SANN). This research is important due to the increasing trend of diarrhea cases and the need for accurate statistical models to support public health policies. The results indicate that the BFGS method achieved the best fit, with five significant predictors: distance to the nearest hospital, rainfall, distance to waste disposal site, elevation, and distance to the nearest river. Population density and slope were not statistically significant but retained for theoretical relevance. These findings emphasize the importance of environmental and infrastructural factors in diarrhea prevention and support spatially targeted public health interventions.
ESTIMASI PARAMETER DAN PENGUJIAN HIPOTESIS MODEL GTW LOG LOGISTIC 3-PARAMETER REGRESSION
The Geographically and Temporally Weighted Logistic Lindley Three-Parameter Regression (GTWLL3R) is a local regression model developed to analyze spatial-temporal heterogeneity using the flexibility of the three-parameter log-logistic distribution. Unlike global regression models that assume constant parameters across observations, GTWLL3R allows model parameters to vary across locations and time periods. This study aims to estimate the parameters of the GTWLL3R model using the Maximum Likelihood Estimation (MLE) approach. Since the log-likelihood function does not have a closed-form solution, parameter estimation is carried out numerically using the Newton–Raphson iterative algorithm. This study derives the likelihood function, parameter estimation procedure, variance–covariance matrix based on the observed information matrix, and statistical hypothesis testing model. The validity of statistical inference is established through the Hessian matrix, where the covariance matrix is obtained from the negative inverse Hessian matrix. Based on MLE theory, the parameter estimators are theoretically consistent and asymptotically normally distributed under regularity conditions. This research is limited to theoretical and methodological development without simulation or empirical validation. Future studies are recommended to conduct simulation analyses and apply the GTWLL3R model to real spatial-temporal datasets to evaluate estimator performance and model accuracy.
PERBANDINGAN PERAMALAN MENGGUNAKAN DOUBLE EXPONENTIAL SMOOTHING HOLT DAN FUZZY TIME SERIES MARKOV CHAIN UNTUK DATA INFLASI INDONESIA
Inflation is one measure of a country’s economic condition. The inflation rate in Indonesia had decreased to 1.33% in June 2021 when the Covid-19 pandemic was hitting. Forecast of the inflation rate is needed for monitoring economic condition. The Double Exponential Smoothing Holt method can be used on data that contains trend elements and uses two smoothing parameters in the analysis, while the Fuzzy Time method Series Markov Chain is a development of the Fuzzy Time Series method with the Markov chain concept which does not have any conditions in its analysis. The results of forecasting analysis from the Double Exponential Smoothing Holt method obtained an MAE value of 0.35957, while from the Fuzzy Time Series Markov Chain method obtained an MAE value of 0.22462. Thus, the Fuzzy Time Series Markov Chain model is a better model in forecasting than the Double Exponential Smoothing Holt method. That model is reused to analyze forecasting and get a MAPE value of 8.79% which means that the Fuzzy Time Series Markov Chain model predicts Indonesia’s inflation data very well.
PENGELOMPOKAN DAERAH RAWAN KRIMINALITAS DI INDONESIA TAHUN 2021 MENGGUNAKAN METODE K-MEANS & FUZZY C-MEANS
Crime is one of the aspects that can influence national stability and security. In this study, crime data is used to cluster crime-prone areas and is considered whether these areas require extra surveillance or not. This research employs the K-means and Fuzzy C-Means methods. The K-means method groups data based on the similarity of data with cluster centroids. This algorithm is relatively efficient in complexity and easy to understand. K-means explicitly allocates data to specific clusters. On the other hand, Fuzzy C-Means is capable of placing a data point that lies between two or more other clusters into a single cluster. This is due to each data point having a degree of membership to determine its grouping, making the chances of failure to converge or stable cluster centers very low. The optimal number of clusters is selected using validation through the Davies Bouldin Index and Calinski Harabasz Index. The research results indicate that clustering crime-prone areas using both methods yield the same outcome of 2 clusters. Cluster 1 exhibits a higher crime rate compared to cluster 2, as indicated by the higher average values of group members. The lowest Davies Bouldin Index is 1.04948, and the highest Calinski Harabasz Index is around 24.36783.
