Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Journal of Information Systems and Business Technology
Najwa Rena Amanda
Universitas Pamulang
Computer Science & IT Library & Information Science

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Analisis dan Perancangan Sistem Informasi PPDB Berbasis Web Menggunakan Metode Prototype dan Evaluasi Kualitas Berdasarkan Standar ISO/IEC 25010 (On Project PT. Teknologi Informatika Solusindo) Najwa Rena Amanda; Nisrina Fitriyani; Chairul Anwar
Journal of Information Systems and Business Technology Vol 2 No 3 (2026): Journal of Information Systems and Business Technology
Publisher : PT Jurnal Cendekia Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Kajian ini difokuskan pada upaya merancang dan membangun Sistem Informasi Penerimaan Peserta Didik Baru (PPDB) berbasis web dalam lingkup proyek bersama PT. Teknologi Informatika Solusindo. Pengembangan sistem ini dilakukan guna mengatasi kendala inefisiensi administrasi dan manajemen data pendaftaran dengan menerapkan metode prototype sebagai model pengembangan perangkat lunak yang adaptif terhadap kebutuhan pengguna. Pasca implementasi, penilaian mutu perangkat lunak dilakukan secara terstruktur menggunakan kerangka standar internasional ISO/IEC 25010, dengan instrumen kuesioner yang disebarkan kepada 31 partisipan yang mencakup pengelola sistem, panitia PPDB sekolah, dan calon peserta didik. Temuan evaluasi memperlihatkan bahwa capaian persentase pada tiap dimensi kualitas umumnya berkisar antara 75% sampai dengan 85%. Secara keseluruhan, akumulasi nilai rata-rata yang diperoleh sistem mencapai 80,60%, angka yang menempatkannya pada kategori kualitas Sangat Baik. Perolehan ini mencerminkan bahwa platform yang dikembangkan mampu menjalankan fungsi-fungsi utama dengan tepat sasaran, dilengkapi perlindungan data yang cukup, antarmuka yang ramah pengguna, serta kemampuan berjalan lintas perangkat yang baik. Kendala sinkronisasi server di saat lonjakan akses sempat menjadi catatan, namun secara keseluruhan pendekatan prototype berhasil menghasilkan sistem yang selaras dengan kebutuhan lapangan dan sangat layak untuk menggantikan proses pendaftaran manual.
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

Show Abstract | Download Original | Original Source | Check in Google Scholar

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 Chairul Anwar Devira Nazra Suhendra Muhamad Yumni Airennn Nisrina Fitriyani