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Penerapan Algoritma Genetika untuk Optimasi Penjadwalan Tebangan Hutan Ipung Permadi; subanar subanar
JUITA : Jurnal Informatika JUITA Vol. 1 Nomor 1, Mei 2010
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Scheduling Cuts Away Forest is one of problem met at forestry area. All important problem in finalizing this problem is to determine forest check which will be cut away with a purpose to maximizes yield wood volume in each period cuts away and remain to maintains everlasting forest concept. Method which has been developed to finalize this problem is apply linear program with simplex method. At this method every step is taken based on exact formula is assessed unsatisfying good to finalize this problem. Genetics algorithm is one of alternative of solution of scheduling problems cuts away this forest. This idea of this algorithm comes from the Evolution Theory of Charles Darwin, which is only the best route was choosen. An individual was being choosen from a parent population and then recombined to another individual that has been choosen from another parent population to create a new individu. This new individual expected to be better from the rest individu at the population. With this method, the genetic algorithm found to be able to offer a best Scheduling Cuts Away Forest Problem.

A New Approach of Fuzzy-Wavelet Method’s Implementation in Time Series Analysis Seng Hansun; Subanar Subanar
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 5, No 2 (2011): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.2020

Abstract— Recently, many soft computing methods have been used and implemented in time series analysis. One of the methods is fuzzy hybrid model which has been designed and developed to improve the accuracy of time series prediction. Popoola has developed a fuzzy hybrid model which using wavelet transformation as a pre-processing tool, and commonly known as fuzzy-wavelet method. In this thesis, a new approach of fuzzy-wavelet method has been introduced. If in Popoola’s fuzzy-wavelet, a fuzzy inference system is built for each decomposition data, then on the new approach only two fuzzy inference systems will be needed. By that way, the computation needed in time series analysis can be pressed. The research is continued by making new software that can be used to analyze any given time series data based on the forecasting method applied. As a comparison there are three forecasting methods implemented on the software, i.e. fuzzy conventional method, Popoola’s fuzzy-wavelet, and the new approach of fuzzy-wavelet method. The software can be used in short-term forecasting (single-step forecast) and long-term forecasting. There are some limitation to the software, i.e. maximum data can be predicted is 300, maximum interval can be built is 7, and maximum transformation level can be used is 10. Furthermore, the accuracy and robustness of the proposed method will be compared to the other forecasting methods, so that can give us a brief description about the accuracy and robustness of the proposed method. Keywords— fuzzy, wavelet, time series, soft computing

Fuzzy C-Means Clustering Model Data Mining For Recognizing Stock Data Sampling Pattern Sylvia Jane Annatje Sumarauw; Subanar Subanar
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 1, No 2 (2007): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.2278

AbstractCapital market has been beneficial to companies and investor. For investors, the capital market provides two economical advantages, namely deviden and capital gain, and a non-economical one that is a voting .} hare in Shareholders General Meeting. But, it can also penalize the share owners. In order to prevent them from the risk, the investors should predict the prospect of their companies. As a consequence of having an abstract commodity, the share quality will be determined by the validity of their company profile information. Any information of stock value fluctuation from Jakarta Stock Exchange can be a useful consideration and a good measurement for data analysis. In the context of preventing the shareholders from the risk, this research focuses on stock data sample category or stock data sample pattern by using Fuzzy c-Me, MS Clustering Model which providing any useful information jar the investors. lite research analyses stock data such as Individual Index, Volume and Amount on Property and Real Estate Emitter Group at Jakarta Stock Exchange from January 1 till December 31 of 204. 'he mining process follows Cross Industry Standard Process model for Data Mining (CRISP,. DM) in the form of circle with these steps: Business Understanding, Data Understanding, Data Preparation, Modelling, Evaluation and Deployment. At this modelling process, the Fuzzy c-Means Clustering Model will be applied. Data Mining Fuzzy c-Means Clustering Model can analyze stock data in a big database with many complex variables especially for finding the data sample pattern, and then building Fuzzy Inference System for stimulating inputs to be outputs that based on Fuzzy Logic by recognising the pattern.Keywords: Data Mining, AUz..:y c-Means Clustering Model, Pattern Recognition

