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Budiman, Haldi
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Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Neuroscience Transportation

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Klasifikasi Ekspor Impor Produk Pertanian dengan Metode Deep Learning Oktavia, Samita; Mambang, Mambang; Prasetya, M. Riko Anshori; Nurhaeni, Nurhaeni; Naparin, Husni; Budiman, Haldi
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 7, No 5 (2024): Oktober 2024
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v7i5.8083

Abstract

Abstrak - Perubahan nilai impor dan ekspor memiliki pengaruh signifikan terhadap pertumbuhan ekonomi suatu negara, dengan inflasi berperan penting dalam mempengaruhi neraca perdagangan. Teknik pembelajaran mesin, khususnya Deep Learning yang merupakan subset dari Machine Learning, menawarkan solusi efektif untuk mengklasifikasi dan mendiagnosis pola dalam data ekspor-impor pertanian. Menggunakan Artificial Neural Network (ANN), yang terinspirasi oleh struktur otak manusia, teknik ini dapat memproses data kompleks untuk mendukung pengambilan keputusan yang tepat dalam analisis perdagangan pertanian. Penelitian ini fokus pada penerapan Deep Learning untuk mengidentifikasi pola ekspor-impor pertanian dengan akurasi tinggi, mencapai 93.7%, dan precision sebesar 89.6%, Recall sempurna sebesar 100% dan F-Measure yang tinggi pada 94.5% menunjukkan keseimbangan antara precision dan recall.Kata kunci: Artificial Neural Network, Deep Learning, Ekspor, Impor, Klasifikasi. Abstract - The changes in import and export values have a significant impact on a country's economic growth, with inflation playing a crucial role in influencing the trade balance. Machine learning techniques, particularly Deep Learning, a subset of Machine Learning, offer effective solutions for classifying and diagnosing patterns in agricultural export-import data. Using Artificial Neural Networks (ANN), inspired by the structure of the human brain, this technique can process complex data to support accurate decision-making in agricultural trade analysis. This research focuses on the application of Deep Learning to identify agricultural export-import patterns with high accuracy, achieving 93.7%, precision of 89.6%, perfect recall of 100%, and a high F-Measure of 94.5%, indicating a balance between precision and recall.Keywords: Artificial Neural Network, Classification, Deep Learning, Ekspor, Impor
Review of Original Differential Evolution Algorithm: Research Trends, Original Setting Parameters Wang, ShirLi; Budiman, Haldi; Ramadhani, Siti; FooNg, Theam; Morsidi, Farid
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 10, No 2 (2024): December 2024
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v10i2.29903

Abstract

Abstract: Differential Evolution (DE) has emerged as a widely embraced optimization algorithm, consistently showcasing robust performance in the IEEE Congress on Evolutionary Computation (CEC) competitions.Purpose: This study aims to pinpoint key regulatory parameters and manage the evolution of DE parameters. We conduct an exhaustive literature review spanning from 2010 to 2021 to identify and analyze evolving trends, parameter settings, and ensemble methods associated with original differential evolution.Method: Our meticulous investigation encompasses 1,210 publications, comprising 543 from ScienceDirect, 12 from IEEE Xplore, 424 from Springer, and 231 from WoS. Through an initial screening process involving title and abstract skimming to identify relevant subsets and eliminate duplicate entries, we excluded 762 articles from full-text scrutiny, resulting in 358 articles for in-depth analysis.Findings: Our findings reveal a consistent utilization of tuning parameters, self-adaptive mechanisms, and ensemble methods in the final collection. These results deepen our understanding of DE's success in CEC competitions.Value: offer valuable insights for future research and algorithm development in optimization fields.  
Co-Authors FooNg, Theam Mambang Morsidi, Farid Naparin, Husni Nurhaeni Nurhaeni Oktavia, Samita Prasetya, M. Riko Anshori Ramadhani, Siti Wang, ShirLi