ANALISIS KINERJA PEGAWAI PUSBINDIKLAT PENELITI LIPI BERDASARKAN POLA PEMANFAATAN INTERNET MELALUI PENDEKATAN WEB USAGE MINING

Main Article Content

Sutrisno Heru Sukoco
Imas Sukaesih Sitanggang
Heru Sukoco

Abstract

Pengukuran kinerja pegawai dalam penggunaan layanan internet dapat dilakukan sebagai bagian dari penilaian kinerja. Pendekatan web usage mining melalui pengamatan rekam jejak akses internet yang tersimpan pada proxy server merupakan salah satu cara yang dapat diterapkan untuk memahami perilaku pengguna. Penelitian ini bertujuan untuk mendapatkan gambaran perilaku pegawai Pusbindiklat Peneliti LIPI dalam memanfaatkan layanan internet, mengukur level produktivitas pegawai berdasarkan lama waktu akses terhadap situs yang tidak mendukung pekerjaan dan memetakan kategori situs yang diakses apakah medukung tugas fungsi jabatannya. Penerapan algoritme clustering K-Means digunakan untuk memudahkan memahami pola akses pengguna. Data yang digunakan adalah log proxy server dan nilai prilaku pegawai Pusbindiklat Peneliti LIPI  periode Agustus-Desember 2016. Hasil penelitian menunjukkan pola pemanfaatan internet oleh pegawai Pusbindiklat Peneliti LIPI belum sepenuhnya mendukung tugas fungsi jabatannya. Sekitar 83% pegawai menggunakan internet untuk mengakses situs yang tidak mendukung pekerjaan berada pada level rendah (0-4 jam per minggu). Berdasarkan hasil tersebut dapat disimpulkan bahwa prilaku penggunaan internet yang dilakukan pegawai Pusbindiklat Peneliti LIPI  tidak mempengaruhi produktivitas secara signifikan.

Abstract

Measurement of employee performance in the use of internet services can be conducted as part of employee’s performance target. Web usage mining approach through observation of internet access records stored in the proxy server can be applied in understanding user behavior. This study aims to obtain an overview of employee behavior in utilizing internet services in Pusbindiklat Peneliti LIPI, measure the level of employee productivity based on the length of time access to sites that do not support the work and map the category of sites accessed to the task dutyof employee.  K-Means clustering algorithm is used to group  user access patterns. The data used are proxy server logs and employee’s performance target in Pusbindiklat Peneliti LIPI  in period of August-December 2016. The results shows that  the pattern of Internet use by employees Pusbindiklat Peneliti LIPI  do not fully support the job function. About 83% of employees use the internet to access sites do not support jobs at low level access (ranging from 0-4 hours per week). Based on these results, it can be concluded that the behavior of internet use by employees of Pusbindiklat Peneliti LIPI does not affect their productivity significantly.

 

Keywords: clustering, K-Means, log proxy server, performance of employees, web usage mining

Article Details

Section
Informatics
Author Biography

Sutrisno Heru Sukoco, Program Studi Magister Ilmu Komputer, Institut Pertanian Bogor

Pusbindiklat Peneliti LIPI

Jl. Raya Bogor KM 46 Cibinong-Bogor

References

Cadez, I., Heckerman, D., Meek, C., Smyth, P., & White, S. (2003). Model-Based Clustering and Visualization of Navigation Patterns on aWeb Site. Data Mining and Knowledge Discovery, 7, 399–424.

Chitraa, V., & Davamani, A. S. (2010). A Survey on Preprocessing Methods for Web Usage Data. International Journal of Computer Science and Information Security, 7(3), 78–83. https://doi.org/10.2200/S00191ED1V01Y200904ICR006

Chitraa, V., & Thanamani, A. S. (2012). An Enhanced Clustering Technique for Web Usage Mining. International Journal of Engineering Research & Technology (IJERT), 1(4), 1–5.

Coker, B. L. S. (2011). Freedom to surf: The positive effects of workplace Internet leisure browsing. New Technology, Work and Employment, 26(3), 238–247. https://doi.org/10.1111/j.1468-005X.2011.00272.x

Dong, D. (2009). Exploration on Web Usage Mining and Its Application. Analysis, 1–4. https://doi.org/10.1109/IWISA.2009.5072860

Fathonah, N., & Hartijasti, Y. (2014). the Influence of Perceived Organizational Injustice Towards Workplace Personal Web Usage and Work Productivity in Indonesia. South East Asian Journal of Management, 8(2), 151–166.

Kerkhofs, J., Vanhoof, K., & Pannemans, D. (2001). Web usage mining on proxy servers: a case study. Proceedings of Data Mining for Marketing Applications Workshop at ECML/PKDD 2001, September 3-7 2001, Freiburg (Germany).

Kim, S. J., & Byrne, S. (2011). Conceptualizing personal web usage in work contexts: A preliminary framework. Computers in Human Behavior, 27(6), 2271–2283. https://doi.org/10.1016/j.chb.2011.07.006

Lüderitz, S. (2006). Pre-processing of webserver logs for data mining. Berlin. Diakses dari https://people.cs.kuleuven.be/~bettina.berendt/teaching/2007w/adb/Lecture/OtherSlides/luederitz-presentation1-slides_2006_07_10.pdf tanggal 15 September 2016

Nithya, P., & Sumathi, P. (2012). Novel Pre-Processing Technique for Web Log Mining by Removing Global Noise , Cookies and Web Robots. International Journal of Computer Applications, 53(17), 1–6.

Pamutha, T., Chimphlee, S., Kimpan, C., & Sanguansat, P. (2012). Data Preprocessing on Web Server Log Files for Mining Users Access Patterns. International Journal of Research and Reviews in Wireless Communications (IJRRWC), 2(2), 92–98.

Roiha, N. U. (2017). Segmentasi Pengguna Web Menggunakan Metode Genetic K-Means Algorithm. Tesis. Institut Teknologi Sepuluh November.

Weinreich, H., Obendorf, H., & Herder, E. (2006). Data cleaning methods for client and proxy logs. Workshop on Logging Traces of Web Activity: The Mechanics of Data Collection; 2006 Mei 23; Edinburgh (GB): Dalhousie University.

Xu, J., & Liu, H. (2010). Web User Clustering Analysis Based on K-means Algorithm. Proceedings of the International Conference on Information Networking and Automation (ICINA), 2, V2-6-V2-9. https://doi.org/10.1109/ICINA.2010.5636772

Yusriani, E., & K. Suprapto, Y. (2016). Pemodelan Prediksi Pola Akses Website Pemerintah menggunakan Classification via Regression. Jurnal Masyarakat Telematika Dan Informasi, 7(1), 1–12.

Zhang, Y., Dai, L., & Zhou, Z.-J. (2010). A New Perspective of Web Usage Mining: Using Enterprise Proxy Log. 2010 International Conference on Web Information Systems and Mining, 38–42. https://doi.org/10.1109/WISM.2010.20

Zubi, Z. S., Saleh, M., & Raiani, E. (2014). Using Web Logs Dataset via Web Mining for User Behavior Understanding. International Journal of Computers and Communications, 8, 103–111.