THE INFLUENCE OF ONLINE SOCIAL INTERACTION ON INTERNET ADDICTION AMONG ADOLESCENCE

Internet addiction is a serious problem affecting individuals of all ages. There are many reasons for internet addiction. One of these reasons is online social interaction. Online social interaction on the internet is the strongest and most common activity that makes adolescents dependent on the internet. Social interaction online facilitate the need to make friends and the need to obtain a personal achievement. Therefore, individuals spend a long time on the Internet. This study aims to determine the effect of online social interaction on internet addiction in adolescents. This research was conducted in the Purbalingga / Indonesia district. The study was conducted with 70 adolescents aged 15-18 years. As data collection tool, Online Social Interaction Questionnaire and Internet Addiction Questionnaire have been used. To test whether the data shows normal distribution, Kolmogorov Smirnov Normality Test was performed. The results of the test showed that the data obtained in the study showed normal distribution. Regression Analysis were used in the study. The research findings show that online social interaction has an effect on internet addiction (R2=0.351; p<0.05). The Regression Analysis applied indicated that online social interaction explain 35.1% of the total variance in internet addiction. According to the results of the study, online social interaction needs to be reduced in order to reduce internet addiction in adolescents. Even so, internet addiction is not only caused by online social interaction, but there are 64.9% of other factors that influence it.


Introduction
The internet presented an extraordinary advancement in information technology. The internet provides opportunities for humans to connect socially more easily, to educate themselves, improve economic conditions, and to be free from the shame of various obstacles that make them powerless. Young and De Abreu (2010) suggested that the practicality provided by internet technology does indeed improve the welfare and quality of life but for some people, the internet can have an effect on mental disorders.
Practicality of using the internet makes people interested in using the internet. This can be seen from the increase in internet usage based on the results of a survey of the Indonesian Internet Users Network Association (APJII). Starting from 2014 there was an increase in internet usage in Indonesia to 34.9% or around 88.1 million people in Indonesia (APJII, 2014). Currently there is an increase in internet use in Indonesia to 54.68% or approximately 143.26 million people Indonesia (APJII, 2017) Based on the data above, the increase in internet usage for three years has increased by 19.8%.
The APJII survey results in 2017 (2017) among adolescents in the 13-18 year age group were 75.5% and the high increase between the ages of 19-34 years was 74.23%. This shows that most teenagers have become internet users. The use of the internet in adolescents aims to update information, relating to work, free time, socialization, education and entertainment. APJII (2017); Head of Information and Public Relations Center of the Ministry of Communication and Information, (2014). Suprapto and Nurcahyo (2005) suggested that data on an increase in the number of internet users correlated with an increase in internet addiction.
Internet addiction also correlates with the amount of time a person spends when using the internet, this statement was stated by (Yao, He, Ko, & Pang, 2014) that participants who access the internet for a longer period or longer duration of use, have a greater tendency to become addicted to the internet or participants who use the internet with high frequency will be addicted to the internet. Another opinion according (Jiang, 2014) that the degree of duration connected internet (internet connectedness) who tend to show symptoms of Internet Addiction Disorder (IAD) based on criteria for pathological gambling in the DSM-IV. Young and Rogers, (1998) distinguish internet users into two; The first is non-dependent internet users who access the internet for four to five hours per week. Both categories of addictive internet users (dependent) who access the internet as much as 20 to 80 hours per week.
The results of the 2017 APJII survey also showed an indication of addictive or dependent internet use behavior based on the length of time of use. The survey states that internet usage between 4-7 hours per day is 29.63% and more than 7 hours per day is 26.48%, or it can be said that 26.48% of internet users have a duration of use of 49 hours per week. This fact can be categorized ¼ internet users fall into the second category of internet addiction (dependent).
Knowing that around 26.48% of internet users in Indonesia experience internet addiction based on the duration of use, it certainly raises concerns about the effects of internet addiction. (Chou, Liu, Yang, Yen, & Hu, 2015;Wee et al., 2014;Ybarra & Mitchell, 2005) suggested that internet addiction can cause considerable side effects on adolescent life, such as anxiety, depression, physical decline and mental health, interpersonal relationships , and decreased performance.
The time of internet use cannot be the only indicator that determines a person is diagnosed with internet addiction (Müller et al., 2016). In general, addicted behavior has clinical signs, namely preoccupation (addiction) to opium objects; not having behavioral control (loss of control); and perpetuating opiate behavior continuously despite the harmful consequences. Neto and Barros, (2000) suggests that a weld adolescents who are addicted to the internet because he did not obtain self-satisfaction when social relationships directly or face to face and therefore the individual must rely on social interaction online to meet their needs in social interaction. When online, people feel excited, happy, free and feel needed and supported, otherwise when offline individual feel lonely, anxious, unfulfilled, even frustrating. Another opinion was put forward (Mesch, 2012;Ybarra & Mitchell, 2005) that Individuals who experience anxiety in social interaction see online interaction as a safe way to interact rather than having to meet face to face. Morahan-Martin (2007) suggests that there is an increasingly strong consensus that social interactions made possible by the internet play an important role in the development of internet abuse. Morahan-Martin, (2008) argues that in addition to online social interactions that result in internet addiction, it also results in social anxiety and loneliness. Opinions from these experts indicate that currently online social interaction is more interesting than face-to-face interaction directly when social skills are needed to be able to work together with other individuals. Without social skills, collaboration is impossible. Most teens today who use online social interaction tend to be good in cognitive but lacking in terms of socialization. The weak ability to socialize adolescents is now a major problem that has caused a number of cases to emerge. Based peng observations lead author of many aspects of internet addiction can happen in Indonesia. For this reason researchers aim to find out the relationship between online social interaction with internet addiction to internet addiction.

