Pengelompokan Komentar Dataset Sentipol dengan Modified K-Means Clustering

Clustering is a technique in data mining thatgroups data sets into similar data clusters. One of thealgorithms that is commonly used for clustering is K-Means.However, the K-Means algorithm has several weaknesses, oneof them is the random factor in initial centroid selection, sothat cluster result is inconsistent even though it is tested withthe exact same data. The Modified K-Means algorithm focuseson selecting the initial centroid to overcome inconsistencies ofcluster results in the K-Means method. The test was conductedusing sentipol dataset and only focused on comment data.Furthermore, the specified number of clusters is 3 based on thenumber of existing comment labels (positive, negative, andneutral). According to testing result proves that Modified KMeans algorithm produces better purity value than K-Meansalgorithm. Modified K-Means algorithm produces average ofpurity value 0,42, while K-Means produces average of purityvalue 0,391. Meanwhile, from testing related to random factorsconducted 5 times with the same attributes and test data, theresults of the cluster on the Modified K-Means algorithm didnot change, so automatically the resulting purity value was alsothe same. Whereas in the K-Means algorithm, the clusterresults always change in each test, so the result of purity valueis also likely to change.

POSITIONING CITRA PKS DI MATA KONSTITUEN (SEBUAH PENDEKATAN BRAND ASSOCIATION TERHADAP KONSTITUEN PKS DI KELURAHAN JATI, KECAMATAN PULOGADUNG, JAKARTA TIMUR )

This research analyzes the Positioning of PKS image to its constituents in Jati Village, Pulogadung Sub-district. The positioning of this party image uses the theory of brand association and positioning theory. This research uses quantitative approach with survey method and cluster analysis. The largest cluster result shows that PKS image is still good and its positioning is the Da'wah Party.

Study on Nitrogen, Phosphor and Chemical Oxygen Demand of Differnt Categories of Aquaculture Lakes by Means of Principal Component Analysis, Factor Analysis and Cluster Analysis

The author measured TN, NH4+-N, NO2--N, NO3--N, TP, PO43--P and CODMn of the five aquaculture lakes that is, Liangzi Lake, Futou Lake, Chaipo Lake, Nanhu Lake and Yezhi Lake, in Wuhan in 2007, and had analyzed the results by means of Principal Component Analysis (PCA), Factor Analysis (FA) and Cluster Analysis (CA). The PCA result showed that the two principal components were nutrient factor (including TN, NO2--N, NO3--N, TP, PO43--P and CODMn) and ammonium absorption factor (NH4+-N), with their function expression integrated. The results of FA and PCA were in conformity, with their factor score function expression integrated. R-mode cluster result indicated that the seven hydrochemical indexes could be divided into four categories, i.e. NO3 - -N, TP, TN and NO2--N make up of one category, and another three categories were PO43--P, NH4+-N and CODMn, respectively. Q-mode cluster result showed that the five lakes can be divided into two categories, Futou Lake and Liangzi Lake is one category, and Chaipo Lake, Yezhi Lake and Nanhu Lake is the other, which has been caused by urban waste water, domestic sewage and aquaculture production.

Molecular Dynamics Simulation of Thermal Conductivity and Viscosity of a Nanofluid: Effect of Nanoparticle Aggregation

Effect of nanoparticle aggregation on the thermal conductivity and viscosity of nanofluids is studied by molecular dynamics simulation in this work. Thermal conductivity and viscosity of the nanofluid are calculated using Green-Kubo method and results show that the nanoparticle aggregation induces a significant enhancement of thermal conductivity in nanofluid, while the increase of viscosity is moderate. The results also indicate that different configurations of the nanoparticle cluster result in different enhancements of thermal conductivity and increase of viscosity in the nanofluid. The differences between equilibrium molecular dynamics (EMD) approach and non-equilibrium molecular dynamics (NEMD) approach in obtaining the thermophysical properties of nanofluids are also discussed.