Stock Data Clustering of Food and Beverage Company Shofwatul Uyun; Subanar Subanar
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 1, No 2 (2007): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.2279

AbstractCluster analysis can be defined as identifying groups of similar objects to discover distribution of patterns and interesting correlations in large data sets. Clustering analysis is important in the fields of pattern recognition and pattern classification. Over the years many methods have been developed for clustering data. In general, clustering methods can be categoried into two categories, i.e., fuzzy clustering and hard clustering. Fuzzy C-means is one of many methods of clustering based on fuzzy approach, while K-Means and K-Medoid are methods clustering based on crisp approach.This study aims to apply Fuzzy C-Means, K-Means and K-Medoid methods for clustering stock data in a jbod and beverage company. The main goal is to find a clustering method that can produce optimal clusters, The resulting clusters are validated using Dunn'• Index (DI). It is expected that the result of this reseach can be used to support decision making in the food and beverage company.Keywords : Clustering, Fuzzy C-Means, K-Means, K-Medoid, Cluster Validity, Dunn's Index (Dl)

Simulasi Antrian Jaringan Multi Server Menggunakan Metode Open Jackson I Wayan Supriana; Subanar Subanar
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 7, No 2 (2013): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.3358

AbstrakAntrian paket data pada jaringan komputer memiliki model antrian jaringan, dimana proses transmisi yang rumit sehingga tidak dapat diselesaikan secara analitik. Pemodelan dan simulasi diperlukan untuk menyelesaikan masalah antrian jaringan. Model antrian dalam penelitian ini adalah jaringan terbuka dengan analisis paket data menggunakan model server tunggal. Waktu pelayanan paket memiliki distribusi Eksponensial dan distribusi Erlang yang digunakan sebagai pembanding. Jika waktu pelayanan paket data menggunakan distribusi Eksponensial maka model menjadi M/M/1, sedangkan waktu pelayanan paket data menggunakan distribusi Erlang dengan parameter m dan µ, maka model menjadi M/E[m]/1. Penelitian ini menggunakan metode open Jackson untuk melakukan simulasi antrian jaringan guna menghitung nilai karakteristik jaringan. Pengujian sistem simulasi menggunakan packet switching network pada server jaringan komputer Jurusan Ilmu Komputer Universitas Udayana untuk mengetahui performansi sistem yang menggunakan distribusi waktu pelayanan berbeda. Hasil pengujian menunjukkan bahwa waktu pelayanan distribusi Eksponensial memiliki karakteristik yang lebih baik dari distribusi Erlang pada parameter m-Erlang ≥ 2. Kata kunci— antrian jaringan, distribusi,sistem performansi, multi server. AbstractQueue data packet at computer network having a network queueing model, with complicated transmission process so that it can not be solved analytically. Modeling and simulation are needed to resolve the issue queue network. Queueing model in this research is an open network with the analysis of data packet using a single server model. Service time packet has Exponential distribution and Erlang used as comparison. If the service time of data packet using the Exponential distribution, then the model become M/M/1, whereas the service time using the Erlang distribution with parameter m and µ, then the model becomes M/E[m]/1. This research uses an open Jackson method to perform queueing network simulations to calculate the characteristics of network queueing system. Examination of simulation system uses data packets on a computer network server of Department Computer Science University of Udayana to determine system performance using with different service time distribution. The result of examination indicate that service time of Exponential distribution has better characteristic then Erlang distribution at parameter m-Erlang ≥2. Keywords— queueing network, distribution, system performance, multiple server

PENDEKATAN ALGORITMA GENETIKA DALAM MENYELESAIKAN PERMASALAHAN FUZZY LINEAR PROGRAMMING Siska Dewi Lestari; Subanar Subanar
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 5, No 3 (2011): November
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.5211

