Pengembangan Modul Mata Pelajaran Pendidikan Pancasila dan Kewarganegaraan (PPKn) untuk Siswa SMK

The learning process in the clasroom has been focused on teacher centered learning even though student actually also have the potential to gain knowledge independently. The implementation of learning the Pancasila and Citizenship Education subjects has so far impressed that only teachers have extensive knowledge so that lectures and notes are a way of providing material to students. Meanwhile, to get the desired competence, the learning process must be supported by adequate learning facilities. Answering this problem, teachers as facilitators in achieving student competence are required to creatively manage learning. One way is to find alternative solutions, including by developing Modules for learning. The development of this module aims to encourage students to seek their own knowledge without having to depend on the teacher. The development model chosen is based on the Borg and Gall models. The development process is carried out in five main steps, namely: (1) Conducting analysis, (2) Designing the initial product, (3) Validating and revising, (4) Small-scale field trials, and (5) Large group field trials. Based on the results of field trials, the PPKn subject modules are very well qualified, meaning that the PPKn subject modules are interesting, according to the needs and characteristics of students. Furthermore, with the results of the large group trial which concluded that by using the PPKn subject module, student scores increased, this was indicated by the value that was above the value of the minimum completeness criteria. With the existence of the PPKn subject module for vocational students, in the future students will no longer depend on the material presented by the teacher in class, but are able to obtain their knowledge independently. Furthermore, in the future learning is no longer focused on teachers but is already focused on students. Then for perfection in the future it is better if the trial use of the module is more expanded so that the results can be a reference for teachers to prepare learning teaching materials

Pelatihan Installasi dan Operasional Blended Learning untuk Admin Fakultas

The Learning process at Faculty of Education and Vocational Education (FKIP)) and the Faculty of Administrative Sciences (FIA) lecturers has been providing materials by displaying lecture materials using projector, as well as writing lecture materials on the blackboard. The student quiz is conducted after several meetings, the students are required to work on and complete the quiz given by the lecturer and then immediately collected after the time is up or finished. To give and collect the task, the lecturer give it during the lecture period and there are also lecturers who give the task at the end of the lecture after the lecturer delivered the lecture material, and the collection is done during the meeting in the class or on the next day schedule in the form of hardcopy so that the lecturer must certain to correct it. To face the problems faced by the Faculty of Education (FKIP) and Faculty of Administrative Sciences (FIA) the authors suggest with the use of electronic-based learning media that is Blended Learning. &nbsp

Analisa Sentimen Publik Mengenai Perekonomian Indonesia pada Masa Pandemi Covid-19 di Twitter Menggunakan Metode Klasifikasi K-NN dan Svm

Pandemi global virus Covid-19 yang sedang mewabah dunia kini telah memberi berbagai pengaruh pada sektor seperti pendidikan, kesehatan, pariwisata, transportasi termasuk perekonomian di Indonesia. Fenomena ini menuai berbagai tanggapan dari masyarakat yang kerap menjadikan media sosial, salah satunya Twitter sebagai alat untuk melakukan proses pertukaran informasi. Pendapat yang terkandung dapat dilakukan analisis menggunakan teknik text mining yaitu proses analisis sentimen yang merupakan cara untuk mengetahui pandangan ataupun opini seseorang terhadap suatu fenomena, baik itu berupa pandangan positif, negatif maupun netral. Data yang diambil merupakan data hasil crawling menggunakan API Twitter dan sebagai data pendukung digunakan pengambilan data melalui kuesioner kepada pengguna Twitter di Indonesia. Dataset yang digunakan pada penelitian ini berjumlah 422 data yang terdiri dari 211 data berlabel positif dan 211 data berlabel negatif. Metode yang dipakai dalam penelitian ini adalah metode K-Nearest Neighbors (K-NN) dan Support Vector Machine (SVM). Berdasarkan hasil pengujian menggunakan confusion matrix didapatkan akurasi dari analisis sentimen menggunakan metode K-NN sebanyak 76%. Sedangkan akurasi dari analisis sentimen menggunakan metode SVM sebanyak 78%