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All Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jiko (Jurnal Informatika dan komputer) Edukasia: Jurnal Pendidikan dan Pembelajaran
Dillah, Salsa
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Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Mathematics Other

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ANALYSIS OF FUZZY C-MEANS IN PERSONALITY CLUSTERING BASED ON THE OCEAN MODEL Pamput, Jessicha; Dillah, Salsa; Muthmainnah, Aindri; Surianto, Dewi Fatmarani
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 3 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i3.8369

Abstract

Personality is the pattern of an individual's behavior in daily life, reflected in their thoughts, feelings, and actions. The Big Five Personality Traits Model, known as OCEAN, helps to understand the complexity of human personality through five main traits. The identification and classification of personality, particularly among students, impacts academic performance, personal development, anxiety levels, and risky behaviors. Collaboration between educators, mental health professionals, and career advisors is crucial to creating an educational environment that supports students' holistic development. The Fuzzy C-Means (FCM) method is used to identify students' personalities with adequate accuracy. This study adopts the OCEAN model with FCM to efficiently identify and classify students' personalities. Data were obtained from 142 respondents, resulting in 27% of respondents being classified in cluster 1, 21% in cluster 2, 18% in cluster 3, 16% in cluster 4, and 18% in cluster 5. This study has important implications for students, educators, and educational institutions to understand that learning patterns, social interactions, and decision-making processes can be influenced by an individual's personality.
Classification of Livin' by Mandiri Customer Satisfaction Using MLP with BM25 and TF-IDF Feature Weighting Mardiah, Aina; Dillah, Salsa; Surianto, Dewi Fatmarani; Fadilah, Nur; Zain, Satria Gunawan
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 3, August 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i3.2248

Abstract

The increasing use of mobile banking applications such as Livin' by Mandiri requires an analysis of customer satisfaction based on user reviews. This study classifies customer satisfaction levels using the Multi-Layer Perceptron (MLP) algorithm with two feature extraction methods, namely BM25 and TF-IDF. A total of 1,143 reviews were collected from the Google Play Store and App Store. Three test scenarios were applied: (1) comparison of feature extraction methods, (2) application of Synthetic Minority Over-Sampling Technique (SMOTE), and (3) application of Synonym Replacement-based Easy Data Augmentation (EDA) technique. The evaluation results show that the combination of BM25 and data augmentation produces the highest performance, with 97% accuracy and 98% precision, recall, and F1-score, respectively. BM25 proved to be more effective in understanding the context of reviews, while data augmentation improved the quality of representation, especially for minority classes such as neutral sentiment. These findings make a significant contribution to the improvement of Livin' by Mandiri digital services and serve as a reference for the development of review-based satisfaction classification systems in the digital banking sector.
Students Understanding of Network Virtualization in Computer Network Courses at Universitas Negeri Makassar Arifin, Afrisal; Rezky Anisar, Muh. Alief; Baso, Fadhlirrahman; Dillah, Salsa; Muliadi, Muliadi; Surianto, Dewi Fatmarani; Parenreng, Jumadi Mabe
EDUKASIA Jurnal Pendidikan dan Pembelajaran Vol. 6 No. 2 (2025)
Publisher : LP. Ma'arif Janggan Magetan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62775/edukasia.v6i2.1653

Abstract

Students’ understanding of network virtualization concepts in Computer Network courses remains limited, particularly in institutions with restricted access to advanced practical facilities. This study aims to analyze students’ comprehension of network virtualization and its relationship with learning experiences, technical skills, and perceptions of simulation tools. A quantitative cross-sectional survey was conducted with 202 students who had completed the Computer Network course and gained experience using Cisco Packet Tracer. The questionnaire was validated through expert judgment and construct validity testing via Exploratory Factor Analysis, and its reliability was confirmed. Findings indicate that four dimensions conceptual understanding, learning experience, technical skills, and perception of simulation tools consistently form a framework explaining students’ understanding of virtualization. Correlation analysis revealed that technical skills exert the strongest influence, while learning experiences and perceptions contribute additional support to conceptual comprehension. These results highlight the importance of integrating simulation media such as Cisco Packet Tracer into Computer Network courses, not only as a means of developing practical skills but also as a strategy to enhance students’ conceptual grasp of virtualization technologies increasingly demanded in the digital industry. This study contributes to the literature by emphasizing the cognitive dimension of simulation-based learning, an aspect often overlooked in prior research that mainly focused on technical performance.
ANALYSIS OF FUZZY C-MEANS IN PERSONALITY CLUSTERING BASED ON THE OCEAN MODEL Pamput, Jessicha; Dillah, Salsa; Muthmainnah, Aindri; Surianto, Dewi Fatmarani
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 3 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i3.8369

Abstract

Personality is the pattern of an individual's behavior in daily life, reflected in their thoughts, feelings, and actions. The Big Five Personality Traits Model, known as OCEAN, helps to understand the complexity of human personality through five main traits. The identification and classification of personality, particularly among students, impacts academic performance, personal development, anxiety levels, and risky behaviors. Collaboration between educators, mental health professionals, and career advisors is crucial to creating an educational environment that supports students' holistic development. The Fuzzy C-Means (FCM) method is used to identify students' personalities with adequate accuracy. This study adopts the OCEAN model with FCM to efficiently identify and classify students' personalities. Data were obtained from 142 respondents, resulting in 27% of respondents being classified in cluster 1, 21% in cluster 2, 18% in cluster 3, 16% in cluster 4, and 18% in cluster 5. This study has important implications for students, educators, and educational institutions to understand that learning patterns, social interactions, and decision-making processes can be influenced by an individual's personality.
Co-Authors Arifin, Afrisal Baso, Fadhlirrahman Dewi Fatmarani Surianto Jumadi Mabe Parenreng MARDIAH, AINA Muliadi Muthmainnah, Aindri NUR FADILAH Pamput, Jessicha Rezky Anisar, Muh. Alief