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All Journal Jurnal Media Infotama Jurnal Pseudocode
Nuri David Maria Veronica
Universitas Muhammadiyah Bengkulu
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Implementasi Algoritme Naïve Bayes Berbasis Particle Swarm Optimization Dalam Penentuan Pemberian Kredit Muhammad Husni Rifqo; Nuri David Maria Veronica
Jurnal Pseudocode Vol 6, No 1 (2019): Volume 6 Nomor 1 Februari 2019
Publisher : Universitas Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (979.446 KB) | DOI: 10.33369/pseudocode.6.1.1-12

Abstract

Sewa guna usaha (leasing) atau sering disingkat SGU adalah kegiatan pembiayaan dalam bentuk penyediaan barang modal baik secara sewa guna usaha dengan hak opsi (finance lease) maupun sewa guna usaha (lessee) selama jangka waktu tertentu berdasarkan pembayaran secara angsuran, kegiatan ini bisa dikatakan sebagai kegiatan kredit, kredit adalah penyediaan uang atau tagihan yang dapat dipersamakan dengan itu, berdasarkan persetujuan atau kesepakatan pinjam meminjam antara lembaga keuangan dengan pihak lain yang mewajibkan pihak peminjam melunasi utangnya setelah jangka waktu tertentu dengan pemberian bunga.Pengajuan aplikasi kredit oleh calon pelanggan sekarang sangatlah mudah, hal ini dikarenakan pengajuan kredit bisa dilakukan oleh semua orang sepanjang memenuhi syarat tertentu. Persaingan perusahaan penyedia kredit menjadi sangat pesat dan prediksi konsumen kredit adalah hal yang sangat penting. Dari permasalahan ini diperlukan suatu model yang mampu mengklasifikasikan sekaligus memprediksi pelanggan mana saja yang bermasalah dan tidak bermasalah. Model Naïve Bayes berbasis Particle Swarm Optimization (PSO) ternyata mampu meningkatkan akurasi dalam menganalisa kelayakan kredit, semakin besar data set yang digunakan maka akurasi model Naïve Bayes berbasis Particle Swarm Optimization (PSO) akan semakin meningkat, akurasi yang didapat oleh model ini untuk data set yang digunakan adalah: Agiing 2010 (96,75%), Agiing 2011 (97,95%), Japan credit approval (84,77%) dan Australia credit approval (87,83%).Kata Kunci: Analisa kredit, penilaian kredit, Naïve Bayes berbasis PSO.
Klasifikasi Kualitas Buah SawitMenggunakan Metode Gray Level Co-occurrence Matrix Dengan Variasi Arah Obyek Ridho Ikhlasul; Ardi Wijaya; Nuri David Maria Veronica; Rozali Toyib
Jurnal Media Infotama Vol 20 No 1 (2024): April 2024
Publisher : UNIVED Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmi.v20i1.5679

Abstract

The harvest of fresh fruit bunches (FFB) from palm fruit is done with fruit that is of good quality, namely fruit that is dark red in color or has lots of knots, so that the quality of the palm oil produced increases. Low quality produced will of course further weaken competitiveness. Determining the quality classification of palm oil can be done through image processing techniques. Classification is the process of declaring a data object to one of the previously defined categories. Categorization of data sets can involve grouping them into similar types of classification or identifying similar characteristics across a number of observations. Classification is supported by the concept of pattern recognition, classifying the quality of palm oil products as categories produced. Efforts to achieve the expected quality standards by using quality control. This classification of palm oil is divided into quality classes, namely good and not good. Knowing the quality of palm oil is done by making an application for palm oil quality using image processing. There are quite a lot of methods used in image processing technology. One of them is the Gray Level Co-Occurrence Matrix (GLCM) method. Gray Level Co-Occurrence Matrix (GLCM) is a method used for texture analysis/feature extraction, feature acquisition is obtained from matrix pixel values, which have certain values ​​and form an angle pattern. Palm Fruit Quality Classification Using Gray Level Co-occurrence Matrix With Variations in Object Direction. It is hoped that this application can help determine the quality of palm fruit more easily and efficiently.
Co-Authors Muhammad Husni Rifqo Ridho Ikhlasul Toyib, Rozali Wijaya, Ardi