Application of Clustering Methods on Sexual Harassment Cases
DOI:
https://doi.org/10.54066/jptis.v2i3.2382Keywords:
Data Mining, Clustering, Sexual HarassmentAbstract
Sexual harassment is one of the most common crimes in Indonesia, this act of sexual harassment can occur in daily life regardless of time, whether at work, on the street, or at home. Sexual abuse can come from unknown people, people who have hate, even people we care about. To solve problems that often occur in some cases including cases of sexual harassment that often occur in women based on certain factors, resulting in trauma to the victims who suffer physically, sexually, and psychologically, are required quick action to reduce the number in cases of sexual abuse in the area that often occur using clustering methods so that later it is expected to help the agency in socializing so that the community is more alert while in the place. From the testing conducted using 20 sexual harassment cases data there are 3 groups, namely group 1 there are 9 data and 2 groups there are 5 data and group 3 there are 6 data and it can be known that in cluster 1 is a group in the case data on sexual harassment based on the factors that are many causes with a total of 9 data and located in the age group (X) is 12-16 years, and for the group Sexual Harassment (Y) namely Physical Harassment and causing factor (Z) that is a lot due to individual factors.
References
Adawiah, N., Suliatiyowati, N., & Jajuli, M. (2021). Klasterisasi Kasus Kekerasan Terhadap Anak dan Perempuan Berdasarkan Algoritma K-Means. Generation Journal, 5(2), 69–80.
Dewi, ratna. (2023). Aplikasi Matlab untuk Simulasi Pengolahan Sinyal ( agus Prijono, Ed.; 1st ed.). Zahir Publishing.
Hasan, Z., Novriyanti, F., Ramadhani Putri, A. T., & Al Munawwaroh, R. (2023). FAKTOR PENYEBAB TERJADINYA PELECEHAN DAN KEKERASAN SEKSUALTERHADAP ANAK DI BAWAH UMUR DI KOTA BANDAR LAMPUNG. JHM, 4(2), 84–91.
Laksana, F., Hidayat, R., & Dan Dewi, Y. (2023). Pengelompokan jumlah kekerasan pada anak menggunakan K-Means 123. Computational Intelligent Journal, 5(2), 123–135.
Laksana, F., Hidayat, R., & Dewi, Y. (2023). Pengelompokan Jumlah Kekerasan Terhadap Anak Berdasarkan Jenis Pelecehan Seksual Di Kabupaten Banyumas Menggunakan Penerapan Algoritma K-Means. INDEXIA : Informatic and Computational Intelligent Journal, 5(2), 123–135.
Matheos Sariole, F., & Hakim, L. (2024). Klasifikasi Barang Menggunakan Metode Clustering K-Means Dalam Penentuan Prediksi Stok Barang. Jurnal Sains Dan Teknologi, 5(3), 846–854. https://doi.org/10.55338/saintek.v5i1.2709
Prasetyo. (2012). Data Mining Konsep dan Aplikasi menggunakan MATLAB (Nikodemus WK, Ed.; 1st ed., Vol. 1). ANDI OFFSET.
Relita Buaton, Zarlis, M., Efendi, S., & Yasin, V. (2019). DATA MINING TIME SERIES (1st ed., Vol. 1). Wade Group.
Relita, B., Sundari, Y., & Maulita Yani. (2016). Clustering Tindak Kekerasan Pada Anak Menggunakan Algoritm Dengan Perbandingan Jarak Kedekatan Manhattan City Dan Eu oritma K-Means an Euclidean. MEANS (Media Informasi Analisa Dan Sistem), 1(2548–6985), 47–53.
Sari, K. indriyanti purnama, Farida, L. nur, Khayati, N., Maidaliza, Asmaret, D., Pramana, I., Meinarisa, Melvia Girsang, B., Alfianto, A. G., & Suminah. (2022). KEKERASAN SEKSUAL (Agustiawan, Ed.; 1st ed., Vol. 1). CV. Media Sains Indonesia. www.medsan.co.id
Sundari, M. A., Pane, R., & Rohani, R. (2023). Data Mining Clustering Korban Kejahatan Pelecehan Seksual dengan Kekerasan Berdasarkan Provinsi Menggunakan Metode AHC. Building of Informatics, Technology and Science (BITS), 5(1). https://doi.org/10.47065/bits.v5i1.3499
Wandana, J., Defit, S., & Sumijan, S. (2020). Klasterisasi Data Rekam Medis Pasien Pengguna Layanan BPJS Kesehatan Menggunakan Metode K-Means. Jurnal Informasi Dan Teknologi. https://doi.org/10.37034/jidt.v2i4.73
