Prediksi Penambahan Piutang Iuran Jaminan Sosial Ketenagakerjaan menggunakan Algoritma K-Nearest Neighbor
DOI:
https://doi.org/10.29408/edumatic.v6i1.5255Keywords:
BPJS ketenagakerjaan, cross validation, dues receivable, KNNAbstract
There are several issues with Social Security Organizing Agency (BPJS) employment at the moment, one of which is contribution receivable. To reduce the BPJS contribution receivables, BPJS has done various ways. However, the resulting effort is not maximal enough to reduce the number of receivables in BPJS. This study aims to provide input by predicting the addition of receivables from social security contributions made by several companies or organizations. This study used the K-Nearest Neighbor (KNN) Algorithm with a cross-validation technique. KNN is a very simple classification method in classifying an image based on the closest distance to its neighbors. This study conducted data processing from BPJS use, which amounted to 1193 data. The data is then preprocessed so that the processed data is clean from missing and noise, this data uses 70:30 data splitting. After the preprocessing and splitting of data were carried out, the next step was to do modeling using KNN, so the cross-validation to improve the accuracy of results obtained from the KNN algorithm. The results obtained from this research get the highest accuracy of 92% with the Optimal K value being 6, then the ROC curve gets 94% accuracy. From these results, it can be said that the use of cross-validation can increase the accuracy of this study.
References
Anam, M. K., Mahendra, M. I., Agustin, W., Rahmaddeni, & Nurjayadi. (2022). Framework for Analyzing Netizen Opinions on BPJS Using Sentiment Analysis and Social Network Analysis (SNA). Intensif, 6(1), 2549–6824. https://doi.org/10.29407/intensif.v6i1.15870
Anam, M. K., Pikir, B. N., Firdaus, M. B., Erlinda, S., & Agustin. (2021). Penerapan Naïve Bayes Classifier , K-Nearest Neighbor dan Decision Tree untuk Menganalisis Sentimen pada Interaksi Netizen dan Pemeritah. Matrik: Jurnal Manajemen, Teknik Informatika, Dan Rekayasa Komputer, 21(1), 139–150. https://doi.org/10.30812/matrik.v21i1.1092
Ardiansyah, M., Sunyoto, A., & Luthfi, E. T. (2021). Analisis Perbandingan Akurasi Algoritma Naïve Bayes Dan C4.5 untuk Klasifikasi Diabetes. Edumatic: Jurnal Pendidikan Informatika, 5(2), 147–156. https://doi.org/10.29408/edumatic.v5i2.3424
Azis, H., Purnawansyah, P., Fattah, F., & Putri, I. P. (2020). Performa Klasifikasi K-NN dan Cross Validation Pada Data Pasien Pengidap Penyakit Jantung. ILKOM Jurnal Ilmiah, 12(2), 81–86. https://doi.org/10.33096/ilkom.v12i2.507.81-86
Baharuddin, M. M., Azis, H., & Hasanuddin, T. (2019). Analisis Performa Metode K-Nearest Neighbor Untuk Identifikasi Jenis Kaca. ILKOM Jurnal Ilmiah, 11(3), 269–274. https://doi.org/10.33096/ilkom.v11i3.489.269-274
Fansyuri, M. (2020). Analisa algoritma klasifikasi k-nearest neighbor dalam menentukan nilai akurasi terhadap kepuasan pelanggan (study kasus pt. Trigatra komunikatama). Humanika: Jurnal Ilmu Sosial, Pendidikan, Dan Humaniora, 3(1), 29–33. https://doi.org/https://doi.org/10.33050/tmj.v6i1.1531
Frastian, N. (2018). Implementasi Komparasi Algoritma Klasifikasi Menentukan Kelulusan Mata Kuliah Algoritma Universitas Budi Luhur. STRING (Satuan Tulisan Riset Dan Inovasi Teknologi), 3(1), 1–8. https://doi.org/10.30998/string.v3i1.2334
Hidayat, W., Utami, E., Iskandar, A. F., Hartanto, A. D., & Prasetio, A. B. (2021). Perbandingan Performansi Model pada Algoritma K-NN terhadap Klasifikasi Berita Fakta Hoaks Tentang Covid-19. Edumatic: Jurnal Pendidikan Informatika, 5(2), 167–176. https://doi.org/10.29408/edumatic.v5i2.3664
Kusrini, & Luthfi, E. T. (2009). Algoritma Data Mining. Yogyakarta: ANDI.
