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Rendi Cahya Wihandika
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Optimasi Fuzzy Time Series Dengan Algoritme Genetika Untuk Meramalkan Jumlah Pengangguran di Jawa Timur Radifah Radifah; Budi Darma Setiawan; Rendi Cahya Wihandika
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 8 (2018): Agustus 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Unemployment becomes one of the important points that are occurred in Indonesia. High unemployment rate has an impact on the economic and poverty levels of Indonesians especially in East Java. The increase number of unemployment can reduce the income and productivity of society. Several factors that are causing the increase of unemployment make the government difficult to overcome the numbers of unemployment annually that experience ups and downs. So, by predicting the number of unemployment in East Java, it can facilitate the government in overcoming the unemployment rate and expanding the workforce especially in East Java. The method that is used in this study is Fuzzy Time Series that use Genetic Algorithm. The best genetic algorithm parameter values are by testing to the genetic algorithm parameters and producing the best average fitness value. The result of genetic algorithm parameter test are with the population size of 525, the combination of crossover rate and mutation rate of 0,8 and 0,2 and at generation of 1200 which reaches the most optimal average fitness value is 13,840314614 with Root Mean Square Error(RMSE) value equal to 0,0722526928.
Pengenalan Sidik Jari Balita Menggunakan Metode Zone Based Linear Binary Pattern dan Extreme Learning Machine Dea Valentina; Yuita Arum Sari; Rendi Cahya Wihandika
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 2 (2019): Februari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Fingerprint recognition is one of the technological developments that is feasible since childhood. Along with the increasing number of toddlers, an introduction system is needed that is able to uniquely identify toddlers with the biometric patterns they have. Toddler fingerprint patterns have a low contrast between ridges and valleys and have a size (distance between ridges) that is smaller than adult fingerprints making it difficult to design accurate algorithms that are able to extract important features and match them in a strong way. In this study, the process begins with pre-processing and then uses the Zone Based Linear Pattern method to extract features on toddler fingerprints and the Extreme Learning Machine (ELM) classification method to recognize the identity of the fingerprint owner. The test results using a combined binary pattern for the Zone Based Linear Binary Pattern extraction method, the gaussian filtering, opening and adaptive thresholding technique for pre-processing images with dimensions of 200x200 in the image, the z-score method for normalizing data and the number of hidden neurons by 50 with binary sigmoid activation function for ELM classification produces the best accuracy of 72.33%. Based on these results it can be concluded that the Zone Based Linear Binary Pattern and Extreme Learning Machine methods can be used to recognize toddler fingerprints.
Klasifikasi Penerimaan Program Keluarga Harapan (PKH) Menggunakan Metode Learning Vector Quantization (Studi Kasus Desa Kedungjati) Vidya Capristyan Pamungkas; Lailil Muflikhah; Rendi Cahya Wihandika
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 3 (2019): Maret 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Poverty is a condition of someone inability to fulfill basic needs for a decent life. The poverty rate is increases, especially in Jombang Regency from year to year until 2017 reaching 131.16 people, various ways have been carried out by the government to reduce poverty, one of which is Program Keluarga Harapan or PKH, Kedungjati Village officer doing survey head of family with manual method by visiting each head of family and recording one by one the criteria. Classification system of Program Keluarga Harapan using Learning vector quantization (LVQ). LVQ is a classification method that has a pattern where the output of each unit is a representation of a class or category. The weight vector of each unit's output is a vector representation to a class. Weight vector have rules during training. As a classification method, LVQ does a lot of training repeatedly process until get maximum results, so LVQ can minimize errors that occur in the process. LVQ method do training and testing process to obtain the classification results. In this case using 5 test parameters with the best results, that is learning rate 0.7, DecAlpha 0.3, Epoch 2, and MinAlpha 0.01, using 2 weight vector to represent class 0 and class 1, get the results of an accuracy of 100%.
Co-Authors Budi Darma Setiawan Dea Valentina Lailil Muflikhah Radifah Radifah Vidya Capristyan Pamungkas Yuita Arum Sari