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.
Data files:
  1. [1] A. Kasim, L. W. Badu, and S. Y. Imran, “Mechanism of Rehabilitation Against Narcotics Abuse by Children,” Estud. Law J., Vol.3, No.1, pp. 219-233, 2021.
  2. [2] M. E. Gondokesumo and N. Amir, “Legality of Marijuana Use in the Need for Medical Treatment in Indonesia (Judging From Law Number 36 of 2009 Concerning Health and Law Number 35 of 2009 Concerning Narcotics),” J. Equity Law Gov., Vol.1, No.2, pp. 119-126, 2021.
  3. [3] S. Widiasyam, O. Haris, and S. A. Abdullah, “Criminal Law Study on Narcotics Abuse Rehabilitation,” Indonesian J. Crim. Law Stud. (IJCLS), Vol.5, No.1, pp. 55-62, 2020.
  4. [4] M. Laila, “The Role Of The Medan Police In Law Enforcement Of Criminal Acts Of Narcotics,” J. Law Sci., Vol.3, No.4, pp. 164-175, 2021.
  5. [5] A. Pasinringi, “The Performance Of The National Narcotics Agency In Illegal Drugs Prevention Efforts Of Palu City, Central Sulawesi, Indonesia,” J. Public Adm. Gov., Vol.2, No.1, pp. 1-7, 2020.
  6. [6] Rezkiansyah, F. U. Puluhulawa, and L. M. Tijow, “Law Enforcement Against Narcotics Crime Recidivists,” Estud. Law J., Vol.2, No.1, pp. 206-214, 2020.
  7. [7] Mahkamah Agung Republik Indonesia, “Sistem Informasi Pnelusuran Perkara.” [Accessed April 16, 2022]
  8. [8] P. Shah, P. Swaminarayan, and M. Patel, “Sentiment analysis on film review in Gujarati language using machine learning,” Int. J. Electr. Comput. Eng., Vol.12, No.1, pp. 1030-1039, 2022.
  9. [9] R. Kosasih and A. Alberto, “Sentiment analysis of game product on shopee using the TF-IDF method and naive bayes classifier,” Ilk. J. Ilm., Vol.13, No.2, pp. 101-109, 2021.
  10. [10] M. Jaiswal, S. Das, and Khushboo, “Detecting spam e-mails using stop word TF-IDF and stemming algorithm with Naïve Bayes classifier on the multicore GPU,” Int. J. Electr. Comput. Eng., Vol.11, No.4, pp. 3168-3175, 2021.
  11. [11] J. J. Stephen and P. Prabu, “Detecting the magnitude of depression in Twitter users using sentiment analysis,” Int. J. Electr. Comput. Eng., Vol.9, No.4, pp. 3247-3255, 2019.
  12. [12] D. D. Albesta, M. L. Jonathan, M. Jawad, O. Hardiawan, and D. Suhartono, “The impact of sentiment analysis from user on Facebook to enhanced the service quality,” Int. J. Electr. Comput. Eng., Vol.11, No.4, pp. 3424-3433, 2021.
  13. [13] I. Paik and H. Mizugai, “Recommendation system using weighted TF-IDF and naive bayes classifiers on RSS contents,” J. Adv. Comput. Intell. Intell. Inform., Vol.14, No.6, pp. 631-637, 2010.
  14. [14] S. Fransiska, Rianto, and A. I. Gufroni, “Sentiment Analysis Provider By.U on Google Play Store Reviews with TF-IDF and Support Vector Machine (SVM) Method,” Sci. J. Informatics, Vol.7, No.2, pp. 2407-7658, 2020.
  15. [15] D. Pratmanto, R. Rousyati, F. F. Wati, A. E. Widodo, S. Suleman, and R. Wijianto, “App Review Sentiment Analysis Shopee Application in Google Play Store Using Naive Bayes Algorithm,” J. Phys. Conf. Ser., Vol.1641, No.1, Article No.012043, 2020.
  16. [16] T. Cao, C. Vo, S. Nguyen, A. Inoue, and D. Zhou, “A Kernel k-Means-Based Method and Attribute Selections for Diabetes Diagnosis,” J. Adv. Comput. Intell. Intell. Inform., Vol.24, No.1, pp. 73-80, 2020.
  17. [17] S. Ubukata, S. Sekiya, A. Notsu, and K. Honda, “Noise Rejection Approaches for Various Rough Set-Based C-Means Clustering,” J. Adv. Comput. Intell. Intell. Inform., Vol.24, No.6, pp. 738-749, 2020.
  18. [18] H. Li, R. Fan, Q. Shi, and Z. Du, “Class imbalanced fault diagnosis via combining K-means clustering algorithm with generative adversarial networks,” J. Adv. Comput. Intell. Intell. Inform., Vol.25, No.3, pp. 346-355, 2021.
  19. [19] Y. Kanzawa and S. Miyamoto, “Generalized fuzzy c-means clustering and its property of fuzzy classification function,” J. Adv. Comput. Intell. Intell. Inform., Vol.25, No.1, pp. 73-82, 2021.
  20. [20] R. Kitajima and I. Kobayashi, “Latent topic estimation based on events in a document,” J. Adv. Comput. Intell. Intell. Inform., Vol.16, No.5, pp. 603-610, 2012.
  21. [21] Y. Xi, G. Chen, B. Li, and Y. Tang, “Topic evolution analysis based on cluster topic model,” J. Adv. Comput. Intell. Intell. Inform., Vol.20, No.1, pp. 66-75, 2016.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on May. 10, 2024