With the rapid advancement of information technology, maintaining a stable and efficient computer network has become essential, particularly in educational settings. SMK Negeri 1 Surabaya encounters difficulties in managing its limited bandwidth because of the large number of users accessing the network simultaneously. This research focuses on applying bandwidth management through the Queue Tree technique combined with Per Connection Queuing (PCQ) on the school's computer network using Mikrotik hardware. The approach aims to enhance network performance by improving throughput, minimizing delays, and stabilizing jitter. Evaluation of Quality of Service (QoS) indicators—including throughput, packet loss, delay, and jitter—demonstrated favorable outcomes, with all metrics categorized as good. Findings confirm that using Queue Tree and PCQ methods contributes to fairer bandwidth distribution, mitigates network congestion, and improves overall network quality in the educational environment. This study offers a practical solution to support teaching and administrative processes by providing a more reliable and efficient network infrastructure.

Indonesian people mostly tend to ignore health, especially dengue fever because the symptoms that arise are similar to common fever, this causes people with common fever symptoms to be reluctant to see a doctor. This study aims to help the public recognize the symptoms of dengue fever early by using the implementation of the fuzzy method in the dengue fever disease diagnosis system that uses specific datasets as a reference. Mamdani's fuzzy method, which is based on linguistic frameworks and fuzzy concepts, allows for the management of knowledge from experts for intuitive decision-making. The test results using the Confusion Matrix, the system showed an accuracy of 92.8%, an average precision of 90.6%, a recall of 95.8%, and an F1-Score of 92.6%, with an effectiveness value of 100%. This study proves that the system is able to provide information about dengue fever, diagnose based on symptoms, and offer solutions for its treatment.

Item-based collaborative filtering is a popular technique in recommendation systems that aims to provide suggestions for films to watch or services to users based on similarities between items. In this approach, the similarity between items is calculated using metrics such as cosine similarity, allowing the prediction of user preferences for items that have never been rated. This research implements Item-based collaborative filtering using datasets from Kaggle. Experimental results show that the resulting model is able to provide recommendations with significant improvements in evaluation metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of 3.05 and 3.26. This shows that the smaller the value, the better.

Fundraising activities have become a common method to achieve various objectives, including charitable activities and gathering funds from investors for companies. However, cases of fund misuse often occur, leading to inappropriate use of funds or even unfair loss. Therefore, an innovative approach is needed to enhance trust and transparency in the fundraising process. In this study, the use of smart contracts on a blockchain network will be employed as a solution to ensure the security and integrity of each transaction in fundraising activities. By utilizing smart contracts, all transactions in fundraising activities can be verified by all users on the blockchain network. Transaction data recorded on the blockchain is permanent and tamper-proof, preventing any manipulation or fund misuse. Leveraging smart contracts and blockchain technology, fundraising activities can be conducted more efficiently, transparently, and securely. This approach helps improve trust among involved parties and reduces the risk of fund misuse