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All Journal PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Smatika Jurnal : STIKI Informatika Jurnal
Mardi Yudhi Putra
Bina Insani University
Computer Science & IT Decision Sciences, Operations Research & Management

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Implementasi Sistem Penerimaan Mahasiswa Baru Universitas Bina Insani Putro Dwi Mulyo; Mardi Yudhi Putra
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 15 No 02 (2025): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v15i02.1954

Abstract

New student admission is a very important process in higher education that requires fast, accurate, and organized information. At Bina Insani University, this process was previously done manually and using WhatsApp messaging services directly by the admin, which caused delays in response, repeated questions, and difficulties in managing registration documents. The purpose of this research is to build a new student admission information system that can speed up the registration process, reduce admin workload, and centralize registration documents digitally. This research uses the Extreme Programming (XP) method with data collection techniques through observation, interviews, and literature studies. This system is implemented using CodeIgniter with MySQL database and integrates real-time notifications via WhatsApp and Email using RabbitMQ architecture as a message broker. The results of the system implementation show that automatic notifications can improve the efficiency of information services, reduce repetitive questions, and speed up the response and document distribution process. The system also facilitates the search for registration documents with Google Drive integration. In conclusion, this system makes a significant contribution in improving the quality of new student admission services at Bina Insani University.
Enhancing Transformer Performance through Contextual Labeling: A Case Study on Student Mental Health Prediction Mardi Yudhi Putra; Dwi Ismiyana Putri; Rika Apriani; Renaldi Triharsono; Dewi Mufadilah
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 14 No. 1 (2026): March 2026
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v14i1.11800

Abstract

 Early identification of stress and depression among university students is essential to support timely psychological intervention, yet traditional counseling methods often rely on manual, self-initiated reporting that may overlook students experiencing emotional distress. This study aimed to develop a text-based mental-health detection framework using transformer models supported by contextual labeling to analyze student-generated social-media content. The research was conducted through three stages: problem exploration with the Student Affairs Division, data collection from questionnaires and 993 social-media text entries, and comprehensive data preprocessing involving cleaning, normalization, deduplication, and lexicon-based weak labeling. The cleaned dataset was used to fine-tune two transformer architectures—RoBERTa for sequence classification and T5 for text-to-text classification—and to construct a majority-vote ensemble. Model performance was evaluated using accuracy, precision, recall, F1-score, and confusion matrices. The results showed that the T5 model achieved the most balanced performance across all categories, particularly in distinguishing neutral and stress expressions, while RoBERTa and the ensemble exhibited strong prediction bias toward a single class. The findings demonstrated that contextual preprocessing combined with transformer-based modeling effectively supported automated detection of student emotional states. This study concluded that transformer models, especially T5 with contextual labeling, offered a promising foundation for developing early-warning systems that can be integrated into university counseling services and further enhanced through expanded datasets, expert-validated annotations, and explainable-AI components.
Co-Authors Dewi Mufadilah Dwi Ismiyana Putri Putro Dwi Mulyo Renaldi Triharsono Rika Apriani