The Impact of Social Environment on Juvenile Delinquency in Tanjung Baru Subdistrict
Downloads
In society, the phenomenon of juvenile delinquency is a serious concern because it can have a negative impact on individuals and their environment. One factor that is believed to play a role in the emergence of juvenile delinquent behavior is the social environment around them. Tanjung Baru sub-district, as an area that has certain social characteristics, is interesting to be the focus of research in identifying the impact of the social environment on juvenile delinquency. The main objective of this study is to analyze the impact of the social environment on juvenile delinquency in Tanjung Baru Sub-district. This study aims to determine the extent to which social environmental factors such as family, school, and peers affect the level of juvenile delinquency in the area. The methods to be used in this research are survey method and qualitative analysis. A survey will be conducted to collect data on the social characteristics of adolescents and their level of delinquency. In addition, in-depth interviews with teenagers, parents, teachers, and community leaders will be conducted to gain a deeper understanding of the social environmental factors that influence juvenile delinquency. The results of this study indicate a significant relationship between the social environment and the level of juvenile delinquency in Tanjung Baru Sub-district. Factors such as family disintegration, lack of supervision from parents, and negative influence from peers have a major contribution to the increase in juvenile delinquency in the area. Based on the results of the study, it can be concluded that the social environment plays an important role in shaping juvenile delinquent behavior. Therefore, efforts to reduce the level of juvenile delinquency in Tanjung Baru Sub-district should involve various parties, including families, schools, and local communities, to create a healthier environment and support positive adolescent development.
Abbas, J., Aman, J., Nurunnabi, M., & Bano, S. (2019). The Impact of Social Media on Learning Behavior for Sustainable Education: Evidence of Students from Selected Universities in Pakistan. Sustainability, 11(6), 1683. https://doi.org/10.3390/su11061683
Albalawi, R., Yeap, T. H., & Benyoucef, M. (2020). Using Topic Modeling Methods for Short-Text Data: A Comparative Analysis. Frontiers in Artificial Intelligence, 3, 42. https://doi.org/10.3389/frai.2020.00042
Alharbi, A. S. M., & de Doncker, E. (2019). Twitter sentiment analysis with a deep neural network: An enhanced approach using user behavioral information. Cognitive Systems Research, 54, 50–61. https://doi.org/10.1016/j.cogsys.2018.10.001
Bankó, P., Lee, S. Y., Nagygyörgy, V., Zrínyi, M., Chae, C. H., Cho, D. H., & Telekes, A. (2019). Technologies for circulating tumor cell separation from whole blood. Journal of Hematology & Oncology, 12(1), 48. https://doi.org/10.1186/s13045-019-0735-4
Binks, A. P., LeClair, R. J., Willey, J. M., Brenner, J. M., Pickering, J. D., Moore, J. S., Huggett, K. N., Everling, K. M., Arnott, J. A., Croniger, C. M., Zehle, C. H., Kranea, N. K., & Schwartzstein, R. M. (2021). Changing Medical Education, Overnight: The Curricular Response to COVID-19 of Nine Medical Schools. Teaching and Learning in Medicine, 33(3), 334–342. https://doi.org/10.1080/10401334.2021.1891543
