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All Journal Jurnal Ilmiah Mahasiswa FEB International Journal of Advances in Intelligent Informatics
Fauzi, Muhammad Rifqi
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Computer Science & IT Education

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Imputation of missing microclimate data of coffee-pine agroforestry with machine learning Nurwarsito, Heru; Suprayogo, Didik; Sakti, Setyawan Purnomo; Prayogo, Cahyo; Yudistira, Novanto; Fauzi, Muhammad Rifqi; Oakley, Simon; Mahmudy, Wayan Firdaus
International Journal of Advances in Intelligent Informatics Vol 10, No 1 (2024): February 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v10i1.1439

Abstract

This research presents a comprehensive analysis of various imputation methods for addressing missing microclimate data in the context of coffee-pine agroforestry land in UB Forest. Utilizing Big data and Machine learning methods, the research evaluates the effectiveness of imputation missing microclimate data with Interpolation, Shifted Interpolation, K-Nearest Neighbors (KNN), and Linear Regression methods across multiple time frames - 6 hours, daily, weekly, and monthly. The performance of these methods is meticulously assessed using four key evaluation metrics Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The results indicate that Linear Regression consistently outperforms other methods across all time frames, demonstrating the lowest error rates in terms of MAE, MSE, RMSE, and MAPE. This finding underscores the robustness and precision of Linear Regression in handling the variability inherent in microclimate data within agroforestry systems. The research highlights the critical role of accurate data imputation in agroforestry research and points towards the potential of machine learning techniques in advancing environmental data analysis. The insights gained from this research contribute significantly to the field of environmental science, offering a reliable methodological approach for enhancing the accuracy of microclimate models in agroforestry, thereby facilitating informed decision-making for sustainable ecosystem management.
FAKTOR - FAKTOR YANG MEMENGARUHI RISIKO BISNIS BANK (STUDI PADA BANK BUMN TAHUN 2004 – 2017) Fauzi, Muhammad Rifqi
Jurnal Ilmiah Mahasiswa FEB Vol. 7 No. 2
Publisher : Fakultas Ekonomi dan Bisnis Universitas Brawijaya

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Abstract

Penelitian ini bertujuan untuk mengetahui pengaruh kondisi permodalan yang diproksikandengan Capital Adequacy Ratio (CAR), Tingkat kredit bermasalah atau Non Performing Loan (NPL),Loan to Deposit Ratio (LDR), Net Interest Margin (NIM), dan Ukuran perusahaan (SIZE) terhadapBusiness Risk yang diukur dengan standar deviasi dari Return on Asset (ROA). Metode yang dipakaidalam penelitian ini adalah analisis regresi data panel. Populasi yang digunakan dalam penelitian iniadalah 4 perusahaan perbankan BUMN pada tahun 2004 – 2017. Sampel dalam penelitian ini dipilihdengan menggunakan metode purposive sampling dengan kriteria perusahaan perbankan yangmelaporkan publikasi keuangan tahunan (annual report). Dari hasil analisis tersebut menunjukkanbahwa secara simultan semua variabel independen berpengaruh signifikan terhadap risiko bisnis bank.Secara parsial NPL, LDR, NIM, dan Size berpengaruh positif signifikan terhadap risiko bisnis bank.Dan CAR berpengaruh negatif signifikan terhadap risiko bisnis bank. Adjusted R2 senilai 0.69 artinyadari 5 variabel independen dapat menjelaskan pengaruh variabel dependen sebesar 69%. Dan 31%lainnya dijelaskan pada faktor lain yang tidak dijelaskan pada penelitian ini.Kata Kunci: Risiko Bisnis Bank, CAR, NPL, LDR, NIM, SIZE 
Co-Authors Cahyo Prayogo, Cahyo Didik Suprayogo Heru Nurwarsito Novanto Yudistira Oakley, Simon Setyawan Purnomo Sakti Wayan Firdaus Mahmudy