Article Per Year (5 Year)
p-Index From 2021 - 2026
6.287
P-Index
This Author published in this journals
All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Jurnal Statistika Universitas Muhammadiyah Semarang BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Zeta - Math Journal J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika Jambura Journal of Mathematics M A T H L I N E : Jurnal Matematika dan Pendidikan Matematika Jurnal Varian Mandalika Mathematics and Educations Journal Inferensi JAMBURA JOURNAL OF PROBABILITY AND STATISTICS Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Jurnal Litbang Edusaintech ARRUS Journal of Mathematics and Applied Science Mathematics and Applications (MAp) Journal Jurnal Siger Matematika Journal of Research Applications in Community Services Eksponensial Asian Journal of Science, Technology, Engineering, and Art Mikailalsys Journal of Mathematics and Statistics
Dani, Andrea Tri Rian
Universitas Mulawarman
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Transportation Other

Published : 47 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 47 Documents
Search

 1   2   3   4   5 
Comparative Analysis Of Neural Network Model Selection And Data Transformation For Rainfall Forecasting Permata, Regita Putri; Dani, Andrea Tri Rian
Mathline : Jurnal Matematika dan Pendidikan Matematika Vol. 10 No. 3 (2025): Mathline : Jurnal Matematika dan Pendidikan Matematika
Publisher : Universitas Wiralodra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31943/mathline.v10i3.763

Abstract

The selection of input models in neural networks significantly influences predictive accuracy in time series forecasting. This study evaluates different input models for neural networks in rainfall prediction using data from the Wonorejo Reservoir, Surabaya. The neural network inputs are determined based on significant lags identified through the Partial Autocorrelation Function (PACF) and ARIMA models. Simulation results indicate that the best Feed Forward Neural Network (FFNN) model utilizes PACF-derived input lags and is trained using the Rprop+ algorithm with a logistic activation function. Meanwhile, the optimal Deep Learning Neural Network (DLNN) model employs the Rprop- algorithm with a logistic activation function. The best-performing model for rainfall forecasting, based on the lowest Root Mean Squared Error of Prediction (RMSEP), is the FFNN model with an (8,4,1) architecture. To further refine the model, we applied a stepwise selection process to eliminate non-significant lag inputs. However, results show that this optimization had no substantial impact, as RMSEP increased after the stepwise procedure.
Estimasi Produksi Beras dengan Estimator Campuran Spline Truncated – Kernel di Jawa Timur Dani, Andrea Tri Rian; Putra, Fachrian Bimantoro; Budiantara, I Nyoman; Ratnasari, Vita
Jambura Journal of Mathematics Vol 7, No 2: August 2025
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v7i2.33379

Abstract

This study aims to apply a nonparametric regression model using a mixed estimator of Truncated Spline and Kernel to estimate Rice Production in East Java Province. This model combines several predictor variables, namely Harvested Area of Rice Plants, Rice Productivity, Population, and Human Development Index. The selection of the best combination of variables is based on the lowest Generalized Cross-Validation (GCV) value to obtain a stable and accurate model. The results show that the model with a combination of variables Harvested Area of Rice Plants and Rice Productivity set as Truncated Spline components with three knot points, and Population and Human Development Index as Kernel components produces a minimum GCV value of 85,504,949, RMSE of 242,723.6, and R² of 91.24%. This model successfully captures non-linear relationship patterns and provides more stable estimates. The implication of this finding is that the resulting model can be used to design more efficient agricultural policies, by considering the factors that interact dynamically in rice production.
Pendampingan Desain Infografis dengan Statistika dan Sains Data Bagi Siswa/Siswi MAN 1 Kota Samarinda Muhammad Fathurahman; Dani, Andrea Tri Rian; Fauziyah, Meirinda; Darnah; Goenjatoro, Rito; Hayati, Memi Nor; Prangga, Surya; Siringoringo, Meiliyani; Oroh, Chiko Zet
Journal of Research Applications in Community Service Vol. 4 No. 3 (2025): Journal of Research Applications in Community Service
Publisher : Universitas Nahdlatul Ulama Sunan Giri Bojonegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32665/jarcoms.v4i3.5158

