Comparison of RStudio and Python Performance on Green Computing-Based Big Data Analytics - Salman Salman; Heni Sulastri; Muhammad Al Husaini (Rekayasa Hijau : Jurnal Teknologi Ramah Lingkungan, 2026)
AbstrakPerkembangan data berskala menengah mendorong kebutuhan platform analitik yang tidak hanya cepat, tetapi hemat sumber daya. Penelitian ini bertujuan membandingkan performa R (melalui RStudio) dan Python terhadap Big Data Analytics berbasis Green Computing pada data skala menengah. Melalui metode komparatif pada dataset ‘‘AI4I 2020 Predictive Maintenance’’, penelitian ini mengukur waktu eksekusi, utilisasi CPU, penggunaan memori, dan konsumsi energi. Hasil pengujian menunjukkan RStudio mencatat waktu eksekusi 19.916,67 ms, utilisasi CPU 0,08%, penggunaan memori 75,43 MB, dan konsumsi energi 440,95 J, sedangkan Python mencatat 270.447,73 ms, utilisasi CPU 1,14%, penggunaan memori 689,67 MB, dan konsumsi energi 7.178,24 J. Temuan menunjukkan bahwa perbedaan performa bersifat kontekstual terhadap karakteristik workload dan desain pipeline analitik, serta menegaskan adanya trade-off antara efisiensi komputasi dan fleksibilitas ekosistem dalam kerangka Green Computing. Kata kunci: green computing, big data analytics, benchmarking, rstudio, python AbstractThe growth of medium scale data is driving the need for analytics platforms that are not only fast, but also resource-efficient. This research aims to compare the performance of R (via RStudio) and Python on Green Computing based Big Data Analytics on medium scale data. Through comparative methods on the ‘‘AI4I 2020 Predictive Maintenance’’ dataset, this research measures execution time, CPU utilization, memory usage and energy consumption. Test results show that RStudio recorded an execution time of 19,916.67 ms, CPU utilization 0.08%, memory usage 75.43 MB, and energy consumption 440.95 J, while Python recorded 270,447.73 ms, CPU utilization 1.14%, memory usage 689.67 MB, and energy consumption 7,178.24 J. The findings show that the performance differences are contextual to the characteristics. workload and analytical pipeline design, and emphasizes the existence of a trade-off between computing efficiency and ecosystem flexibility in the Green Computing framework. Keywords: green computing, big data analytics, benchmarking, rstudio, python