Article Per Year (5 Year)
p-Index From 2021 - 2026
1.288
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika UNP Journal of Statistics and Data Science STATISTIKA International Journal of Applied Mathematics, Sciences, and Technology for National Defense Parameter: Jurnal Matematika, Statistika dan Terapannya Indonesia Berdampak: Jurnal Pengabdian Kepada Masyarakat
Hendri, Eko Primadi
Politeknik Transportasi Darat Indonesia – STTD, Indonesia
Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Mathematics Physics Social Sciences Other

Published : 7 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 7 Documents
Search

 1 
MODEL PERAMALAN NILAI TUKAR RUPIAH TERHADAP DOLLAR SINGAPURA MENGGUNAKAN METODE HYBRID ARIMA-ANN Fadhlia, Sarah; Hendri, Eko Primadi; Cahyaningtyas A, Deasy Dwi
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 5 No. 3 (2024): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v5i3.720

Abstract

This research aims to predict the Rupiah exchange rate against the Singapore Dollar using the hybrid ARIMA-ANN method. The hybrid model is used to increase prediction accuracy by utilizing the ARIMA model to capture linear patterns and the ANN model to capture non-linear patterns. The data used in this research is data on the Rupiah exchange rate against the Singapore Dollar. The ARIMA model used for hybrid modeling is ARIMA (1,1,1) because it has an AIC value of 2144.93 which is smaller than other ARIMA models. The residuals from the ARIMA model (1,1,1) are used for ANN modeling. ANN modeling uses 3 inputs, 1-10 hidden layers, and 1 output layer. Based on the analysis results, the ARIMA (1,1,1) - ANN (3,10,1) hybrid model has an RMSE value of 52.092 which is smaller than other ARIMA-ANN hybrid models. Therefore, the hybrid ARIMA (1,1,1) - ANN (3,10,1) model is more effective in predicting the Rupiah exchange rate against the Singapore Dollar.
Time Series Clustering of Rice Productivity Using Trimming Gaussian Mixture Models Fadhlia, Sarah; Hendri, Eko Primadi
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 4 No 3 (2025): Parameter: Jurnal Matematika, Statistika dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/parameterv4i3pp381-394

Abstract

This study investigates the application of the Trimming Gaussian Mixture Model (TGMM) for clustering monthly rice productivity time series data in West Java from 2018 to 2023. TGMM is a robust clustering approach that reduces the influence of outliers by trimming a specified portion of the data prior to parameter estimation. The dataset, sourced from Open Data Jabar, was analyzed to identify the most representative number of clusters using the Silhouette Score. The optimal clustering solution was achieved with two main clusters (k = 2) and a trimming proportion of 15%. The results revealed three distinct regional groups: two dominant clusters characterized by moderate-stable and high-consistent productivity patterns, and a separate group of outliers marked by low and highly fluctuating productivity. Cluster stability was assessed using the Adjusted Rand Index (ARI), yielding values of 0.41 (bootstrap) and 0.545 (subsampling), which indicate a reasonably consistent clustering structure. These findings demonstrate the effectiveness of TGMM in capturing underlying productivity patterns while accounting for noise and outliers, suggesting its potential as a robust decision-support tool for data-driven agricultural planning and policy formulation.
Times series data analysis: The Holt-Winters model for rainfall prediction In West Java Eko Primadi Hendri; Sarah Fadhlia
International Journal of Applied Mathematics, Sciences, and Technology for National Defense Vol. 2 No. 1 (2024): International Journal of Applied Mathematics, Sciences, and Technology for Nati
Publisher : FoundAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/app.sci.def.v2i1.325

Abstract

Time series data analysis is used to analyze data that considers time and data characteristics to predict future events. One of the time series data is rainfall data. Rainfall data has a seasonal pattern because there is a pattern that repeats itself over a certain period. Data analysis that considers the characteristics of seasonal patterns is the Holt-Winters method. The Holt-Winters model is divided into two, namely additive and multiplicative models. This research aims to compare the Holt-Winters additive and multiplicative methods to see the accuracy in predicting rainfall data in West Java. The additive model has level parameter I±=0,435, trend parameter I²=0, seasonal parameter I³=1, and RMSE value 140,174. The multiplicative model has level parameter I±=0,936, trend parameter I²=0, seasonal parameter I³=0,247, and RMSE value 150,020. The additive model has a smaller RMSE value so it can predict future rainfall with greater accuracy.
Portable system for real-time traffic volume and speed estimation using YOLOv10 Ida Bagus Sradha Nanda; Masrono Yugihartiman; Eko Primadi Hendri; I Made Suartika
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp300-309

Abstract

Accurate traffic data is essential for effective transportation planning and policymaking. However, in many regions, especially those lacking intelligent infrastructure, data collection remains dependent on manual methods that are labor-intensive, time-consuming, and susceptible to human error. While advanced systems such as closed-circuit television (CCTV) and area traffic control systems (ATCS) offer automation, their high cost and infrastructure requirements limit widespread adoption. This study proposes a portable, low-cost, and real-time traffic monitoring system based on the YOLOv10 object detection algorithm. The system operates using only a smartphone-grade camera (1080 p, 60 fps) and a standard laptop, eliminating the need for expensive installations. It detects, classifies, and counts vehicles as they pass through a predefined region of interest (ROI), and also estimates their speed based on time–distance measurements. Field evaluations using five one-hour urban traffic videos showed excellent agreement with manual counts, achieving a mean absolute percentage error (MAPE) of just 0.30%. Speed estimation trials conducted on sample clips also demonstrated consistent and plausible results. These findings highlight the system’s potential as a scalable and accurate alternative for traffic monitoring in infrastructure-limited environments.
LASSO-Regularized Binary Logistic Regression on Imbalanced Mode Choice Data Eko Primadi Hendri; Novi Urbaningrum; Sarah Fadhlia
Statistika Vol. 25 No. 2 (2025): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v25i2.8126

