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Dr. Muhammad Ahsan
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Inferensi
Published by Institut Teknologi Sepuluh Nopember Surabaya
ISSN : 0216308X     EISSN : 27213862     DOI : http://dx.doi.org/10.12962/j27213862
Core Subject : Science, Education, Social, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Mathematics Social Sciences
The aim of Inferensi is to publish original articles concerning statistical theories and novel applications in diverse research fields related to statistics and data science. The objective of papers should be to contribute to the understanding of the statistical methodology and/or to develop and improve statistical methods; any mathematical theory should be directed towards these aims; and any approach in data science. The kinds of contribution considered include descriptions of new methods of collecting or analysing data, with the underlying theory, an indication of the scope of application and preferably a real example. Also considered are comparisons, critical evaluations and new applications of existing methods, contributions to probability theory which have a clear practical bearing (including the formulation and analysis of stochastic models), statistical computation or simulation where the original methodology is involved and original contributions to the foundations of statistical science. It also sometimes publishes review and expository articles on specific topics, which are expected to bring valuable information for researchers interested in the fields selected. The journal contributes to broadening the coverage of statistics and data analysis in publishing articles based on innovative ideas. The journal is also unique in combining traditional statistical science and relatively new data science. All articles are refereed by experts.
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 11 Documents
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Search results for , issue "Vol 8, No 3 (2025)" : 11 Documents clear
Estimation of Stunting and Wasting in Sumatra 2022 with Nadaraya-Watson Kernel and Penalized Spline Oktarina, Cinta Rizki; Nugroho, Sigit; Sriliana, Idhia; Novianti, Pepi; Sunandi, Etis; Pahlepi, Reza
Inferensi Vol 8, No 3 (2025)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v8i3.23330

Abstract

This study aims to estimate the prevalence of Stunting and Wasting in Sumatra in 2022 using nonparametric regression methods, specifically the Nadaraya-Watson Kernel and Penalized Spline regression models. Both models were applied to assess the relationship between these two correlated response variables and various predictor variables, such as low birth weight, sanitary facilities, poor population, and exclusive breastfeeding. The results showed that the Nadaraya-Watson Kernel regression, particularly using the Gaussian kernel, provided the best fit with minimal prediction error, as indicated by its low Generalized Cross-Validation (GCV) value of 0.024 and high R-squared values (0.9992 for Stunting and 0.9995 for Wasting). In contrast, the Epanechnikov kernel and Biweight kernel produced higher GCV values (0.110 and 0.356, respectively), indicating less optimal performance. For the Penalized Spline model, optimal parameters were determined with a smoothing parameter λ of 5 and 3 knots, which balanced model flexibility and smoothness. This research underscores the potential of nonparametric regression techniques in capturing complex relationships in health data and provides insights for improving interventions aimed at addressing child malnutrition in Indonesia.
Forecasting Tourist Arrivals in Bali: A Grid Search-Tuned Comparative Study of Random Forest, XGBoost, and a Hybrid RF-XGBoost Model Waciko, Kadek Jemmy; Susanti, Leni Anggraini; Muayyad, Muayyad; Fakhrurozi, Rifqi Nur
Inferensi Vol 8, No 3 (2025)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v8i3.23334

Abstract

Tourism planning, infrastructure growth, and economic stability. This study presents an extensive comparative evaluation of Random Forest (RF), Extreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM), and a novel Hybrid RF-XGBoost model in the prediction of monthly international tourist arrivals. A full time series dataset of a ten-year period (2014–2024) from the Central Bureau of Statistics of Bali was used for training and testing the models. Hyperparameter optimization using Grid Search with cross-validation (Grid Search CV) was used for all the machine learning models to obtain best predictive performance. Two robust metrics, Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE), were used to assess forecasting accuracy. Results show that the Random Forest model outperforms all competitors with lowest RMSE (41,772.68) and MAPE (6.30%), indicating high forecasting precision and robustness, especially during structural breaks such as the COVID-19 pandemic. The hybrid model also performs well, with LSTM indicating higher error rates, illustrating its shortcomings on small-to-medium-scale tourism time series. Besides, the study provides six-month ahead predictions (January–June 2025) with 95% prediction intervals, showing an ongoing trend of recovery. The findings affirm the superiority of bagging-based ensemble methods over polynomial-based methods in capturing nonlinearity, seasonality, and exogenous shocks in tourist demand. The study plugs the growing amount of data-driven tourism analytics by offering a reproducible, high-precision forecasting model for developing countries and seasonally driven destinations.
The Application of the K-Medoid Classification Method for Analyzing Crime Rates in South Sulawesi Annas, Suwardi; Aswi, Aswi; Irwan, Irwan
Inferensi Vol 8, No 3 (2025)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v8i3.21464

