SISTEM INFORMASI PENJADWALAN KUNJUNGAN TEKNISI MENGGUNAKAN ALGORITMA GENETIKA PADA PT SOLUSINDO BINTANG PRATAMA
DOI:
https://doi.org/10.31000/jika.v8i4.12183Abstrak
Penelitian ini bertujuan untuk mengatasi tantangan penjadwalan kunjungan teknisi di PT Solusindo Bintang Pratama dengan mengembangkan aplikasi berbasis web menggunakan algoritma genetika. Permasalahan utama adalah penentuan rute kunjungan yang masih dilakukan secara manual, mengakibatkan pemborosan waktu dan sumber daya. Algoritma genetika dipilih karena kemampuannya dalam menyelesaikan masalah optimasi kompleks seperti Travelling Salesman Problem. Aplikasi ini dirancang untuk menghasilkan rute kunjungan optimal, meningkatkan efisiensi operasional perusahaan, dan mengurangi risiko kesalahan. Hasil penelitian menunjukkan bahwa aplikasi ini berhasil meningkatkan efisiensi waktu dan penghematan bahan bakar, serta mengurangi risiko kesalahan dalam menentukan rute. Implementasi sistem juga menekankan pentingnya data input yang akurat. Meskipun memerlukan pelatihan awal bagi teknisi, sistem ini menunjukkan potensi signifikan dalam meningkatkan responsivitas perusahaan terhadap kebutuhan pelanggan. Secara keseluruhan, aplikasi ini memberikan kontribusi signifikan terhadap peningkatan efisiensi operasional di PT Solusindo Bintang Pratama dan dapat menjadi model untuk diterapkan pada industri layanan teknis lainnya. Penerapan lebih lanjut dan pengembangan berkelanjutan diharapkan dapat meningkatkan kinerja sistem dan memperluas fungsionalitasnya.Referensi
Aswandi, Cokrowibowo, S., & Irianti, A. (2021). Model Penentuan Rute Terpendek Penjemputan Sampah Menggunakan Metode MTSP dan Algoritma Genetika . Journal of Applied Computer Science and Technology (JACOST), 2(1), 43–48.
Ghofany, M. S. Al, Wijaya, I. G. P. S., & Maududi, N. (2020). Sistem Informasi Penjadwalan Pembelajaran pada SMAN 5 Mataram. JBegaTI, 1(1), 68–78.
Hanafi, M. I., Junior, B. C., & Rosita, Y. D. (2023). Optimasi Rute Penyebaran Brosur Bimbingan Belajar Menggunakan Algoritma Genetika. JINTEKS, 5(2), 265–270.
herdiansah, A., Sugiyani, Y., Septarini, R. S., & Mahpud, M. (2022). Penerapan Metode Pemodelan UML (Unified Modelling Language) dan RAD (Rapid Application Development) pada Pembangunan Sistem Informasi Akademik Sekolah (A. Wahdi, Ed.; 1st ed.). CV. Dewa Publishing.
Ibrahim, M. R., & Kuswanto, H. (2022). Perancangan Aplikasi Pelayanan Kursus Mengemudi Menggunakan Metode Waterfall Pada LPK/LKP Indera Magelang Berbasis Web. JIKA (Jurnal Informatika), 6(3), 242–248. https://doi.org/10.31000/jika.v6i3.6121
Mubarok, A. Y., & Chotijah, U. (2021). Penerapan Algoritma Genetika Untuk Mencari Optimasi Kombinasi Jalur Terpendek Dalam Kasus Travelling Salesman Problem. Jurnal Teknologi Terpadu, 7(2), 77–82.
Muhammad, Febrianty, & Sentanu, I. G. E. P. S. (2023). Manajemen Pengambilan Keputusan. Penertbit Perkumpulan Rumah Cemerlang Indonesia.
Muziafa, S. (2022). Struktur Data dan Implementasi Algoritma (Vol. 1).
Pratama, A. S. J., Khamid, A., & Rosita, Y. D. (2023). Pencarian Rute Optimal Wisata Mojokerto dalam Kasus Traveling Salesman Problem Menggunakan Algoritma Genetika. Jurnal Informatika Teknologi dan Sains, 5(2), 283–288.
Ramadhan, G. C., W, P. B., & Rosita, Y. D. (2023). Penentuan Rute Optimal Untuk Jasa Pengiriman Barang Menggunakan Algoritma Genetika. Jurnal Teknologi Informasi dan Multimedia, 5(1), 48–55.
Ramadhan, Mgs. M. Z., & Angelia, F. (2023). Mengoptimalkan Pengembangan Aplikasi Mobile Melalui Perbandingan Metode Pengembangan Perangkat Lunak (Waterfall, Prototype, Mobile-D, Agile, RAD) . Jurnal Ilmiah Teknologi Infomasi Dan Sains, 3(2), 13–19.
Sinaga, R. P., & Marpaung, F. (2023). Perbandingan Algoritma Cheapest Insertion Heuristic Dan Nearest Neighbor Dalam Menyelesaikan Traveling Salesman Problem . Jurnal Riset Rumpun Matematika dan Ilmu Pengetahuan Alam, 2(2), 238–247.
Suputra, I. P. G. H., & Pramartha, C. R. A. (2020). Rekomendasi Rute Perjalanan Wisata Berbasis Web Menggunakan Algoritma Genetika . Jurnal Ilmu Komputer, 13(1), 21–27.
Syawal, M., Belluano, P. L. L., & Manga, A. R. (2021). Implementasi Algoritma Genetika Untuk Penjadwalan Laboratotium Fakultas Ilmu Komputer Universitas Muslim Indonesia . Indonesian Journal of Data and Science, 2(1), 29–37.
Udjulawa, D., & Oktarina, S. (2022). Penerapan Algoritma ANT Colony Optimization untuk Pencarian Rute Terpendek Lokasi Wisata.. Klik - Jurnal Ilmu Komputer, 3(1), 26–33.
Wahyuningsih, D., & Helmud, E. (2020). Penerapan Algoritma Genetika Untuk Optimasi Penjadwalan pada MTS Negeri 1 Pangkalpinang. Jurnal Sisfokom (Sistem Informasi dan Komputer), 9(3), 435–441.
Werner, F., Burtseva, L., & Sotskov, Y. (2020). Heuristic Scheduling Algorithms. mdpi.
Zen, H. R. R., & Nuryasin, I. (2024). Penerapan Whitebox Testing pada Pengujian Sistem Menggunak. JOISIE (Journal Of Information Systems And Informatics Engineering), 8(1), 101–111.
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