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All Journal Jurnal Sains Teknologi dan Sistem Informasi Jurnal Sains dan Teknologi
Arosochi Yosua Daeli
Universitas Pelita Harapan
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Dentistry Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Veterinary

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Klasifikasi Tingkat Kelunturan Warna Kain Menggunakan KNN, SVM, dan Random Forest Romindo Romindo; Triandes Sinaga; Kevin Bastian Sirait; Arosochi Yosua Daeli; Jepronel Saragih
INSOLOGI: Jurnal Sains dan Teknologi Vol. 5 No. 3 (2026): Juni 2026
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/insologi.v5i3.8721

Abstract

The laundry industry faces challenges in maintaining service quality, particularly regarding fabric color fading after washing. Assessments that are still performed manually tend to be subjective and inconsistent, so a more objective automated classification system is required. This study aims to apply and compare three algorithms, namely KNN, SVM, and Random Forest, to classify the level of fabric color fading based on digital images. The features used comprise color in the RGB and HSV spaces as well as shape in the form of area and shape ratio, all extracted automatically. A total of 300 images were divided into 250 training data and 50 testing data, then mapped into three categories, namely not faded, fairly faded, and faded. The testing results show that Random Forest delivers the best performance with an accuracy of 0.96, followed by SVM at 0.94 and KNN at 0.88. All models faced difficulties in recognizing the minority class due to data imbalance. This study proves that the machine learning approach, particularly Random Forest, is able to assess color fading levels more accurately and consistently than manual evaluation, while supporting quality control in the laundry industry.
Segmentasi Investor Cryptocurrency Menggunakan Metode K-Means: Studi terhadap Faktor-Faktor yang Mendorong Keputusan Investasi Arosochi Yosua Daeli; Vicky Darmana; Kevin Bastian Sirait; Triandes Sinaga
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.
Co-Authors Jepronel Saragih Kevin Bastian Sirait Romindo Romindo Triandes Sinaga Vicky Darmana