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All Journal Jurnal Minfo Polgan (JMP) Ascendia: Journal of Economic and Business Advancement
Dea Putri Sartika
Universitas Labuhanbatu
Computer Science & IT Economics, Econometrics & Finance Library & Information Science Mathematics Social Sciences

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Evaluasi Efektivitas Mekanisme Enkripsi End-to-End Dalam Mengurangi Resiko Kebocoran Data Pada Layanan Komunikasi Berbasis Cloud Trisalia Purba Lia; Sahat Parulian Sitorus; Dea Putri Sartika; Ajeng Pratiwi; Zakaria Hasibuan
Jurnal Minfo Polgan Vol. 14 No. 2 (2025): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v14i2.15744

Abstract

Keamanan data merupakan komponen utama dari layanan komunikasi berbasis cloud, karena data bisa bahkan sering kali hilang dan disimpan di beberapa server. Enkripsi End-to-End (E2EE) muncul sebagai mekanisme perlindungan yang memungkinkan hanya penerima dan pengirim yang bisa dapat mengakses konten komunikasi dari server cloud, sedangkan pengguna ketiga tidak bisa mengakses data cloud tersebut. Artikel ini mengevaluasi efektivitas E2EE dalam mengurangi risiko kerusakan data dalam layanan komunikasi berbasis cloud dengan tinjauan literatur, analisis teori keamanan, dan simulasi konseptual vektor ancaman seperti akses tidak otentik, penyadapan, dan kerusakan metadata. Hasil menunjukkan bahwa E2EE secara substansial menurunkan kemungkinan kebocoran data jika diimplementasikan dengan manajemen kunci dan endpoint security yang memadai. Namun, ada tantangan signifikan: metadata dan informasi kontekstual tetap berisiko bocor, serta performa sistem dapat terpengaruh. Temuan ini menekankan perlunya penerapan E2EE disertai kebijakan keamanan menyeluruh dan edukasi pengguna.
Penerapan Data Mining Naive Bayes dalam Memprediksi Dampak Faktor Ekonomi terhadap Siswa Sekolah Dasar di Lingkungan Perkebunan Trisalia Purba; Dea putri sartika; Ajeng Pratiwi; Zakaria Hasibuan
Journal of Economic and Business Advancement Vol. 1 No. 3 (2026): March: Ascendia: Journal of Economic and Business Advancement
Publisher : CV SCRIPTA INTELEKTUAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65310/24dznc68

Abstract

This study aims to analyze the application of data mining using the Naive Bayes algorithm to predict the impact of economic factors on elementary school students living in plantation areas. The dataset used in this research consists of student records obtained from SDN 113820 Perkebunan Berangir, including variables such as distance from home to school, student age, early childhood education background, ownership of the Kartu Indonesia Pintar (KIP), and parental income. A total of 95 records were utilized as training data and processed using Orange software version 3.39.0. The evaluation results indicate that the Naive Bayes model achieved an accuracy of 85.3% with an Area Under Curve (AUC) value of 1.000 for both classes, demonstrating excellent classification capability. Although some misclassification occurred in the non-eligible class, the overall performance confirms that Naive Bayes is an effective and reliable classification method for analyzing socioeconomic factors in educational contexts. The findings suggest that this approach can support data-driven decision-making in educational assistance programs.
Co-Authors Ajeng Pratiwi Sitorus, Sahat Parulian Trisalia Purba Trisalia Purba Lia Zakaria Hasibuan