REKOMENDASI PEKERJAAN BIDANG EKONOMI : SISTEM REKOMENDASI MENGGUNAKAN CONTENT BASED
DOI:
https://doi.org/10.33480/inti.v20i1.6786Keywords:
Content-Based , Economics , MLP Classifier, Recommendation SystemAbstract
The recommendation system was developed to assist students of the Institut Teknologi dan Bisnis Widya Gama Lumajang, particularly those from the Faculty of Economics and Business, in determining their preferred career options. This system helps students by providing various job references that match their individual criteria. The data was collected from a tracer study, which includes information such as academic grades, non-academic achievements, job positions, company names, salaries received. From the total dataset, 1,120 records were deemed valid and used in the research process. The aim of this research is to assist students by providing job recommendations based on similar criteria between current students and alumni. The method applied in this study is quantitative experimental research based on data mining, with the main approach being Content-Based filtering and the MLP (Multi-Layer Perceptron) Classifier algorithm. The data was split into two parts: 65% for training and 35% for testing. This division aims to allow the model to learn from most of the data while also being tested for accuracy using unfamiliar data. The recommendation model was developed using the MLP Classifier algorithm with a hidden_layer_size configuration of 100 neurons and a max_iter of 200 iterations. For the initial test, 10 sample data points were used to evaluate the model’s performance. During training, the loss value was monitored to assess how well the model understood the data and adjusted its internal weights. With this configuration, the system is expected to provide accurate job recommendations based on the user’s profile and academic history.
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Crismastiana Koloman, Raihan Maulana, Raisya Dwi Zahra Putri, & Wahyu Abadi Harahap. (2023). Sistem Rekomendasi Pekerjaan di bidang IT Menggunakan Algoritma Content-Based Filtering. Journal of Creative Student Research, 1(6), 78–88. https://doi.org/10.55606/jcsrpolitama.v1i6.2992
Databoks Kata Data. (2021, Mei). BPS: Sarjana yang Menganggur Hampir 1 Juta Orang pada Februari 2021. https://databoks.katadata.co.id/ketenagakerjaan/statistik/b52bbe8b99077f1/bps-sarjana-yang-menganggur-hampir-1-juta-orang-pada-februari-2021
Fajriansyah, M., Adikara, P. P., & Widodo, A. W. (2021). Sistem Rekomendasi Film Menggunakan Content Based Filtering. 5(6), 2188–2199. https://doi.org/10.37729/intek.v8i1.6286
Fitria, A., Zaman, S., & Yaqin, M. A. (2024). Sistem Rekomendasi Lowongan Pekerjaan Menggunakan Content-based filtering. 10(3), 421–427. https://doi.org/10.26418/jp.v10i3.83801
Ginting, E. T. B., & Pratama, I. (2023). Sistem Rekomendasi Jurusan SMK Menggunakan Metode Content-Based Filtering Di Kabupaten Sleman. 3(2), 291–300. https://doi.org/10.47233/jsit.v3i2.954
Graha Nusantara. (2025, July 29). Krisis Kecocokan Pendidikan dan Dunia Kerja di Indonesia: Mengapa Banyak Lulusan Tak Terserap? https://grahanusantara.id/krisis-kecocokan-pendidikan-dan-dunia-kerja-di-indonesia-mengapa-banyak-lulusan-tak-terserap?utm_source=chatgpt.com
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua, T.-S. (2017). Neural Collaborative Filtering (No. arXiv:1708.05031). arXiv. https://doi.org/10.48550/arXiv.1708.05031
Hong, W., Zheng, S., Wang, H., & Shi, J. (2013). A Job Recommender System Based on User Clustering. Journal of Computers, 8(8), 1960–1967. https://doi.org/10.4304/jcp.8.8.1960-1967
Huda, A. A., Fajarudin, R., & Hadinegoro, A. (2022). Sistem Rekomendasi Content-based Filtering Menggunakan TF-IDF Vector Similarity Untuk Rekomendasi Artikel Berita. Building of Informatics, Technology and Science (BITS), 4(3), 1679–1686. https://doi.org/10.47065/bits.v4i3.2511
Humairo, A., Herdiani, A., & Puspitasari, S. Y. (2023). Pembangunan Recommender System Menggunakan Content Based Filtering pada Aplikasi Service Desk. LOGIC: Jurnal Penelitian Informatika, 1(1), 20. https://doi.org/10.25124/logic.v1i1.6427
Jepriana, I. W., & Hanief, S. (2020). ANALISIS DAN IMPLEMENTASI METODE ITEM-BASED COLLABORATIVE FILTERING UNTUK SISTEM REKOMENDASI KONSENTRASI DI STMIK STIKOM BALI. 9.
