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All Journal Epsilon: Jurnal Matematika Murni dan Terapan Jurnal Ilmu Komputer dan Agri-Informatika Journal of Data Science and Software Engineering
Akhmad Yusuf
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Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Transportation

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Jaringan Saraf Radial Basis Probabilistic untuk Identifikasi Morfologi Benih Padi Rawa Kalimantan Selatan Oni Soesanto; Akhmad Yusuf; Dindin H Mursyidin; M Syahid Pebriadi
Jurnal Ilmu Komputer & Agri-Informatika Vol. 4 No. 1 (2015)
Publisher : Departemen Ilmu Komputer - IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (502.557 KB) | DOI: 10.29244/jika.4.1.14-21

Abstract

Machine vision berbasis jaringan saraf tiruan dan pemrosesan gambar digital merupakan metode alternatif yang dapat dilakukan untuk mengidentifikasi dan mengevaluasi keragaman varietas padi. Berbeda dengan metode pengamatan langsung yang memiliki tingkat subjektivitas tinggi dan metode kimiawi (PCR) yang bersifat destruktif dan mahal, machine vision berbasis jaringan saraf tiruan menawarkan sistem identifikasi dan evaluasi secara cepat, praktis, murah, akurat, serta bersifat non-destruktif. Paper ini membahas machine vision berbasis jaringan saraf tiruan sebagai teknologi alternatif untuk identifikasi varietas padi rawa Kalimantan Selatan berdasarkan ciri morfologinya, yaitu area, perimeter, major axis, minor axis, circularity, aspect ratio, roundness, dan feret untuk setiap sampel benih padi. Dalam paper ini, sistem identifikasi varietas benih padi menggunakan jaringan saraf radial basis probabilistic dengan optimalisasi bobot hidden center menggunakan algoritme orthogonal least square. Dari proses learning dihasilkan performa pelatihan sebesar 88.32% dan performa pengujian sebesar 88.21% dengan tingkat keberhasilan pada proses pelatihan dari masing-masing varietas bayar papuyu, bayar putih, benih kuning, benih putih, ketan, siam gadis, siam unus, dan karan dukuh masing-masing sebesar 100.00%, 92.59%, 88.89%, 92.59%, 92.59%, 81.48%, 100.00%, dan 100.00%. Untuk proses pengujian, tingkat keberhasilan masing-masing varietas ialah 100.00%, 87.50%, 88.89%, 100.00%, 88.89%, 88.89%, 100.00%, dan 100.00%.Kata Kunci: benih padi, machine vision, morfologi, RBP-OLS
ALGORITMA GENETIKA PADA PENYELESAIAN AKAR PERSAMAAN SEBUAH FUNGSI Akhmad Yusuf; Oni Soesanto
EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN Vol 6, No 2 (2012): JURNAL EPSILON VOLUME 6 NOMOR 2
Publisher : Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/epsilon.v6i2.87

Abstract

The Genetic Algorithm is one approach to determining global optimum that is based on the theory of evolution. Outline the steps in this procedure starting with establishing a set of potential solutions and making changes with some iterations with genetic algorithms to get the best solution. Calculation the root of a function is actually a classic problem in mathematics. For that, various methods have been numerically developed. From the results of the implementation of genetic algorithm to find the root of the equation of a function h (x1, x2) = 1000 (x1-2x2) 2+ (1-x1) 2 in can be that FitMax (genome 9) = 10, FitMin (genome 107) = 0, FitAvr = 0.153, FitTot = 30.6, Best Genome: 10011001001000110010, x1 = 1 and x2 = 0.5 and this is the same as the exact value or value actually from the root of the equation
IMPLEMENTASI METODE CONVOLUTIONAL NEURAL NETWORK UNTUK PREDIKSI HARGA SAHAM LQ45 Aris Pratama; Dwi Kartini; Akhmad Yusuf; Andi Farmadi; Irwan Budiman
Journal of Data Science and Software Engineering Vol 1 No 02 (2020)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (195.714 KB)

Abstract

Stock are securities of ownership of a company. Investments in the stock market on average can produce a return rate of 10-30% per year, this amount is about two to three times higher than the rate of return on deposits or savings in banks which are only 5-10 % every year. One problem is the stock price is fluctuating or changing due to certain factors. This study compares several window size data with different amounts of data, aiming to find window size data with a more accurate amount of data for stock price predictions. Convolutional neural network algorithm with window size data of 7 days, 14 days, 21 days and 28 days in the amount of data 1 year and 2 years for stock price predictions. The results of this study are the convolutional neural network algorithm with a data window size of 7 days at the amount of data 2 years is more accurate than the window size data and the amount of other data. Because the smallest error result is 0.000201587.
Co-Authors Andi Farmadi Aris Pratama Dindin H Mursyidin Dwi Kartini Irwan Budiman M Syahid Pebriadi Oni Soesanto