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K-Means Clustering On Rice Harvest Data For Planting Season Recommendation In Subak Cepaka, Tabanan Dewi, Ni Made Cahyani; Hidayat, Ahmad Tri
Compiler Vol 14, No 2 (2025)
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/compiler.v14i2.3504

Abstract

The Subak farming system in Tabanan Regency, Bali, is vital as a primary rice granary but faces challenges in determining optimal planting patterns. Planting decisions based only on inherited experience often do not match climate conditions, reducing productivity and increasing crop failure risks. This study implements the K-Means Clustering algorithm on five years of historical rice harvest data (2020–2024) to generate accurate planting season recommendations. Monthly data were analyzed and grouped into three categories: rainy, dry, and transitional seasons. The clustering results were integrated into a mobile application that provides farmers with accessible recommendations through an interactive interface and visualization. The effectiveness of the clustering model was evaluated using the Silhouette Score, which indicated good separation and cohesion among clusters, while efficiency was assessed through processing time and algorithm simplicity, confirming that K-Means performed the task with minimal computational cost. This system enables farmers to make data-driven planting decisions, optimize productivity, and support sustainable food security in Bali.
Assessing the Impact of Face Recognition and QR Code-Based Attendance Systems on Payroll Processing and Business Efficiency Fuady, Aflah Firduas; Hidayat, Ahmad Tri
Journal of Information System and Education Development Vol. 3 No. 4 (2025): Journal Of Information System And Education Development
Publisher : Manna wa Salwa Foundation (Yayasan Manna wa Salwa)

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Abstract

Businesses have had to use new technologies to make their operations more efficient in the digital age, especially when it comes to managing their employees. This study examines the utilization of Face Recognition and QR Code technologies in a dual-mode attendance and payroll system to improve accuracy and efficiency in the management of attendance and payment processing. This research intends to examine the effects of this system on cutting down processing time, diminishing human errors, and enhancing overall cost effectiveness. The research utilized an experimental design with a case study methodology at PT TOTO SUKSES ABADI, where the system was evaluated and its performance assessed prior to and following installation. The results show that the dual-mode system cut down on the time it took to process payroll, got rid of human errors in attendance, and made the whole operation run more smoothly. Also, workers said they were happier since payroll handling was faster and more precise. The research indicates that the amalgamation of Face Recognition with QR Code technology can significantly improve the efficacy of human resource management