This study aims to find a model for strengthening digital literacy at the University of Darussalam Gontor. This research uses descriptive qualitative method. Data are collected through observation and interviews with five lecturers in the Communication Study Program at the University of Darussalam Gontor. This research found a model of strengthening digital literacy through the use of e-learning. The model includes elements of communication and collaboration in the form of active participation in learning and research activities. It consists of individual competence components in the form of use skills, critical understanding, and communicative abilities. This research contributes to a model of strengthening digital literacy through the use of e-learning.

<p>This research proposed automated hierarchical classification of scanned documents with characteristics content that have unstructured text and special patterns (specific and short strings) using convolutional neural network (CNN) and regular expression method (REM). The research data using digital correspondence documents with format PDF images from pusat data teknologi dan informasi (technology and information data center). The document hierarchy covers type of letter, type of manuscript letter, origin of letter and subject of letter. The research method consists of preprocessing, classification, and storage to database. Preprocessing covers extraction using Tesseract optical character recognition (OCR) and formation of word document vector with Word2Vec. Hierarchical classification uses CNN to classify 5 types of letters and regular expression to classify 4 types of manuscript letter, 15 origins of letter and 25 subjects of letter. The classified documents are stored in the Hive database in Hadoop big data architecture. The amount of data used is 5200 documents, consisting of 4000 for training, 1000 for testing and 200 for classification prediction documents. The trial result of 200 new documents is 188 documents correctly classified and 12 documents incorrectly classified. The accuracy of automated hierarchical classification is 94%. Next, the search of classified scanned documents based on content can be developed.</p>

Pemanfaatan e-learning dalam pembelajaran juga menjadi sebuah inovasi dalam menyongsong era society 5.0. Relasi mahasiswa santri UNIDA Gontor dengan dunia luar dibatasi dengan berbagai regulasi yang ada, termasuk juga dalam pemanfaatan media digital. Etika komunikasi digital menjadi hal penting yang harus dimiliki mahasiswa santri dalam setiap interaksi melalui media digital. Tujuan penelitian ini untuk mengetahui strategi penanaman etika komunikasi digital di pesantren dalam menyongsong era society 5.0 melalui pemanfaatan e-learning. Penelitian ini berupa kualitatif deskriptif. Pengumpulan data melalui wawancara dan observasi dengan pengelola program studi dan dosen-dosen pengampu mata kuliah dasar Ilmu Komunikasi Universitas Darussalam Gontor yang merupakan lembaga pendidikan tinggi berbasis pesantren. Teknik analisis data dilakukan berdasarkan teori Milles dan Huberman. Keabsahan data penelitian dilakukan dengan triangulasi sumber dan metode. Hasil penelitian menunjukkan bahwa strategi penanaman etika komunikasi digital di Universitas Darussalam Gontor dengan memanfaatkan e-learning dilakukan berdasarkan standar literasi media islam daring. Terdapat tujuh standar literasi media islam daring yang dijadikan rujukan dalam menanamkan etika komunikasi digital kepada para mahasiswa santri meskipun pelaksanaannya belum secara maksimal. Ketujuh standar tersebut terdiri dari prinsip produksi konten, etika distribusi informasi, jaminan akurasi dan komitmen anti hoak, semangat amar ma’ruf nahi munkar, asas hikmah dalam dakwah, prinsip interaksi digital, dan prinsip kebebasan. Pembelajaran e-learning mata kuliah dasar ilmu komunikasi menjadi sarana dalam menanamkan etika komunikasi digital menyongsong era society 5.0. Kontribusi penelitian ini berupa strategi penanaman etika komunikasi digital melalui pemanfaatan e-learning menyongsong era society 5.0 di lembaga pendidikan tinggi berbasis pesantren.

Pasar Lama Tangerang is a tourist attraction in the city of Tangerang. With the development of current technology, the public can provide an overview of how the facilities and services are provided by expressing opinions on the internet. However, it is difficult to distinguish which opinions belong to positive or negative opinions. Sentiment analysis is needed to overcome this problem. The stage in sentiment analysis starts with collecting data first, then the data is processed. Furthermore, the data that has been propagated is given a sentiment classification using the K-Nearest Neighbor (KNN) algorithm. Then the classification results obtained an accuracy of 83% with a value of k = 1 of 120 data divided by 92 positive and 28 negative comments. Sentiment analysis is made using the R and Rstudio programming languages as supporting software.

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