This study explores the application of machine learning algorithms, specifically Linear Regression and Decision Tree Regressor, for predicting student academic performance using academic grade data from Kaggle. The analyzed factors include attendance, assignment grades, midterm exam grades, and final exam grades. The research methodology encompasses data collection, preprocessing, model development, training, and validation. This study contributes to the field of educational data analytics by demonstrating how machine learning can provide actionable insights into students' learning patterns and academic outcomes. The findings emphasize the effectiveness of Linear Regression for linearly distributed data and Decision Tree Regressor for capturing complex, non-linear relationships. The implications of this research suggest that machine learning models can assist educators in identifying key factors influencing student performance, enabling targeted interventions to enhance learning outcomes. Future research should explore larger, more diverse datasets and incorporate ensemble methods, such as Random Forest or Gradient Boosting, to improve model generalization and prediction accuracy. Additionally, integrating socio-economic and psychological factors could provide a more holistic perspective on academic achievement.

Perkembangan teknologi yang semakin meningkat seiring dengan berjalanya waktu dimana semua pengguna harus mempelajari teknologi yang semakin berkembang. Dengan perkembangan yang semakin meningkat maka ancaman dalam pengguna internet akan semakin meningkat akan serangan siber dengan mencuri data atau informasi peribadi melalui website. Maka perlu dilakukan pengujian kerentanan keamanan pada website untuk mengurangi ancaman dari serangan siber dengan memberikan solusi yang dapat digunakan sebagai alat pengukuran akan tingkatan bahaya untuk memperbaiki keamanan yang ada pada website. Tujuan penelitian mencari apa saja kerentanan yang ada pada website Fakultas Teknologi Informasi pada Universitas Sebelas April dan menganalisis dampak yang terjadi jika ancaman tersebut ada pada website dan memberikan solusi dengan metode yang digunakan NIST SP 800-115 yang terdiri dari 4 tahapan yaitu planning, discovery, attack, reporting.  Berdasarkan hasil scanning yang dilakukan mendapatkan 11 kerentanan serta dari hasil pengujian belum ditemukan kerentanan sehingga masih dibilang aman karena belum mendapatkan kerentanan pada website tersebut. Penelitian ini memberi saran untuk melakukan pengujian ulang serta eksplorasi metode lain untuk menemukan kerentanan.

Technological advancements have brought significant transformations across various fields, including the application of machine learning in recommendation and classification systems. Machine learning leverages data processing, utilizes algorithms, and efficiently identifies patterns to produce accurate recommendations and predictions. This study aims to review machine learning-based recommendation system approaches, analyze model performance, and compare the algorithms used. A literature review was conducted by examining journals published in the past five years, focusing on algorithm implementation. The findings indicate that the Naïve Bayes algorithm delivers the best performance, achieving an accuracy of up to 97%. This algorithm is particularly well-suited for processing small to medium-sized datasets with high efficiency. The research provides comprehensive insights into the performance and limitations of various algorithms, serving as a valuable guide for future developments in the field.