{
  "abstract": "Social media use can adversely affect students’ academic functioning and psychological well-being when disengagement becomes difficult. This study compared Random Forest (RF), XGBoost, and Support Vector Machine (SVM) for predicting self-reported difficulty disengaging from social media among university students. A cross-sectional survey dataset comprising 118 responses was analyzed, including 75 positive (63.6%) and 43 negative (36.4%) cases. The binary target represented a behavioral proxy rather than a clinically validated diagnosis of social media addiction. Using balanced class weighting and 10-fold stratified cross-validation, SVM achieved the highest mean accuracy (77.2%) and ROC-AUC (0.804), followed by RF (73.0%) and XGBoost (66.8%). However, the performance difference between SVM and RF was not statistically significant (Nadeau–Bengio corrected *p* = 0.102), while RF demonstrated greater resampling stability. The findings provide preliminary evidence for algorithm selection in behavioral proxy classification and support future digital well-being research. Interpretation is limited by the small, single-institution sample and single-item outcome measure; therefore, the results should not be used for clinical screening or institutional deployment without external validation using established psychometric instruments.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "name": "Supardi"
    },
    {
      "name": "Djoko Soetarno"
    },
    {
      "name": "Rahmat Sulaiman"
    },
    {
      "name": "Chandra Kirana"
    },
    {
      "name": "Ari Amir Alkodri"
    },
    {
      "name": "Anisah"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/b7y5j13wkqfnf.bib",
  "cited_by": 0,
  "doi": "10.63158/journalisi.v8i5.1735",
  "fulltext_url": "https://journal-isi.org/index.php/isi/article/view/1735",
  "id": "b7y5j13wkqfnf",
  "issns": [
    "2656-5935",
    "2656-4882"
  ],
  "landing_url": "https://doi.org/10.63158/journalisi.v8i5.1735",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "QA1-939"
    ],
    [
      "lcc",
      "QA75.5-76.95"
    ],
    [
      "openalex",
      "3312|Impact of Technology on Adolescents"
    ]
  ],
  "openalex": "W7223476784",
  "pdf_url": "https://journal-isi.org/index.php/isi/article/download/1735/1034",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-24",
  "publisher": "Asosiasi Doktor Sistem Informasi Indonesia",
  "ris": "https://research.draconitic.com/paper/b7y5j13wkqfnf.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:44:42Z",
      "native_id": "24669ea8ddca40e38c5cafa5b2a93718",
      "source": "doaj",
      "url": "https://doi.org/10.63158/journalisi.v8i5.1735"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "lcc:QA1-939",
      "subfield": "general"
    }
  ],
  "title": "Comparative Analysis of Machine Learning Algorithms for Predicting Difficulty Disengaging from Social Media Among Students",
  "title_text": "Comparative Analysis of Machine Learning Algorithms for Predicting Difficulty Disengaging from Social Media Among Students",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:44:42Z",
  "url": "https://research.draconitic.com/paper/b7y5j13wkqfnf",
  "venue": "Journal of Information Systems and Informatics",
  "versions": []
}