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All Journal Jiko (Jurnal Informatika dan komputer) Prosiding Seminar Nasional Pendidikan dan Agama
Sherli Sherli
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Religion Humanities Computer Science & IT Social Sciences

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Peningkatan Pemahaman Iman dan Implementasi Nilai Kristiani melalui Project-Based Learning pada Siswa Kelas 10 di Tanjung Palas Barat Sherli Sherli; Gregorius Daru Wijoyoko
PROSIDING SEMINAR NASIONAL PENDIDIKAN DAN AGAMA Vol. 6 No. 2 (2025): PROSIDING SEMINAR NASIONAL PENDIDIKAN DAN AGAMA
Publisher : Sekolah Tinggi Pastoral Kateketik (STPKat) Santo Fransiskus Assisi Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/semnaspa.v6i2.3175

Abstract

Catholic Religious and Character Education (PAK-BP) aims to form students’ character based on Christian values; however, its implementation in schools often remains theoretical. This classroom action research (CAR) seeks to enhance students’ understanding of faith and the application of Christian moral values through the Project-Based Learning (PJBL) method. PJBL emphasizes contextual and experiential learning through projects relevant to students’ daily lives. The research subjects were Grade 10 students in Tanjung Palas Barat, with instruments including classroom observations, project rubrics, and written reflections. The findings show that the use of PJBL effectively improved students’ understanding of faith and their ability to apply Christian moral values in various social, environmental, and school projects. These projects included community service, environmental stewardship, and faith-based school activities that fostered collaboration, responsibility, and compassion. The outcomes also align with Church teachings as stated in the Catechism of the Catholic Church (nos. 1700–1789) and Amoris Laetitia, emphasizing moral formation and faith lived through love. In conclusion, the implementation of PJBL in Catholic Religious Education successfully internalized Christian values in a contextual and meaningful way. This method enables students not only to learn about faith but also to practice it through real-life actions rooted in Gospel values.
SYSTEMATIC REVIEW: COMPARISON OF ARTIFICIAL INTELLIGENCE METHODS FOR PAIN DETECTION Fiona Angeline; Sherli Sherli; Devin Tanadi; Rendi Winata; Christnatalis HS
JIKO (Jurnal Informatika dan Komputer) Vol 9 No 2 (2026)
Publisher : Program Studi Teknik Informatika Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v9i2.11648

Abstract

Objective pain detection remains a major challenge in healthcare because conventional assessments rely on subjective self-reports. Advances in artificial intelligence (AI) have enabled more objective approaches by analyzing biological signals and human expressions. Facial expressions and functional Near-Infrared Spectroscopy (fNIRS) are widely studied due to their complementary characteristics. Facial expressions are non-invasive, easy to capture, and strongly associated with visible pain responses, making them suitable for real-time applications. In contrast, fNIRS measures brain activity related to pain perception, providing a more objective physiological perspective. This study follows PRISMA guidelines for systematic literature review. Studies were identified from major databases, screened for relevance, assessed for eligibility, and included for final analysis. A total of 45 studies were selected. Previous research shows that AI, especially deep learning, is effective in analyzing facial expressions and fNIRS signals for pain detection. However, few studies systematically compare these modalities in a unified framework. This review highlights a shift from single-modality to hybrid and multimodal approaches integrating facial and fNIRS data. Deep learning models, particularly CNNs for facial analysis and hybrid machine learning–deep learning methods for physiological signals, dominate recent studies. Multimodal fusion consistently outperforms single-modality approaches, improving accuracy and robustness in pain detection tasks
Co-Authors Devin Tanadi Fiona Angeline Gregorius Daru Wijoyoko HS, Christnatalis Rendi Winata