Articles
<![CDATA[Modeling Text Independent Speaker Identification with Vector Quantization ]]>
<![CDATA[Syeiva Nurul Desylvia]]>;
<![CDATA[Agus Buono]]>;
<![CDATA[Bib Paruhum Silalahi]]>
<![CDATA[ TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 15, No 1: March 2017 ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/telkomnika.v15i1.4656
<![CDATA[Speaker identification is one of the most important technology nowadays. Many fields such as bioinformatics and security are using speaker identification. Also, almost all electronic devices are using this technology too. Based on number of text, speaker identification divided into text dependent and text independent. On many fields, text independent is mostly used because number of text is unlimited. So, text independent is generally more challenging than text dependent. In this research, speaker identification text independent with Indonesian speaker data was modelled with Vector Quantization (VQ). In this research VQ with K-Means initialization was used. K-Means clustering also was used to initialize mean and Hierarchical Agglomerative Clustering was used to identify K value for VQ. The best VQ accuracy was 59.67% when k was 5. According to the result, Indonesian language could be modelled by VQ. This research can be developed using optimization method for VQ parameters such as Genetic Algorithm or Particle Swarm Optimization.]]>
<![CDATA[Optimization of Parallel K-means for Java Paddy Mapping Using Time-series Satelite Imagery ]]>
<![CDATA[Alvin Fatikhunnada]]>;
<![CDATA[Kudang Boro Seminar]]>;
<![CDATA[Liyantono Liyantono]]>;
<![CDATA[Mohamad Solahudin]]>;
<![CDATA[Agus Buono]]>
<![CDATA[ TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 3: June 2018 ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/telkomnika.v16i3.6876
<![CDATA[Spatiotemporal analysis of MODIS Vegetation Index Imagery widely used for vegetation seasonal mapping both on forest and agricultural site. In order to provide a long-terms of vegetation characteristic maps, a wide time-series images analysis is needed which require high-performance computer and also consumes a lot of energy resources. Meanwhile, for agriculture monitoring purpose in Indonesia, that analysis has to be employed gradually and endlessly to provide the latest condition of paddy field vegetation information. This research is aimed to develop a method to produce the optimized solution in classifying vegetation of paddy fields that diverse both spatial and temporal characteristics. The time-series EVI data from MODIS have been filtered using wavelet transform to reduce noise that caused by cloud. Sequential K-means and Parallel K-means unsupervised classification method were used in both CPU and GPU to find the efficient and the robust result. The developed method has been tested and implemented using the sample case of paddy fields in Java Island. The best system which can accommodate of the extend-ability, affordability, redundancy, energy-saving, maintainability indicators are ARM-based processor (Raspberry Pi), with the highest speed up of 8 and the efficiency of 60%.]]>
<![CDATA[Fuzzy-based Spectral Alignment for Correcting DNA Sequence from Next Generation Sequencer ]]>
<![CDATA[Kana Saputra S]]>;
<![CDATA[Wisnu Ananta Kusuma]]>;
<![CDATA[Agus Buono]]>
<![CDATA[ TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 2: June 2016 ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/telkomnika.v14i2.2395
<![CDATA[Next generation sequencing technology is able to generate short read in large numbers and in a relatively short in single running programs. Graph based DNA sequence assembly used to handle these big data in assembly step. The graph based DNA sequence assembly is very sensitive to DNA sequencing error. This problem could be solved by performing an error correction step before the assembly process. This research proposed fuzzy inference system (FIS) model based spectral alignment method which can detect and correct DNA sequencing error. The spectral alignment technique was implemented as a pre-processing step before the DNA sequence assembly process. The evaluation was conducted using Velvet assembler. The number of nodes yielded by the Velvet assembler become a measure of the success of error correction. The results shows that FIS model based spectral alignment created small number of nodes and therefore it successfully corrected the DNA reads.]]>
<![CDATA[Estimating Parameter of Nonlinear Bias Correction Method Using NSGA-II in Daily Precipitation Data ]]>
<![CDATA[Angga Wahyu Pratama]]>;
