Judul: Penelusuran Artikel Kasus Tindak Pidana Korupsi (Tipikor) Menggunakan Metode Self Organizing Maps (SOM)
Abstrak: Nowadays, there are so many corruption articles in the internet with so many topics. Pusat Teknologi Informasi dan Komunikasi Badan Pengkajian dan Penerapan Teknologi (PTIK BPPT) conducts a research to make a system to cluster the corruption articles with related topic based on the keywords without changing the topology of the articles and can be visualized to the two dimension maps with information of related article and share rate. Self Organizing Maps (SOM) is the clustering method that is being used in the research. SOM can cluster massive article with high dimension to lower dimension without changing its topology. In this research, tools for measuring the clusters are Vector Quantization Error (VQE), cohesion, and separation. Method of computation cohesion and separation that used in this research is prototype-based cohesion and separation that focus in computing on the centroid. Finally, the conclusion of this research is that SOM clusters the articles based on the similarity of the topic. Neighbouring clusters mean that they have resemblance topics. Clusters that far-set from the other clusters means that they have less or do not have resemblance each other.
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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: Reproductive Characteristics of the Rainbow Water Snake,  Enhydris enhydris (Schneider, 1799) (Familia: Homalopsidae) for Sustainable  Harvesting Recommendations
Abstrak: Ular-air pelangi (Enhydris enhydris) merupakan ular akuatik bertubuh gempal, yang memiliki persebaran cukup luas yakni dari Asia Selatan hingga Asia Tenggara. Ular air-pelangi bereproduksi secara vivipar dan dapat ditemukan di Indonesia mulai dari Sumatera hingga Sulawesi. Ular ini tidak toleran terhadap air asin, sehingga seluruh populasinya dibatasi oleh laut. Ular ini merupakan komiditas ekspor yang dipanen dari alam untuk dimanfaatkan daging dan kulitnya. Oleh karena itu, dikhawatirkan populasi Ular air-pelangi di alam terancam, akibat pemanenan dalam jumlah besar yang terus dilakukan. Informasi biologi reproduksi ular pelangi masih sangat minim. Informasi ini bersifat fundamen untuk pengelolaan pemanenan berkelanjutan. Penelitian ini bertujuan untuk mengetahui karakteristik reproduksi Ular-air pelangi berdasarkan ukuran dan kondisi reproduksi ular-ular yang dipanen di rumah pemotongan ular di Cirebon, Jawa Barat. Hasil dari penelitian ini dapat menjadi dasar penentuan ukuran yang layak panen dari ukuran minimum individu reproduktif Ular-air pelangi di Indonesia. Sampel Ular-air pelangi ditangkap dari alam dan diproses di rumah pemotongan di Cirebon, Jawa Barat. Penelitian ini dimulai dengan pengambilan data, meliputi ukuran SVL (Snout-Vent Length / Panjang moncong-kloaka) (cm), TL (tail length / panjang ekor) (cm), massa tubuh (gram), dan skor lemak. Khusus pada individu jantan, diamati pula skor vas deferens, panjang dan lebar testis (mm). Kemudian, pada individu betina, diamati kondisi dan lebar oviduk (mm), lebar folikel primer (mm), keberadaan dan diameter folikel sekunder (mm), keberadaan dan diameter embrio (mm), serta keberadaan corpus luteum dan corpus albicans. Data yang telah dikumpulkan kemudian dikelompokkan berdasarkan karakteristik reproduksi untuk dianalisis secara deskriptif. Hasil penelitian menunjukkan bahwa Ular-air pelangi jantan reproduktif memiliki SVL berkisar antara 38.36±5.44 cm. Kedua testis individu jantan reproduktif menjadi turgid dengan panjang minimum 10.43±3.31 mm dan lebar minimum 3.47±1.18 mm. Pada individu betina, ukuran SVL 39.57±6.85 cm. Individu betina yang pernah melakukan reproduksi sebelumnya berukuran SVL minimum 44.77±7.26 cm. Oviduk individu betina yang sudah matang mengalami penebalan dan dengan lebar minimum 4.93±2.46 mm. Ditemukan juga alokasi folikel sekunder dan embrio yang asimetri di ovarium dan oviduk individu betina yang secara signifikan mengalokasikan lebih banyak folikel sekunder di ovarium dan embrio di oviduk kanan dengan rata-rata rasio (kanan/kiri) 1.45±0.91 dan 1.58±1.15. Hasil dari penelitian ini menunjukkan bahwa pemanenan Ular-air pelangi dapat dilakukan pada individu-individu yang pernah berreproduksi. Individu jantan dengan SVL > 44 cm dan betina dengan > 53 cm layak panen. Persyaratan tangkap yang didasari ketentuan SVL ini dapat mendukung pemanenan yang berkelanjutan yaitu dengan memanen individu-individu dewasa yang telah memberi kontribusi terhadap populasi pada ukuran tersebut.
Keyword: Enhydris enhydris, Ular-air, Pemanenan Berkelanjutan, Reproduksi