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All Journal Jurnal Gaussian Indonesian Journal of Mathematics and Natural Sciences Teknosains: Media Informasi Sains dan Teknologi GRADIEN SEMIRATA 2015 Indonesian Journal of Applied Statistics BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Martabe : Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Masyarakat Bumi Raflesia Jurnal Berdaya Mandiri Dharma Raflesia : Jurnal Ilmiah Pengembangan dan Penerapan IPTEKS Inferensi Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi MATHunesa: Jurnal Ilmiah Matematika Journal Of Human And Education (JAHE) Jurnal Matematika Integratif
Idhia Sriliana
Universitas Bengkulu
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Estimation of Stunting and Wasting Prevalence in Southern Part of Sumatra Using Nadaraya-Watson Kernel and Penalized Spline Oktarina, Cinta Rizki; Nugroho, Sigit; Sriliana, Idhia
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 10, No 2 (2026): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v10i2.36177

Abstract

This study aims to estimate the prevalence of stunting and wasting in the southern region of Sumatra using a bivariate nonparametric regression framework based on the Nadaraya-Watson Kernel and Penalized Spline estimators with Penalized Weighted Least Squares (PWLS). The analysis utilizes data from the 2023 Indonesian Toddler Nutrition Survey, comprising 60 regencies and cities across five provinces, namely Bengkulu, South Sumatra, Lampung, Jambi, and Bangka Belitung. By jointly modeling stunting and wasting as correlated response variables, this study seeks not only to compare methodological performance, but also to provide empirical insights into the nonlinear patterns underlying child nutritional outcomes influenced by maternal-child health and socioeconomic conditions. Model performance was evaluated using the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²). The empirical results indicate that the Nadaraya-Watson Kernel estimator outperforms the Penalized Spline approach, yielding a substantially lower prediction error (MSE = 0.0008), high goodness-of-fit values (R² of 99.98% for stunting and 99.95% for wasting), and relatively small RMSE values of 0.038 and 0.017, respectively. These findings suggest that the kernel-based estimator provides stable and accurate predictions within the data structure considered, particularly in capturing complex nonlinear relationships between predictors and nutritional outcomes. Furthermore, the results reveal that the effects of health-related and socioeconomic factors vary across different prevalence levels, underscoring the importance of nonparametric methods in accommodating heterogeneous and nonlinear response patterns. In line with previous evidence emphasizing integrated, multisectoral approaches to child nutrition improvement, the findings highlight the relevance of combining health interventions with broader social protection strategies. Nevertheless, the interpretation of results is subject to methodological caution, given the limited sample size and the aggregated nature of the data. Overall, this study demonstrates the potential of bivariate nonparametric regression as a complementary analytical tool for health data analysis and evidence-based policy formulation related to stunting and wasting reduction.
LITERASI STATISTIKA DESKRIPTIF BERBASIS WEB MENGGUNAKAN R-SHINY UNTUK SISWA SMA MUHAMMADIYAH 4 KOTA BENGKULU Sriliana, Idhia; Afandi, Nur; Dyah Pangesti, Riwi; Wijuniamurti, Susi; Fairuzindah, Athaya; M. Syarlan
Jurnal Pengabdian Masyarakat Bumi Rafflesia Vol. 9 No. 1 (2026): APRIL: Jurnal Pengabdian Kepada Masyarakat Bumi Raflesia
Publisher : Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/bumir.v9i1.9342

Abstract

Tujuan dari kegiatan Pengabdian Kepada Masyarakat (PKM) ini adalah untuk meningkatkan literasi statistika deskriptif siswa SMA Muhammadiyah 4 Kota Bengkulu melalui pemanfaatan aplikasi berbasis web menggunakan R-Shiny. Literasi statistika deskriptif menjadi keterampilan penting dalam memahami, mengolah, dan menyajikan data secara sistematis. Tahapn-tahapan yang dilakukan antara lain perancangan, persiapan, implementasi, dan evaluasi, dengan produk utama berupa aplikasi Kalkulator Statistika berbasis R-Shiny, modul pembelajaran, implementation arrangement, dan poster edukatif. Evaluasi kegiatan dilakukan dengan pemberian pre-test dan post-test guna mengetahui peningkatan literasi siswa. Nilai rata-rata pre-test ke post-test siswa meningkat dari 46,66 menjadi 74,66 setelah pelatihan. Hal ini sejalan dengan hasil uji paired sample t-test yang menghasilkan nilai t-statistik sebesar 6,18 dengan p-value 0,00 (<0,05) yang berarti ada peningkatan signifikan antara nilai pre-test yang menggambarkan nilai sebelum pelatihan dengan nilai post test yang mencerminkan nilai sesudah pelatihan. Uji normalitas data dilakukan menggunakan statistik Kolmogorov-Smirnov dan Shapiro-Wilk yang menyimpulkan bahwa data memiliki sebaran normal (p>0,05). Dengan demikian, kegiatan literasi statistika deskriptif berbasis web menggunakan R-Shiny terbukti efektif dalam meningkatkan pemahaman konsep statistika deskriptif siswa serta berkontribusi dalam memperkuat integrasi teknologi informasi dalam pembelajaran dan menumbuhkan minat terhadap bidang statistika dan analisis data.
PREDIKSI HARGA SAHAM PT BANK NEGARA INDONESIA (PERSERO) TBK MENGGUNAKAN MODEL STOKASTIK GEOMETRIC BROWNIAN MOTION : (STUDI KASUS: DATA HARGA SAHAM BBNI 2024) Rizal, Jose; Rahma Sholeha, Tari; Hidayati, Nurul; Novianti, Pepi; Sriliana, Idhia
MATHunesa: Jurnal Ilmiah Matematika Vol. 14 No. 1 (2026)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/mathunesa.v14n1.p227 - 234