PERAMALAN JUMLAH PENUMPANG PENERBANGAN DOMESTIK PADA TIGA BANDARA UTAMA INDONESIA MENGGUNAKAN MODEL GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR)
Forecasting the number of airplane passengers requires a model related to time and location because the data on the number of airplane passengers is space time series data. Generalized Space Time Autoregressive (GSTAR) is an approach to forecast time series data that show a connection between time and location. The GSTAR model can be applied to data with heterogeneous location characteristics. In this research, the GSTAR model is used to form a forecasting model for the number of domestic flight passengers at three major airports in Indonesia Juanda Airport, Soekarno Hatta Airport, and Hasanuddin Airport, using optimal location weighting. The residual of the GSTAR model satisfies the white noise assumption. The location weights used are uniform weights, inverse distance weights, and cross-correlation normalization. The outcome model is constructed with a first-order differencing, an autoregressive order of 1, and spatial order of 1, resulting in the formation of the GSTAR (11)I(1) model. The most optimal model generated is the GSTAR (11)I(1) model utilizing inverse distance weights due to its smallest sMAPE value compared to other weightings, which stands at 8,71%. This sMAPE value signifies a highly precise forecast accuracy.
IMPLEMENTASI ALGORITMA FUZZY K-NEAREST NEIGHBOR UNTUK KLASIFIKASI PENYAKIT DIARE
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.
ANALISIS VALUE AT RISK ASET TUNGGAL DENGAN PENDEKATAN EKSPANSI CORNISH-FISHER MOMEN KELIMA
Stocks are tangible proof of ownership in a corporation. In investment, the greater the return obtained, the greater the risk received. Value at risk is used as a risk measurement tool by estimaing the worst loss at a given level of confidence and time stage. VaR generally assumes that the data used meets the assumption of normality. However, data in the field often shows deviations from the assumption of normality due to fluctuating movements. The Cornish-Fisher Expansion is one approach in calculating VaR that does not require normality assumptions. The Cornish-Fisher Expansion method generally takes into account the first 4 quantiles, namely mean, variance, skewness, and kurtosis in VaR predictions. However, in its development this method can be extended beyond the fourth moment. The data used in this study is PT Aneka Tambang Tbk's daily closing stock price data for the period January 4, 2021 to December 31, 2022 totaling 493 data. Calculation results using the Cornish-Fisher Expansion method to the fifth moment show that the estimated value of VaR is higher than the regular Cornish-Fisher Expansion.
REGRESI ROBUST ESTIMASI-M UNTUK PEMODELAN TINGKAT PENGANGGURAN TERBUKA (TPT) DI PROVINSI JAWA TENGAH
Regression analysis can be used to overcome the outlier problem with robust regression. The M-estimation robust regression method is an estimation approach that is the simplest computationally and theoretically. The purpose of using the robust regression method is because there is an outlier problem in the data. This research will create a model on open unemployment rate data in Central Java in 2022 which involves variables including average length of schooling, population, number of poor people, minimum wage, and per capita expenditure. Based on the Ordinary Least Squares (OLS) regression equation, it shows that there is a violations of assumptions and outlier data is detected. So it is necessary to carry out robust regression to overcome the outlier problem. In the test carried out using M-estimation robust regression, the best robust regression model was obtained = -5,899 + 0,0192 X3+ 0,0000024 X4+0,00036 X5. The M-estimation robust regression model can be concluded that total variation in the open unemployment rate in Central Java in 2022 is explained by the number of poor people, minimum wages, and per capita expenditure by 31.39%, and the MSE value is 2.143382.
KLASIFIKASI MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBOR DAN C5.0 PADA IMBALANCE CLASS DATA DENGAN SMOTE
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.
PENGUKURAN RISIKO KREDIT DAN PEMBENTUKAN SKENARIO RATING OBLIGASI KORPORASI MENGGUNAKAN CREDIT METRICS DAN SIMULASI MONTE CARLO
Indonesia's economic recovery after the pandemic has positively impacted the capital market, leading investors to invest in fixed-income instruments like bonds. Bonds have their own risks, including default risk and future cash value (valuation). To measure bond risk, investors use the Credit Metrics method and Monte Carlo simulation to simulate bond rating scenarios. For this project, five bonds from companies listed in the PEFINDO25 Index are used. The credit risk of each bond is calculated based on the mean and standard deviation. HEAL bonds have the smallest mean at IDR 13.16 billion, while MYOR bonds have the largest mean at IDR1 542.87 billion. The rating scenario shows that DSNG, HEAL, MYOR, TINS, and TOBA bonds are more stable in their initial ratings, but DSNG and TOBA bonds have potential downgrade to CCC. Sixteen portfolio combinations were formed, with a mean valuation of IDR 2,446.63 billion for a portfolio of five bonds and IDR 639.23 billion for the three bond portfolios. These valuations can be used as a consideration for investors when making bond investment decisions.