Implementasi Data Mining Klasifikasi Fuel Surcharge Menggunakan Algoritma Naive Bayes Studi Kasus PT Pelabuhan Indonesia (Persero) Regional 2 Tanjung Priok
DOI:
https://doi.org/10.35870/jimik.v5i3.960Keywords:
Data Mining, Fuel Surcharge, Tanjung PriokAbstract
In the application of fuel surcharge in PT Pelabuhan Indonesia (Persero) Regional 2 Tanjung Priok, it is necessary to display data that can help become the basis for the imposition of fuel surcharge from data on the movement of pilot vessels / tugboats which is the basis for the imposition of fuel surcharge in PT Pelabuhan Indonesia (Persero) Regional 2 Tanjung Priok, this research was conducted to help provide information to workers, Kesyahbandaran Otoritas Pelabuhan Utama and ship service users or shipping agents in more detail related to the imposition of fuel surcharge in the PT Pelabuhan Indonesia (Persero) Regional 2 Tanjung Priok environment, using data mining methods in fuel surcharge peneran as a consideration to make it easier to understand, easier to see and easier to operate. With this data mining method, it will help the company to become an important data and facilitate workers in the data collection section related to fuel surcharge in which there is a recap of the number of ship movements, the number of notes, the strength of groos tons (GT), house power (HP) of the ship and payment status. The use of data mining methods is expected to be a solution for workers in data collection and become a history of fuel surcharge collection within PT Pelabuhan Indonesia (Persero) Regional 2 Tanjung Priok.
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Agung, A. S., Fauzi, A. A., Risal, A. A. N., & Adiba, F. (2023). Implementasi Teknik Data Mining terhadap Klasifikasi Data Prediksi Curah Hujan BMKG Di Sulawesi Selatan. Jurnal Tekno Insentif, 17(1), 22-23. DOI: https://doi.org/10.36787/jti.v17i1.955.
Agusetiana, E., & Fitrani, A. S. (2022, August). Implementasi Data Mining Pada Pelanggan Telkom Menggunakan Metode K-Nearest Neighbor untuk Memprediksi Status Pelayanan. In Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) (Vol. 6, No. 1, pp. 115-119). DOI: https://doi.org/10.29407/inotek.v6i1.2461.
Arifin, A. A. A., Handoko, W., & Efendi, Z. (2022). Implementasi Metode Naive Bayes Untuk Klasifikasi Penerima Program Keluarga Harapan. J-Com (Journal of Computer), 2(1), 21-26. DOI: https://doi.org/10.33330/j-com.v2i1.1577.
Choeriyah, S. S., Fanhas, R. S., Fathah, A., & Pebriyansyah, H. (2022). Implementasi Algoritma k-Nearest Neighbor (k-NN) dalam Klasifikasi Status Gizi Balita. Cipasung Techno Pesantren: Scientific Journal, 16(2), 70-78.
Damuri, A., Riyanto, U., Rusdianto, H., & Aminudin, M. (2021). Implementasi Data Mining dengan Algoritma Naïve Bayes Untuk Klasifikasi Kelayakan Penerima Bantuan Sembako. JURIKOM (Jurnal Riset Komputer), 8(6), 219-225. DOI: http://dx.doi.org/10.30865/jurikom.v8i6.3655.
Etriyanti, E., Syamsuar, D., & Kunang, N. (2020). Implementasi Data Mining Menggunakan Algoritme Naive Bayes Classifier dan C4. 5 untuk Memprediksi Kelulusan Mahasiswa.
Herlena, A. C. P. (2023). Implementasi Data Mining Untuk Klasifikasi Stuting Gizi Pada Balita di Surabaya Menggunakan Metode K-Medoids. Jurnal Publikasi Teknik Informatika, 2(1), 61-67.
Ikhromr, F. N., Sugiyarto, I., Faddillah, U., & Sudarsono, B. (2023). Implementasi Data Mining Untuk Memprediksi Penyakit Diabetes Menggunakan Algoritma Naives Bayes dan K-Nearest Neighbor. INTECOMS: Journal of Information Technology and Computer Science, 6(1), 416-428. .
