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Journal : Jurnal Sains dan Teknologi

Pengembangan Web Pemesanan Tiket pada Perusahaan Startup Skilldemy Reynaldo Fang; Jefri Junifer Pangaribuan
INSOLOGI: Jurnal Sains dan Teknologi Vol. 1 No. 4 (2022): Agustus 2022
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/insologi.v1i4.575

Abstract

Advances in technology like this are something that cannot be avoided in everday life because this happens in tandem with advances in the development of science. The web is a service that makes it easier for users to interact with other users and browse information from the internet. Skilldemy is a startup company fromt the city of Medan, which is engaged in education. Before seminars or workshops are held, instructors or students must first carry out an online-based administrative process via Messages from Instagram, while instructor who want to share their knowledge through seminars or workshops can coordinate via Messages from Whatsapp to discuss what materials will be brought. Because of these problems, it is necessary to build an online seminar or workshop ticket booking website to meet user needs in terms of ticket reservations. The result showed that the design of this website-based ticket booking system made it easier for the participants with the features provided in terms of information and ticket reservations.
Analisis Kualitas Wine Menggunakan Machine Learning dengan Pendekatan SMOTE dan Seleksi Fitur Triandes Sinaga; Kevin Bastian Sirait; Pangaribuan, Jefri Junifer; Barus, Okky Putra; Romindo, Romindo
INSOLOGI: Jurnal Sains dan Teknologi Vol. 4 No. 3 (2025): Juni 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/insologi.v4i3.5436

Abstract

Conventional wine quality assessment remains reliant on subjective expert judgment, which introduces potential bias and inconsistency in quality control processes. This study aims to develop an objective and automated machine learning-based classification model to enhance the accuracy of wine quality prediction. To address the issue of class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied, along with ANOVA F-test-based feature selection to optimize model performance. The White Wine Quality dataset from the UCI Machine Learning Repository (4,898 samples, 11 numerical features) was utilized to evaluate five classification algorithms: Naïve Bayes, Decision Tree, Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN). Before SMOTE application, the Random Forest model achieved an accuracy of only 67.55%. After implementing SMOTE and parameter tuning, the Random Forest (Tuned) model demonstrated the best performance with 90.29% accuracy, 89.99% precision, 90.29% recall, and 89,97%.  % F1-score. Additionally, Decision Tree and KNN algorithms also exhibited notable improvements. SMOTE effectively balanced extreme minority class representations (quality levels 3 and 9). The most influential features in quality classification were alcohol content, density, and chlorides. These findings indicate that the proposed framework offers a reliable, objective, and scalable solution for automated wine quality control in industrial production environments.