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Segmentasi Pelanggan Ritel Global dan Inggris Menggunakan RFM dan K-Means Clustering
Prayitno Prayitno
; Irawan Irawan
; Marrylinteri Istoningtyas
Prosiding Seminar Nasional Ilmu Teknik
Vol 2
, No 2
(2026)
Transaction logs in online retail provide opportunities for data-driven customer segmentation. This study segments customers at two scopes global (all countries) and United Kingdom (UK) using Recency, Frequency, and Monetary (RFM) features derived from the Online Retail transaction dataset. After cleaning cancellations and invalid records, RFM variables are computed per customer and normalized. K-Means clustering is applied separately for global and UK data, while the number of clusters is selec...
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Analisis Tata Kelola Teknologi Informasi Menggunakan Framework COBIT 5.0 Pada SMP Negeri 5 Merlung
Hanif Umi Azizah
; Marrylinteri Istoningtyas
; Della Selfia Riyani
Prosiding Seminar Nasional Ilmu Teknik
Vol 2
, No 2
(2025)
SMP Negeri 5 Merlung is a public junior high school in Merlung Subdistrict that has utilized the DAPODIK system for online data processing management, enabling efficient sending and receiving of information to the government. This research analyzes IT governance on the DAPODIK system using the COBIT 5 framework, specifically the MEA01 domain (Monitor, Evaluate and Assess Performance and Conformance), which focuses on monitoring and evaluating performance and conformance. The research background...
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Optimasi Software Effort Estimation Menggunakan Random Forest
Maria Rosario Borroek
; Jasmir Jasmir
; Fachruddin Fachruddin
; Marrylinteri Istoningtyas
; Yosefina Venus
Prosiding Seminar Nasional Ilmu Teknik
Vol 2
, No 2
(2025)
Software development effort estimation is crucial as it is one of the key factors for successful software development. This research employs Random Forest to estimate software development effort. To achieve better results, the study combines the Random Forest method with Genetic Algorithm. The results show that the China dataset provides more accurate estimation compared to the Desharnais dataset, because the China dataset uses relevant feature selection for estimation.
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