<![CDATA[Agus Buono]]>;
<![CDATA[Rahmat Hidayat]]>;
<![CDATA[Hastuadi Harsa]]>
<![CDATA[ TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 1: February 2018 ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/telkomnika.v16i1.6848
<![CDATA[Nonlinear (NL) method is the most effective bias correction method for correcting statistical bias when observation precipitation data can not be approximated using gamma distribution. Since NL method only adjusts mean and variance, it does not perform well in handling bias on quantile values. This paperpresents a scheme of NL method with additional condition aiming to mitigate bias on quantile values. Non-dominated Sorting Genetic Algorithm II (NSGA-II) was applied to estimate parameter of NL method. Furthermore, to investigate suitability of application of NSGA-II, we performed Single Objective Genetic Algorithm (SOGA) as a comparison. The experiment results revealed NSGA-II was suitable when solution of SOGA produced low fitness. Application of NSGA-II could minimize impact of daily bias correction on monthly precipitation. The proposed scheme successfully reduced biases on mean, variance, first and second quantile However, biases on third and fourth moment could not be handled robustly while biases on third quantile only reduced during dry months.]]>
<![CDATA[Purchase Recommendation and Product Inventory Management using Content Based Filtering with Sequential Pattern Mining Approach ]]>
<![CDATA[Aditya Cipta Raharja]]>;
<![CDATA[Imas Sukaesih Sitanggang]]>;
<![CDATA[Agus Buono]]>
<![CDATA[ Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol 3, No 4, November 2018 ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Malang ]]>
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DOI: 10.22219/kinetik.v3i4.663
<![CDATA[Today, the product sales at XYZ Bookstore are increase in accordance to the trend in society. In that case, high sales must be supported by good supply and on target. Product sold based on needs of consumers will make possibility to achieve high sales. Using the Sequential Pattern Mining approach, we can specify sales patterns of products in relation to another products. SPADE (Sequential Pattern Discovery using Equivalence classes) is an algorithm that can be used to find sequential patterns in a large database. This algorithm finds frequent sequences of the sales transaction data using database vertical and join process of the sequence. The results of SPADE algorithm is frequent sequences which are used to form the rules. Those can be used as predictors of other items that will be purchased by consumers in the future. The result of this study is a lot of unique sequence appears that can provide the best advice for Merchandiser Officer, for example, there are 1.468 sequences that prove the customer who bought the product in Children’s Book category will always bought the same thing in the others day. This research produce some recommendation, one of the recommendation is Children's Book category has a very high chance of being a Best Seller for a long time so that the purchasing officer on XYZ bookstore should ensure that the product's supply of the category is always safe throughout the year. It means SPADE is successfully used to provide the advice and Merchandiser Officer must ensure the stock of that product is always available to avoid Lost Sales.]]>
<![CDATA[Peningkatan Performansi Multi Objektif NSGA-II Dengan Operator Mutasi Adaptif Pada Kasus Portofolio Reksadana Saham ]]>
<![CDATA[Putri Yuli Utami]]>;
<![CDATA[Yandra Arkeman]]>;
<![CDATA[Agus Buono]]>;
<![CDATA[Irman Hermadi]]>
<![CDATA[ CYBERNETICS Vol 3, No 02 (2019): CYBERNETICS ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Pontianak ]]>
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DOI: 10.29406/cbn.v3i02.2194
<![CDATA[Non-dominated sorting genetic algorihm (NSGA-II) merupakan salah algoritma pencarian solusi optimal dengan mengurutkan solusi berdasarkan pareto-front untuk mengindentifikasi feasible solutions. Performansi algoritme NSGA-II sangat dipengaruhi oleh operator parameter. Salah satu parameter adalah operator mutasi yang memegang kendali untuk diversitas kandidat solusi. Pada riset ini operator mutasi dibuat adaptif dengan menggunakan distribusi probabilitas polinomial (parameter nm). Parameter ini mengontrol kekutatan mutasi dan mengubah nilai mutasi secara adaptif serta mengubah probabilitas mutasi secara dinamik untuk mengatur banyaknya gen yang mengalami mutasi. Berdasarkan hasil penelitian nilai standar deviasi mutasi non-adaptif lebih kecil daripada mutasi adaptif. Nilai standar deviasi merepresentasikan varians sehingga mutasi adaptif memiliki varians yang beragam dibandingkan dengan mutasi non-adaptif. Mutasi adaptif dapat meningkatkan diversitas kromosom sehingga mencapai konvergensi kromosom agar terhindar dari konvergensi dini dengan waktu komputasi yang lebih efektif. Pada kasus portofolio reksadana saham menghasilkan standar deviasi yang lebih besar sehingga solusi yang dihasilkan semakin beragam.]]>
