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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Teknologi Informasi dan Ilmu Komputer Journal of Information Technology and Computer Science INTEGER: Journal of Information Technology Knowledge Engineering and Data Science JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) International Journal of Artificial Intelligence and Robotics (IJAIR) Journal Of Artificial Intelligence And Software Engineering
Mohammad Zoqi Sarwani, Mohammad Zoqi
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Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Pemodelan Regresi Non Linear Menggunakan Algoritma Genetika Untuk Prediksi Kebutuhan Air PDAM Kota Malang Putri Hasan, Vitara Nindya; Mahmudy, Wayan Firdaus; Sarwani, Mohammad Zoqi
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 3 No 1: Maret 2016
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (798.15 KB) | DOI: 10.25126/jtiik.201631170

Abstract

AbstrakSeiring dengan meningkatnya jumlah penduduk di Kota Malang maka meningkat pula kebutuhan konsumen air bersih dari PDAM.  Perubahan pemakaian air tersebut jika tidak diolah dengan baik maka akan menyebabkan beberapa persoalan diantaranya apabila PDAM terlalu banyak mendistribusikan air bersih ke konsumen maka akan berakibat pemborosan air dan sebaliknya apabila distribusi air bersih PDAM kurang maka konsumen akan kekurangan air bersih.  Oleh karena itu dibutuhkan suatu estimasi untuk memperkirakan dengan tepat seberapa besar volume air yang diperlukan di tahun-tahun berikutnya. Permasalahan tersebut dimodelkan dengan persamaan regresi non linear yang terdiri dari variabel bebas (X) dan variabel terikat (Y). Algoritma Genetika digunakan untuk memilih variabel mana saja yang perlu dilibatkan dalam persamaan regresi. Proses reproduksi menggunakan one-point-crossover dan random mutation, untuk proses seleksinya menggunakan model elitism selection. Dari uji coba didapatkan parameter terbaik yaitu ukuran populasi sebanyak 225, generasi terbaik sebanyak 1750 generasi, kombinasi cr : mr adalah 0,6 : 0,4 dengan nilai fitness tertinggi yaitu 107.997.  Hasil akhir berupa model regresi dengan melibatkan sesedikit mungkin variable bebas dan mean square error (MSE) terkecil..Kata kunci: Regresi Non Linear, Algoritma Genetika, Prediksi, Pemakaian air PDAM  AbstractAlong with the increasing population in Malang the consumer water consumption from PDAM also increase.  The change of water consumption if it is not treated properly , it will cause some problems when the PDAM has too many of water to distribute to consumers it will result in wastage of water and otherwise if the distribution of water less than normal, then the consumer will get a shortage of water.  Therefore it is necessary to estimate for predict exactly how much the water volume needed in subsequent years.  This problem will be modeled with non linear regression that consist of the independent variable (X) and the dependent variable (Y). Genetic Algorithm is applied to determine which variables are involved in the regression model. The reproduction process uses one-point-crossover and random mutation, for the selection process uses a elitism selection models. The numerical experiment obtains the best population size is 225, the best generation as much as 1750 generation, combination of cr : mr is 06 : 0.4 with the highest fitness value is 107.997.  The final result is a regression model that involves less independent variabels with minimum mean square error (MSE).Keywords: Non Linear Regression, Genetic Algorithm, Predict, Water Consumption
Signature Pattern Recognition using Kohonen Network Sari, Nadia Roosmalita; Sarwani, Mohammad Zoqi; Aulia, Yudha Alif; Mahmudy, Wayan Firdaus
Knowledge Engineering and Data Science
Publisher : citeus

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

A signature is a special form of handwriting that used for human identification process. The current identification process is extremely ineffective. People have to manually compare signatures with the previously stored data. This study proposed SOM Kohonen algorithm as the method of signature pattern recognition. This method has able to visualize high-dimensional data. The image processing method is used in this study in pre-processing data phase. The accuracy of SOM Kohonen was 70 %, indicated the method used was good enough for pattern recognition.
Co-Authors Agus Naba Aprilia, Yunita Nur Aulia, Yudha Alif Chanafi, Imam Hidayat, Faris Mohamad Khoiron Nadhiroh, Irni Irana Ainin Nadia Roosmalita Sari Putri Hasan, Vitara Nindya Putri, Nabila Rudi Hariyanto Salafudin, Muhammad Shubkhan Sani, Dian Ahkam Saputro, Harinudin Setiawan, Muhamad Agus Suharyadi, Heri Udin, Muhammad Wayan Firdaus Mahmudy