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All Journal JURNAL MULTIDISIPLIN ILMU AKADEMIK Journal of Information Systems and Business Technology
Devira Nazra Suhendra
Universitas Pamulang
Religion Humanities Computer Science & IT Law, Crime, Criminology & Criminal Justice Library & Information Science Medicine & Pharmacology Social Sciences

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Perancangan Sistem Informasi Monitoring Kinerja Pegawai Berbasis Website Menggunakan Metode Prototype (On Project PT. Teknologi Informatika Solusindo) Devira Nazra Suhendra; Anastasya Huang; Chairul Anwar
JURNAL MULTIDISIPLIN ILMU AKADEMIK Vol. 3 No. 3 (2026): JUNI
Publisher : CV. KAMPUS AKADEMIK PUBLISHING

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61722/jmia.v3i3.10381

Abstract

Proses monitoring kinerja pegawai di PT. Teknologi Informatika Solusindo masih dilakukan secara manual, sehingga menyebabkan keterlambatan pelaporan, kesalahan pencatatan, dan kurangnya transparansi penilaian. Penelitian ini merancang sistem informasi monitoring kinerja pegawai berbasis website menggunakan metode Prototype dengan teknologi PHP, MySQL, HTML, dan CSS. Pengujian kualitas dilakukan menggunakan standar ISO/IEC 25010 terhadap 32 responden. Hasil pengujian menunjukkan Functional Suitability 81%, Reliability 75%, Performance Efficiency 75%, Usability 78%, Security 68%, Compatibility 76%, Maintainability 75%, dan Portability 75%, seluruhnya masuk kategori Baik hingga Sangat Baik. Sistem dinyatakan layak digunakan dan mampu mendukung proses operasional perusahaan secara lebih efektif dan efisien
Segmentasi Pelanggan dan Prediksi Churn E-Commerce Menggunakan K-Means Clustering dan Random Forest: Studi Kasus Olist Brazil Muhamad Yumni Airennn; Devira Nazra Suhendra; Najwa Rena Amanda
Journal of Information Systems and Business Technology Vol 2 No 3 (2026): Journal of Information Systems and Business Technology
Publisher : PT Jurnal Cendekia Indonesia

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Abstract

Among 93,357 customers analyzed from the Olist Brazil e-commerce platform, nearly four in ten were found to be in a state of permanent churn a condition invisible to conventional transaction reporting without data-driven segmentation. This study proposes a two-stage analytical pipeline integrating RFM-based (Recency, Frequency, Monetary) K-Means Clustering with a Random Forest Classifier for churn prediction, structured within the CRISP-DM framework. Data were drawn from the Olist Brazilian E-Commerce Public Dataset covering 115,653 orders between 2016 and 2018. Churn was operationalized as customers with recency exceeding 180 days and a transaction frequency of one, yielding a churn proportion of 56.4% across the sample. Clustering at K=4 (Silhouette Score=0.526) partitioned customers into four behaviorally distinct segments: Active (53%, churn rate 29%), Lost (39%, churn rate 100%), Big Spender (4%, churn rate 60%), and Loyal (3%, churn rate 0%). Cluster labels were subsequently incorporated as input features into the Random Forest model a design decision that proved consequential, as the cluster variable emerged as the single strongest predictor with a feature importance score of 0.826, outweighing all individual behavioral features combined. The model achieved an ROC-AUC of 0.897, accuracy of 82.9%, precision of 97.7%, recall of 71.5%, and F1-Score of 82.6%. These results demonstrate that customer segmentation, when embedded within a predictive pipeline rather than used in isolation, yields substantial gains in churn detection capability.
Co-Authors Anastasya Huang Chairul Anwar Muhamad Yumni Airennn Najwa Rena Amanda