Jobstreet. (2025, Mei). Terdapat 739 lowongan pekerjaan di bidang ekonomi. Jobstreet. https://id.jobstreet.com/id/ekonomi-jobs?utm_source=chatgpt.com
Levid, J. F., Wijaya, D., Irsyad, H., & Rahman, A. (2025). Penerapan Smart, Edas, Dan Cosine Similarity Dalam Rekomendasi Lowongan Pekerjaan Di Era Digital. 3(3), 85–92. https://doi.org/10.58369/biit.v2i3.128
Muhammad Alkaff, Husnul Khatimi, & Andi Eriady. (2020). Sistem Rekomendasi Buku Menggunakan Weighted Tree Similarity dan Content Based Filtering. 20(1), 193–202. https://doi.org/DOI: 10.30812/matrik.v20i1.617
Musto, C., Franza, T., Semeraro, G., De Gemmis, M., & Lops, P. (2018). Deep Content-based Recommender Systems Exploiting Recurrent Neural Networks and Linked Open Data. Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization, 239–244. https://doi.org/10.1145/3213586.3225230
Nurfalah, F., Asriyanik, & Pambudi, A. (2022). Sistem Rekomendasi Event Online Menggunakan Metode Content Based Filtering. Elkom : Jurnal Elektronika dan Komputer, 15(2), 271–279. https://doi.org/10.51903/elkom.v15i2.736
Permana, R. M., Hadiana, A. I., & Sabrina, P. N. (2024). Rekomendasi Pemilihan Sepeda Motor Menggunakan Metode Content Based Filtering Dan Item Based Colaborative Filtering. 12(2), 207–217. https://doi.org/0.33592/jutis.v12i2.5149
Pratama, R. V., & Hasrullah, H. (2025). Pengembangan Sistem Rekomendasi Buku untuk Meningkatkan Minat Baca dengan Pendekatan Hybrid Filtering. Jurnal Inovasi Global, 3(1), 2182–2191. https://doi.org/10.58344/jig.v3i1.255
Purkar, M., Joshi, O., Salape, A., Patil, A., Kulkarni, V., & Futane, P. (2021). Recommendation System for Workers & Customers for Informal Jobs. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3833762
Raharjo, P. N., Handojo, A., & Juwiantho, H. (2022). Sistem Rekomendasi Content Based Filtering Pekerjaan dan Tenaga Kerja Potensial menggunakan Cosine Similarity. 10(2), 1–6. https://doi.org/10.11591/eei.v10i5.3157
Silveira, T., Zhang, M., Lin, X., Liu, Y., & Ma, S. (2019). How good your recommender system is? A survey on evaluations in recommendation. International Journal of Machine Learning and Cybernetics, 10(5), 813–831. https://doi.org/10.1007/s13042-017-0762-9
Yanisa Putri, K. S., I Made Agus Dwi Suarjaya, & Wayan Oger Vihikan. (2024). Sistem Rekomendasi Skincare Menggunakan Metode Content Based Filtering dan Collaborative Filtering. Decode: Jurnal Pendidikan Teknologi Informasi, 4(3), 764–774. https://doi.org/10.51454/decode.v4i3.601
Yanti, F. R., & Yahfizham, Y. (2024). Implementasi Sistem Informasi Pengolahan Data Alumni Membantu Akreditas dan Bursa Kerja Metode Content Based Filtering. Journal of Information System Research (JOSH), 5(4), 1102–1114. https://doi.org/10.47065/josh.v5i4.5546
Yusuf, M., & Cherid, A. (2021). Implementasi Algoritma Cosine Similarity Dan Metode TF-IDF Berbasis PHP Untuk Menghasilkan Rekomendasi Seminar. 9(1), 8–16.
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Copyright (c) 2025 Abdur Rouf, Hasyim Asy’ari , Maysas Yafi Urrohman, Febriane Devi Rahmawati
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