Model Sistem Monitoring URL Menggunakan Plugin Browser dengan Pendekatan NLP dan SDMIL untuk Perlindungan Anak
DOI:
https://doi.org/10.24014/sitekin.v20i1.19889Abstract
Internet adalah salah satu kemajuan teknologi yang berdampak ke banyak orang termasuk anak-anak. Dengan internet anak-anak dapat mengakses halaman web yang memiliki banyak informasi termasuk untuk mendukung pendidikan anak. Namun di internet banyak pula halaman web yang berisikan konten tidak pantas untuk dilihat anak-anak seperti pornografi. Bebasnya anak dalam mengakses konten halaman web ini menyulitkan orang tua untuk memantau perilaku anak di internet. Oleh karena itu pada penelitian ini diajukan sebuah model sistem monitoring untuk perlindungan anak dari konten negatif internet. Model sistem ini dikembangkan dengan arsitektur microservice dengan masing-masing service memiliki fungsi untuk mendeteksi konten negatif. Service pertama didukung dengan google API content filtering untuk menyaring konten website untuk orang dewasa. Service kedua dibuat dengan pendekatan NLP (Natural Language Processing) untuk menyaring tulisan-tulisan negatif. Service ketiga dibuat dengan pendekatan SDMIL atau Strongly Supervised Deep MIL (Multiple Instant Learning) untuk mendeteksi gambar dan video yang tidak cocok untuk anak-anak. Service keempat menyediakan layanan untuk kustomisasi URL negatif yang bisa diatur sendiri. Terakhir service kelima untuk log yang dapat membantu developer memantau kesalahan sistem. Model ini juga diintegrasikan dengan perangkat mobile sehingga orang tua dapat menerima laporan pengaksesan internet anak secara real-time.References
N. Alqahtani, “A state of the art review of Internet risks on children,” 2017 2nd Int. Conf. Anti-Cyber Crimes, ICACC 2017, pp. 108–112, 2017.
T. Weru, J. Sevilla, J. Olukuru, L. Mutegi, and T. Mberi, “Cyber-smart children, cyber-safe teenagers: Enhancing internet safety for children,” 2017 IST-Africa Week Conf. IST-Africa 2017, vol. 1, pp. 2–9, 2017.
M. Eneman, “Internet filtering: A solution to harmful and illegal content?,” Proc. - 2019 IEEE SmartWorld, Ubiquitous Intell. Comput. Adv. Trust. Comput. Scalable Comput. Commun. Internet People Smart City Innov. SmartWorld/UIC/ATC/SCALCOM/IOP/SCI 2019, pp. 354–359, 2019.
S. N. Hamade, “Parental Awareness and Mediation of Children’s Internet Use in Kuwait,” Proc. - 12th Int. Conf. Inf. Technol. New Gener. ITNG 2015, pp. 640–645, 2015.
Y. Kim and T. Nam, “An efficient text filter for adult web documents,” 8th Int. Conf. Adv. Commun. Technol. ICACT 2006 - Proc., vol. 1, pp. 438–440, 2006.
O. Jing, “Research on English Text Information Filtering Algorithm Based on SVM,” Proc. 2020 IEEE Int. Conf. Power, Intell. Comput. Syst. ICPICS 2020, pp. 1001–1004, 2020.
D. Yan, J. Liu, and F. Yang, “Design and implementation of text filtering with no semantic accidental injury,” Proc. - 2011 4th IEEE Int. Conf. Broadband Netw. Multimed. Technol. IC-BNMT 2011, pp. 61–65, 2011.
S. C. Kalkan, B. Gozutok, A. Al Nahas, A. Kulunk, and H. Y. Erdinc, “Image Enhancement Effects on Adult Content Classification,” INISTA 2020 - 2020 Int. Conf. Innov. Intell. Syst. Appl. Proc., pp. 2–7, 2020.
M. A. Mofadde and S. Sadek, “Adult image content filtering: A statistical method based on Multi-Color Skin Modeling,” 2010 IEEE Int. Symp. Signal Process. Inf. Technol. ISSPIT 2010, no. Icctd, pp. 366–370, 2010.
Z. Zhao and A. Cai, “Combining multiple SVM classifiers for adult image recognition,” Proc. - 2010 2nd IEEE Int. Conf. Netw. Infrastruct. Digit. Content, IC-NIDC 2010, pp. 149–153, 2010.