Abstract

Kegiatan pengabdian masyarakat ini bertujuan untuk memberikan pendampingan desain infografis yang mengintegrasikan ilmu statistika dan sains data serta meingkatkan literasi data bagi siswa dan siswi MAN 1 Kota Samarinda. Dalam era digital yang ditandai dengan kemudahan akses informasi, masih terdapat kekurangan pemahaman di kalangan siswa mengenai pemanfaatan teknologi, khususnya dalam desain infografis berbasis statistika dan sains data. Infografis merupakan alat yang efektif untuk menyajikan informasi secara visual yang membantu mempercepat pemahaman data kompleks menjadi lebih mudah dipahami. Aplikasi Canva dipilih sebagai platform dalam pendampingan ini karena kemudahan penggunaannya, yang memungkinkan siswa untuk berkreasi secara mandiri. Berdasarkan hasil tes awal, siswa belum memanfaatkan dengan optimal pengembangan ilmu data sains dalam pembuatan desain infografis. Oleh karena itu, kegiatan ini dirancang untuk memberikan pemahaman dan keterampilan praktis kepada peserta agar mereka dapat menggunakan teknologi visual dalam mengelola dan menyampaikan informasi berbasis data dengan lebih efektif dan inovatif. Melalui metode pengabdian ini, diharapkan terjadi peningkatan pemahaman dan keterampilan dalam penggunaan desain infografis serta pemanfaatan sains data literasi siswa yang dapat diterapkan dalam kegiatan belajar mengajar, terutama dalam pengolahan dan penyajian data statistik.
A Simulation Study of Interval Estimation in Nonparametric Regression Using the Truncated Spline Estimator Puspitasari, Melda; Dani, Andrea Tri Rian; Fauziyah, Meirinda
Mandalika Mathematics and Educations Journal Vol 7 No 3 (2025): Edisi September
Publisher : FKIP Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jm.v7i3.9625

Abstract

This study examines interval estimation in truncated spline nonparametric regression using simulated data. The study aims to determine the impact of sample size, variance, and knot points on the performance of the truncated spline estimator. The results show that as the sample size increases, both the Generalized Maximum Likelihood (GML) and Mean Square Error (MSE) values decrease, while the coefficient of determination increases. This study also reveals that increasing the variance leads to higher GML and MSE values, as well as a lower coefficient of determination. Furthermore, the truncated spline nonparametric regression model achieves optimal performance with three knot points. The results showed that the more knot points, the GML and MSE values will decrease, while the coefficient of determination increases. The results of this study show that the determination of sample size, variance, and knot points significantly affects the accuracy and efficiency of the truncated spline nonparametric regression model, allowing it to serve as a reference for applying truncated spline nonparametric regression more effectively to produce a more optimal model that aligns with the characteristics of the data.
A Computatioal Analysis of Kernel-Based Nonparametric Regression Applied to Poverty Data Adrianingsih, Narita Yuri; Dani, Andrea Tri Rian; I Nyoman Budiantara; Dandito Laa Ull; Raditya Arya Kosasih
Mandalika Mathematics and Educations Journal Vol 7 No 3 (2025): Edisi September
Publisher : FKIP Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jm.v7i3.9802

Abstract

This research aims to model the relationship between poverty and socioeconomic variables in Nusa Tenggara Timur Province, Indonesia. The purpose of the study is to assess the effectiveness of nonparametric regression, specifically using kernel methods, to provide a more accurate representation of the complex and nonlinear relationships between predictor variables and poverty levels. The study focuses on several key variables, including average years of schooling, labor force participation rate, percentage of households with access to electricity, population density, illiteracy rate, and life expectancy. The research utilized a kernel regression approach, comparing the performance of different kernel functions, including Gaussian, Epanechnikov, Triangle, and Quartic kernels. The model’s performance was evaluated using metrics such as Mean Squared Error (MSE), Generalized Cross Validation (GCV), and the coefficient of determination (R²). The results showed that the Gaussian kernel function provided the most accurate predictions for poverty levels, with the best balance between model complexity and error.
Forecasting Maximum Water Level Data for Post Sangkuliman using An Artificial Neural Network Backpropagation Algorithm Mislan, Mislan; Dani, Andrea Tri Rian
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 2 (2024): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i2.20112