Bock, A., Modabber, A., Kniha, K., Lemos, M., Rafai, N., & Hölzle, F. (2018). Blended learning modules for lectures on oral and maxillofacial surgery. British Journal of Oral and Maxillofacial Surgery, 56(10), 956–961. https://doi.org/10.1016/j.bjoms.2018.10.281
Bora, A., Balasubramanian, S., Babenko, B., Virmani, S., Venugopalan, S., Mitani, A., de Oliveira Marinho, G., Cuadros, J., Ruamviboonsuk, P., Corrado, G. S., Peng, L., Webster, D. R., Varadarajan, A. V., Hammel, N., Liu, Y., & Bavishi, P. (2021). Predicting the risk of developing diabetic retinopathy using deep learning. The Lancet Digital Health, 3(1), e10–e19. https://doi.org/10.1016/S2589-7500(20)30250-8
Bryant, C., Szejda, K., Parekh, N., Deshpande, V., & Tse, B. (2019). A Survey of Consumer Perceptions of Plant-Based and Clean Meat in the USA, India, and China. Frontiers in Sustainable Food Systems, 3, 11. https://doi.org/10.3389/fsufs.2019.00011
Budd, J., Miller, B. S., Manning, E. M., Lampos, V., Zhuang, M., Edelstein, M., Rees, G., Emery, V. C., Stevens, M. M., Keegan, N., Short, M. J., Pillay, D., Manley, E., Cox, I. J., Heymann, D., Johnson, A. M., & McKendry, R. A. (2020). Digital technologies in the public-health response to COVID-19. Nature Medicine, 26(8), 1183–1192. https://doi.org/10.1038/s41591-020-1011-4
Chen, H., Guo, J., Wang, C., Luo, F., Yu, X., Zhang, W., Li, J., Zhao, D., Xu, D., Gong, Q., Liao, J., Yang, H., Hou, W., & Zhang, Y. (2020). Clinical characteristics and intrauterine vertical transmission potential of COVID-19 infection in nine pregnant women: A retrospective review of medical records. The Lancet, 395(10226), 809–815. https://doi.org/10.1016/S0140-6736(20)30360-3
Chick, R. C., Clifton, G. T., Peace, K. M., Propper, B. W., Hale, D. F., Alseidi, A. A., & Vreeland, T. J. (2020). Using Technology to Maintain the Education of Residents During the COVID-19 Pandemic. Journal of Surgical Education, 77(4), 729–732. https://doi.org/10.1016/j.jsurg.2020.03.018
Debnath, R., Bardhan, R., Reiner, D. M., & Miller, J. R. (2021). Political, economic, social, technological, legal and environmental dimensions of electric vehicle adoption in the United States: A social-media interaction analysis. Renewable and Sustainable Energy Reviews, 152, 111707. https://doi.org/10.1016/j.rser.2021.111707
Dutta, P., Choi, T.-M., Somani, S., & Butala, R. (2020). Blockchain technology in supply chain operations: Applications, challenges and research opportunities. Transportation Research Part E: Logistics and Transportation Review, 142, 102067. https://doi.org/10.1016/j.tre.2020.102067
Dwivedi, Y. K., Hughes, D. L., Coombs, C., Constantiou, I., Duan, Y., Edwards, J. S., Gupta, B., Lal, B., Misra, S., Prashant, P., Raman, R., Rana, N. P., Sharma, S. K., & Upadhyay, N. (2020). Impact of COVID-19 pandemic on information management research and practice: Transforming education, work and life. International Journal of Information Management, 55, 102211. https://doi.org/10.1016/j.ijinfomgt.2020.102211
Dziuban, C., Graham, C. R., Moskal, P. D., Norberg, A., & Sicilia, N. (2018). Blended learning: The new normal and emerging technologies. International Journal of Educational Technology in Higher Education, 15(1), 3. https://doi.org/10.1186/s41239-017-0087-5
Feng, H., Wang, X., Duan, Y., Zhang, J., & Zhang, X. (2020). Applying blockchain technology to improve agri-food traceability: A review of development methods, benefits and challenges. Journal of Cleaner Production, 260, 121031. https://doi.org/10.1016/j.jclepro.2020.121031
