Comparison Digital Internet Addiction Test Bahasa Version and Manual Internet Addiction Test Bahasa Version in Adolescent
Keywords:
Internet Addiction Test, Digital Mental Health Care, adolescentsAbstract
Internet Addiction Test (IAT) is the most common internet addiction questionnaire that is used for internet addiction screening. In the digital era and impact of the COVID-19 pandemic that suggests contactless to prevent widespread infection, we tried to develop the Digital Internet Addiction Test in Bahasa version (E-IAT Ina) to measure internet addiction with contactless and real-time usage. Still, it is needed to evaluate the effectiveness of the usual questionnaire using the manual Internet Addiction test in Bahasa version (M IAT). This study aimed to compare the effectiveness of E-IAT Ina with manual IAT Ina (M-IAT Ina). E-IAT Ina was developed with expert systems forward chaining method web-based applicaton which built using HTML, Laravel and Flowbite framework. This is a cross-sectional study, conducted by 93 students ranging from 12-20 years old. Totally 22 participants from junior high school, 32 participants from senior high school, and 39 participants from college students in Denpasar city were collected by Google form using purposive sampling. Each participant fills out M-IAT Ina and E-IAT Ina directly. The effectiveness of the tools for the score test was analyzed with a Paired t-test and the level of internet addiction was analyzed with the Wilcoxon test using SPSS 25 software. There was no significant difference between E-IAT Ina and M-IAT in all stages of adolescence, where adolescents 12-14 years (p=0,101), 15-17 years (p=0,184), and 18-20 years (p=0,142) by scoring. There was also no significant difference in the level of addiction (p= 0.013, p>0.000). Digital Internet Addiction Test in Bahasa version application (E-IAT Ina) could be used to measure internet addiction in adolescents with the same effectiveness as manual Internet Addiction Test Bahasa version (M-IAT Ina) with contactless and real-time usage to prevent the widespread COVID-19 infection in digital mental health care.
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