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All Journal Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Kapas: Kumpulan Artikel Pengabdian Masyarakat STATISTIKA International Journal of Applied Mathematics, Sciences, and Technology for National Defense Parameter: Jurnal Matematika, Statistika dan Terapannya
Fadhlia, Sarah
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Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Education Mathematics Physics Social Sciences Other

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Times series data analysis: The Holt-Winters model for rainfall prediction In West Java Hendri, Eko Primadi; Fadhlia, Sarah
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.
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.
Layanan Penguasaan Konten Uji Statistik Pada Metode Penelitian Korelasional Fadhlia, Sarah; Susiati, Susiati; Arifin, Deasy Dwi Cahyaningtyas
Kapas: Kumpulan Artikel Pengabdian Masyarakat Vol 3, No 2 (2024)
Publisher : Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/ks.v3i2.3183

Abstract

Pengolahan data selalu ada kaitannya dengan ilmu statistika. Pemilihan metode dan alat uji statistik memiliki kriteria tertentu, disesuaikan dengan kondisi data penelitian. Kendala yang sering terjadi adalah masih banyak yang salah dalam mendefinisikan konten uji statistik diantaranya perubahan bentuk data hingga interpretasi hasil dari pengujian dengan menggunakan aplikasi statistika. Atas dasar itulah kami dari tim abdimas Universitas Indraprasta, mencoba untuk memberikan layanan penguasaan konten uji statistika khususnya terkait korelasi kepada peserta, melalui kegiatan pengabdian kepada masyarakat yang merupakan salah satu tridarma Dosen. Kegiatan pengabdian masyarakat kami lakukan di Yayasan Griya Konseling Pancawaskita dengan peserta yang terdiri dari calon konselor. Hasil dari kegiatan ini para peserta dapat memahami konsep dan metodelogi uji korelasi untuk keperluan penelitian.
LASSO-Regularized Binary Logistic Regression on Imbalanced Mode Choice Data Hendri, Eko Primadi; Urbaningrum, Novi; Fadhlia, Sarah
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.
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.
Co-Authors Arifin, Deasy Dwi Cahyaningtyas Cahyaningtyas A, Deasy Dwi Hendri, Eko Primadi Susiati, Susiati Urbaningrum, Novi