Articles

Found 35 Documents
Search

OPTIMASI BIAYA DISTRIBUSI RANTAI PASOK TIGA TINGKAT DENGAN MENGGUNAKAN ALGORITMA GENETIKA ADAPTIF DAN TERDISTRIBUSI Indra, Zulfahmi; 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 | Full PDF (584.647 KB) | DOI: 10.22146/ijccs.6546

Abstract

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.
STATISTICAL INFERENCE FOR MODELING NEURAL NETWORK IN MULTIVARIATE TIME SERIES Urwatul Wutsqa, Dhoriva; Subanar, Subanar; Guritno, Suryo; Soejoeti, Zanzawi
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)

Abstract

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.
PERAMALAN BEBAN LISTRIK DAERAH ISTIMEWA YOGYAKARTA DENGAN METODE SINGULAR SPECTRUM ANALYSIS (SSA) Utami, Herni; Sari, Yunita Wulan; Subanar, Subanar; Abdurakhman, Abdurakhman; Gunardi, Gunardi
MEDIA STATISTIKA Vol 12, No 2 (2019): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (740.583 KB) | DOI: 10.14710/medstat.12.2.214-225

Abstract

This paper will study forecasting model for electricity demand in Yogyakarta and forecast it for 2019 until 2024. Usually, electricity demand data contain seasonal. We propose Singular Spectral Analysis-Linear Recurrent Formula (SSA-LRF) method. The SSA process consists of decomposing a time series for signal extraction and then reconstructing a less noisy series which is used for forecasting. The SSA-LRF method will be used to forecast h-step ahead. In this study, we use monthly electricity demand in Yogyakarta for 11 year (2008 to 2018). The forecasting results indicates that the forecast using window length of L=26 have good performance with MAPE of 1.9%.
Forecasting electricity load demand using hybrid exponential smoothing-artificial neural network model Sulandari, Winita; Subanar, Subanar; Suhartono, Suhartono; Utami, Herni
International Journal of Advances in Intelligent Informatics Vol 2, No 3 (2016): November 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v2i3.69

Abstract

Short-term electricity load demand forecast is a vital requirements for power systems. This research considers the combination of exponential smoothing for double seasonal patterns and neural network model. The linear version of Holt-Winter method is extended to accommodate a second seasonal component. In this work, the Fourier with time varying coefficient is presented as a means of seasonal extraction. The methodological contribution of this paper is to demonstrate how these methods can be adapted to model the time series data with multiple seasonal pattern, correlated non stationary error and nonlinearity components together. The proposed hybrid model is started by implementing exponential smoothing state space model to obtain the level, trend, seasonal and irregular components and then use them as inputs of neural network. Forecasts of future values are then can be obtained by using the hybrid model. The forecast performance was characterized by root mean square error and mean absolute percentage error. The proposed hybrid model is applied to two real load series that are energy consumption in Bawen substation and in Java-Bali area. Comparing with other existing models, results show that the proposed hybrid model generate the most accurate forecast
MODEL REPRESENTASI INFORMASI DAN PENGETAHUAN UNTUK PROYEK-PROYEK PERUSAHAAN DENGAN MENGGUNAKAN SEMANTIK ONTOLOGI Azhari, Azhari; Subanar, Subanar; Wardoyo, Retantyo; Hartati, Sri
JUTI: Jurnal Ilmiah Teknologi Informasi Vol 7, No 2, Juli 2008
Publisher : Teknik Informatika, ITS Surabaya

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

Abstract

This paper presents the utilization of knowledge management system for information and knowledge model development of enterprise projects. This information and knowledge management model is based on ontology semantic data model. The ontology data model is new technique for representing information and knowledge base on more semantically conception of meanings of objects, their properties, and relations between them that may arise within certain domain knowledge. The concern of the knowledge management model is to ensure that the model allows the process of creation, access, and utilization of data in a semantically manner (for querying process) and information or knowledge of enterprise projects. The experimentation shows that project ontology model has satisfied all consistent, valid, complete, and correct ontology model criteria and can be used for semantic reasoning computation. A prototype of the proposed model can access information and knowledge from the knowledge ontology model.   Kata Kunci: knowledge management system, semantic data model, ontology model, semantiq query, enterprise projects
DEVELOPMENT OF A SPATIAL PATH-ANALYSIS METHOD FOR SPATIAL DATA ANALYSIS Sulistyo, Wiwin; Subanar, Subanar; Pulungan, Reza
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 4: August 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1162.002 KB) | DOI: 10.11591/ijece.v8i4.pp2456-2467

