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Journal of Engineering, Technology and Computing (JETCom)
Published by Yayasan Bina Internusa Mabarindo
ISSN : 2829372X     EISSN : 28280512     DOI : -
Core Subject : Science, Engineering,
Automotive Engineering Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering
Journal of Engineering, Technology and Computing (JETCom) Merupakan Media Publikasi Ilmiah untuk Para Akademisi dan Peneliti, yang berasal dari hasil Pemikiran dan Penelitian sesuai Cakupan dan Fokus yang ada dalam Manajemen Informasi Jurnal ini. Pengelola/Penyunting mengingatkan kepada semua calon penulis bahwa kami hanya menerima pengiriman kertas/Naskah Jurnal yang berasal dari Penelitian asli (Original) dari penulis, tidak berasal dari Plagiasi hasil penelitian dari peneliti orang lain. Journal of Engineering, Technology and Computing (JETCom) Terbit tiga (3) kali setahun pada bulan Maret, Juli dan November Semua Penulis Wajib mematuhi Aturan Standar Format Template Jurnal, dan harus sesuaikan Format Naskah Penulis dengan Format Template yang berlaku di Journal of Engineering, Technology and Computing (JETCom), maka kami mengingatkan, sebaiknya penulis download dahulu Format Template Artikel/Jurnal , selanjutnya pengelola akan Langsung Menolak (Riject) Naskah yang tidak sesuai Format Template Jurnal kami dan tidak sesuai dengan Cakupan dan Fokus (Scope & Focus) Journal of Engineering, Technology and Computing (JETCom) mencakup semua aspek Teknik Terapan, Teknik Komputer, Ilmu Terapan, Teknologi informasi, Ilmu Komputer/Informatika, Sistem Informasi, dan Komputasi.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 2 Documents
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Search results for , issue "Vol. 4 No. 3 (2025): Journal of Engineering, Technology and Computing (JETCom - November 2025)" : 2 Documents clear
MACHINE LEARNING OPTIMIZATION USING CORRELATION FEAUTER SELECTION AND SMOTE-ENN FOR EDUCATOR SENTIMENT: Indonesia Jupriadi, Jupriadi; Anggarawan, Anthony; Hairani, Hairani
Journal of Engineering, Technology and Computing (JETCom) Vol. 4 No. 3 (2025): Journal of Engineering, Technology and Computing (JETCom - November 2025)
Publisher : Yayasan Bina Internusa Mabarindo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63893/jetcom.v4i3.314

Abstract

Abstrak : Tenaga pendidik memiliki peran strategis dalam kemajuan pendidikan nasional, sehingga pemahaman terhadap sentimen mereka penting dalam meningkatkan kesejahteraan dan kualitas layanan pendidikan. Dalam pengolahan data sentimen, seringkali ditemui tantangan seperti ketidakseimbangan data antara sentimen mayoritas dan minoritas, serta tingginya jumlah fitur yang menyebabkan dimensionalitas data menjadi besar.Tujuan penelitian ini adalah menganalisis sentimen attitude tenaga pendidik di Indonesia menggunakan metode klasifikasi Machine Learning, dengan pendekatan seleksi fitur berbasis korelasi dan penyeimbangan data melalui Synthetic Minority Over-sampling Technique–Edited Nearest Neighbours (SMOTE ENN). Model klasifikasi dibangun menggunakan algoritma Naïve Bayes dan Support Vector Machine (SVM).Hasil penelitian menunjukkan bahwa SVM memberikan akurasi lebih tinggi dibandingkan Naïve Bayes, baik pada data asli maupun setelah penerapan SMOTE ENN. Akurasi Naïve Bayes meningkat dari 61% menjadi 89% setelah seleksi fitur berbasis korelasi, sedangkan SVM meningkat dari 69% menjadi 97%. Penelitian ini membuktikan bahwa kombinasi SVM, SMOTE ENN, dan seleksi fitur berbasis korelasi mampu meningkatkan akurasi klasifikasi sentimen tenaga pendidik di Indonesia secara signifikan. Kata kunci : analisis sentimen, tenaga pendidik, SVM, SMOTE, seleksi fitur Abstract : Educators play a strategic role in the progress of national education, so understanding their sentiments is important for improving their well-being and the quality of educational services. In sentiment data processing, challenges are often encountered, such as data imbalance between majority and minority sentiments, and a high number of features leading to high data dimensionality. The purpose of this study is to analyze the sentiment of Indonesian educators' attitudes using Machine Learning classification methods, with a correlation-based feature selection approach and data balancing through Synthetic Minority Over-sampling Technique–Edited Nearest Neighbours (SMOTE ENN). Classification models were built using the Naïve Bayes and Support Vector Machine (SVM) algorithms. The research results show that SVM provides higher accuracy compared to Naïve Bayes, both on the original data and after applying SMOTE ENN. Naïve Bayes' accuracy increased from 61% to 89% after correlation-based feature selection, while SVM's increased from 69% to 97%. This study proves that the combination of SVM, SMOTE ENN, and correlation-based feature selection can significantly improve the accuracy of sentiment classification for Indonesian educators Keywords: Sentiment analysis, Educators, SMOTE, Feature selection
KEY SECURITY INTEGRATION IN THE AES ALGORITHM USING THE LUC ALGORITHM ON IMAGE FILES Bustami, Harya; Fauzi, Achmad; Khair, Husnul
Journal of Engineering, Technology and Computing (JETCom) Vol. 4 No. 3 (2025): Journal of Engineering, Technology and Computing (JETCom - November 2025)
Publisher : Yayasan Bina Internusa Mabarindo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63893/jetcom.v4i3.315

Abstract

The rapid growth of information technology requires a reliable data security system, especially for sensitive image files. The Advanced Encryption Standard (AES) is a symmetric algorithm that is fast and efficient; however, its main weakness lies in the vulnerability of key distribution. To address this issue, this research integrates the Lucas (LUC) algorithm to secure AES keys. LUC was chosen as it is an asymmetric algorithm utilizing public and private key pairs, ensuring the confidentiality of AES encryption keys. The purpose of this study is to design and implement an image file security system by integrating AES and LUC using Python programming language with key storage in text format. The encryption process was applied to JPG and PNG files with a maximum size of 3 MB, and the encrypted as well as decrypted results were tested through several experiments. The results indicate that the system successfully encrypted and decrypted image files while maintaining AES key confidentiality through LUC protection. The integration of these two algorithms enhances data security and can serve as a reference for the development of secure digital file systems in the future.

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