K. Dong, L. Guo, and Q. Fu, “An adult image detection algorithm based on Bag-Of- Visual-Words and text information,” 2014 10th Int. Conf. Nat. Comput. ICNC 2014, pp. 556–560, 2014.
Y. Liu, J. Ouyang, and J. Liu, “Bimodal codebooks based adult video detection,” 2017 IEEE Glob. Conf. Signal Inf. Process. Glob. 2017 - Proc., vol. 2018-Janua, pp. 1397–1400, 2018.
H. Bouirouga, S. E. Lrit, A. Jilbab, and D. Aboutajdine, “Recognition of adult video by combining skin detection features with motion information,” Int. Conf. Multimed. Comput. Syst. -Proceedings, 2011.
L. Yin, M. Dong, W. Deng, J. Guo, and B. Zhang, “Statistical color model based adult video filter,” Proc. 2012 IEEE Int. Conf. Multimed. Expo Work. ICMEW 2012, pp. 349–353, 2012.
D. Patel, V. Khan, R. K. Shukla, and M. Kherajani, “A customized children friendly and secure search engine,” 2nd Int. Conf. Data, Eng. Appl. IDEA 2020, pp. 2–6, 2020.
J. Zeniarja et al., “Search Engine for Kids with Document Filtering and Ranking Using Naive Bayes Classifier,” Proc. - 2018 Int. Semin. Appl. Technol. Inf. Commun. Creat. Technol. Hum. Life, iSemantic 2018, pp. 560–564, 2018.
O. R. Hammoud and I. A. Tarkhanov, “Blockchain-based open infrastructure for URL filtering in an Internet browser,” 14th IEEE Int. Conf. Appl. Inf. Commun. Technol. AICT 2020 - Proc., 2020.
D. Demirol, G. Tuna, and R. Das, “A simple logging system for safe Internet use,” IDAP 2017 - Int. Artif. Intell. Data Process. Symp., pp. 2–6, 2017.
C. C. Chiu and C. S. Yang, “A defense tool to prevent inappropriate website on internet,” Proc. - 2019 Int. Conf. Intell. Comput. Its Emerg. Appl. ICEA 2019, pp. 51–54, 2019.
A. Jiwasiddi, R. P. N. Suci, R. T. Herman, and P. Weiss, “News website perceived quality; A comparative study for news websites in Indonesia,” Proc. 2016 Int. Conf. Inf. Manag. Technol. ICIMTech 2016, no. November, pp. 325–328, 2017.
J. Sterne, “plug-in | software | Britannica,” Encyclopedia Britannica. [Online]. Available: https://www.britannica.com/technology/plug-in. [Accessed: 29-Nov-2022].
G. Ajam, C. Rodriguez, and B. Benatallah, “API Topic Issues Indexing, Exploration and Discovery for API Community Knowledge,” Proc. - 2020 46th Lat. Am. Comput. Conf. CLEI 2020, pp. 178–185, Oct. 2020.
D. Geethika et al., “Anomaly Detection in High-Performance API Gateways,” 2019 Int. Conf. High Perform. Comput. Simulation, HPCS 2019, pp. 995–1001, 2019.
Google Inc, “Content filtering | Tenor | Google Developers.” [Online]. Available: https://developers.google.com/tenor/guides/content-filtering. [Accessed: 29-Aug-2022].
R. Kumar and V. Sahula, “Intelligent Approaches for Natural Language Processing for Indic Languages,” Proc. - 2021 IEEE Int. Symp. Smart Electron. Syst. iSES 2021, pp. 331–334, 2021.
“NLTK :: Natural Language Toolkit.” [Online]. Available: https://www.nltk.org/. [Accessed: 30-Aug-2022].
Y. Wang, X. Jin, and X. Tan, “Strongly-
Supervised Deep Multiple Instance Learning,” 2016.
A. Gangwar, E. Fidalgo, E. Alegre, and V. González-Castro, “Pornography and child sexual abuse detection in image and video: A comparative evaluation,” IET Semin. Dig., vol. 2017, no. 5, pp. 37–42, 2017.
Cloudmersive, “Video and Media Services API - Cloudmersive APIs.” [Online]. Available: https://cloudmersive.com/video-and-media-services-api. [Accessed: 30-Aug-2022].
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