Published: 2025-10-01
Penerapan Metode Fuzzy Tsukamoto Untuk Mendukung Pengambilan Keputusan Berdasarkan Data Jumlah Resi dan Profit
DOI: 10.35870/jtik.v9i4.3917
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Abstract
Data-driven decision-making in the logistics sector often encounters challenges due to fluctuating shipment volumes and unpredictable profit variations. This study implements the Fuzzy Tsukamoto method to process shipment quantity and profit data, enabling a decision-making model that is more responsive to uncertainty. The fuzzification process converts numerical data into fuzzy representations, followed by the application of if-then rules in the inference stage to determine appropriate decisions. The final results are then transformed back into numerical values through the defuzzification process. Evaluation results indicate high accuracy, with a Root Mean Squared Error (RMSE) of 0.07 and a Mean Absolute Error (MAE) of 0.05. These findings suggest that the Fuzzy Tsukamoto method effectively enhances decision-making by accounting for data variations and operational uncertainties. In practical applications, this model can assist logistics companies in optimizing shipment distribution, resource allocation, and delivery planning with greater precision, thereby improving operational efficiency and profitability.
Keywords
Fuzzy Tsukamoto ; Decision-Making ; Shipment Quantity ; Profit ; Fuzzification ; Defuzzification
Article Information
This article has been peer-reviewed and published in the Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi). The content is available under the terms of the Creative Commons Attribution 4.0 International License.
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Issue: Vol. 9 No. 4 (2025)
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Section: Computer & Communication Science
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Published: %750 %e, %2025
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License: CC BY 4.0
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Copyright: © 2025 Authors
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DOI: 10.35870/jtik.v9i4.3917
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Ferryma Arba Apriansyah
Program Studi Magister Teknologi Informasi, Universitas Teknologi Yogyakarta.
Arif Pramudwiatmoko
Program Studi Magister Teknologi Informasi, Universitas Teknologi Yogyakarta.
Muhammad Senoaji Wibowo
Program Studi Magister Teknologi Informasi, Universitas Teknologi Yogyakarta.
Evi Widiyastuti
Program Studi Magister Teknologi Informasi, Universitas Teknologi Yogyakarta.
Tri Agung Jiwandono
Program Studi Magister Teknologi Informasi, Universitas Teknologi Yogyakarta.
Vatma Sari
Program Studi Manajemen, Universitas Mercu Buana Yogyakarta.
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Auliana, S., & Mansyuri, U. (2022). Penggunaan metoda fuzzy Tsukamoto untuk menentukan produksi barang elektronik. Jurnal Ilmiah Sistem Informasi. https://doi.org/10.46306/sm.v2i2.29.
-
Bitencourt, H. V., & Guimarães, F. G. (2021, November). High-dimensional multivariate time series forecasting in IoT applications using embedding non-stationary fuzzy time series. In 2021 IEEE Latin American conference on computational intelligence (LA-CCI) (pp. 1-6). IEEE.
-
Burhanuddin, A. (2023). Analisis Komparatif Inferensi Fuzzy Tsukamoto, mamdani dan Sugeno Terhadap Produktivitas Padi di Indonesia. LEDGER: Journal Informatic and Information Technology, 2(1), 48-57. https://doi.org/10.20895/ledger.v2i1.1013.
-
Chai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)?–Arguments against avoiding RMSE in the literature. Geoscientific model development, 7(3), 1247-1250.
-
Fauzi, J. A., Rahman, N. J. N., & Handayani, A. N. (2020). Determining the Articles Acceptance Using Logic of Fuzzy Inference System Tsukamoto. Letters in Information Technology Education (LITE), 3(1), 1-8.
-
Febriani, W., Nurcahyo, G. W., & Sumijan, S. (2019). Diagnosis Penyakit Rubella Menggunakan Metode Fuzzy Tsukamoto. Jurnal Sistem Informasi dan Teknologi Komputer. https://doi.org/10.37034/jsisfotek.v1i3.3.
-
Fuad, M., Wattimena, F. Y., & Rizani, A. (2023). Investment Decision Making in Digital Business Using Tsukamoto Fuzzy Logic. International Journal Software Engineering and Computer Science (IJSECS), 3(2), 144-150. https://doi.org/10.35870/ijsecs.v3i2.1525.
-
Harim, M., Adiningsi, S., & Sajiah, A. M. (2023, November). Identifikasi Tingkat Kepuasan Mahasiswa Terhadap Pelayanan Fakultas Dengan Logika Fuzzy Metode Tsukamoto. In Prosiding Seminar Nasional Pemanfaatan Sains dan Teknologi Informasi (Vol. 1, No. 1, pp. 391-400).
-
Ismunu, R. S., Purnomo, A. S., & Subardjo, R. Y. S. (2020). Sistem pakar untuk mengetahui tingkat kecemasan mahasiswa dalam menyusun skripsi menggunakan metode multi factor evaluation process dan inferensi fuzzy tsukamoto.
