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All Journal Jurnal Padegogik Jurnal Ilmu Komputer
Eko Kristianto
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Mathematics Social Sciences Other

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PEMBELAJARAN NUMBER HEAD TOGETHER DAN ING NGARSA SUNG TULADHA UNTUK MENINGKATKAN KEMAMPUAN KOMUNIKASI MATEMATIS Eko Kristianto; Louise M Saija
Jurnal Padegogik Vol 4 No 1 (2021): Jurnal Padegogik: Februari 2021
Publisher : LPPM Universitas Advent Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35974/jpd.v4i1.2460

Abstract

Mathematical communication ability is important because it can train students thinking and reasoning, develop creative activities, develop problem solving skills and develop ability to convey information. But previous researchs shows that mathematical communication skill still not high, so it still needs to be improved. The purpose of this study is to know the Junior High School students' mathematical communication improvements, and whether there are different improvement between students who acquire Number Head Together cooperative learning model and Ing Ngarsa Sung Tuladha. This comparative designed study used 7 essay mathematical communication problems and response questionaire as the research instruments. The sample of this study were students in two grades VII of SMP Negeri 3 Parongpong, West Bandung. The result shows that the students’ mathematical communication improvement of those who acquire Number Head Together cooperative learning model and Ing Ngarsa Sung Tuladha are both in the moderate category, and they are not different statistically. Another result shows that students really like to learn using Number Head Together or Ing Ngarsa Sung Tuladha cooperative learning model.
Analisis Kinerja Sistem Deteksi Intrusi Jaringan Internet Of Things Berbasis Metode Ensemble Eko Kristianto; Arya Adhyaksa Waskita; Thoyyibah Tanjung
Jurnal Ilmu Komputer Vol 2 No 2 (2024): Jurnal Ilmu Komputer (Edisi Desember 2024)
Publisher : Universitas Pamulang

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Abstract

Network intrusion has rapidly evolved, posing significant risks to IT infrastructure. To address this, ensemble learning, known for its robust classification capabilities, is applied to IoT network traffic using the public RT_IOT2022 dataset. Models such as CatBoost, Extreme Gradient Boost (XGBoost), and LightGBM were developed and evaluated. The dataset was normalized using the Normalizer and MinMaxScaler functions from the scikit-learn framework. Model training was conducted with an 80:20 fixed data split for training and testing, along with 5-fold cross-validation. Testing revealed that XGBoost with MinMaxScaler and the 80:20 split achieved the highest accuracy of 99.89%. However, accuracy decreased to 94.04% when using 5-fold cross-validation. Nevertheless, XGBoost with MinMaxScaler consistently demonstrated the fastest computation time across all schemes. For instance, it required only 15 seconds for the fixed split scheme compared to 59 seconds for 5-fold cross-validation. These findings highlight the efficiency and accuracy of XGBoost when combined with MinMaxScaler under specific validation schemes.
Co-Authors Arya Adhyaksa Waskita Louise M Saija Thoyyibah Tanjung