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All Journal INTEGER: Journal of Information Technology KERNEL: Jurnal Riset Inovasi Bidang Informatika dan Pendidikan Informatika
Dimara, Denis Lizard Sambawo
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Computer Science & IT Other

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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: April 2025
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
Penerapan Convolutional Neural Network (CNN) dalam Klasifikasi Citra MRI untuk Deteksi Tumor Otak Manusia Dimara, Denis Lizard Sambawo; Putri, Shintyadhita Wirawan; Amelia, Rizky; Arishandy, Zalfa Ibtisamah; Rizki, Agung Mustika
KERNEL: Jurnal Riset Inovasi Bidang Informatika dan Pendidikan Informatika Vol 4, No 2 (2023)
Publisher : Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.kernel.2023.v4i2.6960

Abstract

Brain tumors are deadly diseases with a high mortality rate, making early diagnosis crucial to improving patient survival rates. However, manual diagnosis through Magnetic Resonance Imaging (MRI) often requires significant time and is prone to errors. This study developed an MRI image classification method using the EfficientNetB3-based Convolutional Neural Network (CNN) architecture to detect brain tumors. The dataset used was obtained from Kaggle, consisting of 253 brain MRI images, including 98 normal and 155 abnormal images. The data were preprocessed through normalization and resizing to 224x224 pixels. The model employed transfer learning techniques using pretrained weights from ImageNet, enhanced with additional layers to improve performance. Evaluation was conducted using metrics such as accuracy, precision, recall, F1-score, AUC, as well as confusion matrix and classification report analyses. The results showed that the EfficientNetB3 model achieved an overall accuracy of 86%, demonstrating its capability to support brain tumor diagnosis processes quickly and accurately. This implementation is expected to provide a significant contribution to early detection of brain tumors and improve patient care quality in the medical field.
Co-Authors Agung Mustika Rizki, Agung Mustika Arishandy, Zalfa Ibtisamah Fetty Tri Anggraeny Manalu, Daniel Putri, Shintyadhita Wirawan Rizky Amelia Siagian, Pangestu Sandya Etniko Sunarko, Victor Immanuel