Sieva: K-Means And 360-Degree Feedback For Sugarcane Harvest Evaluation at PTPN II Kwala Madu

Muhammad Zarwin Syahputra; Raissa Amanda Putri; Muhamad Alda

doi:10.55537/cosie.v5i4.1966

Authors

Muhammad Zarwin Syahputra Universitas Islam Negeri Sumatera Utara
Raissa Amanda Putri Universitas Islam Negeri Sumatera Utara
Muhamad Alda Universitas Islam Negeri Sumatera Utara

DOI:

https://doi.org/10.55537/cosie.v5i4.1966

Keywords:

Sugarcane harvest evaluation, Information system, K-Means Clustering, 360-Degree Feedback

Abstract

Sugarcane harvest yields at PT Perkebunan Nusantara II (PTPN II) Kwala Madu often fall short of targets, and harvest evaluation is still largely manual, slow, and open to subjective bias. This study developed SIEVA, a web-based harvest evaluation information system that combines two evaluation perspectives: K-Means clustering of plot-level average harvest results (outcome-based) and 360-Degree Feedback on employee competencies (competency-based). The system was built with the Rapid Application Development model using PHP and MySQL, based on weighing-station records and employee assessment data collected from September 2022 to April 2023. K-Means with three clusters (good, fair, and poor) grouped 30 plots into 10, 11, and 9 plots after five iterations, with final centroids of 5,812, 5,278, and 4,601 [VERIFY FROM SIEVA OUTPUT: the reported centroids and the Silhouette and Davies–Bouldin indices must be checked against the final system output before submission.]. Black-box testing by one expert across ten function groups indicated that the functions performed as specified. The contribution is applied: an information-system model that digitalizes outcome-based and competencybased evaluation in plantation operations, not a new algorithm. Quantitative validation of the cluster structure, tests with more users, and evidence of the system's effect on management decisions remain future work.

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