Abstract

Neural Network (NN) is an information processing system that has characteristics similar to biological neural networks. One of the algorithms in NN is Backpropagation Neural Network (BPNN). BPNN is an excellent method for dealing with complex pattern recognition problems. In this research, maximum water level forecasting was carried out at Sangkuliman Post using a Backpropagation Neural Network. This research aims to obtain modeling for forecasting maximum water level, as well as forecasting results using the best model. The research results show that the best model is five neurons in hidden layer 1 and 3 neurons in hidden layer 2 with the backpropagation algorithm, the activation function used is binary sigmoid, the learning rate is 0.001, and the maximum iteration is 10,000,000 with the smallest RMSE result being 1.816. The forecast results for the following 12 periods are 1.672, 1.779, 1.523, 1.271, 1.752, 1.692, 1.335, 1.479, 1.750, 1.779, 1.340, 1.269, and 1.754. Forecasting results can be used by various parties in decision-making and planning in multiple fields, as an example to see the patterns of biological and vegetable life around Sangkuliman Post. Based of forecasting results, certain months show an increase in maximum water levels. 
A District/City Profiling Based on Poverty Indicators in East Nusa Tenggara Using the Centroid Linkage Algorithm Dani, Andrea Tri Rian; Candra, Yossy; Putra, Fachrian Bimantoro; Fauziyah, Meirinda
Zeta - Math Journal Vol 10 No 2 (2025): November
Publisher : Universitas Islam Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31102/zeta.2025.10.2.81-91

Abstract

Poverty is a complex multidimensional phenomenon that significantly impacts human life. Poverty has always been a problem that the government has discussed regionally, centrally, and internationally. The issue of poverty is interesting to approach and analyze using a statistical approach, namely cluster analysis. Cluster analysis is used to group objects based on their level of similarity. In this research, the algorithm used is the Centroid Linkage Algorithm. The Centroid Linkage algorithm was chosen based on its advantages in the grouping process. Distance similarity measurement uses Squared Euclidean. The data used are district/city poverty indicators in East Nusa Tenggara Province. The analysis results show that two optimal clusters were obtained with their distinguishing characteristics. Hopefully, the results of this analysis can be used as a reference in formulating policies for alleviating poverty.
Nonparametric Spline Truncated Regression with Knot Point Selection Method Generalized Cross Validation and Unbiased Risk Handayani, Tutik; Sifriyani, Sifriyani; Dani, Andrea Tri Rian
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 7, No 3 (2023): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v7i3.14034

Abstract

Nonparametric regression approaches are used when the shape of the regression curve between the response variable and the predictor variable is assumed to be unknown. Nonparametric excess regression has high flexibility. A frequently used nonparametric regression approach is a truncated spline that has excellent ability to handle data whose behavior is variable at certain sub-intervals. The aim of this study was to obtain the best model of multivariable nonparametric regression with linear and quadratic truncated spline approaches using Generalized Cross Validation (GCV) and Unbiased Risk (UBR) methods and to find out the factors influencing stunting prevalence in Indonesia in 2021. The data used are the prevalence of stunting as a response variable and the predictor variable used by the percentage of infants receiving Exclusive breastfeeding for 6 months, the percentage of households with proper sanitation, the percentage of toddlers receiving Early Childhood Cultivation (IMD), the percentage of the poor population, and the percentage of pregnant womenIt's a risk. Results show that the best linear and quadratic nonparametric spline truncated regression model in modeling the stunting prevalence is linear truncated spline using the GCV method with three knot points. This model has the minimum GCV value of 7.29 with MSE value of 1.82. Factors influencing the incidence of stunting in Indonesia in 2021 include the percentage variable of infants receiving Exclusive breastfeeding for 6 months, the percentage of households with proper sanitation, the percentage of poor people, and the percentage of pregnant women at risk of KEK. 
ANALISIS KLASIFIKASI ARTIST MUSIC MENGGUNAKAN MODEL REGRESI LOGISTIK BINER DAN ANALISIS DISKRIMINAN DANI, ANDREA TRI RIAN; RATNASARI, VITA; NI'MATUZZAHROH, LUDIA; AVIANTHOLIB, IGAR CALVERIA; NOVIDIANTO, RADITYA; ADRIANINGSIH, NARITA YURI
Jambura Journal of Probability and Statistics Vol 3, No 1 (2022): Jambura Journal Of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjps.v3i1.13708

