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All Journal Jurnal Ilmiah Kursor INTEGER: Journal of Information Technology
Sunarko, Victor Immanuel
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Computer Science & IT Decision Sciences, Operations Research & Management

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Implementasi K-Fold Dalam Prediksi Hasil Produksi Agrikultur Pada Algoritma K-Nearest Neighbor (KNN) Sunarko, Victor Immanuel; Dimara, Denis Lizard Sambawo; Siagian, Pangestu Sandya Etniko; Manalu, Daniel; Anggraeny, Fetty Tri
INTEGER: Journal of Information Technology Vol 10, No 1 (2025): April
Publisher : Fakultas Teknologi Informasi Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.integer.2024.v10i1.6892

Abstract

Sektor agrikultur khususnya pertanian di Indonesia merupakan tulang punggung perekonomian, dengan tenaga kerja pertanian mencapai 38,14 juta orang pada Februari 2023, atau 27,52% dari total tenaga kerja nasional. Meskipun memiliki potensi besar, sektor ini menghadapi tantangan signifikan, termasuk lahan terbatas, perubahan iklim, dan kelangkaan air, yang mengharuskan penerapan pertanian berkelanjutan. Penelitian ini bertujuan untuk meningkatkan efisiensi produksi pertanian melalui penerapan kecerdasan buatan (AI) dan analisis data. Metodologi yang digunakan meliputi pembagian data untuk memprediksi hasil produksi pertanian dengan algoritma k-nearest neighbour (KNN). Uji skenario dilakukan dengan pendekatan k-fold cross-validation dan hold-out data sharing. Hasil penelitian menunjukkan akurasi tertinggi sebesar 98,36% menggunakan k-fold cross-validation dan 97,42% dengan metode hold-out.Kata Kunci: KNN, K-Fold, Hold-Out, Prediksi, Agrikultur
Convolutional layer exertion on few-shot learning for brain tumor classification Sunarko, Victor Immanuel; Puspaningrum, Eva Yulia; Widiastuty, Riana Retno; Hadi, Surjo; Awang, Mohd Khalid; Mas Diyasa, I Gede Susrama
Jurnal Ilmiah Kursor Vol. 13 No. 2 (2025)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v13i2.430

Abstract

Brain tumors, though relatively rare, pose a significant threat due to their critical location within the brain, impacting essential bodily functions. Accurate and timely diagnosis is vital, but traditional diagnostic methods are time-intensive and rely heavily on large labeled datasets. This study addresses these challenges by proposing a Few-Shot Learning (FSL) framework enhanced with Convolutional Neural Networks (CNNs) to classify brain tumors using MRI images. By employing the Matching Network architecture, the model leverages limited training data through an N-way-K-shot setup. Training results demonstrated accuracy levels of 71.58% (1-shot) and 82.89% (5-shot) for 1-layer CNNs, 66.65% (1-shot) and 84.03% (5-shot) for 3-layer CNNs, and 63.43% (1-shot) and 84.94% (5-shot) for 5-layer CNNs. However, validation accuracy revealed overfitting concerns, with the highest performance at 51.56% (1-layer, 1-shot). These results underscore the potential of FSL in medical imaging while highlighting the need for advanced augmentation and feature representation techniques to improve generalization.
Co-Authors Awang, Mohd Khalid Dimara, Denis Lizard Sambawo Eva Yulia Puspaningrum Fetty Tri Anggraeny Hadi, Surjo Manalu, Daniel Mas Diyasa, I Gede Susrama Siagian, Pangestu Sandya Etniko Widiastuty, Riana Retno