Abstract

This research employs the k-medoid clustering method to analyze districts and cities in South Sulawesi based on their crime rates. As the population grows, employment opportunities tend to diminish, which can increase stress levels and, consequently, the likelihood of criminal behavior. To evaluate the distribution of criminal incidents across South Sulawesi, the k-medoid method is used to cluster regions. Unlike other clustering methods, k-medoid utilizes the median as the cluster center (medoid), which enhances its robustness against outliers. Specifically, the Partitioning Around Medoids (PAM) algorithm is applied, where initial objects are randomly selected to represent clusters. If the error value is high, the cluster centers are adjusted until the error is minimized. The dataset comprises crime incidence data for South Sulawesi in 2020, focusing on various types of crime. The analysis identified an optimal number of three clusters based on the Silhouette coefficient. Cluster 1 includes 11 regions, Cluster 2 consists of 8 regions, and Cluster 3 contains 5 regions. These clusters provide a comprehensive overview of the crime conditions across different regions within each cluster.
Application of Bisecting K-Means Method in Grouping Earthquake Data (Case Study: Earthquakes in Indonesia 2023) Rais, Zulkifli; Hafid, Hardianti; Risqi, Shopia
Inferensi Vol 8, No 3 (2025)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v8i3.23335

Abstract

Earthquakes are natural disasters that frequently occur in Indonesia, threatening the safety and resilience of its communities. This study aims to analyze the descriptive and clustering results of earthquake data in Indonesia. The data used in this study include various variables such as latitude, longitude, magnitude, and depth as the main features. The method used in this study is Bisecting K-means, and the Davies Bouldin Index test is used to determine the number of clusters. The study results indicate the formation of 3 groups, where cluster 1 falls into the deep earthquake category, cluster 3 falls into the intermediate earthquake category, and cluster 2 falls into the shallow earthquake category, with an average Davies-Bouldin Index value of 0.4758.
Comparison of Ordinal Logistic Regression and Artificial Neural Network in Stunting Prevalence Classification Risnawati, May; Fathurahman, M.; Prangga, Surya
Inferensi Vol 8, No 3 (2025)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v8i3.22287

Abstract

The prevalence of under-five stunting in one of the crucial health problems in Indonesia. Stunting is a growth and development disorder in children due to chronic malnutrition and repeat infections that can have a negative impact on children’s physical and cognitive development. This study aims to analyse the accuracy of the classification of the prevalence of stunting on regencies/cities in Indonesia, in 2022 using two methods, namely Ordinal Logistic Regression (OLR) and Artificial Neural Network (ANN). OLR is development of logistic regression applied to response variables with more the two categories that have levels or ranks, while ANN is a method that mimics the function of the biological nervous system and is designed for complex information processing. This study used two proportions of data splitting namely 80:20 and 90:10. Each method produce two models, OLR 1 and OLR 2 for the OLR method, and ANN 1 and ANN 2 for the ANN method. The results show that the ANN 1 model with 80:20 data proportion performs better than other models with an accuracy of 63.37%.
Comparison of ARIMA, LSTM, and Ensemble Averaging Models for Short-Term and Long- Term Forecasting of Non-Stationary Time Series Data Pratiwi, Windy Ayu; Sumertajaya, I Made; Notodiputro, Khairil Anwar
Inferensi Vol 8, No 3 (2025)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v8i3.22643

Abstract

This study aims to forecast the highest weekly selling rate of the Indonesian Rupiah (IDR) against the US Dollar (USD) and identify the most accurate model among ARIMA, LSTM, and Ensemble Averaging. The evaluation results indicate that ARIMA achieves an accuracy of 97.75%, demonstrating strong performance in short-term forecasting, while LSTM achieves 99.98% accuracy, excelling in capturing complex and dynamic patterns in long-term predictions. The Ensemble Averaging approach attains the highest accuracy of 99.99%, proving to be the optimal solution by combining ARIMA’s stability with LSTM’s adaptability, resulting in more precise and stable predictions. The findings of this study highlight that the ensemble approach is more effective than individual models, as it balances accuracy and prediction stability across various forecasting scenarios. This method serves as a reliable tool for addressing market volatility and contributes significantly to the advancement of financial and economic forecasting techniques that are more adaptive and accurate.
Small Area Estimation of Child Poverty on Java Island In 2021 (Comparison of EBLUP and Hierarchical Bayes) Istiana, Nofita; Tanur, Erwin; Ubaidillah, Azka; Sitanggang, Yuliana Ria Uli; Nainggolan, Rosalinda
Inferensi Vol 8, No 3 (2025)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v8i3.23311

