Using Expert System Application to Diagnose Online Game Addiction in Junior High School Students: Case Study in Five Big City in Indonesia
Bibliographic record
Abstract
The development of computer technology gradually increases. An artificial intelligence-based system also begins to be developed and used in various fields. One product of artificial intelligence is an expert system, used for psychological field. This study aimed to explain and describe the using of expert system to diagnose online game addiction to Junior High School Students. This is based on online game addiction phenomenon happening to Indonesian student especially in Junior High School. The implementation of this expert system used certainty factor method. Steps for developing this system were divided into four, namely designing expert system or architecture of expert system, representing knowledge, designing database, and testing and implementing the system. The results indicate that this system is divided into two domains, including user and admin. The user domain is provided for users who are willing to do online consultations using system expert application. Meanwhile, the admin domain is provided for an admin to manage each datum and question from the user who conducts an online consultation. From 1000 samples, it is obtained that 69% amongst total samples of Junior High School Student have a low-level addiction to online game, 25% experience medium-level addiction, and 6% are highly addicted.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".