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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Infomedia
Latifa, Khoiriya
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Computer Science & IT

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SISTEM PREDIKSI HARGA BITCOIN MENGGUNAKAN METODE ARIMA ( AUTOREGRESSIVE MOVING AVERAGE) Setiaji, Venanda Try; Latifa, Khoiriya; Harjanta, Aris Trijaka
Jurnal Infomedia: Teknik Informatika, Multimedia, dan Jaringan Vol 10, No 1 (2025): Jurnal Infomedia
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jim.v10i1.7255

Abstract

Volatilitas harga Bitcoin di Indonesia, yang semakin populer sebagai alternatif investasi, menimbulkan tantangan bagi investor dalam pengambilan keputusan akibat fluktuasi ekstrem. Penelitian ini bertujuan mengembangkan sistem prediksi harga Bitcoin berbasis web menggunakan metode AutoRegressive Integrated Moving Average (ARIMA) untuk memberikan informasi prediktif yang akurat. Metode meliputi pendekatan kuantitatif dengan Agile Scrum, pengumpulan data historis 365 hari via API CoinGecko, pengujian stasionaritas menggunakan Augmented Dickey-Fuller, dan optimasi parameter ARIMA. Hasil penelitian menunjukkan sistem dengan antarmuka inklusif (halaman home, prediksi, analisis model) yang menghasilkan prediksi 30 hari ke depan dengan interval kepercayaan 95%, mencerminkan tren pasar berdasarkan data historis. Pembahasan mengkonfirmasi efektivitas ARIMA dalam menangkap pola harga, meskipun terbatas pada tren linier. Simpulan menegaskan keberhasilan sistem dalam mendukung investasi, dengan saran untuk pembaruan data berkala dan analisis faktor eksternal guna meningkatkan akurasi.
Implementation and Comparative Analysis of CNN and Transfer Learning Models (EfficientNetB0, MobileNetV2, and ResNet50) for Rice Leaf Disease Detection Based on Digital Images Utami, Tri Wahyu; Novita, Mega; Latifa, Khoiriya
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11616

Abstract

Rice leaf diseases significantly reduce agricultural productivity, making early and accurate detection essential, particularly in rice-producing regions such as Indonesia. This study proposes an automated rice leaf disease detection system based on Convolutional Neural Networks (CNN) and transfer learning. The dataset, obtained from the Mendeley Data Repository, consists of 6,889 images classified into eight categories: Bacterial Leaf Blight, Brown Spot, Healthy Rice Leaf, Leaf Blast, Leaf Scald, Narrow Brown Leaf Spot, Rice Hispa, and Sheath Blight. The dataset was divided into 70% training, 15% validation, and 15% testing. A baseline CNN model and three pre-trained models—EfficientNetB0, MobileNetV2, and ResNet50—were evaluated using accuracy, precision, recall, F1-score, and confusion matrix analysis. The baseline CNN achieved a test accuracy of 48.26%, while EfficientNetB0 achieved 58.41%. In contrast, MobileNetV2 and ResNet50 demonstrated significantly better performance, with test accuracies of 79.98% and 76.60%, respectively. MobileNetV2 exhibited the most balanced performance across all classes, showing superior generalization capability and computational efficiency. The best-performing model was integrated into a Streamlit-based application, enabling real-time rice leaf disease detection through image upload. The results confirm that transfer learning substantially improves classification accuracy and robustness compared to conventional CNNs. This study highlights the potential of lightweight deep learning models for practical implementation in smart agriculture systems and provides a reliable solution for automated rice disease detection in real-world conditions.
Co-Authors Aris Tri Jaka Harjanta, Aris Tri Mega Novita Setiaji, Venanda Try Utami, Tri Wahyu