Repository Universitas Pakuan

Detail Karya Ilmiah Dosen

Boldson Herdianto Situmorang, Prihastuti Harsani

Judul : Classification System of Indonesian Language Skripsi Documents in Computer Science Department Using K-Means Algorithm

Abstrak :
Thesis is a scientific paper created by the student as a final requirement on his final academic education to earn a bachelor's degree. Students of the Computer Science Department at Pakuan University are faced with the difficulty of finding the previous thesis references to determine the desired thesis theme because the clustering of the thesis documents is set based on the writing year only and not based on the theme classifications which includes Software Engineering, Hardware Programming, Artificial Intelligence, and Network Computer. A computer based system will be developed where the data in the Thesis document will be processed through text pre-processing which aims to convert unstructured document data into structured so that it can be read by the system, then grouped using K-Means Algorithm.

Tahun : 2019 Media Publikasi : Jurnal Internasional

Kategori : Jurnal No/Vol/Tahun : 2 / 8 / 2019

ISSN/ISBN : 2277-3878

PTN/S : Universitas Pakuan, UTHM, IPB, Universitas Terbuka Program Studi : ILMU KOMPUTER

Bibliography :
1. S. Kannan and V. Gurusamy. “Preprocessing Techniques for Text Mining.” International Journal of Computer Science & Communication Networks, vol. 5 (1), pp. 7-16.

2. H. L. Sari, D. Suranti and L. N. Zulita, “Implementation of k-means clustering method for electronic learning model.” Journal of Physics: Conf. Series, vol. 930, 012021, 2017.

3. R. Kaur and A. Kaur, “Text document clustering and classification using k-means algorithm and neural networks” Indian Journal of Science and Technology, vol 9 (40), 78-96, 2016.

4. A. ŠiliÄ‡, M. F. Moens, L. Å½makand B. D. BašiÄ‡. “Comparing document classification schemes using k-means clustering.” In International Conference on Knowledge-Based and Intelligent Information and Engineering Systems. pp. 615-624, 2008.

5. R. Malviya and P. Jain. “A Novel Text Categorization Approach based on K-means and Support Vector Machine.” International Journal of Computer Applications, vol. 130 (14), pp. 1-7, 2015.

6. S. T. Deokar. “Text Documents Clustering using K Means Algorithm”. International Journal of Technology and Engineering Science, vol 1 (4), pp. 282-286, 2013.

7. E. Laxmi Lydia, P. Govindaswamy, SK. Lakhsmanaprabu, D. Ramya. “Document Clustering Based On Text Mining K-Means Algorithm Using Euclidean Distance Similarity”. Journal of Advanced Research in Dynamical and Control System, vol 10 (2), pp. 208-214, 2018.

URL : https://www.ijrte.org/wp-content/uploads/papers/v8i2S7/B10330782S719.pdf

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Detail Karya Ilmiah Dosen

Boldson Herdianto Situmorang, Prihastuti Harsani

Judul	:	Classification System of Indonesian Language Skripsi Documents in Computer Science Department Using K-Means Algorithm
Abstrak	:	Thesis is a scientific paper created by the student as a final requirement on his final academic education to earn a bachelor's degree. Students of the Computer Science Department at Pakuan University are faced with the difficulty of finding the previous thesis references to determine the desired thesis theme because the clustering of the thesis documents is set based on the writing year only and not based on the theme classifications which includes Software Engineering, Hardware Programming, Artificial Intelligence, and Network Computer. A computer based system will be developed where the data in the Thesis document will be processed through text pre-processing which aims to convert unstructured document data into structured so that it can be read by the system, then grouped using K-Means Algorithm.
Tahun	:	2019	Media Publikasi	:	Jurnal Internasional
Kategori	:	Jurnal	No/Vol/Tahun	:	2 / 8 / 2019
ISSN/ISBN	:	2277-3878
PTN/S	:	Universitas Pakuan, UTHM, IPB, Universitas Terbuka	Program Studi	:	ILMU KOMPUTER
Bibliography	:	1. S. Kannan and V. Gurusamy. “Preprocessing Techniques for Text Mining.” International Journal of Computer Science & Communication Networks, vol. 5 (1), pp. 7-16. 2. H. L. Sari, D. Suranti and L. N. Zulita, “Implementation of k-means clustering method for electronic learning model.” Journal of Physics: Conf. Series, vol. 930, 012021, 2017. 3. R. Kaur and A. Kaur, “Text document clustering and classification using k-means algorithm and neural networks” Indian Journal of Science and Technology, vol 9 (40), 78-96, 2016. 4. A. ŠiliÄ‡, M. F. Moens, L. Å½makand B. D. BašiÄ‡. “Comparing document classification schemes using k-means clustering.” In International Conference on Knowledge-Based and Intelligent Information and Engineering Systems. pp. 615-624, 2008. 5. R. Malviya and P. Jain. “A Novel Text Categorization Approach based on K-means and Support Vector Machine.” International Journal of Computer Applications, vol. 130 (14), pp. 1-7, 2015. 6. S. T. Deokar. “Text Documents Clustering using K Means Algorithm”. International Journal of Technology and Engineering Science, vol 1 (4), pp. 282-286, 2013. 7. E. Laxmi Lydia, P. Govindaswamy, SK. Lakhsmanaprabu, D. Ramya. “Document Clustering Based On Text Mining K-Means Algorithm Using Euclidean Distance Similarity”. Journal of Advanced Research in Dynamical and Control System, vol 10 (2), pp. 208-214, 2018.
URL	:	https://www.ijrte.org/wp-content/uploads/papers/v8i2S7/B10330782S719.pdf