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All Journal Building of Informatics, Technology and Science Andragogi Jurnal Pendidikan dan Pembelajaran
Farihah, Lailatul
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Computer Science & IT Education Languange, Linguistic, Communication & Media Other

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Perilaku dan Motivasi Anak Angkringan dalam Judi Online di Indonesia Ningsih, Susiyati; Farihah, Lailatul; Prasetya, Benny
Andragogi: Jurnal Pendidikan dan Pembelajaran Vol. 5 No. 1 (2025): Pendidikan dan Pembelajaran
Publisher : Universitas KH. Abdul Chalim Pacet Mojokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31538/adrg.v5i1.1310

Abstract

This study is about "The Behavior and Motivation of Angkringan Children in Gambling (Case Study in Laweyan Village)." The research aims to identify and analyze the behavior and motivation of children working at angkringan (street food stalls) in relation to their involvement in gambling activities in Laweyan Village. The study employs a case study approach to gain a deep and comprehensive understanding of the various social and psychological factors influencing children's engagement in gambling. Data collection was carried out through detailed participant observation, in-depth interviews with various relevant parties, and analysis of pertinent documents.The findings reveal that economic factors are the primary drivers for children to engage in gambling, with urgent financial needs compelling them to seek alternative income sources. The social environment also plays a significant role, particularly in terms of peer influence and community norms that do not strongly condemn gambling activities. The lack of supervision and attention from parents further reinforces children's involvement in these activities.The study concludes that holistic and coordinated interventions from the government, educational institutions, and social organizations are crucial in addressing this issue.
ObeCheck Sebagai Platform Penerapan Metode K-Nearest Neighbors untuk Klasifikasi Obesitas Berbasis Website Farihah, Lailatul; Subekti, Puji
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6726

Abstract

The increase in obesity has become one of the major challenges in the healthcare sector, requiring quick and effective solutions for early classification and diagnosis. This study aims to develop a web-based system using the K-Nearest Neighbors (KNN) method to classify obesity based on user data, thereby assisting the public in early detection of obesity. The dataset used in this research comprises 2,111 records and 17 attributes, covering various factors related to obesity, such as weight, height, age, gender, genetic factors, and lifestyle, including dietary habits and physical activity. This dataset was obtained from the UCI Repository website. The data is processed using the K-Nearest Neighbors (KNN) method to generate an accurate and relevant obesity classification model. To evaluate the performance of the K-Nearest Neighbors (KNN) model, the dataset was split into training and testing data with a ratio of 80:20 and evaluated using a Confusion Matrix, resulting in an accuracy of 89%. Since the model demonstrates good performance in classifying test data, it can be implemented as a web-based system to test new data. This system will produce weight classification results, including categories such as "Underweight," "Normal Weight," "Overweight," and "Obesity." Thus, the public can easily and accurately classify obesity using this system.
Co-Authors Benny Prasetya, Benny Ningsih, Susiyati Puji Subekti