Determination of Training Participants in Community Work Training Centers Using the Naïve Bayes Classifier Algorithm

April Hananto - Universitas Buana Perjuangan Karawang, Karawang, 41361, Indonesia
Agustia Hananto - Universitas Buana Perjuangan Karawang, Karawang, 41361, Indonesia
Baenil Huda - Universitas Buana Perjuangan Karawang, Karawang, 41361, Indonesia
Aviv Yuniar Rahman - Universitas Widyagama Malang, Jl. Borobudur, Malang, 65142, Indonesia
Elfina Novalia - Universitas Buana Perjuangan Karawang, Karawang, 41361, Indonesia
Bayu Priyatna - Universitas Buana Perjuangan Karawang, Karawang, 41361, Indonesia


Citation Format:



DOI: http://dx.doi.org/10.62527/joiv.8.3.1995

Abstract


Community work training centers are skills training institutions that aim to improve the skills of the surrounding community by providing training programs that align with industry needs. Registration of training participants at the Al-Ikhwan Islamic Boarding School community work training centers often faces obstacles, namely, the selection process is still manual, so it takes a long time, and there is a possibility of errors. This study aims to apply the Naive Bayes Classifier Algorithm to determine whether applicants pass training at the Al-Ikhwan Islamic Boarding School community work training centers. This classification method is used to help optimize the applicant selection process by considering administrative factors, income, and training quotas. RapidMiner software is used as a tool to implement the algorithm. This study found that the Naive Bayes Classifier Algorithm can provide good accuracy results in determining applicants who pass the training selection. The test results show that the resulting model has an accuracy of 90.00% in determining passing training participants with data that has the highest chance of passing, namely data that has the attributes of the female gender, age 20 years, last education Senior High School/Vocational High School, student work/student, income 364,912, father's work as laborer, father's income 3912,280, mother's work as an IRT, and mother's income 885,964. This research increases efficiency and accuracy in determining training applicants at the Al-Ikhwan Islamic Boarding School community work training centers.


Keywords


Data Mining; Classification; Acceptance of Participants; Naïve Bayes

Full Text:

PDF

References


A. Rahman and A. Suryanto, “Implementasi Sistem Informasi Seleksi Penerima Beasiswa Dengan Metode Naive Bayes Classifier,” J. Penelit. Pendidik. Indones., vol. 2, no. 3, pp. 1–8, 2017.

I. Issah, O. Appiah, P. Appiahene, and F. Inusah, “A systematic review of the literature on machine learning application of determining the attributes influencing academic performance,” Decis. Anal. J., vol. 7, p. 100204, Jun. 2023, doi: 10.1016/j.dajour.2023.100204.

I. Bahaguna, U. Kumar, K. Arumachalam, V. Shridhar, and A. Sharma, “Determinants of Natural Resources Based Microenterprises Performance in India’S Western Himalayan Region: a Naïve Bayes Classifier Analysis,” Int. J. Small Mediu. Enterp., vol. 6, no. 1, pp. 27–40, 2023, doi: 10.46281/ijsmes.v6i1.2032.

Gagan Suganda, Marsani Asfi, Ridho Taufiq Subagio, and Ricky Perdana Kusuma, “Penentuan Penerima Bantuan Beasiswa Kartu Indonesia Pintar (Kip) Kuliah Menggunakan Naïve Bayes Classifier,” JSiI (Jurnal Sist. Informasi), vol. 9, no. 2, pp. 193–199, 2022, doi: 10.30656/jsii.v9i2.4376.

R. G. Almonte, C. R. Malizon, and J. N. Olimpiada, “Sentiment Analysis on Students’ Satisfaction of the Tanauan City College using Naïve Bayes Algorithm,” in 2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), IEEE, Nov. 2022, pp. 1–5. doi: 10.1109/ICECCME55909.2022.9988637.

R. Amalia, “Penerapan data mining untuk memprediksi hasil kelulusan siswa menggunakan metode naïve bayes,” J. Inform. dan Sist. Inf, vol. 6, no. 1, pp. 33–42, 2020.

