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All Journal Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Merkurius: Jurnal Riset Sistem Informasi dan Teknik Informatika
Lifa Sholiah
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Implementasi Data Mining untuk Analisis Data Penjualan dengan Menggunakan Algoritma Naïve Bayes : (Studi Kasus : KPRI Kokarnaba Baturraden) Lifa Sholiah; Ito Setiawan; Abdillah Teguh Permana; Iqbal Yusuf Azhari; Wakhid Sayudha Rendra Graha Alrashid
Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika Vol. 2 No. 6 (2024): November: Merkurius: Jurnal Riset Sistem Informasi dan Teknik Informatika
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/merkurius.v2i6.475

Abstract

KPRI KOKARNABA Baturraden faces challenges in managing increasingly complex sales data, particularly in identifying the most in-demand products to maximize profit. This study aims to analyze sales patterns using the Naïve Bayes algorithm as a probability-based classification method. The collected sales data were analyzed to identify categories of best-selling and less popular products within the cooperative. The results indicate that the Naïve Bayes algorithm has an accuracy rate of 77.56% in predicting product categories. This research is expected to assist the cooperative in optimizing stock management and improving member satisfaction.
Implementasi Data Mining untuk Clustering Lowongan Pekerjaan Menggunakan Metode Algoritma K-Means Rifqi Mubarok; Akhmal Angga Syahputra; Abdillah Teguh Permana; Lifa Sholiah; Tarwoto
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 9 No 2 (2025): APRIL-JUNE 2025
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v9i2.3438

Abstract

The development of digital technology has transformed the way businesses recruit employees online. This study aims to create an interactive dashboard that facilitates job seekers and companies, using clustering methods with the K-Means algorithm to analyze job posting data in the United States. The data from the Kaggle LinkedIn Job Postings 2023 dataset, consisting of 33,000 records, is processed using the CRISP-DM phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The clustering analysis results in four job categories: low-mid-level general jobs, high-level executive jobs, time-based jobs, and mid-high-level professional jobs. Model evaluation shows good clustering quality with a Silhouette Coefficient of 0.78 and a Davies-Bouldin Index of 0.55. The developed dashboard helps companies plan recruitment and job seekers find positions matching their skills and salary expectations. The practical contribution of this study is modernizing the recruitment process, assisting companies and recruitment agencies in screening candidates more efficiently, and improving job matching through deeper data analysis.
Co-Authors Abdillah Teguh Permana Akhmal Angga Syahputra Iqbal Yusuf Azhari Ito Setiawan Rifqi Mubarok Tarwoto Wakhid Sayudha Rendra Graha Alrashid