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All Journal Jurnal Media Computer Science Jurnal Ilmiah Informatika dan Komputer
Naurah Atikah Nurpadhilah
Universitas Muhammadiyah Bengkulu
Computer Science & IT

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Sentiment Analysis and Characteristics of Youtube User Opinions Toward Samsung and Iphone Brands Using TF-IDF With Naive Bayes and KNN Comparison and Mcnemar Test Naurah Atikah Nurpadhilah; Surya Ade Saputera
Jurnal Media Computer Science Vol 5 No 2 (2026): April
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v5i2.11311

Abstract

The development of social media, particularly YouTube, has generated a large amount of public opinion data that can be utilized to understand user perceptions of products. Samsung and iPhone are two smartphone brands with intense market competition and are frequently discussed in YouTube comment sections. This study aims to compare the performance of the Naive Bayes and K-Nearest Neighbor (KNN) algorithms in sentiment analysis of YouTube comments related to these two brands. The research data were collected through a YouTube comment scraping process using the youtube-comment-downloader library. The research stages included data collection, text pre-processing consisting of case folding, punctuation removal, number removal, stopword removal, and stemming using the Sastrawi library. Furthermore, the text data were transformed into numerical representations using the Term Frequency-Inverse Document Frequency (TF-IDF) method. The classification process was carried out using the Naive Bayes and KNN algorithms and evaluated using accuracy, classification reports, confusion matrices, and the McNemar test to determine the significance of performance differences between the models. In addition, this study also analyzed word distribution based on sentiment and brand using WordCloud visualization. The results indicate that both algorithms are capable of classifying comment sentiments effectively and provide insights into user opinion characteristics toward Samsung and iPhone based on YouTube comments.
Optimizing iPhone Spare Parts Inventory Using K-Medoid Clustering Naurah Atikah Nurpadhilah; Aliya Dwi Ardiyanti; M. Aufa Rafiqi; Nur Ayu Siti Hardianti; Yusa Virginiawan Guntara
Jurnal Ilmiah Informatika dan Komputer Vol. 3 No. 1 (2026): Juni 2026 (In Progress)
Publisher : CV.RIZANIA MEDIA PRATAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69533/informatech.volume3number1.555

Abstract

MR. GADGET store in Bengkulu faces significant challenges in managing iPhone spare parts inventory due to a manual recording system. This study proposes a data science-based solution using the K-Medoid Clustering algorithm to group data based on characteristic similarity. Utilizing a dataset of 471 products, this study compares K-Medoid with conventional partitioning methods (like K-Means), demonstrating its superior robustness against outliers by using actual data points as cluster centers. The clustering quality is evaluated using the Silhouette Score and Davies-Bouldin Index (DBI), yielding best-performing results with a Silhouette Score of 0.681 and a DBI of 0.798. The algorithm generates three main clusters: Fast-Moving, Medium-Moving, and Slow-Moving. The system's functionality is validated through Black Box Testing. The results indicate that this approach provides more accurate procurement recommendations, optimizes inventory turnover, and reduces the risk of inventory imbalance, offering a practical data-driven framework for local retail businesses.
Co-Authors Aliya Dwi Ardiyanti M. Aufa Rafiqi Nur Ayu Siti Hardianti Surya Ade Saputera Yusa Virginiawan Guntara