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All Journal Jurnal Sains Teknologi dan Sistem Informasi Jurnal Sains dan Teknologi Jurnal Pengabdian Masyarakat Bidang Sains dan Teknologi
Jepronel Saragih
Universitas Pelita Harapan
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Transportation 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.
Pelatihan SAP Analytics Cloud dan Pengenalan Canva di SLB Karya Murni Medan Jepronel Saragih; Ronald Belferik; Evander Banjarnahor
ABDIKAN: Jurnal Pengabdian Masyarakat Bidang Sains dan Teknologi Vol. 4 No. 4 (2025): November 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/abdikan.v4i4.6853

Abstract

This community service activity is a collaborative initiative between Universitas Pelita Harapan and SLB Karya Murni Medan, aimed at enhancing digital literacy and technology skills among deaf students. The program focuses on two main areas: Data Science training using SAP Analytics Cloud (SAC) and digital banner and poster design training using Canva. In the SAC training, participants were introduced to fundamental concepts of data analytics, data modeling, data exploration, information visualization, and key features such as Smart Discovery, Designer Mode, and Data Explorer Mode. A COVID-19 dataset was used as a case study to help students understand basic data analysis processes, including how to build models, run simple machine learning algorithms, and generate interactive dashboards. All materials were designed using visual, demonstrative, and applicative approaches to ensure accessibility for students with special needs and to support a gradual learning process. In addition, the Canva training was provided to foster students’ creativity in creating informative and visually appealing digital banners and posters. The results of the program show that the students were able to create simple data models, understand basic visualizations, and produce digital design works. This program provides a meaningful contribution to the empowerment of inclusive education and strengthens sustainable collaboration between academic institutions and special education schools.
Evaluasi Keberhasilan Aplikasi CapCut dalam Pembuatan Video Promosi Produk: Pendekatan DeLone and McLean Riche Riche; Jepronel Saragih; Wenripin Chandra; Suminar Ariwibowo; Segar Napitupulu
SATESI: Jurnal Sains Teknologi dan Sistem Informasi Vol. 5 No. 1 (2025): April 2025
Publisher : Yayasan Pendidikan Penelitian Pengabdian ALGERO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/satesi.v5i1.4181

Abstract

CapCut is a popular video editing application among young people due to its diverse features and ease of use. This application is widely used for creating product promotional videos; however, several limitations affect user experience. Some common issues include video compression when uploaded from a PC, features experiencing bugs, and restrictions on using music outside the application. Therefore, this study aims to analyze the functionality of the CapCut application in supporting promotional video creation using the DeLone and McLean model.  Data collection was conducted online using the purposive sampling method, focusing on online business owners in North Sumatra. The total number of eligible respondents analyzed was 156, based on the Slovin formula. The collected data were analyzed using Structural Equation Modeling - Partial Least Squares (SEM-PLS) with the assistance of SmartPLS software.  The results indicate that system quality and the level of application usage have a positive and significant impact on user satisfaction. Furthermore, user satisfaction significantly contributes to the net benefits gained from using the application. However, the analysis also reveals that direct application usage does not significantly contribute to net benefits perceived by users.  These findings suggest that improving system quality and user experience are key factors in enhancing the benefits of CapCut for users in creating promotional videos. 
Co-Authors Arosochi Yosua Daeli Banjarnahor, Evander Kevin Bastian Sirait Riche Riche Romindo Romindo Ronald Belferik Segar Napitupulu Suminar Ariwibowo Triandes Sinaga Wenripin Chandra