Method
This is a survey research and other research using correlational design with a quantitative approach. Aims to find out whether there is influence between online social interaction and internet addiction in teenagers.

Participants
The population in this study were students of SMK Negeri 1 Kaligondang. The total population is 549 students. Samples were taken using cluster random sampling with a total of 70 students . According to Sugiyono (2010: p.121), this cluster random sampling technique can be used because of the limitations of researchers. Data sources are very broad, and without regard to strata that exist in these populations.

Data Collection Tools
Online Social Interaction Questionnaire: The Online Social Interaction Questionnaire given to students consisted of 20 items with each item having a score of 0-5, so the total score of online social interaction had a range of 0-100. Collection of online social interaction data using a questionnaire based on opinion (Caplan, 2003) The questionnaire lattice is 1) mood swings namely the extent to which a person uses the internet to facilitate some changes in negative affective circumstances; 2) perceived social benefits, namely the extent to which an individual views the use of the internet as a tool to interact with the social environment is better than interacting directly ; 3) perceived social control that is the extent to which a person feels an increase in social control when interacting with others online; 4) Withdrawl is the extent to which a person has difficulty not using social media; 5) compulsiveness, the inability to control, reduce or stop online behavior , feelings of guilt about the time spent using social media; 6) excessive use of the internet ie the extent to which a person feels that he spends a lot of time online: 7) negative effects namely the severity of personal effects, social and professional problems caused by someone's internet use.
Internet Addiction Questionnaire: Internet Addiction Questionnaire given to students consists of 25 items with each item having a score of 0-5, so the total internet addiction score has a range of 0-125. Data collection for internet addiction uses a questionnaire based on opinion (Tao et al., 2010). The questionnaire lattices are, 1) Fun, a strong desire on the internet. Think about internet activities beforehand or anticipate the next online session. Internet use is the dominant activity in daily life; and 2) withdrawal: manifested by disporic mood, anxiety, irritability and boredom after a few days without internet activity. In addition, at least one other symptom must be followed, such as: 3) tolerance, namely the increasing need to use the internet to achieve satisfaction; 4) difficulty in controlling; a continuing and / or unsuccessful desire to control or discontinue using the internet; 5) do not care about the dangerous consequences in the sense of continually using the internet excessively despite having knowledge related to physical and psychological problems due to internet use; 6) communication and social attraction are lost: Hobby interests, entertainment that have a direct impact are lost. Except using the internet; and reduction of negative emotions: 7) to avoid or reduce disporic moods such as feelings of hopelessness, guilt and anxiety.

Statistical Analysis
This research uses descriptive analysis techniques and inferential analysis. Descriptive analysis is used to see the data picture of each variable expressed through the mean, median, mode, frequency distribution and histogram. Furthermore, by analyzing the data parameters of the regression model that will be used. Data processing in this study uses the SPSS version 22.0 program. The study uses a variable free namely social interaction online and the dependent variable is addicted to the Internet. These variables are given the symbols X and Y, the independent variables are given the symbol X which is online social interaction and the dependent variable is given the symbol Y which is internet addiction. Both variables were collected using a questionnaire instrument. The scale used in measuring this aspect is a Likert scale with five alternative answers. The validity of the questionnaire used expert opinions and its reliability was analyzed using Cronbach's Alpha.

