Analisis Efek Samping Obat Anti Tuberkulosis Menggunakan K-Means Clustering di RSUD Prof. Dr. H. Aloei Saboe Kota Gorontalo - Hipmi , Ahmad Fahrian; Wijaya , Made Hariadi; Abas , Siti Nur Rahmatiya; Darmawan , Endang; Lolita , Lolita; Irham , Lalu Muhammad; Surono , Sugiyarto (Fakultas Farmasi Universitas Tjut Nyak Dhien, 2025)
Background: The side effects of anti-tuberculosis drugs (OAT) experienced by patients often interfere with their daily activities and affect their compliance in completing relatively long-term treatment. This is one of the reasons patients discontinue OAT therapy unilaterally, which can lead to treatment failure for tuberculosis (TB). Objective: To analyse the side effects of OAT in patients at Prof. Dr. H. Aloei Saboe General Hospital in Gorontalo City using k-means clustering. Methods: Data including gender, age, OAT regimen, laboratory results, comorbidities, and types of OAT side effects were analyzed using the k-means method to determine patient clustering patterns. Research Results: The analysis yielded three clusters. Cluster 1 (25 patients) was predominantly male (80%), aged 45–54 years, received first-line OAT (88%), experienced elevated SGPT/SGOT levels (88%), had hypertension as the most common comorbidity (28%), and primarily experienced liver dysfunction as the main side effect (96%). Cluster 2 (348 patients) was predominantly male (58%), aged 35–44 years, receiving OAT line 1 (96.1%), no increase in SGPT and SGOT levels (0%), almost no increase in urea and creatinine levels (0.2%), the most common comorbidity being diabetes mellitus (22.1%). The main side effect being gastrointestinal disorders (58.9%). Cluster 3 (40 patients) was predominantly male (70%), aged 45–54 years, received OAT line 1 (97.5%), experienced an increase in urea and creatinine levels (97.5%), and the most common comorbidity was diabetes mellitus (47.5%). The main side effect was renal dysfunction (95%). Conclusion: The k-means algorithm is effective in generating clusters of patient characteristics. This clustering supports specific interventions such as comorbidity therapy management and monitoring of side effect risks, thereby optimising individualised tuberculosis (TB) treatment.