A. Pebdika et al., “KLASIFIKASI MENGGUNAKAN METODE NAIVE BAYES UNTUK MENENTUKAN CALON PENERIMA PIP,” JATI (Jurnal Mhs. Tek. Inform., vol. 7, no. 1, pp. 452–458, 2023.

A. Lia Hananto et al., “Analysis of Drug Data Mining with Clustering Technique Using K-Means Algorithm,” J. Phys. Conf. Ser., vol. 1908, no. 1, 2021, doi: 10.1088/1742-6596/1908/1/012024.

L. Setiyani, M. Wahidin, D. Awaludin, and S. Purwani, “Analisis Prediksi Kelulusan Mahasiswa Tepat Waktu Menggunakan Metode Data Mining Naïve Bayes : Systematic Review,” Fakt. Exacta, vol. 13, no. 1, p. 35, 2020, doi: 10.30998/faktorexacta.v13i1.5548.

H. K. Saputra, “Analisis Data Mining Untuk Pemetaan Mahasiswa Yang Membutuhkan Bimbingan Dan Konseling Menggunakan Algoritma Naïve Bayes Classifier,” J. Teknol. Inf. dan Pendidik., vol. 11, no. 1, pp. 14–26, 2018, doi: 10.24036/tip.v11i1.104.

N. W. Wardani and N. K. Ariasih, “Analisa komparasi algoritma decision tree c4. 5 dan naïve bayes untuk prediksi churn berdasarkan kelas pelanggan retail,” Int. J. Nat. Sci. Eng., vol. 3, no. 3, pp. 103–112, 2019.

J. K. Sethi and M. Mittal, “Efficient weighted naive bayes classifiers to predict air quality index,” Earth Sci. Informatics, vol. 15, no. 1, pp. 541–552, Mar. 2022, doi: 10.1007/s12145-021-00755-7.

F. Prasetya and F. Ferdiansyah, “Analisis Data Mining Klasifikasi Berita Hoax COVID 19 Menggunakan Algoritma Naive Bayes,” J. Sist. Komput. dan Inform., vol. 4, no. 1, p. 132, 2022, doi: 10.30865/json.v4i1.4852.

M. Wongkar and A. Angdresey, “Sentiment Analysis Using Naive Bayes Algorithm Of The Data Crawler: Twitter,” in 2019 Fourth International Conference on Informatics and Computing (ICIC), IEEE, Oct. 2019, pp. 1–5. doi: 10.1109/ICIC47613.2019.8985884.

I. Kurniawan et al., “Perbandingan Algoritma Naive Bayes Dan SVM Dalam Sentimen Analisis Marketplace Pada Twitter,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 10, no. 1, pp. 731–740, 2023.

T. H. Roza et al., “Suicide risk classification with machine learning techniques in a large Brazilian community sample,” Psychiatry Res., vol. 325, p. 115258, Jul. 2023, doi: 10.1016/j.psychres.2023.115258.

R. Panigrahi et al., “Intrusion detection in cyber–physical environment using hybrid Naïve Bayes—Decision table and multi-objective evolutionary feature selection,” Comput. Commun., vol. 188, pp. 133–144, Apr. 2022, doi: 10.1016/j.comcom.2022.03.009.

A. L. Hananto, A. P. Nardilasari, A. Fauzi, A. Hananto, B. Priyatna, and A. Y. Rahman, “International Journal of INTELLIGENT SYSTEMS AND APPLICATIONS IN ENGINEERING Best Algorithm in Sentiment Analysis of Presidential Election in Indonesia on Twitter,” Orig. Res. Pap. Int. J. Intell. Syst. Appl. Eng. IJISAE, vol. 2023, no. 6s, pp. 473–481, 2023.

P. Padeli, A. Martono, and S. Sudaryono, “Application of Naive Bayes Model, SVM and Deep Learning Predicting,” Cices, vol. 9, no. 1, pp. 93–101, 2023, doi: 10.33050/cices.v9i1.2584.

