Model Reduksi Pada Parameter Markov

As well known that the model reduction for large-scale linear dynamical systems based on Krylov subspace is called moments matching. In this method, one or more interpolation points is needed to construct a certain Krylov subspace. In this paper, we propose the proof of theorem for moment matching at Markov parameter using the theorem which is obtained from standard block Arnoldi algorithm

Counter Example: The Algorithm of Determinant of Centrosymmteric Matrix based on Lower Hessenberg Form

The algorithm for computing determinant of centrosymmetric matrix has been evaluated before. This algorithm shows the efficient computational determinant process on centrosymmetric matrix by working on block matrix only. One of block matrix at centrosymmetric matrix appearing on this algorithm is lower Hessenberg form. However, the other block matrices may possibly appear as block matrix for centrosymmetric matrix’s determinant. Therefore, this study is aimed to show the possible block matrices at centrosymmetric matrix and how the algorithm solve the centrosymmetric matrix’s determinant. Some numerical examples for different cases of block matrices on determinant of centrosymmetric matrix are given also. These examples are useful for more understanding for applying the algorithm with different cases

Teorema Kekonvergenan Fungsi Terintegral Riemann .

Teorema kekonvergenan merupakan bagian yang penting dalam mempelajari teori integral. Limit fungsi barisan fungsi yang terintegral Riemann pada suatu interval belum tentu fungsi tersebut juga terintegral Riemann pada interval itu. Dengan demikian diperlukan syarat lain agar limit fungsi juga terintegral Riemann. Tulisan ini bertujuan membahas syarat cukup agar limit fungsi dari barisan fungsi yang konvergenan di mana-mana juga terintegral Riemann. Selanjutnya, dibahas juga syarat cukup agar limit fungsi dari barisan fungsi yang konvergen hampir di mana-mana juga terintegral Riemann

MODEL REDUKSI UNTUK SISTEM MIMO

Model reduksi berdasarkan subruang Krylov biasa disebut pemadanan momen. Dalam paper ini, dikaji mengenai teknik mereduksi system dinamik linear untuk sistem MIMO menggunakan algorima Arnoldi-blok yang dimodifikasi. Modifikasi dilakuan dengan cara menyisipkan dekomposisi QR termodifikasi. Bagian akhir paper ini, diberikan keterhandalan algoritma yang diusulkan melalui pembahasan contoh model reduksi. Kata Kunci: Arnoldi-blok, MIMO, dan pemadanan mome

Dilated Convolutional Neural Network for Skin Cancer Classification Based on Image Data

Skin cancer is a disorder of cell growth in the skin. Skin cancer has a big impact, causing physical disabilities that can be seen directly and high treatment costs. In addition, skin cancer also causes death if nor treated properly. Generally, dermatologists diagnose the presence of skin cancer in the human body by using the Biopsy process. In this study, the Dilated Convolutional Neural Network method was used to classify skin cancer image data. Dilated Convolutional Neural Network method is a development method of the Convolutional Neural Network method by modifying the dilation factors. The Dilated Convolutional Neural Network method is divided into two stages, including feature extraction and fully connected layer. The data used in this study is HAM1000 dataset. The data are dermoscopic image datasets which consists of 10015 images data from 7 types of skin cancer. This study conducted several experimental scenarios of changes in the value of d, which are 2,4,6, and 8 to get the optimal results. The parameters used in this study are epoch = 100, minibatch size = 8, learning rate = 0.1, and dropout = 0.5. The best results in this study were obtained with value of d=2 with the value of accuracy is 85.67% and the sensitivity is 65.48%

SISTEM INFORMASI PERENCANAAN PRODUKSI DAN PENJADWALAN POLA TANAM HORTIKULTURA DENGAN MODEL LINEAR PROGRAMMING DAN FUZZY TIME SERIES

