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All Journal Enthusiastic : International Journal of Applied Statistics and Data Science Bulletin of Applied Mathematics and Mathematics Education Emerging Statistics and Data Science Journal
Danarwindu, Ghiffari Ahnaf
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Mathematics

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Statistical Perspective of Dengue Hemorrhagic Fever in West Java: Insights from Two-Way RE Model Danarwindu, Ghiffari Ahnaf; Fadhlurrahman, Muhammad Ghani
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 4 Issue 2, October 2024
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol4.iss2.art4

Abstract

The Indonesian Ministry of Health has reported an alarming increase in Dengue Hemorrhagic Fever (DHF) cases, particularly in West Java Province. Given this trend, collaborative research and surveillance efforts are crucial to understanding and managing DHF cases in Indonesia. The panel data regression model in dengue fever cases will provide new insights into modeling. This research aimed to identify the most appropriate random effects model for estimating a dataset with four different variables. This study involved panel data variables on the effect of population density, percentage of poor people, percentage of households with access to clean water, and proper sanitation on DHF cases in West Java Province. This method emphasized selecting the best model from one-way and two-way Random Effects (RE) models and identifying what factors influenced the increase of DHF cases in West Java province. The best model obtained was a two-way RE Model with three significant variables. Based on the selected variables in the model, West Java Province needs to pay attention to the distribution of housing and economic activity in each district because population density is a crucial concern for the local government.
Perbandingan Metode Peramalan Volume Transaksi Sistem Resi Gudang: Prophet, Exponential Smoothing dan Sarima: Perbandingan Metode Peramalan Volume Transaksi Sistem Resi Gudang: Prophet, Exponential Smoothing dan Sarima Noviani Sugianto, Vickie Ashri; Danarwindu, Ghiffari Ahnaf; Prihatmoko, Harry
Emerging Statistics and Data Science Journal Vol. 3 No. 2 (2025): Emerging Statistics and Data Science Journal
Publisher : Statistics Department, Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/esds.vol3.iss.2.art8

Abstract

Fluktuasi harga komoditas saat panen raya sering menyebabkan rendahnya pendapatan petani dan kesulitan akses pembiayaan. Sistem Resi Gudang (SRG) dirancang sebagai solusi untuk menstabilkan harga dan memberi akses pembiayaan tanpa agunan tambahan, serta mendukung ketahanan pangan nasional. Meskipun SRG terus berkembang, implementasinya masih menghadapi tantangan seperti keterbatasan kapasitas gudang, infrastruktur yang belum merata, dan perbedaan karakteristik komoditas. Peramalan volume komoditas yang masuk diperlukan untuk mengoptimalkan penggunaan gudang dan mendukung kebijakan logistik serta penyimpanan. Penelitian ini membandingkan tiga metode peramalan deret waktu yaitu Prophet, Exponential Smoothing (Holt-Winters), dan SARIMA. Menggunakan data bulanan volume Resi Gudang dari Januari 2022 hingga Desember 2024. Evaluasi akurasi model dilakukan dengan Mean Absolute Scaled Error (MASE). Prophet dengan konfigurasi multiplicative memberikan akurasi tertinggi dengan MASE 0,4134, namun menghasilkan prediksi negatif pada awal 2025. Holt-Winters menghasilkan prediksi yang lebih stabil dan realistis meski nilai MASE-nya lebih tinggi (0,7875). SARIMA memiliki performa terendah dengan MASE 0,9097. Hasil ini menunjukan bahwa pemilihan model tidak hanya bergantung pada nilai error, tetapi juga pada hasil yang diperoleh. Peramalan volume SRG yang akurat dapat meningkatkan efisiensi operasional gudang, mencegah kekurangan kapasitas, serta mendukung stabilitas harga dan pengambilan kebijakan strategis.
Pipeline on microarray data analysis: Pre-processing Fajriyah, Rohmatul; Kongchouy, Noodchanath; Ayudhaya, Wanvisa Saisanan Na; Yotenka, Rahmadi; Danarwindu, Ghiffari Ahnaf
Bulletin of Applied Mathematics and Mathematics Education Vol. 5 No. 1 (2025)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/bamme.v5i1.12539

Abstract

Bioinformatics is blooming and its data are store in some repository offline and or online. Yet some basic concepts are not fully disseminated. The paper intends to provide the reader with a review of one important concept in the pipeline bioinformatics data analysis of microarray, pre-processing. In pre-processing, there are four steps, background correction, normalization, probe correction and summarization. Each step consists of several methods, and we describe each method to give a better understanding on how it works theoretically. We focused on microarray data from Affymetrix platform with single-color chip.
Co-Authors Ayudhaya, Wanvisa Saisanan Na Fadhlurrahman, Muhammad Ghani Kongchouy, Noodchanath Noviani Sugianto, Vickie Ashri Prihatmoko, Harry Rohmatul Fajriyah Yotenka, Rahmadi