The purpose of this study is to examine how deep learning influences students’ mathematical critical thinking skills. In mastering mathematics—which requires reasoning, analysis, and systematic review—critical thinking is essential to address the challenges of the 21st century. This research employs a quasi-experimental design using a pretest-posttest control group model. The subjects of the study were eighth-grade junior high school students, divided into an experimental group and a control group. The main instruments used were a mathematical critical thinking skills test and a learning activity observation sheet. The findings indicate that students who participated in instruction based on a deep learning approach showed a significant improvement in critical thinking skills compared to those who received conventional instruction. Unlike students who underwent standard learning methods, those exposed to deep learning strategies experienced a notable enhancement in their critical thinking abilities. Based on these findings, the implementation of deep learning is recommended more broadly in mathematics classroom instruction.

With the advancement of technology and computing capabilities of computers, various applications and algorithms that were previously not applicable to portable devices such as laptops and smartphones can now be applied. Artificial intelligence (AI) is one of the increasingly popular techniques that are now a part of everyday life. AI is an umbrella term that refers to the simulation of human intelligence by machines that use big data to perform various tasks. AI has benefited many aspects of human life, including education. However, AI also has significant negative effects. This study aims to study the influence of artificial intelligence on students' critical thinking skills. This study uses qualitative research and literature analysis. However, the study also identified several negative impacts, including reliance on technology, unassured information quality, social isolation, and ethical and privacy concerns. Therefore, a balanced and strategic approach is needed in integrating AI in education to ensure that its benefits can be maximized, while its negative impact is minimized.

Data communication is a field of science that studies the process of sending data, as well as how data can be received from sending and receiving terminals. A computer network is a set of "interconnections" between 2 or more autonomous computers connected by cable or wireless transmission media. Cisco packet tracer is a network simulation application developed by Cisco as a simulator as a learning medium for Cisco devices. Peer-to-Peer (P2P) network simulation using Cisco Packet Tracer software is an effective method for understanding, designing, and analyzing P2P networks in a controlled environment. The method used is literature study and carrying out simulation practice on Cisco Packet Tracer software to simulate a network. This research method aims to outline the steps and procedures required to simulate a Peer-to-Peer (P2P) network using Cisco Packet Tracer software. This abstract presents a brief view of the simulation approach used to model and examine the performance of a P2P network using Cisco Packet Tracer contains implementation steps for peer to peer network simulation. Through simulation, in-depth performance analysis can be carried out on various aspects of P2P networks, such as throughput, latency and reliability. This allows researchers and practitioners to understand how P2P networks behave under various conditions. Thus, P2P network simulation with Packet Tracer provides a useful tool for researchers and practitioners to understand and optimize P2P network performance in various scenarios.