Analisis Efek Samping Obat Anti Tuberkulosis Menggunakan K-Means Clustering di RSUD Prof. Dr. H. Aloei Saboe Kota Gorontalo
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Latar Belakang: Efek samping obat anti tuberkulosis (OAT) yang dialami pasien sering mengganggu aktivitas sehari-hari pasien dan berdampak pada kepatuhan pasien dalam menyelesaikan pengobatan yang relatif lama. Hal ini menjadi salah satu penyebab pasien menghentikan terapi OAT secara sepihak, akibatnya akan berdampak pada kegagalan pengobatan tuberkulosis (TB). Tujuan: Menganalisis efek samping OAT pada pasien di RSUD Prof. Dr. H. Aloei Saboe Kota Gorontalo menggunakan k-means clustering. Metode: Data meliputi jenis kelamin, usia, lini OAT, hasil laboratorium, komorbiditas dan jenis efek samping OAT, di analisis menggunakan metode k-means untuk menentukan pola pengelompokan pasien. Hasil Penelitian: Analisis menghasilkan 3 (tiga) klaster. Klaster 1 (25 pasien) mayoritas laki-laki (80%), usia 45-54 tahun, menerima lini OAT 1 (88%), mengalami kenaikan nilai SGPT SGOT (88%), komorbiditas terbanyak hipertensi (28%), dan efek samping utama gangguan hati (96%). Klaster 2 (348 pasien) didominasi laki-laki (58%), usia 35-44 tahun, menerima lini OAT 1 (96,1%), tidak ada kenaikan nilai SGPT SGOT (0%), hampir tidak ada kenaikan nilai ureum kreatinin (0,2%), komorbiditas terbanyak diabetes melitus (22,1%) dan efek samping utama gangguan gastrointestinal (58,9%). Klaster 3 (40 pasien) mayoritas laki-laki (70%), usia 45-54 tahun, menerima lini OAT 1 (97,5%), mengalami kenaikan nilai ureum kreatinin (97,5%), komorbiditas terbanyak diabetes melitus (47,5%) dan efek samping utama gangguan ginjal (95%). Kesimpulan: Algoritma k-means efektif dalam menghasilkan klaster karakteristik pasien. Pengelompokan tersebut mendukung intervensi spesifik seperti manajemen terapi komorbiditas dan pemantauan risiko efek samping, sehingga dapat mengoptimalkan pengobatan tuberkulosis (TB) secara individual.
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