Judul: Penerapan SOM untuk Pengenalan Nada pada Angklung Modern
Abstrak: Angklung is an Indonesian cultural heritage from West Java and has been developed following the development of modern music. The purpose of this research is to recognize the tone of angklung by using self-organizing maps (SOM) and mel-frequency cepstrum coefficients (MFCC) feature extraction. The used data were 300 tones of angklung that were played by staccato of 12 diatonic tones, each tone by 25 voices. Tones data were recorded for 3 seconds with some staccato in play. The initial process was making the data into one staccato tone, normalized, and then its characteristics were extracted with 26 cepstral coefficients MFCC. Distribution of training and test data was conducted randomly by the composition of 19 training data and 5 test data for each tone. This research conducted three repetitions on radius 0, 1, and 2 with training data, test data and learning rate of 10 parameter values at different SOM in each of their repetition. The result shows that the best accuracy obtained was 98.33% at a radius of 1 in repetition 1 and 3, as well as at a radius of 2 in repetition 1 with a common learning rate in both radius, 0.0090
Keyword: SOM, MFCC, diatonic tones, clustering, angklung

Judul: Kajian Self-Organizing Maps (SOM) dalam pengelompokan objek (studi kasus: pengelompokan desa/kelurahan di Kabupaten Wajo Sulawesi Selatan)
Abstrak: Clustering is a process of classifying objects into groups which have similarity. The result of clustering will show that objects in one cluster will be more homogeneous than others. There are two methods in classic clustering analysis i.e. hierarchical cluster method and non-hierarchical cluster method. Determination of the number of clusters which formed by them is done subjectively. The cluster other methods also developed by using artificial intelligence. Artificial neural network is an information processing paradigm that inspired by the biology systems, it is neuron. Like brain which process information. Self-organizing maps (SOM) is one of the topology of Unsupervised Artificial Neural Network (Unsupervised ANN) which process does not require monitoring in his training. Application clustering using SOM algorithm is expected to be used as a tool to analyze the data in order to obtain the characteristics of the data that will be grouped. Clustering is used to group the data naturally without based on the specific class target. In this study, SOM compared with clustering method with large data sizes, it was two-step cluster. According to Bacher (2004), two-step cluster method (TSC) was a cluster method which can resolve the problem clustering measurement scale, especially for large data with variables which have categorical and numerical data types. Performance clustering SOM and two-step cluster method compared by the simulation data, afterwards, applying the method of SOM on clustering villages/urbans in Wajo regency, South Sulawesi. The data in this study consisted of two sources i.e. simulated data and secondary data. Simulated data was generated data multivariate distribution (μ,Ʃ) which useful to measure the performance of two-step cluster method and SOM in classifying an object. Secondary data, which used in this study, BPS’s data in Wajo regency, South Sulawesi, was Village Potential Data (VPD) in 2011. Simulation data was the generated data numeric type (M) which consisted of three forms of the population i.e. a). a population consisted of three clusters were clearly separated, b). a population which consisted of three clusters of overlapping (overlap) each other in small numbers, and c). a population that consisted of three clusters of overlapping (overlap) each other in large numbers. The results of methods SOM and TSC showed that simulation data has the good ability to classify data, however, TSC provides better clustering results for large data sizes than SOM. In addition, it is also showed that the larger the number of data, the misclassification of SOM would become larger, nevertheless, the changes were relatively smaller. In the other hand, the larger the number of data the misclassification of TSC method was become smaller. Secondary data, which used in this study, written documentation and identification of used variables about areas/villages in Wajo regency, South Sulawesi, was Village Potential Data (VPD) in 2011 i.e. : X1 (total population), X2 (family farm), X3 (family farm laborer), X4 (family power user), X5 (fuel for daily cooking), X6 (educational facilities), X7 (health personnel), X8 (population mortality). Objects in this research were applied around the village/urbans in Wajo district. The results of clustering with SOM method, village/urbans in the Wajo regency produced
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Judul: Penyakit transmissible gastroenteritas oleh virus corona pada babi
Abstrak: Trasnmissible Gastroenteritis pada babi oleh virus merupakan penyakit infeksius yang ditemukan pertama kali oleh Doyle dan Hutchings tahun 1946 di Indiana USA. Sehubungan dengan tingginya mortalitas akibat pe- nyakit TGE pada anak-anak babi serta kerugian yang beru- pa penurunan berat badan secara drastis pada babi yang dewasa, maka perlu kiranya penyakit ini diperhitungkan bagi suatu usaha peternakan. Walaupun di Indonesia be- lum dilaporkan kejadian penyakit tersebut, tetapi kita perlu waspada terhadap kemungkinan masuknya penyakit bersama dengan babi-babi import yang banyak dijumpai di- peternakan besar di Indonesia. ...
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