Abstract

Path analysis is a method used to analyze the relationship between independent and dependent variables to identify direct and indirect relationship between them. This method is developed by Sewal Wright and initially only uses correlation analysis results in identifying the variables' relationship. Path analysis method currently is mostly used to deal with variables with non-spatial data type. When analyzing variables that have elements of spatial dependency, path analysis could result in a less precise model. Therefore, it is necessary to build a path analysis model that is able to identify and take into account the effects of spatial dependencies. Spatial autocorrelation and spatial regression methods can be used to develop path analysis method so as to identify the effects of spatial dependencies. This paper proposes a method in the form of path analysis method development to process data that have spatial elements. This study also discusses our effort on establishing a method that could be used to identify and analyze the spatial effect on data in the framework of path analysis; we call this method spatial path analysis.
STATISTICAL SIGNIFICANCE TEST FOR NEURAL NETWORK CLASSIFICATION Rezeki, Sri; Subanar, Subanar; Guritno, Suryo
Jurnal Natur Indonesia Vol 11, No 1 (2008)
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (102.606 KB) | DOI: 10.31258/jnat.11.1.64-69

Abstract

Model selection in neural networks can be guided by statistical procedures, such as hypothesis tests, informationcriteria and cross validation. Taking a statistical perspective is especially important for nonparametric models likeneural networks, because the reason for applying them is the lack of knowledge about an adequate functionalform. Many researchers have developed model selection strategies for neural networks which are based onstatistical concepts. In this paper, we focused on the model evaluation by implementing statistical significancetest. We used Wald-test to evaluate the relevance of parameters in the networks for classification problem.Parameters with no significance influence on any of the network outputs have to be removed. In general, theresults show that Wald-test work properly to determine significance of each weight from the selected model. Anempirical study by using Iris data yields all parameters in the network are significance, except bias at the firstoutput neuron.
ASIMTOTIK MODEL MULTIVARIATE ADAPTIVE REGRESSION SPLINE Otok, Bambang Widjanarko; Guritno, Suryo; Subanar, Subanar
Jurnal Natur Indonesia Vol 10, No 2 (2008)
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (153.379 KB) | DOI: 10.31258/jnat.10.2.112-119

Abstract

Parameter estimation in MARS model executed by minimizing penalized least-squarer (PLS). Through somerequirement, asymtotic estimator characteristic from MARS prediction model has been successfully proven. Theresearch result shows that GCV can work properly to determine the best model that applied on MARS model. Solar?s vehicles produce opacity that exceed the standard limit of emition quality which was adjusted in Kepmen LH No.35 Year 1993, as large as 88 percent from 408 percent. Applying years, cylinder volume, type of machine, andvehicle?s radius are the variables that influences the opacity.
Simulation of queue with cyclic service in signalized intersection system Mulyodiputro, Muhammad Dermawan; Subanar, Subanar
International Journal of Advances in Intelligent Informatics Vol 1, No 1 (2015): March 2015
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v1i1.15

Abstract

The simulation was implemented by modeling the queue with cyclic service in the signalized intersection system. The service policies used in this study were exhaustive and gated, the model was the M/M/1 queue, the arrival rate used was Poisson distribution and the services rate used was Exponential distribution. In the gated service policy, the server served only vehicles that came before the green signal appears at an intersection. Considered that there were 2 types of exhaustive policy in the signalized intersection system, namely normal exhaustive (vehicles only served during the green signal was still active), and exhaustive (there was the green signal duration addition at the intersection, when the green signal duration at an intersection finished). The results of this queueing simulation program were to obtain characteristics and performance of the system, i.e. average number of vehicles and waiting time of vehicles in the intersection and in the system, as well as system utilities. Then from these values, it would be known which of the cyclic service policies (normal exhaustive, exhaustive and gated) was the most suitable when applied to a signalized intersection system
UJI NONLINEARITAS YANG DIABAIKAN DALAM TIME SERIES Sutijo, Brodjol; Subanar, Subanar
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 4, No 2 (2004)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v4i2.874

Abstract

Dalam makalah ini akan dibahas tentang pengujian nonlinearitas didasarkan pada pendekatan Neural Network (NN) yang dikemukakan oleh Lee dan White untuk kondisi nonlinear yang terabaikan pada model time series. Pada uji neural network ini, dikembangkan dari model Feedforward neural network dengan menambahkan hubungan langsung dari input ke output. Uji ini akan dibandingkan dengan uji Tsay dan didasarkan pada studi simulasi, baik untuk model linear maupun model nonlinear. Pendekatan uji dengan neural network adalah pendekatan lagrange multiplier, sedangkan uji Tsay didasarkan pada pendekatan regresi dengan menambahkan perkalian komponen dari variabel prediktor. Hasil simulasi secara umum menunjukkan jika model yang dibentuk adalah model linear, kekuatan uji nonlinearitasnya rendah, sedangkan jika yang dibentuk adalah model nonlinear, maka kekutan uji nonlinearnya tinggi. Hasil ini berlaku untuk metode White maupun Tsay.