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All Journal Jurnal Sains Teknologi dan Sistem Informasi Jurnal Minfo Polgan (JMP)
Sirait, Kevin Bastian
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Computer Science & IT Library & Information Science Mathematics Social Sciences

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Segmentasi Investor Cryptocurrency Menggunakan Metode K-Means: Studi terhadap Faktor-Faktor yang Mendorong Keputusan Investasi Arosochi Yosua Daeli; Vicky Darmana; Sirait, Kevin Bastian; Sinaga, Triandes
SATESI: Jurnal Sains Teknologi dan Sistem Informasi Vol. 5 No. 2 (2025): Oktober 2025
Publisher : Yayasan Pendidikan Penelitian Pengabdian ALGERO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/satesi.v5i2.7405

Abstract

The growth of cryptocurrency investors is very rapid with diverse characteristics and various investment driving factors. This study aims to analyze and form investor segmentation in cryptocurrency based on investment driving factors using the K-Means Clustering algorithm. A quantitative approach was applied through online questionnaires to 300 respondents who are cryptocurrency investors, with 289 valid data meeting the research criteria. The variables studied include four driving factors: Fear of Missing Out (FOMO), social media influence, high profit potential, and interest in the investment world. Data were processed through Min-Max normalization, Principal Component Analysis (PCA), and K-Means clustering using Orange Data Mining. The optimal number of clusters was determined using the Silhouette Score, while cluster validation used K-Nearest Neighbors (KNN). ANOVA and Games-Howell tests confirmed significant differences between clusters. The results identified four clusters: Cluster 1 (Emotional Investors, n=37), Cluster 2 (Ambitious Investors, n=156), Cluster 3 (Rational Investors, n=50), and Cluster 4 (Passive Investors, n=46). Cluster 3 is the most optimal in investment decision-making with a profit rate of 90% and zero loss (0%). These findings confirm that optimal investment decisions are driven by rational analysis and logical consideration without excessive emotional influence.
Analisis Sentimen Komentar Toxic pada Video Musik YouTube Menggunakan Metode Naive Bayes Romindo, Romindo; Sirait, Kevin Bastian; Riffan, Valentino; Wijaya, Chailine Garcia
Jurnal Minfo Polgan Vol. 15 No. 1 (2026): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v15i1.16190

Abstract

Platform berbagi video YouTube telah menjadi ruang interaksi digital yang masif, namun seiring pertumbuhannya, fenomena komentar toxic atau negatif semakin marak ditemukan pada konten-konten populer yang bersifat kontroversial. Penelitian ini bertujuan untuk menganalisis pola sentimen komentar toxic pada video musik "Hozier - Take Me to Church" serta mengukur performa algoritma Naive Bayes dalam melakukan klasifikasi sentimen secara otomatis. Data dikumpulkan menggunakan YouTube API dengan total 100.000 data komentar berbahasa Inggris, yang setelah melalui proses pembersihan menghasilkan 51.348 data valid. Proses text preprocessing mencakup lowercasing, penghapusan URL, tanda baca, angka, dan emotikon, diikuti tahap contraction, tokenization, penghapusan stopwords, serta lemmatization menggunakan library Natural Language Toolkit (NLTK). Data kemudian dilabeli ke dalam tiga kelas sentimen: positif, negatif, dan netral, sebelum diklasifikasi menggunakan algoritma Naive Bayes. Evaluasi performa model dilakukan dengan confusion matrix yang menghasilkan nilai akurasi sebesar 74%. Presisi untuk kelas negatif mencapai 96%, netral 97%, dan positif 64%. Nilai recall kelas negatif sebesar 57%, netral 49%, dan positif 99%. Sedangkan F1-score kelas negatif sebesar 71%, netral 65%, dan positif 78%. Dari total data yang diproses, ditemukan 8.475 komentar toxic (negatif), menunjukkan bahwa sebagian besar audiens merespons video tersebut secara positif. Hasil ini membuktikan bahwa algoritma Naive Bayes memiliki kemampuan yang memadai dalam klasifikasi sentimen komentar media sosial.
Co-Authors Arosochi Yosua Daeli Riffan, Valentino Romindo Triandes Sinaga Vicky Darmana Wijaya, Chailine Garcia