JUTI: Jurnal Ilmiah Teknologi Informasi
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RANCANG BANGUN OPTIMASI KEBUTUHAN BAHAN BAKU MENGGUNAKAN ALGORITMA WAGNER-WHITIN
Get PDFLotting or purchasing raw materials is one step in Material Requirement Planning. Lotting technique that already known is the Wagner-Within algorithm. This algorithm is widely used because it provides optimal solutions for problem sizedeterministic dynamic reservation at a particular time period in which the needs of the entire period must be completed. It takes a application system of optimization planning raw material requirements using the Wagner-Whitin algorithm. The development of this process begins with building a power module of demand data using Arima method (1,1,1), then followed by forecasting modules of consumer demand for end product by using the multiplicative decomposition forecasting methods, and ends with the development of Materials Requirement Planning module (MRP I) using the Wagner-Whitin algorithm. The results of the test system with test data is the generation of data will form the same pattern that is likely up from week to week. Forecasting results have high accuracy registration of 99.48%, 99.64% and 99.68%. Wagner-Whitin algorithm always produces the combination of weeks. Result of the combination in the first week will produces the minimum cost for the entire week of production
CATATAN DEWAN REDAKSI
Get PDFJurnal Ilmiah Teknologi Informasi (JUTI) edisi Januari 2009 ini merupakan edisi ketiga dari volume 7. Sebagaimana telah direncanakan, bahwa sejak Januari 2008 silam, kami menyatukan 4 edisi dalam satu volume, sehingga edisi ini merupakan kelanjutan edisi 1 dan 2 yang telah terbit pada bulan Januari 2008 dan Juli 2008.
Kami bersyukur bahwa minat para peneliti untuk mendokumentasikan hasil penelitiannya di JUTI cukup tinggi. Namun demikian, kita semua menyadari bahwa kualitas tata tulis ilmiah yang mendasari penulisan artikel dalam jurnal merupakan hal yang tidak dapat ditawar. Oleh karena itu, usaha peningkatan kualitas yang dilakukan Dewan Redaksi melalui workshop secara reguler terus menerus dilaksanakan. Workshop penulisan artikel di JUTI telah dilaksanakan beberapa kali bekerja sama dengan Fakultas Teknologi Informasi ITS. Para anggota mitra bestari juga tak henti-hentinya memberikan dorongan semangat agar para penulis dapat menghasilkan karya ilmiah yang semakin berkualitas.
Pada edisi ini 8 artikel dari berbagai area penelitian dalam ilmu komputer mulai dari multimedia, kecerdasan buatan dan citra telah berhasil dinyatakan diterima. Semoga seluruh artikel ini mampu menjadi kontribusi nyata para peneliti tanah air sebagai solusi atas berbagai problem dalam dunia industri, sosial kemasyarakatan, dan lain-lain. Kami mengundang para peneliti untuk mempublikasikan hasil penelitiannya di JUTI pada edisi berikutnya yakni Juli 2009
PENERAPAN METODE ANALISA DISKRIMINAN MAJEMUK DENGAN PENDEKATAN TRANSFORMASI FUKUNAGA KOONTZ
Get PDFLinear discriminant analysis is one of method frequently used and developed in the field of pattern recognition. This method tries to find the optimal subspace by maximizing the Fisher Criterion. Application of pattern recognition in highdimensional data and the less number of training samples cause singular within-class distribution matrix. In this paper, we developed Linear Discriminant Analysis method using Fukunaga Koontz Transformation approach to meet the needs of the nonsingular within-class distribution matrix. Based on Fukunaga Koontz Transformation, the entire space of data is decomposed into four subspaces with different discriminant ability (measured by the ratio of eigenvalue). Maximum Fisher Criterion can be identified by linking the ratio of eigenvalue and generalized eigenvalue. Next, this paper will introduce a new method called complex discriminant analysis by transforming the data into intraclass and extraclass then maximize their Bhattacharyya distance. This method is more efficient because it can work even though within-class distribution matrix is singular and between-class distribution matrix is zero
EMBEDDED LINUX BASED ALBUM BROWSER SYSTEM AT MUSIC STORES
Get PDFThe goal of this research is the creation of an album browser system at a music store based on embedded Linux. It is expected with this system; it will help the promotion of said music store and make the customers activity at the store simpler and easier. This system uses NFS for networking, database system, ripping software, and GUI development. The research method used are and laboratory experiments to test the system’s hardware using TPC-57 (Touch Panel Computer 5.7" SA2410 ARM-9 Medallion CPU Module) and software using QtopiaCore. The result of the research are; 1. The database query process is working properly; 2. The audio data buffering process is working properly. With those experiment results, it can be concluded that the summary of this research is that the system is ready to be implemented and used in the music stores
CATATAN DEWAN REDAKSI
Get PDFUcapan terima kasih dan penghargaan yang setinggi-tingginya, kami ucapkan kepada para penulis yang artikelnya telah berhasil diterbitkan dalam Jurnal Ilmiah Teknologi Informasi (JUTI) edisi Juli 2009 ini. Semoga seluruh artikel tersebut bermanfaat untuk kita semua dan makin meningkatkan semangat untuk terus meneliti.
Edisi ke 4 dari volume 7 ini, sebagaimana edisi sebelumnya juga didominasi topik Ilmu Komputer dan Sistem Informasi. Hal ini dapat dipahami, sebab kedua topik dalam bidang teknologi informasi tersebut memang sedang booming di tanah air dan banyak dikembangkan untuk memecahkan berbagai persoalan dalam industri. Oleh karena itu sangat diharapkan bahwa artikel-artikel ini akan dapat dimanfaatkan dengan baik oleh masyarakat luas.
