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METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi
Published by Universitas Methodist Indonesia
ISSN : 25988565     EISSN : 26204339     DOI : 10.46880
Core Subject : Economy, Science,
Computer Science & IT Economics, Econometrics & Finance
Sistem Informasi Sistem Informasi Manajemen Sistem Informasi Akuntansi Manajemen Basis Data Pengembangan Aplikasi Web dan Mobile Sistem Pendukung Keputusan Desain Grafis dan Multimedia Audit Sistem Informasi Topik-topik lain yang Relevan dengan bidang ilmu Manajemen Informatika Topik-topik lain yang Relevan dengan bidang ilmu Kompuerisasi Akuntansi
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 383 Documents
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Segmentasi Pasar Terhadap Generasi Z Berdasarkan Perilaku Belanja Online Menggunakan Metode K-Means Clustering Meri Nova Marito; Ratna Wati Simbolon; Duma Lasmaria Siagian; Bertha Nerpy Siahaan; Ade Linhar P.; Anugrah Zai
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 10 No. 1 (2026): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol10No1.pp244-248

Abstract

The advancement of digital technology greatly affects consumption patterns, especially among Gen Z, towards e-commerce platforms. Gen Z has a high affinity for digital technology. This condition creates a need for more precise market segmentation so that marketing strategies can be tailored to consumer characteristics. This study aims to conduct market segmentation based on online shopping behavior using the K-Means Clustering method. The data used in this study is the Online Retail Dataset obtained from the UCI Machine Learning Repository. The research stages include data preprocessing, attribute selection, data normalization, determination of the number of clusters, and the clustering process using the K-Means algorithm. The variables analyzed include transaction frequency, product purchase quantity, customer transaction value, and transaction time interval. The research results show that customer data can be grouped into several segments with different characteristics, such as active customers with high purchase levels, customers with moderate transactions, and customers with low purchasing activity. Thus, the resulting segmentation can help business actors understand consumer behavior and develop more targeted marketing strategies. In addition, the K-Means Clustering method has been proven effective in grouping customer data based on online shopping patterns. 
Rekomendasi Kata Kunci Produk Variasi Motor Menggunakan Metode Analytical Hierarchy Process Hafidz Aby Pratama; Ni Gusti Ayu Putu Harry Saptarini; Ni Ketut Pradani Gayatri Sarja
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 10 No. 1 (2026): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol10No1.pp278-285

Abstract

The rising trend in motorcycle modification drives business owners to optimize digital marketing strategies through relevant product keywords. However, keyword determination is often conducted subjectively without a structured approach. This study aims to determine keyword recommendations for motorcycle variation products using the Analytical Hierarchy Process (AHP) method. The AHP method works by breaking down multi-criteria problems using pairwise comparison matrix calculations based on Relevance and Search Volume criteria. The results indicate that the AHP method is capable of determining keyword priorities objectively and systematically. The final results show that the keyword "Shockbreaker" ranks first with a score of 0,415. Consistency testing demonstrates a Consistency Ratio (CR) value of 0,00 for the criteria matrix and 0,081 for the search volume matrix. Since the CR values are CR ≤ 0,1, the weighting and decision-making results in this system successfully meet the standards established in the AHP method.
Analisis Efektivitas Algoritma Cosine Similarity dan Boyer-Moore dalam Sistem Pencarian Dokumen Digital Wawan Ade Saputra; Lidya Wati
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 10 No. 1 (2026): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol10No1.pp269-277

Abstract

This study analyzes the effectiveness of the cosine similarity and Boyer-Moore algorithms in digital document retrieval within the SIPANDOK system developed for the Bengkalis Regency Health Office. Cosine similarity measures semantic document relevance via TF-IDF vector weighting, while Boyer-Moore performs direct pattern matching through string heuristics. The system was built using the Rapid Application Development (RAD) methodology and evaluated against 56 documents and 55 test queries using precision, recall, F1-score, accuracy, and execution time metrics. Results indicate that Boyer-Moore achieves higher average recall (66.7%) and F1-score (33.3%), demonstrating superiority in retrieving relevant documents, whereas cosine similarity offers faster execution time (average 0.31 seconds) compared to Boyer-Moore (0.91 seconds). Each algorithm presents distinct advantages depending on whether precision-orientation or recall-orientation is prioritized in document retrieval scenarios.

Page 39 of 39 | Total Record : 383

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