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All Journal Jurnal Ilmu Multidisplin Eduvest - Journal of Universal Studies
Imam Yuadi
Universitas Airlangga, Surabaya, Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Computer Science & IT Economics, Econometrics & Finance Education Health Professions Law, Crime, Criminology & Criminal Justice Neuroscience Public Health Social Sciences

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Implementation of Orange Data Mining for Employee Turnover Prediction of Company X Thomas Wigung Aji Prayitna; Imam Yuadi
Eduvest - Journal of Universal Studies Vol. 5 No. 5 (2025): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v5i5.50814

Abstract

Employee turnover presents a significant challenge in Human Resources, particularly for companies operating across broad geographic areas such as Indonesia. High turnover rates can disrupt organizational continuity, increase recruitment costs, and affect overall performance. To mitigate these impacts, companies need to predict employee turnover likelihood accurately. This study uses the Orange Data Mining platform to compare the effectiveness of various machine learning models in predicting employee turnover. The models evaluated in this research include Support Vector Machine (SVM), Naive Bayes, K-Nearest Neighbors (K-NN), Neural Network, Decision Tree, and Logistic Regression. Model performance was assessed using cross-validation, Receiver Operating Characteristic (ROC) analysis, and confusion matrix metrics such as precision, recall, and false positives. The findings reveal that the Naive Bayes model outperforms the other models, demonstrating the highest precision rate and the lowest false positive rate. These results suggest that Naive Bayes offers a reliable and efficient approach to turnover prediction, enabling Human Resource departments to implement proactive retention strategies. This study implies that data-driven decision-making in HR analytics can substantially improve workforce planning and reduce the operational costs associated with high turnover.
Klasifikasi Penyakit Daun Padi Menggunakan Convolutional Neural Network (CNN) Berbasis Pengolahan Citra Digital untuk Mendukung Ketahanan Pangan Nasional Aditya Dwisusilo; Imam Yuadi
Jurnal Ilmu Multidisiplin Vol. 4 No. 6 (2026): Jurnal Ilmu Multidisplin (Februari - Maret 2026)
Publisher : Green Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/jim.v4i6.1617

Abstract

Penelitian ini mengkaji penyakit daun padi sebagai faktor utama yang menyebabkan penurunan hasil panen di Indonesia, di mana pemeriksaan manual oleh petani atau petugas lapangan sering berlangsung lambat, bersifat subjektif, dan rentan salah identifikasi. Kondisi ini menunjukkan perlunya sistem deteksi yang cepat, konsisten, dan akurat berbantuan teknologi digital. Penelitian ini bertujuan mengembangkan model klasifikasi penyakit daun padi menggunakan Convolutional Neural Network dengan arsitektur EfficientNetB0 yang disesuaikan dalam kerangka pengolahan citra digital. Metode meliputi ekstraksi ciri otomatis, pembagian data secara proporsional menjadi kelompok pelatihan dan pengujian, serta optimasi model menggunakan prosedur komputasi. Kinerja model dinilai melalui akurasi, ketepatan, sensitivitas, skor F1, dan analisis matriks kebingungan. Hasil penelitian menunjukkan bahwa model mencapai tingkat akurasi tinggi pada kisaran 93–96 persen dengan performa yang stabil di seluruh kategori penyakit. Temuan ini menegaskan bahwa model mampu menangkap karakter visual kompleks penyakit daun padi dan memiliki potensi kuat untuk diintegrasikan dalam sistem deteksi dini otomatis. Sistem tersebut dapat meningkatkan ketepatan pemantauan penyakit dan mendukung ketahanan pangan nasional melalui pengurangan kehilangan hasil panen serta peningkatan kualitas pengambilan keputusan budidaya padi.
Co-Authors Aditya Dwisusilo Thomas Wigung Aji Prayitna