Fuzzy linear programming is one of the linear programming developments which able to accommodate uncertainty in the real world. Genetic algorithm approach in solving linear programming problems with fuzzy constraints has been introduced by Lin (2008) by providing a case which consists of two decision variables and three constraint functions. Other linear programming problem arise with the presence of some coefficients which are fuzzy in linear programming problems, such as the coefficient of the objective function, the coefficient of constraint functions, and right-hand side coefficients constraint functions. In this study, the problem studied is to explain the genetic algorithm approach to solve linear programming problems where the objective function coefficients and right-hand sides are fuzzy constraint functions.PT Dakota Furniture study case provides a linear programming formulation with a given objective function coefficients and right-hand side coefficients are fuzzy constraint functions. This study describes the use of genetic algorithm approach to solve the problem of linear programming of PT Dakota to maximize the mean income. The genetic algorithm approach is done by simulate every fuzzy number and each fuzzy numbers by distributing them on certain partition points. Then genetic algorithm is used to evaluate the value for each partition point. As a result, the Final Value represents the coefficient of fuzzy number. Fitness function is done by calculating the value of the objective function of linear programming problems. Empirical results indicated that the genetic algorithm approach can provide a very good solution by giving some limitations on each fuzzy coefficient.Genetic algorithm approach can be extended not only to resolve the case of PT Dakota Furniture, but can also be used to solve other linear programming case with some coefficients in the objective function and constraint functions are fuzzy.Keywords : Genetic Algorithm, Fuzzy Linear Programming, Linear Programming, Two-Phase Simplex Method

Optimasi Biaya Distribusi Rantai Pasok Tiga Tingkat dengan Menggunakan Algoritma Genetika Adaptif dan Terdistribusi Zulfahmi Indra; Subanar Subanar
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 8, No 2 (2014): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.6546

AbstrakManajemen rantai pasok merupakan hal yang penting. Inti utama dari manajemen rantai pasok adalah proses distribusi. Salah satu permasalahan distribusi adalah strategi keputusan dalam menentukan pengalokasian banyaknya produk yang harus dipindahkan mulai dari tingkat manufaktur hingga ke tingkat pelanggan. Penelitian ini melakukan optimasi rantai pasok tiga tingkat mulai dari manufaktur-distributor-gosir-retail. Adapun pendekatan yang dilakukan adalah algoritma genetika adaptif dan terdistribusi. Solusi berupa alokasi banyaknya produk yang dikirim pada setiap tingkat akan dimodelkan sebagai sebuah kromosom. Parameter genetika seperti jumlah kromosom dalam populasi, probabilitas crossover dan probabilitas mutasi akan secara adaptif berubah sesuai dengan kondisi populasi pada generasi tersebut. Dalam penelitian ini digunakan 3 sub populasi yang bisa melakukan pertukaran individu setiap saat sesuai dengan probabilitas migrasi. Adapun hasil penelitian yang dilakukan 30 kali untuk setiap perpaduan nilai parameter genetika menunjukkan bahwa nilai biaya terendah yang didapatkan adalah 80,910, yang terjadi pada probabilitas crossover 0.4, probabilitas mutasi 0.1, probabilitas migrasi 0.1 dan migration rate 0.1. Hasil yang diperoleh lebih baik daripada metode stepping stone yang mendapatkan biaya sebesar 89,825. Kata kunci— manajemen rantai pasok, rantai pasok tiga tingkat, algortima genetika adaptif, algoritma genetika terdistribusi. Abstract Supply chain management is critical in business area. The main core of supply chain management is the process of distribution. One issue is the distribution of decision strategies in determining the allocation of the number of products that must be moved from the level of the manufacture to the customer level. This study take optimization of three levels distribution from manufacture-distributor-wholeshale-retailer. The approach taken is adaptive and distributed genetic algorithm. Solution in the form of allocation of the number of products delivered at each level will be modeled as a chromosome. Genetic parameters such as the number of chromosomes in the population, crossover probability and adaptive mutation probability will change adaptively according to conditions on the population of that generation. This study used 3 sub-populations that exchange individuals at any time in accordance with the probability of migration. The results of research conducted 30 times for each value of the parameter genetic fusion showed that the lowest cost value obtained is 80,910, which occurs at the crossover probability 0.4, mutation probability 0.1, the probability of migration 0.1 and migration rate 0.1. This result has shown that adaptive and distributed genetic algorithm is better than stepping stone method that obtained 89,825. Keywords— management supply chain, three level supply chain, adaptive genetic algorithm, distributed genetic algorithm.