Abstract

Information about child poverty is very important to ensure that children get their rights. Indonesia's decentralized system requires child poverty data in each district/city. Data provision at this level is constrained by a non-specific sample design used for certain age groups, so the sample age group for children is not always sufficient for each district/city. Therefore, direct estimation produces a high relative standard error (RSE), so it requires small area estimation (SAE). SAE that is often used is EBLUP, which assumes that the variable of interest is normally distributed. Child poverty data does not meet the normality assumption, so SAE with Hierarchical Bayes with Beta distribution (HB Beta) is proposed in this study. The result is direct estimation, EBLUP, and HB Beta produce relatively similar estimated values, but HB Beta has the lowest RSE.
Earthquake Point Clustering Using Self Organizing Maps (SOM) In Sulawesi and Maluku Regions Irwan, Irwan; Zaki, Ahmad; Widiyaningrum, Eka Janivia
Inferensi Vol 8, No 3 (2025)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v8i3.23331

Abstract

Earthquakes pose a major threat in Indonesia, particularly in complex tectonic regions like Sulawesi and Maluku. To support disaster mitigation, this research employs the Self Organizing Maps (SOM) method—an unsupervised technique that reduces data dimensionality into an intuitive two-dimensional form—to cluster earthquake data using four key variables: longitude, latitude, magnitude, and depth. The dataset includes 5,275 earthquake records from 2022, sourced from the Meteorology, Climatology, and Geophysics Agency (BMKG). SOM training produced 25 neurons, which were then grouped into three optimal clusters using hierarchical clustering, validated by internal metrics: the lowest Connectivity Index (296.1512), highest Silhouette Index (0.3304), and a Dunn Index of 0.0058. Cluster 1, with 13 neurons, covers eastern Sulawesi and Maluku, featuring medium magnitude and depth. Cluster 2, with 11 neurons, represents central to southern Sulawesi, characterized by low magnitude and shallow depth. Cluster 3, comprising a single neuron, includes western regions with high-magnitude, very deep earthquakes. Keywords⎯ Clustering, Earthquake, Internal Validation, Self Organizing Maps (SOM).
Forecasting Indonesia's Non-Oil and Gas Exports Using Facebook Prophet: A Seasonal and Trend Analysis Erfiani, Erfiani; Wijaya, Ferdian Bangkit
Inferensi Vol 8, No 3 (2025)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v8i3.23337

Abstract

This study aims to analyze and predict the trend of Indonesia's non-oil and gas exports using the Facebook Prophet model, focusing on identifying seasonal patterns, trends, and volatility present in the export data. Monthly export data from 2015 to 2025, sourced from the Statistics Indonesia (BPS), were used as the basis for analysis. The dataset revealed notable seasonal patterns and substantial volatility, particularly in the period following 2020. To model these dynamics, three Prophet model configurations were tested: one considering only annual seasonality, combining both annual and monthly seasonality, and another incorporating only monthly seasonality. The evaluation of these models showed with an initial Mean Absolute Percentage Error (MAPE) of 8.70%. This model was then optimized through hyperparameter tuning. The optimal parameter configuration (changepoint_prior_scale = 0.5, seasonality_prior_scale = 0.01, fourier_order = 3) resulted in a significant improvement, reducing the MAPE to 4.73%. This optimized model demonstrated its capacity to more precisely capture the complex patterns. Furthermore, the study projected Indonesia’s non-oil and gas exports for the period from April 2025 to December 2026. The projections indicate a relatively stable export trend within the range of 20,000 to 22,000 million USD per month, with consistent seasonal patterns.
Spatial Extreme Value Analysis of Extreme Rainfall Using the Extremal-t Process Nuroini, Husna Mir'atin; Sutikno, Sutikno; Purhadi, Purhadi
Inferensi Vol 8, No 3 (2025)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v8i3.23351

Abstract

Indonesia’s diverse topography, consisting of coasts, lowlands, highlands, and mountains, results in a wide range of weather and climate conditions, enabling various hydrological phenomena such as extreme rainfall, hurricanes, high temperatures, and storms. In recent years, global warming has emerged as a major environmental concern, with one of its significant impacts being climate change. This, in turn, increases the frequency and intensity of extreme hydrological events, potentially causing floods, transportation and communication disruptions, infrastructure damage, agricultural losses, and threats to human life. This study aims to identify the best model and estimate the return levels of extreme rainfall in Ngawi Regency from March 1990 to November 2022 using spatial extreme value analysis with max-stable processes and the extremal-t process. Daily rainfall data from 1990 - 2018 were used for model training, while data from 2018 - 2022 were allocated for model testing to validate predictive performance. Parameter estimation was conducted using Maximum Likelihood Estimation (MLE) and Maximum Pairwise Likelihood Estimation (MPLE), solved through the Broyden-Fletcher-Goldfarb-Shanno (BFGS) Quasi-Newton numerical iteration method. The analysis shows that the best trend surface model has average rainfall and variance influenced by latitude, while the distribution shape is unaffected by latitude or longitude, indicating isotropy. Furthermore, the return level prediction demonstrates higher accuracy when applied over a three-year period.

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