<![CDATA[Downscaling Modeling Using Support Vector Regression for Rainfall Prediction ]]>
<![CDATA[Sanusi Sanusi]]>;
<![CDATA[Agus Buono]]>;
<![CDATA[Imas S Sitanggang]]>;
<![CDATA[Akhmad Faqih]]>
<![CDATA[ Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 8: August 2014 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijeecs.v12.i8.pp6423-6430
<![CDATA[Statistical downscaling is an effort to link global scale to local scale variable. It uses GCM model which usually used as a prime instrument in learning system of various climate. The purpose of this study is as a SD model by using SVR in order to predict the rainfall in dry season; a case study at Indramayu. Through the model of SD, SVR is created with linear kernel and RBF kernel. The results showed that the GCM models can be used to predict rainfall in the dry season. The best SVR model is obtained at Cikedung rain station in a linear kernel function with correlation 0.744 and RMSE 23.937, while the minimum prediction result is gained at Cidempet rain station with correlation 0.401 and RMSE 36.964. This accuracy is still not high, the selection of parameter values for each kernel function need to be done with other optimization techniques.]]>
<![CDATA[Optimization of Support Vector Regression using Genetic Algorithm and Particle Swarm Optimization for Rainfall Prediction in Dry Season ]]>
<![CDATA[Gita Adhani]]>;
<![CDATA[Agus Buono]]>;
<![CDATA[Akhmad Faqih]]>
<![CDATA[ Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 11: November 2014 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijeecs.v12.i11.pp7912-7919
<![CDATA[Support Vector Regression (SVR) is Support Vector Machine (SVM) is used for regression case. Regression method is one of prediction season method has been commonly used. SVR process requires kernel functions to transform the non-linear inputs into a high dimensional feature space. This research was conducted to predict rainfall in the dry season at 15 weather stations in Indramayu district. The basic method used in this study was Support Vector Regression (SVR) optimized by a hybrid algorithm GAPSO (Genetic Algorithm and Particle Swarm Optimization). SVR models created using Radial Basis Function (RBF) kernel. This hybrid technique incorporates concepts from GA and PSO and creates individuals new generation not only by crossover and mutation operation in GA, but also through the process of PSO. Predictors used were Indian Ocean Dipole (IOD) and NINO3.4 Sea Surface Temperature Anomaly (SSTA) data. This research obtained an SVR model with the highest correlation coefficient of 0.87 and NRMSE error value of 11.53 at Bulak station. Cikedung station has the lowest NMRSE error value of 0.78 and the correlation coefficient of 9.01.]]>
<![CDATA[Similarity Measurement for Speaker Identification Using Frequency of Vector Pairs ]]>
<![CDATA[Inggih Permana]]>;
<![CDATA[Agus Buono]]>;
<![CDATA[Bib Paruhum Silalahi]]>
<![CDATA[ Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 8: August 2014 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijeecs.v12.i8.pp6205-6210
<![CDATA[Similarity measurement is an important part of speaker identification. This study has modified the similarity measurement technique performed in previous studies. Previous studies used the sum of the smallest distance between the input vectors and the codebook vectors of a particular speaker. In this study, the technique has been modified by selecting a particular speaker codebook which has the highest frequency of vector pairs. Vector pair in this case is the smallest distance between the input vector and the vector in the codebook. This study used Mel Frequency Cepstral Coefficient (MFCC) as feature extraction, Self Organizing Map (SOM) as codebook maker and Euclidean as a measure of distance. The experimental results showed that the similarity measuring techniques proposed can improve the accuracy of speaker identification. In the MFCC coefficients 13, 15 and 20 the average accuracy of identification respectively increased as much as 0.61%, 0.98% and 1.27%.]]>
