A Simple Classifier for Detecting Online Child Grooming Conversation

Gunawan, Fergyanto E.; Ashianti, Livia; Sekishita, Nobumasa

doi:10.12928/telkomnika.v16i3.6745

Cited by 10 publications

(6 citation statements)

References 10 publications

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“…Sari, 2021). Penelitian lain yang juga membahas cyber child grooming, tidak membahas cara pencegahannya agar tidak terjadi pada anak dan hanya berbentuk artikel penelitian (Andaru, 2021;Gunawan et al, 2018;Salamor et al, 2020;Suendra & Mulyawati, 2020).…”

Section: Pendahuluanunclassified

Perancangan Motion Graphic “Awas Predator Online” sebagai Pencegahan Kasus Cyber Child Grooming

Yuwono

Saputra²

2023

ANDHARUPA

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AbstrakMeningkatnya penggunaan teknologi internet dalam kehidupan sehari-hari menyebabkan kenaikan drastis dalam kasus KBGO, salah satunya adalah cyber child grooming. Cyber child grooming mempermainkan psikologis anak dengan penggunaan teknologi agar korban mau menuruti keinginan pelaku yang berujung ke pelecehan seksual terhadap anak. Orangtua memiliki peranan penting untuk melindungi anak mereka sehingga mendorong pembuatan perancangan motion graphic mengenai bahaya cyber child grooming dan pencegahannya agar dapat menjadi media edukasi yang membantu orangtua untuk mengedukasi anaknya. Dengan metode kualitatif pendekatan deskriptif, perancangan pipeline animation, video animasi motion graphic dibuat dengan gaya visual flat design dan narasi yang telah disederhanakan agar dapat dimengerti oleh target audiens dengan pembahasan mengenai deskripsi KBGO dan cyber child grooming, 6 cara umum yang dilakukan pelaku, dampak yang dihasilkan, pencegahan yang dilihat dari 4 sisi yaitu dari segi privasi tubuh dan data diri, segi game online, segi media sosial, dan segi psikologis anak. Diharapkan media karya utama dapat membantu menambah wawasan dan kesadaran orangtua mengenai cyber child grooming, menjadi media edukasi untuk membantu orangtua mengedukasi anaknya, serta mengurangi kasus KBGO dan cyber child grooming di masa depan. Kata Kunci: cyber child grooming, KBGO, motion graphic AbstractThe increase of online technology users in everyday lifes, also increase the online gender-based violence which one of them is cyber child grooming. Cyber child grooming manipulate a child’s psychology with technology so the child will want to do everything the perpetrator says. Parents have important role to ensure their children’s safety which drives the plan to design a motion graphic about the danger of cyber child grooming and how to prevent it so it can be an educative media to help parents educate their children. With qualitative method and descriptive approach, pipeline animation planning, the motion graphic animated video was made with flat design visual style and simplified narration so it can be easily understood by the audience which discuss about online gender-based and cyber child grooming description, 6 usual method of the perpetrator, the impact they make, how to prevent it from 4 sides, body and personal information privacy, online games, social media, and the child psychology. Hopefully the video can help increase parents’ awareness about cyber child grooming, becomes an educative media to help parents educate their children, decrease the online gender-based violence and cyber child grooming cases in the future. Keyword: cyber child grooming, online gender-based violence, motion graphic

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Section: Pendahuluanunclassified

Perancangan Motion Graphic “Awas Predator Online” sebagai Pencegahan Kasus Cyber Child Grooming

Yuwono

Saputra²

2023

ANDHARUPA

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“…1. In [32], a classifier was built to label chats as grooming or non-grooming. The dataset was compiled using 105 grooming chats collected from the PJ website and 45 non-grooming chats collected from the website in [114].…”

Section: Ai Protection Mechanisms Against Child Predatorsmentioning

confidence: 99%

“…The most widely used technique for detecting child predators on chatting platforms is text classification, which utilizes the two available data sources reported in [53], [54]. ML methods used to detect cyberbullying [27], [28], [31], [48] and sexual predatory behavior [25], [29], [30], [32]- [40], [42]- [47], [49]- [52] remain the main focus of research over the last 10 years. In [55], a brief survey of ML algorithms was used to detect child grooming behavior on social media between 2007 and 2016.…”

Section: Introductionmentioning

confidence: 99%

Child Safety and Protection in the Online Gaming Ecosystem

et al. 2022

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Online gaming no longer has limited access, as it has become available to a high percentage of children in recent years. Consequently, children are exposed to multifaceted threats, such as cyberbullying, grooming, and sexting. Although the online gaming industry is taking concerted measures to create a safe environment for children to play and interact with, such efforts remain inadequate and fragmented. There is a vital need to develop laws and policies to regulate and build minimum standards for the industry to safeguard and protect children online on the one hand, while promoting innovations in the gaming industry to preempt such threats. Many tools have been adapted to control threats against children in the form of content filtering and parental controls, thereby restricting contact with children to protect them from child predators. Different approaches utilizing machine learning (ML) techniques to detect child predatory behavior have been designed to provide potential detection and protection in this context. In this paper, we survey online threats to children in the gaming environment and present the limitations of existing solutions that address these threats. We also aimed to present the challenges that ML techniques face in protecting children against predatory behavior by presenting a systematic review of the available techniques in the literature. Therefore, this analysis provides not only recommendations to stakeholders to develop policies and practices that safeguard children when gaming, but also to the gaming industry to continue providing appropriate measures for a safe and entertaining gaming environment.

