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Multidisciplinary Indonesian Center Journal (MICJO) (Vol. 3, No. 1, 2026)

LEGAL DISCOVERY BY JUDGES IN ADDRESSING THE AMBIGUITY OF “DEALER” AND “USER” ELEMENTS IN ARTICLES 114, 112, AND 127 OF THE NARCOTICS LAW

Irwan Triadi, Dhikma Heradika, Abelmart Sihombing, Bayu Giri Atmojo
Abstract: The ambiguity of the elements “dealer” and “user” in Articles 114, 112, and 127 of Law Number 35 of 2009 on Narcotics creates legal uncertainty in the practice of criminal justice. These three provisions often overlap in… erlap in law enforcement, particularly when investigators and public prosecutors apply more severe charges without comprehensively examining the legal construction of the defendant’s actions, including the social and situational context behind them. This study is a normative legal research that examines the doctrine of judicial legal discovery, principles of criminal law, and the principle of proportionality in sentencing in a more in-depth and structured manner. The results of the study indicate that judges have the authority to interpret the elements of narcotics criminal acts systematically, grammatically, and teleologically to clearly distinguish between “abusive users” and “dealers with the intent to distribute.” Legal discovery is needed to prevent overcriminalization and to ensure the protection of the rights of suspects and defendants throughout the entire criminal justice process. This study concludes that the appropriate method of interpretation is an integration of systematic interpretation, teleological interpretation, and the ratio legis of the Narcotics Law.
Global Research
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Journal of International Multidisciplinary Research (Vol. 1, No. 1, 2023)

Preliminary Evaluation of Gaussian Naive Bayes for Multi-Label Hate Speech and Abusive Language Detection on Indonesian Twitter

Handayani, Tri Pratiwi, Hasyim, Wahyudin, Wati, Nursetia
Abstract: Automatic detection of hate speech and abusive language is crucial for combating online toxicity. This study explores Gaussian Naive Bayes for multi-label classification of hate speech on Indonesian Twitter, including target,… rget, category, and level. We combined TF-IDF features with contextual BERT embeddings. The model achieved balanced performance for general hate speech and good non-abusive language detection. However, it exhibited limitations with imbalanced data and specific hate speech types. The classifier consistently favored the majority class (non-hateful/non-abusive) across labels, particularly struggling with HS_Gender, HS_Physical, etc. This suggests difficulty detecting less frequent but potentially severe hate speech, likely due to limited training data. Overall accuracy and F1-scores confirm that while Gaussian Naive Bayes is efficient, it lacks robustness for nuanced multi-label classification with imbalanced datasets. This necessitates exploring alternative approaches for effectively detecting specific and less frequent hate speech.
Global Research Preliminary Evaluation Gaussian Naive Bayes Multi Label Hate Speech Abusive Language Detection Indonesian Twitter
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