Fernandes, N., Costa, D., Costa, D., Keating, J., & Arantes, J. (2021). Predicting COVID-19 Vaccination Intention: The Determinants of Vaccine Hesitancy. Vaccines, 9(10), 1161. https://doi.org/10.3390/vaccines9101161
Hart, C. M. D., Xu, D., Hill, M., & Alonso, E. (2021). COVID-19 and Community College Instructional Responses. Online Learning, 25(1). https://doi.org/10.24059/olj.v25i1.2568
Huang, C., Huang, L., Wang, Y., Li, X., Ren, L., Gu, X., Kang, L., Guo, L., Liu, M., Zhou, X., Luo, J., Huang, Z., Tu, S., Zhao, Y., Chen, L., Xu, D., Li, Y., Li, C., Peng, L., … Cao, B. (2021). 6-month consequences of COVID-19 in patients discharged from hospital: A cohort study. The Lancet, 397(10270), 220–232. https://doi.org/10.1016/S0140-6736(20)32656-8
Jabreel, M., & Moreno, A. (2019). A Deep Learning-Based Approach for Multi-Label Emotion Classification in Tweets. Applied Sciences, 9(6), 1123. https://doi.org/10.3390/app9061123
Jacques, S., Ouahabi, A., & Lequeu, T. (2021). Synchronous E-learning in Higher Education during the COVID-19 Pandemic. 2021 IEEE Global Engineering Education Conference (EDUCON), 1102–1109. https://doi.org/10.1109/EDUCON46332.2021.9453887
Javaid, M., Haleem, A., Vaishya, R., Bahl, S., Suman, R., & Vaish, A. (2020). Industry 4.0 technologies and their applications in fighting COVID-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14(4), 419–422. https://doi.org/10.1016/j.dsx.2020.04.032
Johnston, J., Walshe, G., & Ríordáin, M. N. (2020). Supporting Key Aspects of Practice in Making Mathematics Explicit in Science Lessons. International Journal of Science and Mathematics Education, 18(7), 1399–1417. https://doi.org/10.1007/s10763-019-10016-1
Kamilaris, A., Fonts, A., & Prenafeta-Bold?, F. X. (2019). The rise of blockchain technology in agriculture and food supply chains. Trends in Food Science & Technology, 91, 640–652. set-company-logo-design-ideas-vector.zip
Kaya & Bilge. (2019). Deep Metric Learning: A Survey. Symmetry, 11(9), 1066. https://doi.org/10.3390/sym11091066
Kim, S. (2018). Public Service Motivation, Organizational Social Capital, and Knowledge Sharing in the Korean Public Sector. Public Performance & Management Review, 41(1), 130–151. https://doi.org/10.1080/15309576.2017.1358188
Kirn, A., & Benson, L. (2018). Engineering Students’ Perceptions of Problem Solving and Their Future. Journal of Engineering Education, 107(1), 87–112. https://doi.org/10.1002/jee.20190
König, J., Jäger-Biela, D. J., & Glutsch, N. (2020). Adapting to online teaching during COVID-19 school closure: Teacher education and teacher competence effects among early career teachers in Germany. European Journal of Teacher Education, 43(4), 608–622. https://doi.org/10.1080/02619768.2020.1809650
Kopnov, V. A., Shmurygina, O. V., Shchipanova, D. E., Dremina, M. A., Papaloizou, L., Orphanidou, Y., & Morevs, P. (2018). FUNCTIONAL ANALYSIS AND FUNCTIONAL MAPS OF QUALIFICATIONS IN ECVET CONTEXT. The Education and science journal, 20(6), 90–117. https://doi.org/10.17853/1994-5639-2018-6-90-117
Kukulska-Hulme, A., & Viberg, O. (2018). Mobile collaborative language learning: State of the art: Mobile collaborative language learning. British Journal of Educational Technology, 49(2), 207–218. https://doi.org/10.1111/bjet.12580
