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All Journal JPPPF (Jurnal Penelitian dan Pengembangan Pendidikan Fisika) Contemporary Mathematics and Applications (ConMathA) Jurnal Krakatau (Indonesian of Multidiciplinary Journal) Limits: Journal of Mathematics and Its Applications
Dafik
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Computer Science & IT Education Library & Information Science Materials Science & Nanotechnology Mathematics Physics Social Sciences Other

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Rainbow Connection on Amal(Fn,xz,m) Graphs and Amal(On,xz,m) Graphs Muhammad Usaid Hudloir; Dafik; Adawiyah, Robiatul; Rafiantika Megahnia Prihandini; Arika Indah Kristiana
Contemporary Mathematics and Applications (ConMathA) Vol. 6 No. 2 (2024)
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/conmatha.v6i2.56201

Abstract

Coloring graph is giving a color to a set of vertices and a set of edges on a graph. The condition for coloring a graph is that each color is different for each neighboring member graph. Coloring graph can be done by mapping a different color to each vertex or edge. Rainbow coloring is a type of rainbow connected with coloring edge. It ensures that every graph G has a rainbow path. A rainbow path is a path in a graph where no two vertices have the same color. The minimum number of colors in a rainbow connected graph is called the rainbow connection number denoted by rc(G). The graphs used in this study are the Amal(Fn,xz,m) graph and the Amal(On,xz,m) graph.
Kerangka Aktivitas Research Based Learning Dengan Pendekatan Steam: "Analisis Teknologi Irigasi Kapilaritas Dan Tetes Serta Penyiram Otomatis Berbasis Panel Surya Dengan Smart Sensor Untuk Meningkatkan Literasi Perubahan Iklim Siswa” Okti Anis Safiati; Dafik; Puji Lestari; Syaiful Rahman
KRAKATAU (Indonesian of Multidisciplinary Journals) Vol. 2 No. 1 (2024): Februari
Publisher : Desanta Publisher

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Abstract

Climate change literacy Refers to a deep understanding and awareness of climate change issues, including knowledge of the causes, impacts, and adaptation and mitigation strategies that can be implemented. These skills include not only theoretical knowledge but also practical skills to assess climate-related information, make decisions based on evidence, and participate in dialogue and action aimed at addressing climate challenges. With adequate literacy, individuals and communities can be more effective in identifying sustainable solutions, advocating for science-based policies, and contributing to global and local efforts to address climate change, thereby helping maintain ecological balance and maintaining quality of life for future generations. The aim of this research is to increase climate change literacy among students through the application of analysis and innovative irrigation technology, including capillarity irrigation, drip irrigation, and automatic irrigation systems powered by solar panels with smart sensor integration. The main output of this research is the development of a Research Based Learning (RBL) Activity Framework with a STEAM (Science, Technology, Engineering, Art and Mathematics) Approach which focuses on capillarity and drip irrigation technology, as well as automatic watering powered by solar panels with smart sensor integration. This framework is designed to provide systematic and structured guidance for educators and students in exploring scientific concepts and their application in irrigation technology. The method used in this research is descriptive narrative related to the student activity framework. The results of this research are the results of planting a drip and absorption system and the use of solar panels in an automatic sprinkler system to increase students' climate change literacy. In this research, a Research Based Learning model was applied which was integrated with the STEAM (Science, Technology, Engineering, Art, and Mathematics) approach to increase students' climate change literacy by presenting STEAM problem solving in learning. The STEAM problem raised in this research is the impact of climate change on the environment. The results of this research are in the form of a STEAM problem formulation, integration of RBL_STEAM syntax and including a description of five STEAM studies, and finally the development of a climate change literacy assessment instrument.
Meningkatkan Keterampilan Berpikir Kreatif Siswa: Implementasi Rbl-Stem Dalam Mengembangkan Urban Farming “Bananaponik” Okti Anis Safiati; Rini Purwaningtyas; Siti chususiyah; Diana Puji Lestari; Ida Fitriati; Dafik
KRAKATAU (Indonesian of Multidisciplinary Journals) Vol. 2 No. 2 (2024): Agustus
Publisher : Desanta Publisher

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Abstract

Students' creative thinking abilities are very important in the current era. In an ever-changing world with complex problems and challenges, creative thinking skills enable individuals to generate innovative solutions, think outside the box, and adapt to new situations effectively. Despite the importance of creative thinking skills, there are challenges in fostering and developing creative thinking skills among students. Many traditional education systems prioritize memorization and standardized tests over creativity and critical thinking. This can limit opportunities for students to develop and practice their creativity. To address this, it is necessary to integrate more creative and research-based learning activities into the classroom, allowing students to engage in multiple project assignments. In this research, we implemented the Research Based Learning (RBL) model together with a STEM approach to involve students in bananaponics urban farming development project tasks. Students were given the task of planning to plant several types of vegetables on banana stems in the school backyard and grouping them in two weeks. The results of this research are in the form of a STEM problem formulation, integration of RBL_STEM syntax and including a description of four STEM studies, and finally the development of an assessment instrument for increasing students' creativity abilities.
Computer Vision on Education: Fostering AI Literacy using RBL-STEM with Google Teachable Machine Ridlo, Zainur Rasyid; Dafik; Silvi Putri Ayu Ningsih; Azza Liarista Anggraini
Jurnal Penelitian & Pengembangan Pendidikan Fisika Vol. 11 No. 2 (2025): JPPPF (Jurnal Penelitian dan Pengembangan Pendidikan Fisika), Volume 11 Issue
Publisher : Program Studi Pendidikan Fisika Universitas Negeri Jakarta, LPPM Universitas Negeri Jakarta, HFI Jakarta, HFI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/1.11205

