Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Jurnal Cahaya Mandalika Emerging Information Science and Technology
Azizah, Laila Ma'rifatul
Unknown Affiliation
Humanities Computer Science & IT Languange, Linguistic, Communication & Media

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
IMPLEMENTASI DEVOPS PADA PENGEMBANGAN IMPLEMENTASI SISTEM MONITORING PEMBIMBING TUGAS AKHIR BERBASIS ANDROID Kurnianti, Apriliya; Al Fauzan, Moch Nurul Indra; Azizah, Laila Ma'rifatul
Jurnal Cahaya Mandalika ISSN 2721-4796 (online) Vol. 3 No. 3 (2022)
Publisher : Institut Penelitian Dan Pengambangan Mandalika Indonesia (IP2MI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36312/jcm.v3i3.1380

Abstract

Final or thesis is a scientific work written by a student at the time of completing his studies. Problems that arise when working on a thesis include, sometimes students do not have enough time to complete their final assignments, so they have to procrastinate their work; usually there is also a time limit to complete the final project, so students have to work very quickly; And finally, students often feel stressed when they are working on their final assignment. Ineffective use of time, low motivation to excel, student indiscipline, a guidance process that requires face-to-face and also the busyness of lecturers are the main causes of hampering student thesis. During a pandemic like today, face-to-face guidance is greatly minimized. More guidance is done online. However, online guidance also creates new problems, namely chat history that is often deleted or students who forget to document, record the guidance process or even students forget to record the guidance process. Meanwhile, every revision of the guidance must always be recorded on the thesis monitoring sheet. This study aims to design an android-based online final project monitoring system application. The research method uses the DevOps method as a system development method. The results of the study are in the form of an android-based final project monitoring application.
Understanding Burnout Experiences in Social Media Discourse: Evidence from YouTube User Comments Putri, Nisrina Akbar Rizky; Ardiansyah, Ardiansyah; Widyastuti, Erma; Azizah, Laila Ma'rifatul
Emerging Information Science and Technology Vol. 7 No. 1 (2026): May
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/eist.v7i1.31343

Abstract

Burnout has become an important psychological concern that is increasingly discussed through social media, providing valuable textual data for understanding public experiences of emotional exhaustion, workplace pressure, and coping. This study analyzes sentiment in burnout related YouTube comments using DistilIndoBERT and examines the contribution of back translation to classification performance. The initial dataset consisted of 2,931 comments collected from 5 YouTube videos published between 2021 and 2025 was subjected to a data quality audit that removed exact duplicates, promotional content, spam, and nonmeaningful comments, resulting in 2,829 relevant records. Sentiment labels were assigned through a semi automated process and reviewed by the researchers into positive, neutral, and negative categories. The dataset was divided using stratified sampling into 70% training data, 15% validation data, and 15% test data. Back translation was applied exclusively to the positive and neutral classes in the training set to prevent data leakage, expanding the training data from 1,980 to 3,003 records. Negative sentiment was dominant, accounting for 1,430 comments or 50.55%, followed by neutral sentiment with 846 comments or 29.90% and positive sentiment with 553 comments or 19.55%. DistilIndoBERT achieved 82.4% accuracy, 81.9% macro precision, 81.5% macro recall, and 81.6% macro F1 score on the original dataset. After augmentation, the respective scores increased to 87.1%, 86.8%, 86.2%, and 86.4%. These observed improvements demonstrate the potential of training focused back translation to strengthen DistilIndoBERT classification of burnout discourse.
Co-Authors Al Fauzan, Moch Nurul Indra Ardiansyah Ardiansyah Erma Widyastuti Kurnianti, Apriliya Putri, Nisrina Akbar Rizky