K. Airin Fariza Abu Samah, N. Farhanah Amirah Misdan, M. Nor Hajar Hasrol Jono, and L. Septem Riza, “The Best Malaysian Airline Companies Visualization through Bilingual Twitter Sentiment Analysis: A Machine Learning Classification,” Int. J. Informatics Vis., vol. 6, no. 1, pp. 130–137, 2022.

D. Novianti, “Implementasi Algoritma Naïve Bayes Pada Data Set Hepatitis Menggunakan Rapid Miner,” Parad. Komput. dan Inf., vol. 21, no. 1, pp. 49–54, 2019.

E. Fitriani, “Perbandingan Algoritma C4. 5 Dan Naïve Bayes Untuk Menentukan Kelayakan Penerima Bantuan Program Keluarga Harapan,” Sistemasi, vol. 9, no. 1, pp. 103–115, 2020.

S. A. Yusuf and U. Khasanah, “Kajian Literatur Dan Teori Sosial Dalam Penelitian,” Metod. Penelit. Ekon. Syariah, vol. 80, pp. 1–23, 2019.

Y. Bustomi, A. Nugraha, C. Juliane, and S. Rahayu, “Data Mining Selection of Prospective Government Employees with Employment Agreements using Naive Bayes Classifier,” Sinkron, vol. 8, no. 1, pp. 1–8, 2023, doi: 10.33395/sinkron.v8i1.11968.

D. Fahrudy and S. ‘uyun, “Classification of Student Graduation by Naïve Bayes Method by Comparing between Random Oversampling and Feature Selections of Information Gain and Forward Selection,” Int. J. Informatics Vis., vol. 6, no. 4, pp. 798–808, 2022, doi: 10.30630/joiv.6.4.982.

T. P. Prast, H. Zakaria, and P. Wiliantoro, “Analisis Layanan Pelanggan PT PLN Berdasarkan Media Sosial Twitter Dengan Menggunakan Metode Naïve Bayes Classifier,” OKTAL J. Ilmu Komput. dan Sains, vol. 1, no. 06, pp. 573–582, 2022.

B. Marapelli, S. Kadiyala, and C. S. Potluri, “Performance Analysis and Classification of Class Imbalanced Dataset Using Complement Naive Bayes Approach,” in 2023 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA), IEEE, Jan. 2023, pp. 1–7. doi: 10.1109/ACCTHPA57160.2023.10083369.

Z. M. Ali, N. H. Hassoon, W. S. Ahmed, and H. N. Abed, “The Application of Data Mining for Predicting Academic Performance Using K-means Clustering and Naïve Bayes Classification,” Int. J. Psychosoc. Rehabil., vol. 24, no. 03, pp. 2143–2151, 2020, doi: 10.37200/ijpr/v24i3/pr200962.

M. F. A. Saputra, T. Widiyaningtyas, and A. P. Wibawa, “Illiteracy classification using K means-naïve bayes algorithm,” Int. J. Informatics Vis., vol. 2, no. 3, pp. 153–158, 2018, doi: 10.30630/joiv.2.3.129.

B. Sunarko et al., “Prediction of Student Satisfaction with Academic Services Using Naive Bayes Classifier,” in 2022 6th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), IEEE, Dec. 2022, pp. 228–233. doi: 10.1109/ICITISEE57756.2022.10057736.

T. Arifin and S. Syalwah, “Prediksi keberhasilan immunotherapy pada penyakit kutil dengan menggunakan algoritma naïve bayes,” J. Responsif Ris. Sains dan Inform., vol. 2, no. 1, pp. 38–43, 2020.

A. P. Nardilasari, A. L. Hananto, S. S. Hilabi, and B. Priyatna, “Analisis Sentimen Calon Presiden 2024 Menggunakan Algoritma SVM,” vol. 7, no. 1, pp. 11–18, 2024.

G. S, S. R, D. B. M, and J. J, “Improved Sentimental Analysis to the Movie Reviews using Naive Bayes Classifier,” in 2022 International Conference on Electronics and Renewable Systems (ICEARS), IEEE, Mar. 2022, pp. 1831–1836. doi: 10.1109/ICEARS53579.2022.9752408.