Penelitian tentang perencanaan produksi telah banyak dilakukan, tesis ini menyajikan sistem informasi perencanaan produksi dan penjadwalan pola tanam yang dihadapi oleh petani hortikultura dengan mengkombinasikan dua metode. Metode fuzzy time series digunakan untuk memprediksi jumlah permintaan dan hasil dari metode fuzzy time series menjadi salah satu variabel pada perhitungan Linear Programming. Kombinasi kedua metode ini tepat mewakili dan mendukung pengambilan keputusan penentuan jadwal penanaman dalam kegiatan pertanian hortikultura dengan menggunakan variabel pendukung, data permintaan, data produksi, data jumlah tenaga kerja, data luas lahan, data keuntungan produksi, data jumlah bibit dan data lama tanam, studi kasus yang digunakan adalah tanaman jamur dengan pengambilan data di “Rumah Jamur”. Sistem informasi perencanaan produksi dan penjadwalan pola tanam ini dapat memberikan rekomendasi pola tanam dan jumlah jamur yang harus ditanam dalam satu periode oleh pemilik “Rumah Jamur”, siklus hidup jamur dalam satu periode adalah empat bulan, jumlah penanaman disesuaikan dengan jumlah permintaan yang ada yang sebelumnya telah diprediksi dengan menggunakan fuzzy time series, hasil menunjukan dari empat skenario selang tanam didapatkan nilai pada skenario pertama jarak penanaman satu bulan Rp 5.327.266,00, pada skenario kedua, jarak penanaman dua bulan Rp 6.426.950,00, nilai skenario ketiga, jarak penanaman tiga bulan dengan nilai Rp 11.200.000,00, dan jarak penanaman empat bulan dengan nilai Rp 8.742.400,00 berdasarkan hasil skenario satu, dua, tiga dan empat didapatkan nilai optimal pada skenario ke tiga Rp 11.200.000,00 dengan penanaman bibit jamur tidak semua ditanam di awal, tetapi dipecah dengan penanaman bibit berikutnya diberi jarak tiga bulan sebanyak penanaman bulan pertama 775, kedua 972, ketiga 1172, dan keempat 836. Kata Kunci : Sistem Informasi, Perencanaan Produksi, Penjadwalan Pola Tanam, Fuzzy Time Series, Linear Programming. Research on production planning has been widely performed, This thesis presents the information system production planning and planting patterns scheduling faced by horticulture farmer by combining two methods. Fuzzy time series method used to predict demand. The result of fuzzy time series method will be one of variables in Linear Programming calculation. Combination of both of these methods appropriately represent and support decision making determination of planting schedule in horticulture farming activities by using variable data demand, production, amount of farmers, size of areas, production advantage, amount of seeds and age of the plant, the case study used is mushroom plant with data collection at “Rumah Jamur”. Production planning and planting patterns scheduling information system give planting patterns recommendation and how much mushroom must be planted in one periods by the owner of “Rumah Jamur”, age of mushroom in one period is four months, planting mushroom be adjusted with demand which had previously been predicted by using fuzzy time series, the result is show for four scenario hose planting the value of profit first scenario is Rp 5.327.266,00, second scenario is Rp 6.426.950,00, third scenario is Rp 11.200.000,00, and fourth scenario is Rp 8.742.400,00, based on four scenarios the optimal profit value in third scenario Rp 11.200.000,00 with planting of mushroom divided every three months, in the first month is 775 seeds, in the second month 972 seeds, in third month 1172 seeds and the last month is 836 seeds. Keywords : Production Planning; Information System; Scheduling Planting Patterns; Fuzzy Time Series; Linear Programming

OPTIMASI JUMLAH TOPIK KORPUS MENGGUNAKAN METODE LATENT DIRICHLET ALLOCATION (LDA)