Mustafa, M. S., Ramadhan, M. R., & Thenata, A. P. (2018). Implementasi data mining untuk evaluasi kinerja akademik mahasiswa menggunakan algoritma naive bayes classifier. Creative Information Technology Journal, 4(2), 151-162. DOI: https://doi.org/10.24076/citec.2017v4i2.106.
Nasrullah, A. H. (2021). Implementasi algoritma Decision Tree untuk klasifikasi produk laris. Jurnal Ilmiah Ilmu Komputer Fakultas Ilmu Komputer Universitas Al Asyariah Mandar, 7(2), 45-51. DOI: https://doi.org/10.35329/jiik.v7i2.203.
Nurliana, E., Irawan, B., & Bahtiar, A. (2024). IMPLEMENTASI DATA MINING ALGORITMA K-MEANS UNTUK KLASIFIKASI PENDUDUK MISKIN BERDASARKAN TINGKAT KEMISKINAN DI JAWA BARAT. JATI (Jurnal Mahasiswa Teknik Informatika), 8(1), 1116-1122. DOI: https://doi.org/10.36040/jati.v8i1.8883.
Pratama, F. D., Zufria, I., & Triase, T. (2022). Implementasi Data Mining Menggunakan Algoritma Naïve Bayes Untuk Klasifikasi Penerima Program Indonesia Pintar. Rabit: Jurnal Teknologi dan Sistem Informasi Univrab, 7(1), 77-84. DOI: https://doi.org/10.36341/rabit.v7i1.2217.
Purnawati, N. W., Arsana, I. N. A., Arfyanti, I., Mukhlis, I. R., Sulistyowati, S., Prasetya, F. D., ... & Judijanto, L. (2024). Sistem Informasi: Teori dan Implementasi Sistem Informasi di Berbagai Bidang. PT. Sonpedia Publishing Indonesia.
Putri, S. U., Irawan, E., & Rizky, F. (2021). Implementasi Data Mining Untuk Prediksi Penyakit Diabetes Dengan Algoritma C4. 5. Kesatria: Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen), 2(1), 39-46. DOI: https://doi.org/10.30645/kesatria.v2i1.56.
Sitepu, R., & Manohar, M. (2022). Implementasi Algoritma K-Nearest Neigbor Untuk Klasifikasi Pengajuan Kredit. Jurnal Sistem Informasi, Teknik Informatika dan Teknologi Pendidikan, 1(2), 49-56. DOI: https://doi.org/10.55338/justikpen.v1i2.6.
Solihati, T. I., Hidayanti, N., & Kania, R. Implimentasi Data Mining untuk Evaluasi Kinerja Penelitian Mahasiswa Universitas Banten Jaya dengan Menggunakan Algoritma Naive Bayes Classifier. Jurnal Theorems, 6(2), 135-147.
Sukarna, R. H., & Ansori, Y. (2022). Implementasi Data Mining Menggunakan Metode Naive Bayes Dengan Feature Selection Untuk Prediksi Kelulusan Mahasiswa Tepat Waktu. Jurnal Ilmiah Sains dan Teknologi, 6(1), 50-61. DOI: https://doi.org/10.47080/saintek.v6i1.1467.
Tou, N., & Endraswari, P. M. (2022). Implementasi Data Mining Dalam Klasifikasi Hasil Diagnosa Pasien Bpjs Menggunakan Algoritma Cart. JIKA (Jurnal Informatika), 6(2), 170-179. DOI: http://dx.doi.org/10.31000/jika.v6i2.6164.
Wahyudi, A. K., Azizah, N., & Saputro, H. (2022). Data Mining Klasifikasi Kepribadian Siswa SMP Negeri 5 Jepara Menggunakan Metode Decision Tree Algoritma C4. 5. Journal of Information System and Computer, 2(2), 8-13. DOI: https://doi.org/10.34001/jister.v2i2.392.
Wanto, A., Kom, M., Siregar, M. N. H., Windarto, A. P., Hartama, D., Ginantra, N. L. W. S. R., ... & Prianto, C. (2020). Data Mining: Algoritma dan Implementasi. Yayasan kita menulis.
Wijaya, H. D., & Dwiasnati, S. (2020). Implementasi Data Mining dengan Algoritma Naïve Bayes pada Penjualan Obat. Jurnal Informatika, 7(1), 1-7. DOI: https://doi.org/10.31294/ji.v7i1.6203.
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