Optimasi Nilai Bobot Algoritma Backpropagation Neural Network Dengan Algoritma Genetika

Dasuki, Moh.

doi:10.32528/justindo.v6i1.5280

Cited by 3 publications

(3 citation statements)

References 8 publications

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“…∆𝑣 (Dasuki, 2020). Proses ini dilakukan untuk mengevaluasi penerapan model terhadap dataset yang sudah dilakukan dengan akurasi nilai Root Mean Square Error (RMSE).…”

Section: 𝑧 𝑖𝑛 𝑗unclassified

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Prediksi Kurs Mata Uang Rupiah Terhadap Ringgit Malaysia Menggunakan Algoritma Backpropagation

Tirta,

Nursyarif,

Hasmadi

et al. 2024

Jurnal Penelitian Teknik Informatika, Manajemen Informatika dan

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Permasalahan kemerosotan ekonomi global di era globalisasi ekonomi masih dilakukan penelitian dan dipelajari faktor-faktor penentu pemulihan dalam mencari solusi permasalahan. Diperlukan sebuah analisis pergerakan terhadap nilai tukar agar bisa mengantisipasi terjadinya lonjakan terhadap fluktuasi nilai tukar. Sehingga muncul tantangan baru dalam melakukan prediksi fluktuasi kurs mata uang berdasar permasalahan yang sedang dihadapi. Tujuan dari penelitian untuk meningkatkan akurasi prediksi dengan menerapkan metode Backpropagation. Dataset yang digunakan adalah data kurs mata uang Ringgit Malaysia (MYR) ke Rupiah Indonesia (IDR) dari tanggal 1 Juli s/d 30 Oktober 2023 dengan total data sebanyak 109 data yang didapat dari situs web Investing.Hasil penelitian menggunakan epoch 300, neuron 3, dan learning Rate 0,5 yang menghasilkan nilai RMSE pada pelatihan Data Training 13,601 dan Data Testing 10,721 hal ini menandakan bahwa model mampu memberikan prediksi yang akurat dan mampu menggeneralisasi dengan baik terhadap data yang belum pernah dilihat sebelumnya. Secara keseluruhan pengembangan model prediksi menggunakan Algoritma Backpropagation ini dapat dianggap berhasil dan mempunyai potensi yang bermanfaat dalam pengambilan keputusan terkait prediksi nilai tukar mata uang dalam konteks ekonomi global.

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“…∆𝑣 (Dasuki, 2020). Proses ini dilakukan untuk mengevaluasi penerapan model terhadap dataset yang sudah dilakukan dengan akurasi nilai Root Mean Square Error (RMSE).…”

Section: 𝑧 𝑖𝑛 𝑗unclassified

“…Proses ini melibatkan memberikan setiap sampel data ke model, menghitung kerugian (loss), dan melakukan pembaruan parameter model. (Dasuki, 2020).…”

Section: Membangun Arsitektur Jstunclassified

Prediksi Kurs Mata Uang Rupiah Terhadap Ringgit Malaysia Menggunakan Algoritma Backpropagation

Tirta,

Nursyarif,

Hasmadi

et al. 2024

Jurnal Penelitian Teknik Informatika, Manajemen Informatika dan

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“…Untuk mengatasi beberapa kelemahan mendasar pada Backpropagation, diperlukan alternatif algoritma belajar (learning algorithm). Beberapa peneliti telah mencoba mengatasi permasalahan ini, diantaranya dengan menggunakan Algoritma Genetik pada tahapan pemilihan nilai awal wight untuk sehingga bisa meningkatkan kinerja Backpropagation (Yi, X., 2015;Dasuki, M., 2021). Pada penelitian kali ini, penulis mengusulkan algoritma Adaptif sebagai alternatif algoritma belajar Backpropagation.…”

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Meningkatkan Kinerja Backpropagation Neural Network Menggunakan Algoritma Adaptif

Bohari¹

2022

imtech

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The application of Artificial Neural Networks in various fields of human life is getting wider, especially in the industrial sector. One of the artificial neural network structures that are quite often used is the Feedforward Neural Network with its well-known learning algorithm, namely Backpropagation. However, as reported by several researchers, Backpropagation has several weaknesses such as it takes a long time to converge in the training process, it is quite sensitive to initial weight conditions and is relatively often trapped in a local minima which can thwart the training process. In this study, the Adaptive algorithm is proposed as an alternative to the Backpropagation learning algorithm. The proposed algorithm provides hope in overcoming the weaknesses faced by Bakpropagation. As reported in the test results, compared to Backpropagation, the Adaptive algorithm is much stronger in dealing with variations in the initial weight conditions. From 100 tests in this study for each Backpropagation and Adaptive algorithm, with random variations for the initial weight value, the success rate of the Adaptive algorithm training process reaches 100% compared to Backpropagation which is at the level of 77%. In terms of speed, the Adaptive algorithm has successfully carried out the training process with an average number of iterations of 37 times compared to Backpropagation which requires an average of 162 iterations.

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Identification of Missing Person using CNN

Dhanalakshmi,

Kumar M,

et al. 2024

International Journal of Innovative Science and Research Techno

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Our project aims to leverage Convolutional Neural Networks (CNNs) for the identification of missing persons. CNNs, a class of deep learning algorithms widely used in image recognition tasks, offer promising potential in automating and enhancing the identification process. The project aims to develop a robust system using CNN models to match unidentified individuals with missing person databases, improving identification accuracy and providing closure. The proposed approach demonstrates potential in assisting law enforcement agencies and missing persons organizations by providing a reliable and efficient means of cross-referencing images from various sources, such as surveillance footage, social media, and public records. Additionally, the flexibility of CNNs allows for the integration of other biometric markers, including fingerprints and voice recognition, to increase the accuracy and reliability of identifications. This research underscores the importance of using artificial intelligence and machine learning in social good applications, highlighting the potential for technology to play a transformative role in reuniting families and bringing closure to unresolved cases. Future work will focus on refining the model, enhancing privacy protections, and ensuring ethical use in real-world applications.

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Optimasi Nilai Bobot Algoritma Backpropagation Neural Network Dengan Algoritma Genetika

Cited by 3 publications

References 8 publications

Prediksi Kurs Mata Uang Rupiah Terhadap Ringgit Malaysia Menggunakan Algoritma Backpropagation

Prediksi Kurs Mata Uang Rupiah Terhadap Ringgit Malaysia Menggunakan Algoritma Backpropagation

Meningkatkan Kinerja Backpropagation Neural Network Menggunakan Algoritma Adaptif

Identification of Missing Person using CNN

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