PREDIKSI DINI BATU EMPEDU BERBASIS BIOIMPEDANSI DAN DATA LABORATORIUM MENGGUNAKAN MACHINE LEARNING TEROPTIMASI

• KIRSO
• 14230033

---

ABSTRAK

ABSTRAK

Nama : Kirso

NIM : 14230033

Program Studi : Ilmu Komputer

Fakultas : Teknologi Informasi

Jenjang : Strata Dua (S2)

Peminatan : Software Engineering & Data Science

Judul Tesis : Prediksi Dini Batu Empedu Berbasis Bioimpedansi Dan Data  Laboratorium Menggunakan Machine Learning Teroptimasi

 

Penyakit batu empedu (kolelitiasis) merupakan gangguan gastrointestinal yang seringbersifat asimtomatik pada tahap awal, namun berisiko menimbulkan komplikasi seriusjika tidak terdeteksi. Penelitian ini mengembangkan kerangka kerja machine learning teroptimasi untuk prediksi dini non-invasif menggunakan data bioimpedansi dan pemeriksaan laboratorium rutin. Dataset publik UCI Gallstone digabungkan dengan data rumah sakit lokal dan diperkaya melalui feature engineering untuk membentuk fitur komposit bermakna secara klinis, seperti rasio lipid, indeks komposisi tubuh, skor komorbiditas, dan laju filtrasi glomerulus. Tujuh model klasifikasi dioptimasi menggunakan Random Search dan dievaluasi melalui skema holdout (80:20) serta 10 fold stratified cross-validation. Hasil evaluasi menunjukkan ANN teroptimasi mencapai akurasi 91,19%, recall 95,65%, dan ROC-AUC 0,979, sementara Random Forest teroptimasi memperoleh AUC tertinggi 0,982. Analisis interpretabilitas menggunakan SHAP mengidentifikasi jenis kelamin, total body water, hemoglobin, serta rasio lipid sebagai prediktor utama. Temuan ini menunjukkan potensi pendekatan sebagai sistem pendukung keputusan klinis yang akurat dan hemat biaya untuk skrining dini batu empedu.

Kata kunci: Penyakit Batu Empedu, Pembelajaran Mesin, Optimasi Hiperparameter, Bioimpedansi, Rekayasa Fitur, Interpretabilitas SHAP, Prediksi Dini

KATA KUNCI

machine learning

---

DAFTAR PUSTAKA

DAFTAR PUSTAKA

 

[1] C. Chakraborty and N. Mukherjee, “Bayesian Hybrid Machine Learning of Gallstone Risk,” pp. 1–25, 2025, [Online]. Available: http://arxiv.org/abs/2506.14561

[2] M. W. Jones, C. B. Weir, and M. Marietta, “Gallstones (Cholelithiasis),” in StatPearls [Internet], Updated 20., Treasure Island (FL): StatPearls Publishing, 2025. [Online]. Available: https://www.ncbi.nlm.nih.gov/books/NBK459370/

[3] C. Calolus, “Batu Empedu (Tidak) Perlu Dioperasi?” Accessed: Sep. 01, 2025. [Online]. Available: https://rscarolus.or.id/artikel/batu-empedu-tidak-perludioperasi/

[4] I. Esen, H. Arslan, S. Akturk Esen, M. Gulsen, N. Kultekin, and O. Ozdemir, “Early prediction of gallstone disease with a machine learning-based method from bioimpedance and laboratory data,” Med. (United States), vol. 103, no. 8, p. E37258, 2024, doi: 10.1097/MD.0000000000037258.

[5] Y. Wang et al., “Establishing a preoperative predictive model for gallbladder adenoma and cholesterol polyps based on machine learning: a multicentre retrospective study,” World J. Surg. Oncol. , vol. 23, no. 1, 2025, doi: 10.1186/s12957-025-03671-y.

[6] S. M. Abdu and E. M. Assefa, “Prevalence of gallstone disease in Africa: a systematic review and meta-analysis,” BMJ Open Gastroenterol., vol. 12, no. 1, pp. 1–8, 2025, doi: 10.1136/bmjgast-2024-001441.