Latent Dirichlet Allocation (LDA) merupakan sebuah model probabilitas untuk mengelompokkan topik yang tersembunyi di dalam dokumen dengan jumlah topik yang ditetapkan sebelumnya. Penentuan jumlah topik K yang kurang tepat akan mengakibatkan terbatasnya korelasi kata dengan topik, jumlah topik K terlalu besar atau terlalu kecil menyebabkan ketidakakuratan pengelompokkan topik pada pembentukan model training. Pada penelitian ini bertujuan untuk menentukan jumlah topik korpus yang optimal pada metode LDA dengan menggunakan pendekatan maximum likelihood dan Minimum Description Length (MDL). Proses eksperimen menggunakan artikel berita bahasa Indonesia dengan jumlah dokumen 25, 50, 90, 600 jumlah kata 3898, 7760, 13005, 4365. Hasil penelitian ini menunjukan bahwa pendekatan maximum likelihood dan MDL menghasilkan jumlah topik optimal yang sama. Jumlah topik optimal sangat dipengaruhi oleh parameter alfa dan beta. Banyaknya dokumen tidak mempengaruhi waktu komputasi, akan tetapi jumlah kata yang mempengaruhi waktu komputasi. Waktu komputasi untuk masing masing dataset tersebut adalah 2.9721 detik, 6.49637 detik, 13.2967 detik, dan 3.7152 detik. Penerapan hasil model optimasi jumlah topik LDA sebagai model klasifikasi, menghasilkan rata-rata nilai akurasi tertinggi 60% dengan parameter alfa 0.1 dan beta 0.001. Kata kunci : jumlah topik, likelihood, minimum discription length, latent dirichlet allocation, pengelompokan. Latent Dirichlet Allocation (LDA) is a probability model for grouping hidden topics in documents by the number of predefined topics. Incorrectly, determining the number of K topics will result in limited word correlation with topics, the number of K topics too large or small causes the inaccuracies of grouping topics in the formation of training models. This study aims to determine the optimal number of corpus topics in the LDA method using the maximum likelihood and Minimum Description Length (MDL) approach. The experimental process used an Indonesian news articles with the number of documents such 25, 50, 90, 600 with the number of words 3898, 7760, 13005, 4365. The results showed that the maximum likelihood and MDL approach resulted in the same number of optimal topics. The optimal number of topics is influenced by alpha and beta parameters. In addition, the number of documents does not affect the computation times, but the number of words that affect computing time. Computational times for each of those datasets are 2.9721, 6.49637, 13.2967, and 3.7152 seconds. The optimazation model have results a number of LDA topic as a classification model, this experiment shows highest average accuracy of 60% with alpha 0.1 and beta 0.001. Keywords : number of topic, likelihood, minimum discription length, latent dirichlet allocation, clustering

Perbandingan Metode Trust-region Dengan Metode Newton-raphson Pada Optimasi Fungsi Non Linier Tanpa Kendala

Optimization is the best decision of the objective functions for produce a satisfactory solution. Optimization of multi variables unconstraints is to optimize the the objective functions which contains multi variable function freely without any specific requirements that restrict its function. Trust-Region methods methods is used to optimize multi variables unconstraint , Trust-Region methods quadratic approach of optimizing non linear the objective functions with a certain radius as the limit of the step size according to the quality of the approach. Newton-Raphson methods is a root search method with the the objective functions approaches a point, where the objective functions has a derivative. In this final will be talking about Trust-Region methods will compared with Newton-Raphson methods, and rendered example problem in which only be solved using Trust-Region methods

Optimisation towards Latent Dirichlet Allocation: Its Topic Number and Collapsed Gibbs Sampling Inference Process

Latent Dirichlet Allocation (LDA) is a probability model for grouping hidden topics in documents by the number of predefined topics. If conducted incorrectly, determining the amount of K topics will result in limited word correlation with topics. Too large or too small number of K topics causes inaccuracies in grouping topics in the formation of training models. This study aims to determine the optimal number of corpus topics in the LDA method using the maximum likelihood and Minimum Description Length (MDL) approach. The experimental process uses Indonesian news articles with the number of documents at 25, 50, 90, and 600; in each document, the numbers of words are 3898, 7760, 13005, and 4365. The results show that the maximum likelihood and MDL approach result in the same number of optimal topics. The optimal number of topics is influenced by alpha and beta parameters. In addition, the number of documents does not affect the computation times but the number of words does. Computational times for each of those datasets are 2.9721, 6.49637, 13.2967, and 3.7152 seconds. The optimisation model has resulted in many LDA topics as a classification model. This experiment shows that the highest average accuracy is 61% with alpha 0.1 and beta 0.001