Dewan redaksi mengucapkan terima kasih atas segala jerih payah para anggota mitra bestari dan dewan editor yang telah menyelesaikan tugasnya dengan sangat sempurna. Tak lupa kritik dan saran pembaca sangat kami harapkan untuk penyempurnaan jurnal ini di masa-masa mendatang.
Akhirnya kami berharap agar para peneliti makin giat mendokumentasikan hasil penelitiannya dan berkeinginan untuk menerbitkannya melalui JUTI
PEMODELAN ARIMA DAN DETEKSI OUTLIER DATA CURAH HUJAN SEBAGAI EVALUASI SISTEM RADIO GELOMBANG MILIMETER
Get PDFThe purpose of this paper is to provide the results of Arima modeling and outlier detection in the rainfall data in Surabaya. This paper explained about the steps in the formation of rainfall models, especially Box-Jenkins procedure for Arima modeling and outlier detection. Early stages of modeling stasioneritas Arima is the identification of data, both in mean and variance. Stasioneritas evaluation data in the variance can be done with Box-Cox transformation. Meanwhile, in the mean stasioneritas can be done with the plot data and forms of ACF. Identification of ACF and PACF of the stationary data is used to determine the order of allegations Arima model. The next stage is to estimate the parameters and diagnostic checks to see the suitability model. Process diagnostics check conducted to evaluate whether the residual model is eligible berdistribusi white noise and normal. Ljung-Box Test is a test that can be used to validate the white noise condition, while the Kolmogorov-Smirnov Test is an evaluation test for normal distribution. Residual normality test results showed that the residual model of Arima not white noise, and indicates the existence of outlier in the data. Thus, the next step taken is outlier detection to eliminate outlier effects and increase the accuracy of predictions of the model Arima. Arima modeling implementation and outlier detection is done by using MINITAB package and MATLAB. The research shows that the modeling Arima and outlier detection can reduce the prediction error as measured by the criteria Mean Square Error (MSE). Quantitatively, the decline in the value of MSE by incorporating outlier detection is 23.7%, with an average decline 6.5%
RPLUGIN.ECONOMETRICS: PAKET GRAPHICAL USER INTERFACE OPEN SOURCE UNTUK ANALISIS RUNTUN WAKTU MENGGUNAKAN PERANGKAT LUNAK R
Get PDFR (R Development Core Team, 2009) is one of the open source software that is popular and has become "lingua franca" or standard language for the purposes of computing the current statistics. In this paper, will be introduced and discussed RcmdrPlugin.Econometrics package (Rosadi, Marhadi and Rahmatullah, 2009), which is a GUI version (Graphical User Interface) of R for the purposes of econometric analysis or time series. RcmdrPlugin.Econometrics package is an additional menu (plug-in) which provided for the R Commander, which is the most popular GUI of R. To illustrate the design philosophy of this package, provided also illustrate the usage of the RcmdrPlugin.Econometrics package for the exponential smoothing
IDENTIFIKASI SINYAL ECG IRAMA MYOCARDIAL ISCHEMIA DENGAN PENDEKATAN FUZZY LOGIC
Get PDFThe heart is one of vital organs in human body. Incidence of heart disease can be fatal for the patient. Myocardial ischemia, the disease that is often suffered by the human, is a disease due to clogged heart arteries blood vessels. One of the ways to detect this disease is by reading the graph output of electrocardiogram (ECG) signal. ECG signal represents the condition and activity of the heart. Specialized knowledge, accuration and expertise are required to read ECG graph. To help expert or doctor, expert system based on artificial intelligent, such as Fuzzy Logic approach, can be applied to improve diagnostic accuracy and thoroughness. Fuzzy logic can be applied because of it flexibility to understand the linguistic variables used in identifying myocardial ischemia disease
KINERJA ALGORITMA PARALEL UNTUK PENCARIAN KATA DENGAN METODE BOYER-MOORE MENGGUNAKAN PVM
Get PDFSearch process is one of important activity in data processing. Searching can take more time if conducted in the huge search space. Therefore, it is needed an efficient search technique. One technique that can be used is parallel computing. This article discusses the search process in parallel using the Boyer-Moore algorithm. Parallel scope is emulated with PVM (Parallel Virtual Machine) software. From the research results, it can be concluded that the performance of parallel computing will increase the word searching compared to the computing performance for its word sequencial search consisting of one letter, and will drop to the word search consisting of two letters or more
KERNEL LOGISTIC REGRESSION-LINEAR FOR LEUKEMIA CLASSIFICATION USING HIGH DIMENSIONAL DATA
Get PDFKernel Logistic Regression (KLR) is one of the statistical models that has been proposed for classification in the machine learning and data mining communities, and also one of the effective methodologies in the kernel–machine techniques. Basely, KLR is kernelized version of linear Logistic Regression (LR). Unlike LR, KLR has ability to classify data with non linear boundary and also can accommodate data with very high dimensional and very few instances. In this research, we proposed to study the use of Linear Kernel on KLR in order to increase the accuracy of Leukemia Classification. Leukemia is one of the cancer types that causes mortality in medical diagnosis problem. Improving the accuracy of Leukemia Classification is essential for more effective diagnosis and treatment of Leukemia disease. The Leukemia data sets consists of 7120 (very high dimensional) DNA micro arrays data of 72 (very few instances) patient samples on the state of Leukemia types. In Leukemia classification based upon gene expression, monitoring data using DNA micro array offer hope to achieve an objective and highly accurate classification. It can be demonstrated that the use of Linear Kernel on Kernel Logistic Regression (KLR–Linear) can improve the performance in classifying Leukemia patient samples and also can be shown that KLR–Linear has better accuracy than KLR–Polynomial and Penalized Logistic Regression