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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) KOPERTIP: Jurnal Ilmiah Manajemen Informatika dan Komputer
Siti Aisyah
STMIK IKMI Cirebon
Computer Science & IT Engineering

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Implementasi Algoritma K-Nearest Neighbor Dalam Menentukan Penerima Bantuan Langsung Tunai Anwar Pauji; Siti Aisyah; Agus Surip; Riko Saputra; Irfan Ali
KOPERTIP : Jurnal Ilmiah Manajemen Informatika dan Komputer Vol. 4 No. 1 (2020): KOPERTIP: Jurnal Ilmiah Manajemen Informatika dan Komputer
Publisher : Puslitbang Kopertip Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32485/kopertip.v4i1.114

Abstract

Proses penentuan data dan pemberian bantuan terhadap penerima manfaat sudah dilaksanakan sebelumnya, Namun bantuan yang di terima ada yang tidak sesuai dengan yang diharapkan, hal tersebut disebabkan salah satunya karena penetapan status keluarga miskin selaku penerima bantuan belum maksimal sehingga dalam membagikan bantuan masih belum akurat. Maka dari itu penelitian ini mencoba untuk mengklasifikasi penerima manfaat menggunakan metode K-Nearest Neighbor sehingga penerima bantuan benar-benar tepat sasaran. Kriteria yang digunakan pada penelitian ini yaitu Kondisi Rumah, Pekerjaan, Penghasilan dan Jumlah tanggungan. aplikasi yang digunakan yaitu rapidminer versi 9.10 dengan operator retrive, Cross Validation, Algoritma K-NN, Apply Model dan Performance. Nilai akurasi sebesar 68,82 % dengan rincian sebagai berikut. Prediksi Layak dan Ternyata True Layak Sebesar 104. Prediksi Layak dan Ternyata True Tidak Layak Sebesar 37. Prediksi Tidak Layak dan Ternyata True Layak Sebesar 16. Prediksi Tidak Layak dan Ternyata True Tidak Layak Sebesar 13. Dengan class recall layak sebesar 86,67% dan Class Recall Tidak Layak Sebesar 26%.
Convolutional Neural Networks for Classification Motives and the Effect of Image Dimensions Siti Aisyah; Rini Astuti; Fadhil M Basysyar; Odi Nurdiawan; Irfan Ali
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 1 (2024): February 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i1.5623

Abstract

Although Indonesian batik patterns vary by location, they usually depict local customs and cultures. Each batik has a unique quality and, to correctly identify the batik designs, you need to understand the design patterns. However, many people struggle to identify and categorize these kinds of motivation because they don't have the requisite knowledge, understanding, or access to sufficient information. This study used photo data to classify batik patterns into 15 different groups. Batik Kawung, Megamendung, Lasem, Pole, Machete, Gills, Nutmeg, Karaswasih, Cendrawasih, Geblek Renteng, Bali, Betawi, and Dayak are all included in this category. 1,350 images were used in the research. Google supports the collection of data. To provide the highest level of precision and to evaluate how image dimensions affect the classification of batik designs, this study employs convolutional neural networks (CNNs). The results of this study show that Multi-Layer Perceptron (MLP) is a well-liked deep learning method for data classification, especially in domains where picture classification is involved. The size of the images utilized affects the accuracy of computational neural network (CNN) algorithms. The results showed that the test using training data comparisons of 60%, 30% and 10% resulted in a 01.89% loss of 1.18% and a 100% improvement in accuracy.
Co-Authors Agus Surip Anwar Pauji Basysyar, Fadhil Muhammad Irfan Ali Odi Nurdiawan Riko Saputra Rini Astuti