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All Journal PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Compiler
Rika Apriani
Bina Insani University
Computer Science & IT Decision Sciences, Operations Research & Management

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The Impact of Cloud-Based Information Systems on Organizational Performance in Education: A PIECES Framework Evaluation in Bekasi City Sumardiono Sumardiono; Rika Apriani; Sigit Setiawan; Fazril Mantovani; Reykhando Rifki Awiliyanto; Adrianus Trigunadi Santoso
Compiler Vol 15, No 1 (2026): May
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/compiler.v15i1.3643

Abstract

This study aims to evaluate the impact of cloud-based information system implementation on organizational performance in the education sector, particularly in Bekasi City. The method used is descriptive-quantitative with the PIECES framework approach (Performance, Information, Economy, Control, Efficiency, Service). Data were collected through questionnaires and interviews with 10 respondents consisting of education service operators, teachers, and school operators. The results show that five PIECES dimentions—namely Performance (4.38), Information (4.22), Economy (4.20), Efficiency (4.33), and Service (4.35)—are in the very satisfactory category. However, the Control dimention only obtained a score of 2.85, which is included in the neutral category, indicating weaknesses in system security governance. This study concludes that although cloud systems have improved operational efficiency and service quality, regular information system security audits and the development of standard operating procedures (SOPs) are needed to strengthen the control dimention. The implications of this study serve as a basis for developing better information security governance in the digital transformation of education.
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 Adrianus Trigunadi Santoso Dewi Mufadilah Dwi Ismiyana Putri Fazril Mantovani Mardi Yudhi Putra Renaldi Triharsono Reykhando Rifki Awiliyanto Sigit Setiawan Sumardiono Sumardiono