Abstract

The Geometric brownian motion (GBM) model is widely used in predicting financial instruments such as stocks, because it can overcome the weakness of Brownian motion (BM) which can produce negative values. This study aims to apply the GBM model to predict the daily stock price of PT Bank Negara Indonesia (Persero) Tbk (BBNI) for the period from January to December 2024. The data used is secondary data on daily closing prices obtained from Investing.com, with a distribution of 95% training data and 5% testing data. Parameter drift and volatility are estimated using the Maximum Likelihood Estimation (MLE) method, while model accuracy is evaluated using MAPE and RMSE. The results show that a data proportion of 95%:5% provides the best prediction performance with a MAPE value of 5.724% and an RMSE of 0.267, indicating a high level of accuracy. Thus, the GBM model is reliable enough to describe the price movements of BBNI shares. Future research could develop models that take external factors into account or compare them with other stochastic models.
Penalized Spline Semiparametric Regression for Bivariate Response in Modeling Macro Poverty Indicators Oktarina, Cinta Rizki; Sriliana, Idhia; Nugroho, Sigit
Indonesian Journal of Applied Statistics Vol 8, No 1 (2025)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v8i1.94370

Abstract

Semiparametric spline regression has become an increasingly popular method for modeling data due to its flexibility and objectivity, especially as a parameter estimation method. Spline functions are highly effective in semiparametric regression because they offer unique statistical interpretations by segmenting each predictor variable in relation to the response variable. Bivariate semiparametric regression can be applied to data where observations tend to have disparities between regions, making it suitable for poverty data, particularly the poverty depth index and the poverty severity index. The objective of this research is to analyze the models of the poverty depth index and poverty severity index, as well as to perform segmentation and interpretation of these models. This study utilized observations from 60 districts/cities in the southern part of Sumatra. Several predictor variables were considered, including the percentage of households with a floor area of ≤19 m², labor force participation rate, and life expectancy as parametric components, while the nonparametric components included the average length of schooling and the percentage of households with tap water sources. The estimation methods used were penalized least squares and penalized weighted least squares, involving a full search algorithm for selecting the number and location of knots. The results of the study indicated that the penalized weighted least squares method was the best estimator, with an MSE value of 0.3122 and two knots for each predictor, yielding GCV values of 4.3604 and 4.0794.Keywords: semiparametric regression; bivariate response; poverty; knot; penalized weighted least square
Co-Authors Abdul Aziz, Ali Agwil, Winalia Aprilia, Dinda Aryati, Fitri Baki Swita Cinta Rizki Oktarina Dian Agustina Dian Agustina Dian Agustina Dwi Lorenza, Kenny Dyah Pangesti, Riwi Dyah Setyo Rini Dyah Setyo Rini Ermawati Ermawati Etis Sunandi Fadila, Risfa Fairuzindah, Athaya Famuji, Ahmad Fhaeza, Veronnica Noer Hayadi, Ilham Jose Rizal M. Syarlan Nur Afandi Nurul Hidayati Pepi Novianti Rafflesia, U Rahma Sholeha, Tari Reny Puspasari, Reny Reza Pahlepi, Reza Riry Sriningsih Sigit Nugroho Sihombing, Esther Damayanti Sunandi, Etis Sunandi, Etis Supianti, Filo Susi Wijuniamurti Swita, Baki Ulfasari Rafflesia Ulfasari Rafflesia Wijuniamurti, Susi Winata, Aji Pandu