JOGI ANGGITA SIREGAR, NPM 2209100059 (2026) IMPLEMENTASI ALGORITMA FP-GROWTH PADA SISTEM PERSEDIAAN OBAT DI PUSKESMAS AEK GOTI. Skripsi thesis, Universitas Labuhanbatu.
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Abstract
Pengelolaan persediaan obat di Puskesmas Aek Goti masih menghadapi kendala dalam menentukan pola kebutuhan obat secara akurat, sehingga berpotensi menyebabkan kelebihan maupun kekurangan stok. Penelitian ini bertujuan menerapkan algoritma FP-Growth untuk menganalisis pola asosiasi antarobat berdasarkan data transaksi persediaan obat di Puskesmas Aek Goti. Subjek penelitian berupa 100 data transaksi obat masuk dan keluar yang dikumpulkan pada periode 1 hingga 28 Februari 2026, dengan objek berupa 29 jenis obat yang tercatat dalam transaksi. Data diolah melalui tahap preprocessing berupa konversi ke bentuk matriks biner, kemudian dianalisis menggunakan algoritma FP-Growth pada aplikasi Altair AI Studio (RapidMiner) dengan nilai minimum support sebesar 0,1 (10%) dan minimum confidence sebesar 0,8 (80%). Hasil penelitian menunjukkan 13 item memenuhi nilai minimum support, dengan Paracetamol sebagai obat dengan support tertinggi sebesar 0,73, diikuti Vitamin C sebesar 0,72 dan CTM sebesar 0,58. Proses pembentukan aturan asosiasi menghasilkan 22 aturan yang memenuhi kriteria, dengan nilai confidence tertinggi sebesar 0,839 pada aturan Cetirizine terhadap Ambroxol dan Amoxicillin. Hasil ini dapat dimanfaatkan Puskesmas Aek Goti sebagai dasar perencanaan pengadaan dan pengelolaan stok obat yang lebih efektif dan berbasis data. Kata Kunci : FP-Growth, Data Mining, Association Rules, Persediaan Obat, Puskesmas ================================================================= Medicine inventory management at Puskesmas Aek Goti still faces challenges in accurately determining demand patterns, which can lead to overstock or stockouts. This study applies the FP-Growth algorithm to analyze association patterns among medicines based on inventory transaction data at Puskesmas Aek Goti. The subjects were 100 incoming and outgoing medicine transaction records collected from February 1 to 28, 2026, covering 29 types of medicine. The data were preprocessed into a binary matrix and analyzed using FP-Growth in Altair AI Studio (RapidMiner) with a minimum support of 0.1 (10%) and minimum confidence of 0.8 (80%). The results show 13 items met the minimum support threshold, with Paracetamol having the highest support at 0.73, followed by Vitamin C at 0.72 and CTM at 0.58. The association rule formation produced 22 rules meeting the criteria, with the highest confidence of 0.839 for the rule Cetirizine → Ambroxol and Amoxicillin. These results can serve as a basis for Puskesmas Aek Goti to plan procurement and manage medicine stock more effectively and data-driven. Keywords : FP-Growth, Data Mining, Association Rules, Medicine Inventory, Puskesmas
| Item Type: | Thesis (Skripsi) |
|---|---|
| Uncontrolled Keywords: | FP-Growth, Data Mining, Association Rules, Persediaan Obat, Puskesmas ==================================== FP-Growth, Data Mining, Association Rules, Medicine Inventory, Puskesmas |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General) |
| Divisions: | Fakultas Sains Dan Teknologi > Sistem Informasi |
| Depositing User: | Unnamed user with email repository@ulb.ac.id |
| Date Deposited: | 03 Sep 2026 04:00 |
| Last Modified: | 03 Sep 2026 04:00 |
| URI: | http://repository.ulb.ac.id/id/eprint/2730 |
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