ANALISIS DATA PENILAIAN KINERJA PEKERJA BUILDING MAINTENANCE MENGGUNAKAN METODE KLASIFIKASI DENGAN ALGORITMA DECISION TREE (STUDI KASUS: PT HAREWA)

APRIANI, YUNITA RAHAYU (2023) ANALISIS DATA PENILAIAN KINERJA PEKERJA BUILDING MAINTENANCE MENGGUNAKAN METODE KLASIFIKASI DENGAN ALGORITMA DECISION TREE (STUDI KASUS: PT HAREWA). S1 thesis, Universitas Mercu Buana Bekasi.

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Abstract

Dalam pemeliharaan bangunan, kegagalan memahami kondisi ketidakpastian dapat berpotensi menimbulkan risiko yang akan mempengaruhi tujuan proyek konstruksi yaitu, pada biaya yang optimal namun dengan kualitas yang sesuai terhadap konsep dan spesifikasi proyek yang diinginkan serta waktu pelaksanaan yang tepat. Kualitas hasil pekerja yang melaksanakan tugas dan tanggung jawabnya dapat dilakukan penilaian kinerja pekerja. Dari penilaian kinerja diharapkan, pekerja dapat bekerja dengan baik sehingga membantu perusahaan mencapai tujuan organisasi. PT Harewa merupakan perusahaan yang bergerak di bidang jasa building maintenance. Adanya peranan penting pekerja lapangan ketika dilakukannya proses pemeliharaan bangunan akan berdampak pada kepercayaan customer maupun perusahaan dengan kualitas pekerja. Penelitian ini untuk menganalisis data penilaian kinerja pekerja building maintenance menggunakan metode klasifikasi dengan algoritma Decision Tree bertujuan untuk mengetahui aspek variable dalam meningkatkan kinerja pekerja building maintenance sehingga memberikan hasil keputusan analisis data penilaian kinerja pekerja dengan pemanfaatan algoritma Decision Tree. Berdasarkan hasil dari model Decision Tree, variable yang perlu dilakukan peningkatan kinerja yaitu variable work quality yang nilai rata-rata kurang dari sama dengan 0.300, variable productivity yang kurang dari sama dengan 0.417, variable dependability yang kurang dari sama dengan 0.417. Performance accuracy dalam pengoptimalisasi pemodelan kinerja klasifikasi algoritma Decision Tree mendapati nilai accuracy sebesar 77.50%, precision tertinggi 82.28%, dan recall tertinggi 83.33%. Dengan demikian, pemanfaatan algoritma Decision Tree dapat membuat hasil keputusan analisis data penilaian kinerja pekerja building maintenance sebagai penunjang peningkatan kinerja pekerja building maintenance dalam kualitas pengerjaan building maintenance yang akan mendatang. Kata kunci: Building Maintenance, Data Mining, Decision Tree, Kinerja Pekerja, Klasifikasi. In building maintenance, failure to understand uncertain conditions can potentially lead to risks that will affect the objectives of construction projects, namely, at optimal costs but with quality according to the desired concept and project specifications and the right time of implementation. The quality of results workers carrying out their duties and responsibilities can be assessed by employee performance. The expected performance appraisal, workers can work well so as to help the company achieve organizational goals. PT Harewa is a company engaged in building maintenance services. The important role of field workers when carrying out the building maintenance process will have an impact on customer and company confidence in the quality of workers. This research is analyze the performance appraisal data of building maintenance workers using the classification method with the Decision Tree algorithm. Based on the results of the Decision Tree model, the variables that need to be improved are work quality variables with an average value of less than 0.300, productivity variables less than 0.417, dependability variables less than 0.417. Performance accuracy in optimizing performance modeling of the Decision Tree algorithm classification found an accuracy value of 77.50%, the highest precision was 82.28%, and the highest recall was 83.33%. Thus, the Decision Tree algorithm can produce decision results for analyzing data on the performance appraisal of building maintenance workers as a support for improving the performance of building maintenance workers in the quality of future building maintenance work. Keywords: Building Maintenance, Data Mining, Decision Tree, Worker Performance, Classification.

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