-
Kambalimath, S., & Deka, P. C. (2020). A basic review of fuzzy logic applications in hydrology and water resources. Applied Water Science, 10(8), 1-14.
-
Kuswanto, J., Maulana, B., Vernando, R., & Berta, S. (2023). Penerapan Metode Fuzzy Tsukamoto dalam Penilaian Kinerja Karyawan di Perusahaan Air Minum Kabupaten OKU. Bulletin of Computer Science Research, 4(1), 84-90. https://doi.org/10.47065/bulletincsr.v4i1.312
-
Lubis, R. I. A. N. I., & Nurhayati, S. (2021). Implementation Of Fuzzy Tsukamoto In Production Planning Decision Support Systems. vol, 16, 919-926.
-
Muhandhis, I., Ritonga, A. S., & Murdani, M. H. (2021). Implementasi Metode Inferensi Fuzzy Tsukamoto Untuk Memprediksi Curah Hujan Dasarian Di Sumenep. Jurnal Ilmiah Edutic: Pendidikan Dan Informatika, 8(1), 01-10. https://doi.org/10.21107/edutic.v8i1.8907.
-
Ningsi, I. R., & Kumajas, S. C. (2023). A SISTEM MONITORING SUHU DAN KELEMBABAN GUDANG FARMASI DINAS KESEHATAN KABUPATEN MINAHASA BERBASIS IOT. JOURNAL OF INFORMATICS, BUSINESS, EDUCATION AND INNOVATION TECHNOLOGY, 1(1), 72-84.
-
Nugraha, E., Wibawa, A. P., Hakim, M. L., Kholifah, U., Dini, R. H., & Irwanto, M. R. (2019, November). Implementation of fuzzy tsukamoto method in decision support system of journal acceptance. In Journal of Physics: Conference Series (Vol. 1280, No. 2, p. 022031). IOP Publishing.
-
Nurfadila, S., Lubis, R. S., & Cipta, H. (2023). SISTEM PENDUKUNG KEPUTUSAN UNTUK SELEKSI KARYAWAN BARU DENGAN MENGGUNAKAN METODE FUZZY TSUKAMOTO. JISTech (Journal of Islamic Science and Technology), 8(1), 30-45.
-
Prasetya, G. M. (2024). PENERAPAN LOGIKA FUZZY MENGGUNAKAN METODE TSUKAMOTO DAN MAMDANI DALAM PERENCANAAN PRODUKSI TAHU DI CV SITA (Doctoral dissertation, Universitas Putra Indonesia YPTK).
-
Rezaei, M., Molani, S., Firoozeh, N., Abbasi, H., Vahedifard, F., & Orouskhani, M. (2023). Evolving Tsukamoto Neuro Fuzzy Model for Multiclass Covid 19 Classification with Chest X Ray Images. arXiv preprint arXiv:2305.10421. https://doi.org/10.48550/arXiv.2305.10421.
-
Salari, S., Sadeghi-Yarandi, M., & Golbabaei, F. (2024). An integrated approach to occupational health risk assessment of manufacturing nanomaterials using Pythagorean Fuzzy AHP and Fuzzy Inference System. Scientific Reports, 14(1), 180.
-
Setiawan, T. H., & Prihatini, L. (2023). TSUKAMOTO FUZZY IN OPTIMIZING THE CREDITWORTHINESS ASSESSMENT PROCESS AT SAVINGS AND LOAN COOPERATIVES. BAREKENG: Jurnal Ilmu Matematika dan Terapan, 17(2), 0775-0786. https://doi.org/10.30598/barekengvol17iss2pp0775-0786.
-
Syawari, M. Y. A. (2024). Sistem Inferensi Fuzzy Tsukamoto Untuk Menentukan Tingkat Kualitas Air Pada Kolam Budidaya Ikan Lele. Sienna, 5(1), 95-109. https://doi.org/10.47637/sienna.v5i1.1358.
-
Tabbussum, R., & Dar, A. Q. (2021). Performance evaluation of artificial intelligence paradigms—artificial neural networks, fuzzy logic, and adaptive neuro-fuzzy inference system for flood prediction. Environmental Science and Pollution Research, 28(20), 25265-25282.
-
Widiyantoro, P., Febriyanti, R. D., & Muhamad, C. G. (2024). Penerapan Metode Fuzzy Tsukamoto Untuk Penentuan Harga Rumah Di Kota Bandung. Jurnal Ilmiah Informatika Komputer, 29(1), 60-72.
-
Zhang, H., Xing, B., & Hou, Y. Research on Enterprise Production Decision Making Based on Genetic Algorithm and Monte Carlo Simulation. Journal of Computing and Electronic Information Management, 14(3). https://dx.doi.org/10.54097/m9zzbm54.
-
Ziaei Ghahnavieh, M., Habibi Manesh, H., & Sheikhmoradi, S. (2024). Comparative comparison of fuzzy logic and classical logic. International Journal of Nonlinear Analysis and Applications, 15(12).
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