JACIII Vol.27 No.6 pp. 1168-1174
doi: 10.20965/jaciii.2023.p1168

Research Paper:

Clustering and Topic Modeling of Verdicts of Narcotics Cases Using Machine Learning

Ilmiyati Sari ORCID Icon, Rifki Kosasih ORCID Icon, and Dina Indarti

Department of Informatics, Gunadarma University
Pondok Randu Street No.10, West Jakarta, DKI Jakarta 11750, Indonesia

Corresponding author

September 21, 2022
August 1, 2023
November 20, 2023
narcotics, machine learning, K-means, latent Dirichlet allocation

Narcotics are a grave crime that can lead to addiction, loss of consciousness, and even death. Furthermore, narcotics can damage society’s environment. Narcotics criminal cases have been reported widely in Indonesia. The variety of narcotics cases makes it extremely difficult for judges to make decisions. Therefore, it is necessary to study and analyze the judge’s decisions from the data on the narcotics cases. In this study, we propose using a machine learning approach based on K-means clustering method for clustering and analyzing the verdicts on narcotics cases to see the trend of the verdicts on narcotics cases. In addition, we also use latent Dirichlet allocation (LDA) topic modeling to study the trend of these narcotics cases. Based on the results of the study using K=3 for clustering, there were three categories of verdicts: decisions with light sentences (less than three years), decisions with moderate sentences (three to six years), and decisions with severe sentences (more than ten years). Furthermore, using topic modeling based on the LDA method, the top three topics of narcotics cases based on the verdicts were determined, namely: the first topic refers to verdicts where narcotics perpetrators are found guilty; the second topic refers to verdicts with evidence of marijuana-type narcotics; and the third topic refers to verdicts with evidence of methamphetamine-type narcotics.

Cite this article as:
I. Sari, R. Kosasih, and D. Indarti, “Clustering and Topic Modeling of Verdicts of Narcotics Cases Using Machine Learning,” J. Adv. Comput. Intell. Intell. Inform., Vol.27 No.6, pp. 1168-1174, 2023.
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Last updated on Nov. 24, 2023