Ordinal Regression Model using Bootstrap Approach Bambang Widjanarko Otok; M. Sjahid Akbar; Suryo Guritno; Subanar Subanar
Jurnal ILMU DASAR Vol 8 No 1 (2007)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (225.538 KB)

The aim of the research content three part, thus a to know misclassification and model discriminant analysis with bootstrap approach, model regression ordinal with bootstrap approach, and model MARS with bootstrap approach. The data used is data of secondary related to matrix variance covariance is same and unequal that is (The data worker standard of living and banking performance). The result of this research shows that in determining distinguishing variable between groups there are difference of variable at each method. This matter because of at each method has specification either from fulfilled of assumption and also estimation its. So also at accuracy of classification between groups there is difference especially at matrix of variance covariance unequal at worker standard of living case. As a whole can be concluded that the problem accuracy of classification bootstrap approach at each method give small mistake of goodness at matrix variance covariance unequal and equal.Keywords: classification, bootstrap, discriminant analysis, ordinal regression, MARS.

Statistical Inference for Modeling Neural Network in Multivariate Time Series Dhoriva Urwatul Wutsqa; Subanar Subanar; Suryo Guritno; Zanzawi Soejoeti
Jurnal ILMU DASAR Vol 9 No 1 (2008)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (136.781 KB)

We present a statistical procedure based on hypothesis test to build neural networks model in multivariate time series case. The method involved strategies for specifying the number of hidden units and the input variables in the model using inference of R2 increment. We draw on forward approach starting from empty model to gain the optimal neural networks model. The empirical study was employed relied on simulation data to examine the effectiveness of inference procedure. The result showed that the statistical inference could be applied successfully for modeling neural networks in multivariate time series analysis.

Pendekatan Regresi Ordinal untuk Klasifikasi Tingkat Hidup Pekerja Bambang Widjanarko Otok; Suryo Guritno; Subanar Subanar
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 5, No 1 (2005)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v5i1.915

Masalah klasifikasi (pengelompokkan) pada kelompok yang sudah diketahui pada umumnyamembatasi diri dalam melibatkan sejumlah peubah yang terkait, sehingga mengakibatkan hilangnyasebagian informasi yang justru berkonsekuensi dalam kesimpulan penelitian. Untuk itu upaya yangdilakukan untuk membatasi keterlibatan sejumlah peubah dalam penelitian harus melihat kerangkapermasalahan secara menyeluruh pada kelompok dalam peubah tersebut.Metode klasifikasi yang baik akan menghasilkan sedikit kesalahan klasifikasi atau peluangkesalahan alokasi yang kecil dan juga terpenuhinya asumsi seperti variansi sama pada kelompok.Sehingga diperlukan suatu kajian mengenai masalah klasifikasi dengan pendekatan regresi ordinaldan sebagai kriteria kestabilan klasifikasi digunakan Press-Q.Hasil penelitian menunjukkan bahwa analisis regresi ordinal merupakan suatu metode yangsangat baik dalam masalah klasifikasi dan dalam menentukan variabel yang mempengaruhi padakelompok dan interpretasi model. Selain itu fungsi peluang komulatif yang diperoleh mudahdiinterpretasikan untuk menjelaskan keterkaitan prediksi kedepan dalam pengelompokkan.Secara keseluruhan tingkat ketepatan prediksi model dengan analisis regresi ordinal untukmengelompokkan tingkat hidup pekerja yang dipengaruhi empat variabel (Pendidikan (X1), Statuspekerjaan (X2), Upah/Gaji Sebulan (X3) dan Status perkawinan (X4)) secara keseluruhan sebesar54.6%, dan pengaruh yang signifikan pada pendidikan adalah pendidikan SMA dan SMP, statuspekerjaan bulanan (berbanding terbalik), upah/gaji sebulan sebesar Rp 1.000.000 s/d Rp 1.500.000,dan status perkawinan yang sudah menikah.

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