Decision Model for Best Contraceptive Technique Recommendation Based on Patient's Ideal Profile

Authors

  • Veronika Novia Hugo Fakultas Teknologi dan Informatika, Program Studi Informatika, Institut Bisnis dan Teknologi Indonesia
  • I Gede Iwan Sudipa Fakultas Teknologi dan Informatika, Program Studi Informatika, Institut Bisnis dan Teknologi Indonesia
  • Luh Gede Bevi Libraeni Fakultas Teknologi dan Informatika, Program Studi Informatika, Institut Bisnis dan Teknologi Indonesia
  • Indra Pratistha Fakultas Teknologi dan Informatika, Program Studi Informatika, Institut Bisnis dan Teknologi Indonesia
  • Ketut Jaya Atmaja Fakultas Teknologi dan Informatika, Program Studi Informatika, Institut Bisnis dan Teknologi Indonesia

DOI:

10.33395/sinkron.v10i1.15377

Keywords:

Decision Making Model, Contraception, Profile Matching, Patient Ideal Profile

Abstract

Choosing the right contraceptive method is essential to support the success of family planning programs. Many patients still choose methods without considering their medical conditions, which can lead to failure or side effects. This study designed a decision-making model based on Profile Matching to recommend contraceptive methods according to the patient’s ideal profile. The dataset was obtained from Faskes Level 1 Udayana Denpasar. Validation was conducted through discussions with midwives as experts, referring to the KLOP KB Wheel as the standard issued by the WHO. The evaluation results show a high level of agreement between the model’s recommendations and expert judgments, indicating that the model provides more objective and easily understood recommendations compared to manual approaches.

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References

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How to Cite

Hugo, V. N., Sudipa, I. G. I., Libraeni, L. G. B. ., Pratistha, I., & Atmaja, K. J. (2026). Decision Model for Best Contraceptive Technique Recommendation Based on Patient’s Ideal Profile . Sinkron : Jurnal Dan Penelitian Teknik Informatika, 10(1), 169-180. https://doi.org/10.33395/sinkron.v10i1.15377

Issue

Vol. 10 No. 1 (2026): Article Research January 2026

Section

Articles

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Copyright (c) 2025 Veronika Novia Hugo, I Gede Iwan Sudipa, Luh Gede Bevi Libraeni, Indra Pratistha, Ketut Jaya Atmaja

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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