[7] Y. Miyah, M. Benjelloun, H. El Omari, K. El-Mouhdi, and M. El Feniche, “Recent investigation of the medicinal plants’ effectiveness in the natural management of urinary and gallstones: a review,” Phytomedicine Plus, vol. 5, no. 3, 2025, doi: 10.1016/j.phyplu.2025.100839.

[8] M. C. Staff, “Gallstones - Symptoms & causes - Mayo Clinic.” Accessed: Sep. 04, 2025. [Online]. Available: https://www.mayoclinic.org/diseasesconditions/gallstones/symptoms-causes/syc-20354214

[9] G. P. K. Bali, B. Singh, M. Ashraf, and T. Kamalanathan, “Factors prevailing for gallstone formation,” in Gallstone Formation, Diagnosis, Treatment and Prevention, Elsevier, 2024, pp. 39–50. doi: 10.1016/B978-0-443-16098- 1.00005-9.

[10] “Gallstones - NIDDK.” Accessed: Sep. 04, 2025. [Online]. Available: https://www.niddk.nih.gov/health-information/digestive-diseases/gallstones

[11] “Definition & Facts for Gallstones - NIDDK.” Accessed: Sep. 04, 2025. [Online]. Available: https://www.niddk.nih.gov/health-information/digestivediseases/gallstones/definition-facts?

[12] R. Hasan, F. Allahbakhshi, A. D. Shlyk, and K. Allahbakhshi, “Gallstones as a predictor of elevated cardiovascular disease risk: A meta-analysis and metaregression of over 7.4 million participants,” PLoS One, vol. 20, no. 3, p. e0314661, Mar. 2025, doi: 10.1371/journal.pone.0314661.

[13] W. Y. Dan, Y. S. Yang, L. H. Peng, G. Sun, and Z. K. Wang, “Gastrointestinal microbiome and cholelithiasis: Current status and perspectives,” World J. Gastroenterol., vol. 29, no. 10, pp. 1589–1601, 2023, doi: 10.3748/wjg.v29.i10.1589.

[14] H. Li et al., “The gut microbiota features and the application value in predicting recurrent risks for gallstone patients who underwent laparoscopic cholecystectomy,” mSystems, vol. 10, no. 8, 2025, doi: 10.1128/msystems.01760-24.

[15] Z. Wu, S. Jiang, J. Li, P. Wang, and Y. Chen, “Association between urinary cadmium levels and increased gallstone disease in US adults,” Sci. Rep., vol. 15, no. 1, pp. 1–11, 2025, doi: 10.1038/s41598-025-00648-5.

[16] J. Wang et al., “The association between blood heavy metals and gallstones: A cross-sectional study,” Sci. Total Environ., vol. 904, p. 166735, Dec. 2023, doi: 10.1016/j.scitotenv.2023.166735.

[17] Q. Zhuang et al., “Association between sleep and gallstone disease in United States adults: A cross-sectional study,” BMC Public Health, vol. 24, no. 1, p. 3291, Nov. 2024, doi: 10.1186/s12889-024-20824-y.

[18] S. Hosseini, A. Asadizeidabadi, E. Tarabrin, S. Muraviev, and D. Orlushin, “Asymptomatic Cholecystitis Presents a New Challenge for Correcting Treatment Tactics in Patients with Gallstone Disease Rather Than Being an Unsolvable Problem of Biliary Surgery,” Am. J. Intern. Med., vol. 12, no. 3, pp. 26–32, 2024, doi: 10.11648/j.ajim.20241203.11.

[19] M. S. Ul Hassan et al., “Prevalence and Associated Risk Factors of Acute Pancreatitis in Patients With Gallstones: A Cross-Sectional Study,” Cureus, 116 Program Studi Ilmu Komputer (S2) FTI Universitas Nusa Mandiri May 2025, doi: 10.7759/cureus.84220.