Li, W., Gillies, R., He, M., Wu, C., Liu, S., Gong, Z., & Sun, H. (2021). Barriers and facilitators to online medical and nursing education during the COVID-19 pandemic: Perspectives from international students from low- and middle-income countries and their teaching staff. Human Resources for Health, 19(1), 64. https://doi.org/10.1186/s12960-021-00609-9
Liu, Y., Zhu, Y., & Cui, Y. (2019). Challenges and opportunities towards fast-charging battery materials. Nature Energy, 4(7), 540–550. https://doi.org/10.1038/s41560-019-0405-3
Mahler, D., Großschedl, J., & Harms, U. (2018). Does motivation matter? – The relationship between teachers’ self-efficacy and enthusiasm and students’ performance. PLOS ONE, 13(11), e0207252. https://doi.org/10.1371/journal.pone.0207252
McGrew, S., Breakstone, J., Ortega, T., Smith, M., & Wineburg, S. (2018). Can Students Evaluate Online Sources? Learning From Assessments of Civic Online Reasoning. Theory & Research in Social Education, 46(2), 165–193. https://doi.org/10.1080/00933104.2017.1416320
Montero Perez, M., Peters, E., & Desmet, P. (2018). Vocabulary learning through viewing video: The effect of two enhancement techniques. Computer Assisted Language Learning, 31(1–2), 1–26. https://doi.org/10.1080/09588221.2017.1375960
Oztemel, E., & Gursev, S. (2020). Literature review of Industry 4.0 and related technologies. Journal of Intelligent Manufacturing, 31(1), 127–182. https://doi.org/10.1007/s10845-018-1433-8
Patra, J. K., Das, G., Fraceto, L. F., Campos, E. V. R., Rodriguez-Torres, M. del P., Acosta-Torres, L. S., Diaz-Torres, L. A., Grillo, R., Swamy, M. K., Sharma, S., Habtemariam, S., & Shin, H.-S. (2018). Nano based drug delivery systems: Recent developments and future prospects. Journal of Nanobiotechnology, 16(1), 71. https://doi.org/10.1186/s12951-018-0392-8
Pierce, M., Hope, H., Ford, T., Hatch, S., Hotopf, M., John, A., Kontopantelis, E., Webb, R., Wessely, S., McManus, S., & Abel, K. M. (2020). Mental health before and during the COVID-19 pandemic: A longitudinal probability sample survey of the UK population. The Lancet Psychiatry, 7(10), 883–892. https://doi.org/10.1016/S2215-0366(20)30308-4
Pikhart, M., & Klímová, B. (2020). eLearning 4.0 as a Sustainability Strategy for Generation Z Language Learners: Applied Linguistics of Second Language Acquisition in Younger Adults. Societies, 10(2), 38. https://doi.org/10.3390/soc10020038
Podlewska, S., & Kafel, R. (2018). MetStabOn—Online Platform for Metabolic Stability Predictions. International Journal of Molecular Sciences, 19(4), 1040. https://doi.org/10.3390/ijms19041040
Pouyanfar, S., Yang, Y., Chen, S.-C., Shyu, M.-L., & Iyengar, S. S. (2019). Multimedia Big Data Analytics: A Survey. ACM Computing Surveys, 51(1), 1–34. https://doi.org/10.1145/3150226
Pujadas, G., & Muñoz, C. (2019). Extensive viewing of captioned and subtitled TV series: A study of L2 vocabulary learning by adolescents. The Language Learning Journal, 47(4), 479–496. https://doi.org/10.1080/09571736.2019.1616806
Rao, S. K., & Prasad, R. (2018). Impact of 5G Technologies on Industry 4.0. Wireless Personal Communications, 100(1), 145–159. https://doi.org/10.1007/s11277-018-5615-7
Rosenberg, H., & S. C. Asterhan, C. (2018). “WhatsApp, Teacher?”—Student Perspectives on Teacher-Student WhatsApp Interactions in Secondary Schools. Journal of Information Technology Education: Research, 17, 205–226. https://doi.org/10.28945/4081
Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Computers & Education, 128, 13–35. https://doi.org/10.1016/j.compedu.2018.09.009