Abstract

This study aims to analyze the application of the RBL-STEM learning model using Google Teachable Machine as a computer vision-based learning media to improve AI literacy. The Research Based Learning-STEM (RBL-STEM) learning model is a learning model that integrates research activities in learning using the STEM approach. Convolutional Neural Network (CNN) is a branch of computer vision that uses artificial intelligence algorithms that are very effective in developing AI products to process image-shaped data. This study utilized a mixed methods approach that integrates quantitative and qualitative techniques to explore the improvement of AI literacy. The participants in this study were 139 undergraduate students of science education study program, Faculty of Teacher Training and Education, University of Jember who participated in the study were taking introductory information technology courses for science education, the sample selection method used was purposive sampling. The quantitative method utilized a pre-test and post-test design, which included the analysis of mean scores, standard deviation, and the observed increase in mean scores. The quantitative method used a survey on AI literacy. The pretest mean score was 38.33 with a standard deviation of 13.41, while the posttest mean score was 71.49 with a standard deviation of 9.37 with a Wilcoxon signed rank-test result of -8.468, indicating a significant effect of the RBL-STEM learning model on students' AI literacy. The high standard deviation on the pretest indicates that there is a large variation in the AI literacy level of the students before the learning begins. This is due to students' different backgrounds, experiences and understanding of AI technology. Some students may be familiar with AI, while others have not been exposed to it at all. This inequality causes a wide spread of scores. After the implementation of the RBL-STEM model with Google Teachable Machine, the standard deviation decreased, indicating that this learning not only improved the average AI literacy, but also made the improvement more even. The AI literacy survey results showed an average score of 3.48, indicating that 69% of students showed an understanding of AI literacy. The implementation of the RBL-STEM model of teaching with Google Teachable Machine is able to train students to conduct research integrated in learning activities, the role of Google Teachable machine as an AI-based learning media is able to improve student AI literacy because the use of AI-based learning media creates a new, interactive, and fun learning atmosphere. Based on the findings of the analysis, it can be concluded that the application of the RBL-STEM model has a significant impact in improving students' AI literacy.
Pemodelan Matematika Pada Kasus Kecanduan Game Online Menggunakan Metode Runge-Kutta Orde 14 Arif Fatahillah; Maulida Istiqomah; Dafik
Limits: Journal of Mathematics and Its Applications Vol. 18 No. 2 (2021): Limits: Journal of Mathematics and Its Applications Volume 18 Nomor 2 Edisi No
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

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Abstract

Banyak permasalahan di kehidupan nyata dapat dibentuk kedalam model matematika sehingga dapat dianalisis secara matematik. Salah satunya adalah kasus kecanduan game online yang sedang marak saat ini. Model matematika pada kasus kecanduan game online telah dikembangkan dan dikemas dalam model SEIRS yang berbentuk sistem persamaan diferensial biasa non linier orde satu. Model tersebut sangat kompleks sehingga memerlukan metode numerik untuk menyelesaikannya. Salah satu metode numerik yang efektif adalah metode Runge-Kutta, lebih tepatnya digunakan metode Runge-Kutta orde 14. Penelitian ini akan merumuskan formulasi metode Runge-Kutta orde empat belas dan membuat format pemrograman MATLAB kemudian menganalisis efektifitas metode tersebut dalam menyelesaikan model matematika SEIRS pada kasus kecanduan game online . Efektifitas suatu metode bergantung pada error yang dihasilkan dari eksekusi MATLAB ketika hasilnya semakin kecil (mendekati nol). Metode pengumpulan data yang digunakan adalah metode dokumentasi dan kuesioner. Hasil penelitian ini menunjukkan bahwa metode Runge-Kutta orde empat belas efektif dalam menyelesaikan model SEIRS pada kasus kecanduan game online .
Co-Authors Arika Indah Kristiana Azza Liarista Anggraini Diana Puji Lestari Ida Fitriati Maulida Istiqomah Muhammad Usaid Hudloir Okti Anis Safiati PUJI LESTARI Rafiantika Megahnia Prihandini Ridlo, Zainur Rasyid Rini Purwaningtyas Robiatul Adawiyah Silvi Putri Ayu Ningsih Siti chususiyah Syaiful Rahman