Table 1. Description of Online Social Interaction and Internet Addiction
Furthermore, before carrying out multiple regression analysis, a test requirement analysis must be done. The normal test was carried out to find out whether the study population was normally distributed or not. Calculate the normality of data using the Kolmogorov-Smirnov Test. The results of the normality test are listed in the following Table 2. variables that have been taken has a normal distribution. Statistics show that the smaller the value, the more normal the distribution of data. The normality test results for online social interaction are shown below.

Figure 1. Histogram Test for Online Social Interaction Normality
In the result of Kolmogorov-Smirnov Normality Test, it can be seen that the significance for internet addiction is 0.200. Because the significance value is more than 0.05, it can be concluded that the population in which the data of the internet addiction variable is normally distributed. The normality test results for internet addiction are shown below.

Figure 2. Histogram Test for Internet Addiction Normality
Seen from the two histograms show normal curve lines. It can be concluded that the population in which the data variable online social interaction and internet addiction is normally distributed. Regression analysis simple aim to determine a linear relationship between the variabel independent with variable dependent. Based on data processing using the SPSS 20.0 program, the constant value (a) of 3.390 is obtained , while the regression coefficient b=1.118. The regression equation between online social interaction variables (X) and internet addiction (Y) is Y=3.390+1.118X. The explanation of this regression equation is as follows: Constants (a) of 3.390; meaning that if the value of the online social interaction variable (X) is zero, then the internet addiction variable (Y) is worth 3.390. Regression coefficient of online social interaction variables (b) of 1,118; meaning that if the independent variable online social interaction (X ) has increased by one value then the value (large) then the internet addiction variable (Y) will increase by 1.118. Then the significance value of the online social interaction variable is 0.000 which is a significance value of less than (0.05) meaning that there is an influence between online social interaction with internet addiction. The results of the simple regression coefficients are shown in Table 3 below.

Discussion
Based on the results of the study showed that there is a significant influence between online social interactions (R squares=0.351) on internet addiction. That said, high online social interaction of 35.1% in teens has increased internet addiction. This research provides evidence that online social interaction can predict internet addiction in adolescents. The results of other studies that support is research conducted by (Smahel, Brown, & Blinka, 2012) that online gaming and online social interaction are activities that cause the most internet addiction, both of these activities have a strong tendency to make teens addicted to the internet because it will satisfy the needs of friendship and the need to obtain a personal achievement. Other research from (Lee, Ko, & Chou, 2015) that online social interaction characteristics have an intensity for users to experience the experience of a more positive response, the experience results in repetition in online social interactions that results in excessive internet users and potentially experiencing internet addiction.
Online social interaction will make it easy for teens to meet the needs of being recognized in a friendship. There are various tools used to fulfill online social interaction activities , such as using Facebook, Twitter, LINE, Whatsapp, Instagram etc. The tool used by teens for online social interaction will help teens to meet new friends on the internet. This will make it easier for teens to meet with friends who have similar interests and hobbies, teenage interest in online social interaction will be stronger and make teens addicted to the internet. Research conducted (Kuss & Griffiths, 2011;Rosenbaum & Wong, 2012) that at the same time the internet can increase the use of social applications such as text messages or social sites me, this can be associated with loneliness and reduce real activities in real life and reduce academic achievement, this results in internet addiction. From various explanations of the supporting studies, the conclusion drawn is that there is an online social interaction variable that has a positive relationship with adolescents who experience internet addiction, so it can be said that online social interaction is a predictor that can influence internet addiction in adolescents even though other studies have shown that there are other variables that have a greater influence on online games.

Conclusion
The results of research and discussion, it can be concluded how many things there are: there is a positive and significant influence of online social interaction on internet addiction in vocational adolescents in the Purbalingga patent district. The contribution of the influence of online social interaction on internet addiction in adolescents of a vocational school in Purbalingga district is quite strong, amounting to 35.1%.
Based on the results of the study, and taking into account the limitations of the research, suggestions that can be delivered are: (1) for the government, especially the Education Office in Purbalingga district to be used as input and study in making policies, especially those relating to the socialization of the dangers of excessive internet use in adolescents in the Purbalingga district; (2 ) for school principals and teachers, to be used as input material and internal evaluations to provide understanding for adolescents to control online social interactions and encourage students to be more active in offline social interaction activities or it can be said activities that trigger students to interact more with friends at school; (3) for researcers, it is expected that the result of this research can be used as reference for further research.