Article Per Year (5 Year)
p-Index From 2020 - 2025
0.562
P-Index
This Author published in this journals
All Journal Griya Journal of Mathematics Education and Application
Felicia Eldora
Unknown Affiliation
Education Mathematics

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Prediksi Retur Produk Farmasi dan Klasifikasi Risiko Menggunakan Model ARIMA Felicia Eldora; Panggabean, Suvriadi
Griya Journal of Mathematics Education and Application Vol. 5 No. 2 (2025): Juni 2025
Publisher : Pendidikan Matematika FKIP Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/griya.v5i2.611

Abstract

Pharmaceutical product distribution faces specific challenges, particularly in managing product returns that can affect logistics efficiency and service quality. This study aims to predict the return quantity of pharmaceutical products using the ARIMA (Autoregressive Integrated Moving Average) model and to classify bad goods risk based on the prediction results. The data used consists of monthly return records from a Pharmaceutical Wholesaler (PBF) for a products—Paracetamol Syrup—during the period from January 2023 to December 2024. The research methodology includes data preprocessing, ARIMA model identification and estimation, residual diagnostics, forecasting, and risk classification. The results show that the ARIMA(1,1,1) model provides sufficiently accurate forecasts for Paracetamol Syrup, with predicted returns over the next six months falling into the medium-risk category. These findings offer valuable insights for pharmaceutical wholesalers to anticipate potential losses due to damaged or expired products and to design distribution strategies that are more responsive to return patterns.
Pemodelan Graf Berarah Menggunakan Python untuk Pengelompokan Siswa Berdasarkan Kemampuan Pra-Literasi Felicia Eldora; Simanjuntak, Christina N; Khoiriyati Azmi; Gultom, Ledy Meva Tiurma; Rangkuti, Yulita Molliq
Griya Journal of Mathematics Education and Application Vol. 5 No. 3 (2025): September 2025
Publisher : Pendidikan Matematika FKIP Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/griya.v5i3.638

Abstract

Penelitian ini dilatarbelakangi oleh kebutuhan akan pendekatan yang lebih efisien dalam analisis dan pengelompokan siswa berdasarkan keterkaitan akademik. Tujuan penelitian ini adalah mengimplementasikan graf berarah dalam pemodelan hubungan antar siswa menggunakan bahasa pemrograman Python, sehingga dapat meningkatkan efisiensi analisis keterkaitan pra-literasi. Metode yang digunakan meliputi studi literatur dan analisis berbasis komputasi dengan pustaka NetworkX untuk membangun graf serta Matplotlib untuk visualisasi data. Hasil penelitian menunjukkan bahwa graf berarah yang dibangun dengan NetworkX dapat secara efektif merepresentasikan hubungan antar siswa dalam kelompok belajar. Visualisasi menggunakan Matplotlib memungkinkan identifikasi pola keterkaitan secara sistematis dan lebih intuitif dibandingkan pendekatan berbasis tabel. Selain itu, analisis graf mengungkap siswa yang memiliki peran sentral dalam kelompok, yang dapat menjadi dasar untuk pengelompokan yang lebih optimal. Kesimpulan dari penelitian ini adalah bahwa penerapan graf dalam sistem pendidikan memberikan representasi visual yang lebih jelas dan informatif terhadap hubungan antar siswa. Pendekatan ini dapat menjadi referensi bagi pengelola pendidikan dalam meningkatkan efektivitas pengelompokan siswa dan perancangan strategi pembelajaran yang lebih adaptif
Simulasi Monte Carlo Untuk Prediksi dan Analisis Tindak Pidana di Sumatera Utara Gultom, Ledy Meva Tiurma; Felicia Eldora; Khoiriyati Azmi; Purba, Rut Omega; Karin Aulia Putri; Manullang, Sudianto; Nasution, Alvi Sahrin
Griya Journal of Mathematics Education and Application Vol. 5 No. 3 (2025): September 2025
Publisher : Pendidikan Matematika FKIP Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/griya.v5i3.641

Abstract

Crime data in North Sumatra Province obtained from the Central Statistics Agency shows an upward trend, peaking in 2023. This condition presents an urgent need for swift and appropriate policy intervention. The effectiveness of such policies is measured by their ability to significantly reduce crime rates. This study aims to forecast the trend of criminal acts in North Sumatra for the period 2024–2028 using the Monte Carlo Simulation method, based on historical data from 2000 to 2023. The stochastic simulation approach was chosen to accommodate the uncertainty and variability inherent in crime data. The results provide probabilistic predictions that can serve as a reference for the government in designing more adaptive and responsive crime prevention policies. This model is expected to offer realistic estimates of potential increases or decreases in crime, thereby enabling more targeted and data-driven decisions. Overall, the findings of this study contribute to the development of strategic, risk-based security policies that reflect the dynamic nature of criminal activity.
Co-Authors Gultom, Ledy Meva Tiurma Karin Aulia Putri Khoiriyati Azmi Manullang, Sudianto Nasution, Alvi Sahrin Panggabean, Suvriadi Purba, Rut Omega Simanjuntak, Christina N Yulita Molliq Rangkuti