Mustafa, M. S, & Simpen, I. W. (2019). Implementasi Algoritma K-Nearest Neighbor ( KNN) Untuk Memprediksi Pasien Terkena Penyakit Diabetes Pada Puskesmas Manyampa Kabupaten Bulukumba. Seminar Ilmiah Sistem Informasi Dan Teknologi Informasi, 8(1), 1–10.
Mustafa, M. S., & Simpen, I. W. (2014). Perancangan Aplikasi Prediksi Kelulusan Tepat Waktu Bagi Mahasiswa Baru Dengan Teknik Data Mining (Studi Kasus: Data Akademik Mahasiswa STMIK Dipanegara Makassar). Creative Information Technology Journal, 1(4), 270–281. https://doi.org/10.24076/citec.2014v1i4.27
Nasution, D. A., Khotimah, H. H., & Chamidah, N. (2019). Perbandingan Normalisasi Data untuk Klasifikasi Wine Menggunakan Algoritma K-NN. Computer Engineering, Science and System Journal, 4(1), 78. https://doi.org/10.24114/cess.v4i1.11458
Pikir, B. N., Anam, M. K., Asnal, H., Rahmaddeni, & Fitri, T. A. (2021). Sentiment Analysis of Technology Utilization by Pekanbaru City Government Based on Community Interaction in Social Media. JAIA – Journal Of Artificial Intelligence And Applications, 2(1), 32–40.
Prasetio, A., Bijaksana, M. A., & Suryani, A. A. (2020). Name Disambiguation Analysis Using the Word Sense Disambiguation Method in Hadith. Edumatic: Jurnal Pendidikan Informatika, 4(2), 68-74. https://doi.org/10.29408/edumatic.v4i2.2551
Rhomadhona, H., & Permadi, J. (2019). Klasifikasi Berita Kriminal Menggunakan Naïve Bayes Classifier (NBC) dengan Pengujian K-Fold Cross Validation. Jurnal Sains Dan Informatika, 5(2), 108–117. https://doi.org/10.34128/jsi.v5i2.177
Rifai, M. F., Jatnika, H., & Valentino, B. (2019). Penerapan Algoritma Naïve Bayes Pada Sistem Prediksi Tingkat Kelulusan Peserta Sertifikasi Microsoft Office Specialist (MOS). Petir, 12(2), 131–144. https://doi.org/10.33322/petir.v12i2.471
Samponu, Y. B., & Kusrini, K. (2018). Optimasi Algoritma Naive Bayes Menggunakan Metode Cross Validation Untuk Meningkatkan Akurasi Prediksi Tingkat Kelulusan Tepat Waktu. Jurnal ELTIKOM, 1(2), 56–63. https://doi.org/10.31961/eltikom.v1i2.29
Sari, A. Q., Sukestiyarno, Y. L., & Agoestanto, A. (2017). Batasan Prasyarat Uji Normalitas Dan Uji Homogenitas Pada Model Regresi Linear. Unnes Journal of Mathematics, 6(2), 168–177. https://doi.org/10.15294/ujm.v6i2.11887
Ulfah, A. N., & Anam, M. K. (2020). Analisis Sentimen Hate Speech Pada Portal Berita Online Menggunakan Support Vector Machine (SVM). JATISI (Jurnal Teknik Informatika Dan Sistem Informasi), 7(1), 1–10. https://doi.org/10.35957/jatisi.v7i1.196
Wang, T., Ke, H., Zheng, X., Wang, K., Sangaiah, A. K., & Liu, A. (2019). Big data cleaning based on mobile edge computing in industrial sensor-cloud. IEEE Transactions on Industrial Informatics, 16(2), 1321-1329. https://doi.org/10.1109/TII.2019.2938861
Widiastuti, I. (2017). Pelayanan Badan Penyelenggara Jaminan Sosial (BPJS) Kesehatan di Jawa Barat. Public Inspiration: Jurnal Administrasi Publik, 2(2), 91–101. https://doi.org/10.22225/pi.2.2.2017.91-101
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