Salah satu faktor yang menyebabkan rendahnya hasil belajar siswa adalah penggunaan model pembelajaran terapan yang tidak optimal. Tujuan penelitian ini adalah untuk mengetahui perbedaan kemandirian dan hasil belajar Designing Network menggunakan model Project Based Learning dan modul digital seminar Think Pair Share padasiswakelas XI TKJ di SMKN 3 Malang. Metodepenelitianinimenggunakan Quasi Experiment dengan Post-Test Only Design. Instrumen yang digunakan adalah lembar observasi independensi dan ketermapilan, serta pertanyaan postest dalam bentuk pilihan ganda dengan jumlah 34 pertanyaan. Hasil penelitian menunjukkan bahwa ada perbedaan yang signifikan dalam kemandirian siswa dengan nilai signifikansi Sig. (2-tailed) = 0,003. Namun, tidak ada perbedaan yang signifikan dalam hasil belajar dari domain pengetahuan Sig. (2-tailed) = 0,577 dan Sig keterampilan siswa. (2-tailed) = 0,431.

Amount of information in the form of online news needs to be balanced with the ability of readers to sort or classify subjective or objective news. So that a special system is needed that can be used for online news objectivity classification so that it can help readers to pick up subjective or objective news. This research proposes the development of techniques in machine learning to help sort out news objectivity automatically based on the content of the news. The algorithm proposed is K-Nearest Neighbor (KNN) algorithm. News samples obtained from kompas.com by scrapping occur imbalance classes where the number of objective news and subjective news are not balanced. So that it can affect the performance of the classification algorithm. One technique to overcome the imbalance class is to apply the Synthetic Minority Over-sampling Technique (SMOTE) technique.. SMOTE is the generation of minority data as much as the majority data. This study compares the performance of  KNN algorithm without SMOTE and the performance of KNN algorithm with SMOTE. Based on the results of the study by applying a variety of neighboring k values, namely 1, 3, 5, 7 and 9, it was found that the application of SMOTE could improve the accuracy of the KNN algorithm at values ​​k = 1 and k = 3 with an average increase of 3.36. At values ​​k 5, 7 and 9 the algorithm experiences an average decrease in accuracy of 6.67.

The existence of Covid-19 Pandemic become a challenge for teachers, where the teachers were forced to apply Online learning. Various kinds of online learning media were tried and used. One of Vocational High School (SMK) in Sidoarjo also applies online learning using WhatsApp on the subject of Digital System class X during the Covid-19 Pandemic. This study aims to look at the Effectiveness of Whatsapp in increasing student interest in Digital System Subjects during the Covid-19 Pandemic. This research uses descriptive qualitative research with survey method. The population of this study were 33 students of class X who had completed the digital system subjects during Covid-19 Pandemic. The instrument used was a questionnaire. The data collection technique was carried out by distributing online questionnaires through Google Form to 33 students of class X who had completed the digital system course during Covid-19 Pandemic. The analytical method used was an interactive model of Mile and Hubermen with 4 stages, including collecting data, formulating data, presenting data and drawing conclusions. The result of this research shows a negative response. It can be seen from the results of the study that students prefer to have face-to-face learning than online learning. Moreover, there are several obstacles experienced by students during the learning process using WhatsApp

Artikel ini mengulas yurisdiksi dan administrasi Pengadilan Pidana Internasional(International Criminal Court). Pembentukan Pengadilan Pidana Internasional tersebutdidasarkan atas Statuta Rorna tentang Pidana lnternasional 1998. Pembahasan yurisdiksiICC dikaitkan dengan pokok perkara, waktu, teritorial dan personal. Pembahasanadministrasi ICC, misalnya, dikaitkan dengan apakah suatu kasus dapat diterirna atau tidak.

Convolutional Neural Network (CNN) is an effective Deep Learning (DL) algorithm that solves various image identification problems. The use of CNN for time-series data analysis is emerging. CNN learns filters, representations of repeated patterns in the series, and uses them to forecast future values. The network performance may depend on hyperparameter settings. This study optimizes the CNN architecture based on hyperparameter tuning using Particle Swarm Optimization (PSO), PSO-CNN. The proposed method was evaluated using multivariate time-series data of electronic journal visitor datasets. The CNN equation in image and time-series problems is the input given to the model for processing numbers. The proposed method generated the lowest RMSE (1.386) with 178 neurons in the fully connected and 2 hidden layers. The experimental results show that the PSO-CNN generates an architecture with better performance than ordinary CNN.