Article Per Year (5 Year)
p-Index From 2021 - 2026
0.817
P-Index
This Author published in this journals
All Journal Dinamik POSITIF Krea-TIF: Jurnal Teknik Informatika JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Mattawang: Jurnal Pengabdian Masyarakat JPM: JURNAL PENGABDIAN MASYARAKAT Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen
Riskiono, Sampurna Dadi
Unknown Affiliation
Aerospace Engineering Humanities Astronomy Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Medicine & Pharmacology Public Health Social Sciences Transportation Other

Published : 8 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH)

 1 
Perbandingan Algoritma K-Nearest Neighbor dan Naive Bayes Untuk Klasifikasi Status Penjualan Furniture dengan Data Historis Sucahyo, Bangkit Dwi; Riskiono, Sampurna Dadi
Journal of Information System Research (JOSH) Vol 7 No 2 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i2.8675

Abstract

This study aims to evaluate the performance of two machine learning classification methods, K-Nearest Neighbor (KNN) and Naive Bayes, in predicting the sales status of furniture products at CV. Surya Gemilang. The data used comes from previous sales records and includes details such as product category, product name, price, sales amount, revenue, and sales status, which are labeled "Best Selling" and "Not Selling". This study follows several steps, including data collection, data cleaning and organization, labeling, model training, and performance assessment using accuracy, precision, recall, and F1-score. The research process includes data preprocessing, handling missing values, encoding categorical features, normalizing numeric features, separating training and testing data, model training, and performance evaluation using accuracy, precision, recall, and F1-score. The results show that the KNN algorithm achieves 97% accuracy, 100% precision, 95% recall, and 0.97 F1-score. Meanwhile, the Naïve Bayes algorithm achieved 85% accuracy, 92% precision, 81% recall, and an F1-score of 0.86. These findings indicate that KNN is better able to recognize complex patterns in sales data than Naïve Bayes. The contribution of this research is to provide a machine learning-based classification model that can be used to support production planning and marketing strategies by predicting furniture product sales levels. The results show that KNN achieved 97% accuracy, while Naïve Bayes only achieved 85%. This indicates that KNN is better at identifying complex relationships between features in sales data, while Naïve Bayes is less effective because it assumes all variables are independent. In summary, KNN is more effective in classifying furniture product sales status and can be the basis for making informed business decisions based on data. This research makes a significant contribution to the application of machine learning in small and medium-sized enterprises, helping to improve sales forecasting and develop more effective marketing strategies.
Co-Authors Ade Candra, Ade Adi Sucipto, Adi Ahdan, Syaiful David, Hufajar Wiliam Dedi Darwis Heni Pujiastuti Iqbal, Muhammad Kristianto Kristianto Laksono, Pria Agung Nurrohman , Fatkhur Pasaribu, A. Ferico Octaviansyah Pranita, Elka Prasetyawan, Purwono Sucahyo, Bangkit Dwi Susanto, Try Tohir, Ahmat