Abstract

Characteristics of a song are an important aspect that must be kept authentic by a singer. Using the Spotify API feature, we can extract the characteristics or elements of a song sung by a singer.  There are eight (8) elements that we can get from the extraction of a song, namely: Danceability, Energy, Loudness, Speechiness, Acousticness, Liveness, Valence, and Tempo. Based on the extraction results, we can label the music artist using the classification analysis method. In this study, the labels are music artists, namely Ariana Grande and Taylor Swift. This study aims to obtain the classification of music artist labels using binary logistic regression methods and discriminant analysis. The response variable used in this study is Artist Music (Y) which is categorized into two categories, namely Ariana Grande (Y=0) and Taylor Swift (Y=1). The data will be divided into training and testing data with the proportion of data 90:10 and 80:20. Based on the results of the analysis, the binary regression model that was built, with the proportion of training testing data that is 90:10 has a classification accuracy for data testing of 90.00%.
ESTIMASI MODEL REGRESI SEMIPARAMETRIK SPLINE TRUNCATED MENGGUNAKAN METODE MAXIMUM LIKELIHOOD ESTIMATION (MLE) ADRIANINGSIH, NARITA YURI; DANI, ANDREA TRI RIAN
Jambura Journal of Probability and Statistics Vol 2, No 2 (2021): Jambura Journal Of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjps.v2i2.10255

Abstract

Regression modeling with a semiparametric approach is a combination of two approaches, namely the parametric regression approach and the nonparametric regression approach. The semiparametric regression model can be used if the response variable has a known relationship pattern with one or more of the predictor variables used, but with the other predictor variables the relationship pattern cannot be known with certainty. The purpose of this research is to examine the estimation form of the semiparametric spline truncated regression model. Suppose that random error is assumed to be independent, identical, and normally distributed with zero mean and variance , then using this assumption, we can estimate the semiparametric spline truncated regression model using the Maximum Likelihood Estimation (MLE) method.  Based on the results, the estimation results of the semiparametric spline truncated regression model were obtained  p=(inv(M'M)) M'y 
Co-Authors A'yun, Qonita Qurrota Adhitya Ronnie Effendie, Adhitya Ronnie AINURROCHMAH, ALIFTA Alifta Ainurrochmah Alifta Ainurrochmah Anisar, Anggi Putri AVIANTHOLIB, IGAR CALVERIA Avrilia, Khairunnisa Budi Cahyono Budi, Ennesya Estya Candra, Yossy Chandra, Yossy Dandito Laa Ull Darnah Darnah, Darnah Dimas Nugroho Dwi Seputro Fachrian Bimantoro Putra Fadlirhohim, Rizki Dwi Fauziyah, Meirinda Fidia Deny Tisna Amijaya Goenjatoro, Rito Hardina Sandariria Hinadang, Elen A. I Gusti Bagus Ngurah Diksa I Nyoman Budiantara I Nyoman Budiantara Ibaad, Muhammad Irsadul indarsih, Indarsih Koirudin, Hadi Kosasih, Raditya Arya Krisna Rendi Awalludin Ludia Ni'matuzzahroh Ludia Ni’matuzzahroh M. Fathurahman M. Yogi Riyantama Isjoni Mahmuda, Siti Mar’ah, Zakiyah Meirinda Fauziyah Melisa Arumsari Memi Nor Hayati Mislan Muawanah, Chusnul Muhammad Aldani Zen Mulyadi, Taqriri Kamal Nanda Arista Rizki NARITA YURI ADRIANINGSIH Ni'matuzzahroh, Ludia Nilam Novita Sari Novidianto, Raditya Nurul Istiqomah Oroh, Chiko Zet Puspitasari, Melda Putra, Fachrian Bimantoro Qonita Qurrota A'yun Raditya Arya Kosasih Raditya Novidianto Rahayu, Joana K. Rahmah, Syifa M. Rahmah, Syifa Mutia Rahmania Rahmania Ramadhani, Bagus D. Regita Putri Permata Rifdatun Ni’mah Riry Sriningsih Rito Goejantoro, Rito Sifriyani, Sifriyani Siringoringo, Meiliyani Siswahyudianto Sitinjak, Jesselin Paskalis Solikhah, Arifatus Solikhatun, Solikhatun Sri Wahyuni Sri Wahyuningsih Sri Wigantono Sukamto, Ika Sumiyarsi Surya Prangga Suyitno Suyitno Syaripuddin Syaripuddin Tanur, Erwin Tutik Handayani, Tutik Uha Isnaini Vita Ratnasari Wahyujati, Mohamad Fahruli Watika, Noor Hikmah Zen, Muhammad Aldani