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“…K-NN adalah metode klasifikasi teks dengan pendekatan pembelajaran diawasi berdasarkan jarak terdekat (Putri, Herlambang and Wihandika, 2017). Untuk tujuan klasifikasi, metode mengevaluasi kelaskelas dari data-data yang dipilih (Gunawan, 2018).…”

Section: K-nearest Neigbor(knn)unclassified

Evaluasi Kombinasi Hipernin dan Sinonim untuk Klasifikasi Kebutuhan Non-Functional Berbasis ISO/IEC 25010

Hakim

Rochimah

Fatichah

2019

JTIIK

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Kebutuhan non-fungsional dianggap mampu mendukung keberhasilan pengembangan perangkat lunak. Namun, kebutuhan non-fungsional sering diabaikan selama proses pengembangan perangkat lunak. Hal ini dikarenakan kebutuhan non-fungsional sering tercampur dengan kebutuhan fungsional. Disamping itu, standar kualitas yang beragam menyebabkan kebingungan dalam menentukan aspek kualitas. Pendekataan yang ada menggunakan ISO/IEC 9126 sebagai referensi untuk mengukur aspek kualitas. ISO/IEC 9126 merupakan standar lama yang dirilis pada tahun 2001. Peneliti sebelumnya mengungkapkan ambiguitas dalam enam sub-atribut pada struktur hirarkis ISO/IEC 9126. Hal ini menimbulkan keraguan serius tentang validitas standar secara keseluruhan. Oleh karena itu, standar kualitas yang digunakan sebagai referensi untuk mengukur aspek kualitas pada penelitian ini adalah ISO/IEC 25010. Selain itu, penelitian ini juga mengusulkan suatu sistem untuk mengidentifikasi aspek kualitas kebutuhan non-fungsional dengan menggunakan 1 level hipernim dan 20 sinonim yang disebut skenario 1. Skenario ini akan dibandingkan dengan 2 level hipernim dan 9 sinonim pada masing-masing sinonim yang disebut skenario 2. Kedua skenario tersebut akan menghasilkan dua data latih berbeda. Kedua data latih tersebut akan dibandingkan menggunakan dua model pengujian yaitu berdasarkan ground truth pakar dan sistem dengan menggunakan metode klasifikasi KNN dan SVM. Hasil pengujian menunjukkan skenario 1 terbukti memberikan nilai lebih baik dibandingkan skenario 2 pada kedua model pengujian, dimana nilai precision dari ground truth pakar, KNN, dan SVM masing-masing 49.3%, 81.0%, dan 74.6%. Abstract Non-Functional requirements are considered capable of supporting the success of software development. However, non-functional requirements are often ignored during the software development process. This is because the quality aspects of non-functional requirements are often mixed with functional requirements. in addition, the number of diverse quality standards causes confusion in determining quality aspects. The existing approach uses ISO / IEC 9126 as a reference to measure quality aspects. ISO / IEC 9126 is an old standard released in 2001. Previous researchers revealed ambiguity in six sub-attributes on the hierarchical structure of ISO / IEC 9126. This raises serious doubts about the validity of the overall standard. Therefore, the quality standard used as a reference to measure the quality aspects of this study is ISO / IEC 25010. In addition, this study also proposes a system to identify aspects of the quality of non-functional requirements using 1 hypernym level and 20 synonyms called scenario 1. This scenario will be compared with 2 hypernym levels and 9 synonyms in each synonym called scenario 2. Both scenarios will produce two different training data. The two training data will be compared using two testing models ie based on expert ground truth and systems using the KNN and SVM classification methods. The test results showed scenario 1 is proven to provide a better value than scenario 2 in both testing models, where the precision values of expert ground truth, KNN, and SVM respectively 49.3%, 81.0%, and 74.6%.

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A Simple Classifier for Detecting Online Child Grooming Conversation

Cited by 10 publications

References 10 publications

Perancangan Motion Graphic “Awas Predator Online” sebagai Pencegahan Kasus Cyber Child Grooming

Perancangan Motion Graphic “Awas Predator Online” sebagai Pencegahan Kasus Cyber Child Grooming

Child Safety and Protection in the Online Gaming Ecosystem

Evaluasi Kombinasi Hipernin dan Sinonim untuk Klasifikasi Kebutuhan Non-Functional Berbasis ISO/IEC 25010

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