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All Journal Jurnal VOK@SINDO Journal of Information Systems Engineering and Business Intelligence Fountain of Informatics Journal Konvergensi Jurnal Ilmu Komputer dan Desain Komunikasi Visual Jurnal Berdaya Mandiri Indexia Jurnal Dinamika Informatika (JDI) Journal of Information Technology and Cyber Security
Mandita, Fridy
Universitas 17 Agustus 1945 Surabaya
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Journal : Journal of Information Technology and Cyber Security

 1 
A Child Growth and Development Evaluation Using Weighted Product Method Januantoro, Ardy; Mandita, Fridy
Journal of Information Technology and Cyber Security Vol. 1 No. 1 (2023): January
Publisher : Department of Information Systems and Technology, Faculty of Intelligent Electrical and Informatics Technology, Universitas 17 Agustus 1945 Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30996/jitcs.7613

Abstract

Child development is one of the factors that must be considered in improving a country's education. The level of maturity of human resources is able to maximize starting from childhood. The guidebook of the Ministry of Education and Culture of the Republic of Indonesia (Kemendikbud RI) in 2018 contained six indicators to assess children's learning ability, namely: 1) Moral, 2) Social, 3) Language, 4) Cognitive, 5) Motor, and 6) Art. This study implements these indicators to evaluate children's growth and development. The evaluation method uses the Weighted Product Method (WPM). WPM provides a ranking of the result of the evaluation. In addition, WPM also has an assessment of Beneficial and non-beneficial as a more relevant assessment between indicators. Data were collected by questionnaire at kindergarten schools with the respondents' age average of 5-6 years. The results will be calculated with indicators criteria weights given. The test results recommended for students between 0.65 to 0.62 are as follows: Mahmud, Diko, Cindy, Denny, and Riko. The kindergarten manager can use these recommendations to increase the student's aptitude.
Classification of Volcanic Status Events Using Autocorrelation and Support Vector Machine Methods Mandita, Fridy; Fajriyansah, Muhammad Arif
Journal of Information Technology and Cyber Security Vol. 4 No. 1 (2026): January
Publisher : Department of Information Systems and Technology, Faculty of Intelligent Electrical and Informatics Technology, Universitas 17 Agustus 1945 Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30996/jitcs.133023

Abstract

Volcanic eruption disasters occur frequently in Indonesia due to the high density of active volcanoes, posing persistent risks to surrounding communities and infrastructure. Effective mitigation of these hazards is challenged by limitations in monitoring systems, particularly related to instrumentation coverage and the availability of expert human resources. One critical aspect of volcanic monitoring is the accurate classification of seismic activity, which reflects subsurface volcanic processes and supports timely hazard assessment. This study addresses the challenge of reliably classifying volcanic seismic events by proposing an integrated framework that combines autocorrelation-based signal characterization with Support Vector Machine (SVM)–based multi-class classification, supported by Z-score normalization during data preprocessing. The framework is designed to enhance feature consistency and robustness against noise commonly present in volcanic seismic signals. To evaluate its effectiveness, three SVM kernel functions—linear, polynomial, and radial basis function (RBF)—are systematically assessed under identical experimental conditions. The results demonstrate that the polynomial SVM kernel with a degree of two provides the most reliable classification performance, achieving an accuracy of 0.9605. In addition, the application of Z-score normalization substantially improves model stability and overall performance across all kernel configurations, indicating that feature scaling plays a critical role in SVM-based seismic classification. Performance variations among kernels suggest that non-linear feature representations are better suited to capture the complex characteristics of volcanic seismic signals, while classification errors are primarily influenced by class imbalance in underrepresented event types. These findings indicate that the proposed framework effectively supports automated volcanic seismic signal analysis and has the potential to enhance the reliability of seismic-based volcanic activity monitoring.
Co-Authors Agus Hermanto Aidil Primasetya Armin Anton Breva Yunanda Bramantyo Kusumo Jati Fajriyansah, Muhammad Arif Januantoro, Ardy Lintang Ragadanu Arizona Rizal Kurnia Pratama Supangat Supangat