[20] Y. Saiman, “Batu empedu - Gangguan Hati dan Kandung Empedu - Manual MSD Versi Konsumen.” Accessed: Sep. 04, 2025. [Online]. Available: https://www.msdmanuals.com/id/home/gangguan-hati-dan-kandungempedu/gangguan-kantung-empedu-dan-saluran-empedu/batu-empedu

[21] A. R. Amaliya et al., “Komplikasi Kolesistitis?: Empiema dan Hidrops Kandung Empedu,” Unram Med. J., vol. 12, no. 4, pp. 429–433, 2023, doi: 10.29303/jku.v12i4.985.

[22] M. Matan, “Analisis Bioimpedansi | Aplikasi dan Penggunaannya.” Accessed: Sep. 04, 2025. [Online]. Available: https://www.electricitymagnetism.org/id/analisis-bioimpedansi-aplikasi-dan-penggunaannya/

[23] X. Wen et al., “Clinlabomics: leveraging clinical laboratory data by data mining strategies,” BMC Bioinformatics, vol. 23, no. 1, pp. 1–20, 2022, doi: 10.1186/s12859-022-04926-1.

[24] I. S. Maesaroh, K. N. Syaja’Ah, Y. S. Perkasa, S. Hadianti, D. Riana, and R. R. Nurmalasari, “Cervical Cancer Classification From Pap Smear Using XCeption Model,” in 2024 10th International Conference on Wireless and Telematics (ICWT), IEEE, Jul. 2024, pp. 1–5. doi: 10.1109/ICWT62080.2024.10674721. [25] H. D. Saputra, A. I. E. Efendi, E. Rudini, D. Riana, and A. S. Hewiz, “Segmentation in Identifying the Development of Ground Glass Opacity on CTScan Images of the Lungs,” J. Med. Informatics Technol., pp. 1–6, 2023, doi: 10.37034/medinftech.v1i1.1.

[26] W. Dirsam and M. D. Anasanti, “Improving Obesity Classification with Advanced Machine Learning and Feature Selection Methods,” Int. J. Intell. Eng. Syst., vol. 18, no. 7, pp. 373–387, Aug. 2025, doi: 10.22266/ijies2025.0831.26.

[27] K. Kirso and M. D. Anasanti, “Telematika Improving Alzheimer ’ s Disease Prediction Accuracy using Feature Selection , K Fold Cross Validation , and KNN Imputer Techniques,” Telematika, vol. 18, no. 1, pp. 75–90, 2025, [Online]. Available: https://ejournal.amikompurwokerto.ac.id/index.php/telematika/article/view/30 55

[28] Mahendra and M. D. Anasanti, “Comprehensive Machine Learning Model for 117 Program Studi Ilmu Komputer (S2) FTI Universitas Nusa Mandiri Cervical Cancer Prediction and Risk Factor Identification,” Hum. Behav. Emerg. Technol., vol. 2025, no. 1, 2025, doi: 10.1155/hbe2/6629232.

[29] S. Agustiani and Y. Rianto, “Deep Learning for Histopathological Image Analysis: A Convolutional Neural Network Approach to Colon Cancer Classification,” Telematika, vol. 17, no. 1, pp. 39–51, 2024, doi: https://doi.org/10.21107/rekayasa.v15i2.141580.

[30] N. Merlina, A. Prasetyo, I. Zuniarti, N. A. Mayangky, D. N. Sulistyowati, and F. Aziz, “Improving Early Detection of Cervical Cancer Through Deep Learning-Based Pap Smear Image Classification,” J. Appl. Data Sci., vol. 6, no. 2, pp. 969–980, 2025, doi: 10.47738/jads.v6i2.576.

[31] R. L. Hasanah, Y. Rianto, and D. Riana, “Identification of Acne Vulgaris Type in Facial Acne Images Using GLCM Feature Extraction and Extreme Learning Machine Algorithm,” Rekayasa, vol. 15, no. 2, pp. 204–214, 2022, doi: 10.21107/rekayasa.v15i2.14580.