Abstract

Abstract. Binary logistic regression is a widely used method for modeling mode choice, but it often suffers from reduced predictive accuracy when dealing with high-dimensional datasets and class imbalance. This study implements binary logistic regression with LASSO regularization to identify significant factors influencing transportation mode choice between motorcycles and Trans Metro buses in the CBD of Pekanbaru. Data from 100 respondents were collected through revealed-preference and stated-preference surveys, with class imbalance (71% motorcycle, 29% bus) addressed using the Synthetic Minority Over-sampling Technique (SMOTE). Model performance was evaluated using accuracy, AUC, precision, recall, and F1-score via Repeated Random Subsampling Validation (RRSV). Results show that the LASSO model with SMOTE increased recall from 0.125 to 0.25 and F1-score from 0.143 to 0.267 compared to the non-SMOTE model, with an accuracy of 0.621 and an AUC of 0.613, indicating improved ability to detect the minority class. Statistically significant predictors include occupation, monthly income, and ownership of an alternative vehicle. This study demonstrates that combining LASSO and SMOTE is effective in handling imbalanced data, providing strong quantitative evidence to support urban transport policy planning.
Peningkatan Pengetahuan Rambu Lalu Lintas melalui Penyuluhan kepada Ibu-Ibu PKK di Desa Cimanggis, Kabupaten Bogor Eko Primadi Hendri; Aan Sunandar; Torang Hutabarat; Tatang Adhiatna; Sulistyo Sutanto; Sarah Fadhlia
Indonesia Berdampak: Jurnal Pengabdian kepada Masyarakat Vol. 2 No. 2 (2026): JULI-DESEMBER
Publisher : Indo Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63822/8xnhnb29

Abstract

Limited public understanding of the functions and meanings of traffic signs may affect traffic discipline and increase the risk of traffic violations and accidents. Women members of the Family Welfare Empowerment (PKK) organization play a strategic role as educators within their families; therefore, improving their knowledge of traffic signs is expected to have a broader impact on community traffic safety awareness. This community service program aimed to improve the participants’ knowledge of traffic sign classifications, functions, and meanings through lectures, interactive discussions, and demonstrations. The activity was conducted in RT 001/RW 001, Cimanggis Village, Bojonggede District, Bogor Regency, involving 17 PKK members. The effectiveness of the program was evaluated using a one-group pretest–posttest design. Data were analyzed using descriptive statistics, followed by the Shapiro–Wilk normality test and a paired-samples t-test. The results showed that the mean score increased from 4.65 in the pretest to 7.53 in the posttest, representing an improvement of 2.88 points. The Shapiro–Wilk test indicated that the data were normally distributed (p = 0.207), while the paired-samples t-test revealed a statistically significant improvement in participants’ knowledge after the educational intervention (t = −6.255, p < 0.001). Furthermore, 76.47% of the participants achieved a posttest score of at least seven, meeting the predetermined success indicator. These findings demonstrate that lecture-based education combined with discussion, demonstration, and visual learning media is effective in improving community knowledge of traffic signs and supporting the development of a traffic safety culture within families and the wider community.
Hierarchical Bayesian Modeling with IAR Hexagonal Grids for Reconstructing Incomplete OD Matrices Eko Primadi Hendri; Sarah Fadhlia; Edi Santosa; Rachmat Sadili; Sudirman Anggada
UNP Journal of Statistics and Data Science Vol. 4 No. 3 (2026): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol4-iss3/508

Abstract

Extracting Origin-Destination (OD) matrices in open Bus Rapid Transit systems such as Transjakarta is essential for urban mobility analysis. However, this process is often hindered by incomplete observations, particularly due to missing tap-out data, which leads to extreme sparsity, zero-inflation, and overdispersion in the resulting matrices. This study addresses the problem of probabilistically reconstructing highly sparse OD matrices while accounting for spatial dependencies. To overcome limitations in previous imputation methods—such as ignoring network topology or being affected by the Modifiable Areal Unit Problem (MAUP)—this research proposes a hierarchical Bayesian approach integrating an Intrinsic Autoregressive (IAR) prior within an isotropic hexagonal (H3) tessellation framework. A Negative Binomial distribution is employed to model overdispersed count data, while latent spatial intensities and missing destinations are jointly estimated using Markov Chain Monte Carlo (MCMC). The proposed Spatial IAR model achieves stable convergence with a maximum , whereas the independent non-spatial model fails to converge adequately ( ). Although the independent model produces lower WAIC and LOOIC values (14313.41 and 14313.66) than the Spatial IAR model (14551.88 and 14611.18), the indicates that the apparent predictive superiority is spurious due to inferential instability. Posterior Predictive Checks further confirm that the spatial model successfully reproduces the overdispersion and zero-inflation characteristics of the observed mobility data. Overall, the results demonstrate that spatial regularization is essential for reconstructing high-dimensional sparse urban mobility data and improving the robustness of transportation mobility analysis.
Co-Authors Aan Sunandar Cahyaningtyas A, Deasy Dwi Edi Santosa Fadhlia, Sarah I Made Suartika Ida Bagus Sradha Nanda Masrono Yugihartiman Novi Urbaningrum Rachmat Sadili Sarah Fadhlia Sarah Fadhlia Sarah Fadhlia Sudirman Anggada Sulistyo Sutanto Tatang Adhiatna Torang Hutabarat