Measurement ◽  
2021 ◽  
Vol 177 ◽  
pp. 109279
Author(s):  
Huachen Jiang ◽  
Chunfeng Wan ◽  
Kang Yang ◽  
Youliang Ding ◽  
Songtao Xue

2021 ◽  
Vol 4 (1) ◽  
pp. 10
Author(s):  
Nurfitria Ambarwati ◽  
Gustaman Saragih

<p>Abstract: The purpose of this research at finding out empirical evidence concerning whether clustering technique and vocabulary mastery are effective or not for the student to write recount text. The research methodology used is an experiment. The sampling technique uses random sampling at Private Vocational High Schools in East Jakarta. Data collection is obtained by testing their vocabulary mastery and writing skills. Data analysis to test hypothesis is two ways ANOVA. The research results conclude 1) There is a significant effect of the clustering technique on students’ writing skills with the value (Sig) being 0.000 &lt; 0.05 and F0 = 36.740. 2) A significant effect of vocabulary mastery on students’ writing skills with the value (Sig) is 0.000 &lt; 0.05 and F0 = 72.161. 3) There is a significant interactive effect of clustering technique and vocabulary mastery on students’ writing skills. With the value (sig) is 0.018 &lt; 0.05 and F0 = 5.921. Then all of H0 is rejected, and H1 is accepted. So, there is the effect of clustering technique and vocabulary mastery on students’ writing skills in recount text.<br />Keywords: Clustering Technique, Vocabulary Mastery, Writing Skill, Recount Text</p>


Author(s):  
F. Segovia ◽  
J. M. Gorriz ◽  
J. Ramirez ◽  
D. Salas-Gonzalez ◽  
I. A. Illan ◽  
...  

2017 ◽  
Vol 3 (1) ◽  
pp. 96
Author(s):  
I Nengah Laba

This study aimed to analyze the effectiveness of developing students’ essay writing about tourism topics through word clustering technique. The subjects under study were students sitting on semester 6 at Sekolah Tinggi Pariwisata Bali Internasional (International Bali Institute of Tourism). This classroom action research (CAR) was basically triggered by the fact that the subjects under study have still low capability in English essay writing about Tourism Topics. This study made use of pre-test or initial reflection (IR) and post-test research design using descriptive analysis. There were two cycles in this CAR and each cycle consists of four successive sessions. The IR was intended to establish the real pre-existing English essay writing capability of the subject under study. The Mean of the Pre-test or IR score obtained by the subjects under study was 3.63. The Mean of the Post-test or R scores both in Cycle I and in Cycle II showed an increasing figure which is 4.12 in S1, 5.06 in S2, 5.78 in S3, 6.34 in S4, 6.76 in S5, 7.14 in S6, 7.44 in S7 and 7.82 in S8. These figures showed that the Mean of the Post-test or R scores in each session was much higher than the Mean of Pre-test or IR in essay writing. The data analysis further led to the computation of the Grand Mean score for both Cycles I and Cycle II. The computation of the Grand Mean resulted in Cycle I (XI) was 5.32. The computation of the Grand Mean resulted in Cycle II was 7.29. The difference Mean of Cycle I and cycle II is 1.97 (XII – XI = 7.29 – 5.32 = 1.97). These research findings revealed that developing students’ essay writing about the tourism topics through word clustering technique was very effective. 


Author(s):  
K. Nafees Ahmed ◽  
T. Abdul Razak

<p>Information extraction from data is one of the key necessities for data analysis. Unsupervised nature of data leads to complex computational methods for analysis. This paper presents a density based spatial clustering technique integrated with one-class Support Vector Machine (SVM), a machine learning technique for noise reduction, a modified variant of DBSCAN called Noise Reduced DBSCAN (NRDBSCAN). Analysis of DBSCAN exhibits its major requirement of accurate thresholds, absence of which yields suboptimal results. However, identifying accurate threshold settings is unattainable. Noise is one of the major side-effects of the threshold gap. The proposed work reduces noise by integrating a machine learning classifier into the operation structure of DBSCAN. The Experimental results indicate high homogeneity levels in the clustering process.</p>


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