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INDONESIA
Jurnal Sistem Cerdas
Published by Asosiasi Prakarsa Indonesia Cerdas
ISSN : -     EISSN : 26228254     DOI : -
Core Subject : Science, Education, Engineering,
Automotive Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering
Jurnal Sistem Cerdas dengan eISSN : 2622-8254 adalah media publikasi hasil penelitian yang mendukung penelitian dan pengembangan kota, desa, sektor dan kesistemam lainnya. Jurnal ini diterbitkan oleh Asosiasi Prakarsa Indonesia Cerdas (APIC) dan terbit setiap empat bulan sekali.
Arjuna Subject : Umum - Umum
Articles 13 Documents
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Search results for , issue "Vol. 8 No. 2 (2025)" : 13 Documents clear
Game Innovation; Nglegena World for Elementary Students Latif, Ahmad Rizqi Latif Oktavian; Gandi, Andrian Gandi Wijanarko; Fadloli, M Fadloli Al Hakim
Jurnal Sistem Cerdas Vol. 8 No. 2 (2025)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v8i2.396

Abstract

The use of learning media, especially in the context of developing educational games, has a significant role in facilitating effective learning, especially in introducing abstract material concretely to students. Learning media helps students overcome learning obstacles, create innovative solutions, and increase interest and understanding of learning material. This development discusses the implementation of the Nglegena World educational game, which was developed using RPG Maker MV software, as an innovative alternative in learning Javanese. Nglegena World focuses on learning basic Javanese script, helping students understand the concept and use of Javanese script in an interactive and fun way. By involving teachers and students in the learning process, this game has a positive impact in improving teachers' teaching skills, diversifying learning methods, as well as students' interest, understanding and knowledge of Javanese language and Javanese script. Keywords: learning media, educational games, Nglegena World, Javanese language, Javanese script, RPG Maker MV, innovative learning.
Excessive Permissions Investigation with Data-Driven Account Security with Classification Setiawan, Heri Satria; Pamuji, Agus; Suparman, Rudi
Jurnal Sistem Cerdas Vol. 8 No. 2 (2025)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v8i2.397

Abstract

Many companies lack configuration systems due to the need to protect assets from unauthorized access by individuals or groups. Data mining can help by securing the configuration system to identify accounts in the database. Given the sensitivity of activities on the database system, access permissions are a major concern, especially with unauthorized users. Excessive permissions can compromise database security, making it important to group users into authorized and unauthorized classes. This study uses the decision tree method to extract and investigate factors that affect excessive permissions, and validates the dataset with 10-fold cross-validation to ensure data quality. The final result identifies two classes for user access, showing that the decision tree method performs well with significant values on the AUC curve and the Confusion Matrix
Anxiety Anxiety Detection Based on EEG Signal using 1-D Convolutional Neural Network Classifier Fadhilah Qalbi Annisa
Jurnal Sistem Cerdas Vol. 8 No. 2 (2025)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v8i3.506

Abstract

Anxiety is defined as fear and symptoms of somatic tension experienced when a threat or danger is anticipated. In recent years, biological markers have been explored to detect anxiety noninvasively, one method is Electroencephalography (EEG). Detecting state anxiety using EEG is an intriguing area of research. This study detects the state of anxiety based on an EEG signal using a 1-Dimensional Convolutional Neural Network (1-D CNN). The dataset is provided by the Database for Anxious States based on Psychological Stimulation (DASPS). DASPS is an EEG recording obtained from twenty-three participants for this investigation. The data were analyzed for statistical features, and then a 1-D CNN was employed to classify anxiety levels. The results show that 95.1% of mild and severe anxious conditions can be accurately detected. Furthermore, 94.8% of detection accuracy is achieved when anxiety is classified as normal, mild, moderate, or severe. Overall, this study provides a solid foundation for multi-level anxiety detection by improving the accuracy and selecting better features.

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