Universities certainly have a lot of important documents stored. Documents stored include scientific works in the form of journals. The journal that is kept has an identity so that if needed it will make it easier for people to find it. A library is a place to store scientific work, both in the form of journals and for storing other documents. The problem currently occurring is that there are so many documents or journals stored that it takes a long time for people who want to find information related to journals. The aim of this research is to provide a solution to make it easier for people who want to find information about many journals. The method proposed in this research is to use the information extraction method. Information extraction is the search for structured information such as entities and attributes. The stages carried out start from collecting the dataset which is then carried out preprocessing where this stage takes the form of converting documents with PDF extensions to documents with HTML extension. Apart from that, at this stage data cleaning is also carried out, meaning that at this stage it will be possible to create new paragraphs. Therefore, the new paragraph needs to be removed. Then the next stage is rule-based information extraction based on keywords and rules. The results of this research are that none of the 50 journal abstract documents failed so that the accuracy obtained was 100%. So the information extraction in this research can be used to search for information from journal abstracts.

This research aims to explore the effectiveness of using artificial intelligence (AI) in English language learning. The research method used is mixed-methods, combining quantitative and qualitative approaches. The research participants consist of 100 students of UNIMAR AMNI who utilize AI as a language learning method. The results of the study show a significant improvement in listening, speaking, reading, and writing skills after using AI. These findings support the effectiveness of AI utilization in language learning. Furthermore, this research fills the research gap in the context of AI usage in English language learning in educational institutions. The practical implication of this research is the need for integrating AI into language learning curricula to enhance effective and efficient learning outcomes. This study provides a significant contribution to our understanding of AI implementation in language learning and highlights the immense potential of AI in developing innovative and adaptive teaching methods.

Perguruan  tinggi di Indonesia saat ini sudah mencapai 2000 lebih perguruan tinggi.  Perguruan tinggi bisa tetap eksis salah satu faktornya adalah dengan adanya mahasiswa yang masuk ke perguruan tinggi tersebut. Dalam penerimaan mahasiswa baru setiap perguruan tinggi tentunya tidak sembarangan, mereka akan melakukan seleksi terhadap calon mahasiswa agar mendapatkan mahasiswa dengan kualitas yang mereka harapkan. Ada beberapa cara dalam melakukan seleksi calon mahasiswa, diantaranya ada yang berdasarkan nilai rapor, ada yang melalui tes tertulis, dan ada jalur mandiri. Tidak semua perguruan tinggi melakukan pengolahan data yang masuk secara maksimal. Biasanya mereka masih mengolah data secara manual atau dengan kata lain belum menggunakan machine learning. Masalah yang ada yaitu adanya perguruan tinggi yang belum secara maksimal dalam mengolah data penerimaan calon mahasiswa. Karena belum optimalnya data yang diolah maka ada perguruan tinggi yang belum dapat membuat prediksi dalam penerimaan calon mahasiswa untuk di tahun ajaran berikutnya. Salah satu untuk dapat membuat prediksi di masa mendatang adalah dengan menggunakan data mining.  Metode yang akan digunakan dalam data mining adalah klasifikasi Decision Tree dioptimasi dengan Particle Swarm Optimization. Hasil dari Decision Tree dengan optimasi Particle Swarm Optimization didapatkan akurasi sebesar 99,94%.