Shamantha, R. B., Shetty, S. M., & Rai, P. (2019). Sentiment Analysis Using Machine Learning Classifiers: Evaluation of Performance. 2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS), 21–25. https://doi.org/10.1109/CCOMS.2019.8821650
Swidan, A., Hermans, F., & Smit, M. (2018). Programming Misconceptions for School Students. Proceedings of the 2018 ACM Conference on International Computing Education Research, 151–159. https://doi.org/10.1145/3230977.3230995
Theobald, E. J., Hill, M. J., Tran, E., Agrawal, S., Arroyo, E. N., Behling, S., Chambwe, N., Cintrón, D. L., Cooper, J. D., Dunster, G., Grummer, J. A., Hennessey, K., Hsiao, J., Iranon, N., Jones, L., Jordt, H., Keller, M., Lacey, M. E., Littlefield, C. E., … Freeman, S. (2020). Active learning narrows achievement gaps for underrepresented students in undergraduate science, technology, engineering, and math. Proceedings of the National Academy of Sciences, 117(12), 6476–6483. https://doi.org/10.1073/pnas.1916903117
Torales, J., O’Higgins, M., Castaldelli-Maia, J. M., & Ventriglio, A. (2020). The outbreak of COVID-19 coronavirus and its impact on global mental health. International Journal of Social Psychiatry, 66(4), 317–320. https://doi.org/10.1177/0020764020915212
Unsworth, L., & Mills, K. A. (2020). English language teaching of attitude and emotion in digital multimodal composition. Journal of Second Language Writing, 47, 100712. https://doi.org/10.1016/j.jslw.2020.100712
Walther, J., Brewer, M. A., Sochacka, N. W., & Miller, S. E. (2020). Empathy and engineering formation. Journal of Engineering Education, 109(1), 11–33. https://doi.org/10.1002/jee.20301
Wang, Y., Zhang, D., Du, G., Du, R., Zhao, J., Jin, Y., Fu, S., Gao, L., Cheng, Z., Lu, Q., Hu, Y., Luo, G., Wang, K., Lu, Y., Li, H., Wang, S., Ruan, S., Yang, C., Mei, C., … Wang, C. (2020). Remdesivir in adults with severe COVID-19: A randomised, double-blind, placebo-controlled, multicentre trial. The Lancet, 395(10236), 1569–1578. https://doi.org/10.1016/S0140-6736(20)31022-9
Wehner, S., Elkouss, D., & Hanson, R. (2018). Quantum internet: A vision for the road ahead. Science, 362(6412), eaam9288. https://doi.org/10.1126/science.aam9288
Yi, S., & Liu, X. (2020). Machine learning based customer sentiment analysis for recommending shoppers, shops based on customers’ review. Complex & Intelligent Systems, 6(3), 621–634. https://doi.org/10.1007/s40747-020-00155-2
Yu, X., Wang, C., & Zhou, X. (2018). A Survey on Robust Video Watermarking Algorithms for Copyright Protection. Applied Sciences, 8(10), 1891. https://doi.org/10.3390/app8101891
Zhang, Q., Yang, L. T., Chen, Z., & Li, P. (2018). A survey on deep learning for big data. Information Fusion, 42, 146–157. https://doi.org/10.1016/j.inffus.2017.10.006
Zhao, Y., Chen, X., & Yin, J. (2019). Adaptive boosting-based computational model for predicting potential miRNA-disease associations. Bioinformatics, 35(22), 4730–4738. https://doi.org/10.1093/bioinformatics/btz297
Zhou, F., Yu, T., Du, R., Fan, G., Liu, Y., Liu, Z., Xiang, J., Wang, Y., Song, B., Gu, X., Guan, L., Wei, Y., Li, H., Wu, X., Xu, J., Tu, S., Zhang, Y., Chen, H., & Cao, B. (2020). Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: A retrospective cohort study. The Lancet, 395(10229), 1054–1062. https://doi.org/10.1016/S0140-6736(20)30566-3
Copyright (c) 2024 Eka Putra
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
