LOLA JENNY PITALOKA, NPM 2209100071 (2026) ANALISIS INVENTORY APOTEK DI SILANGKITANG MENGGUNAKAN METODE SUPPORT VECTOR MACHINE (SVM) UNTUK PREDIKSI KEBUTUHAN STOK OBAT. Skripsi thesis, Universitas Labuhanbatu.
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Abstract
Pengelolaan inventory obat di Apotek Silangkitang masih menghadapi kendala dalam menentukan kebutuhan stok secara tepat, sehingga berpotensi menyebabkan kelebihan maupun kekurangan persediaan obat. Penelitian ini bertujuan menerapkan metode Support Vector Machine (SVM) untuk mengklasifikasikan tingkat kebutuhan stok obat berdasarkan data historis penjualan. Objek penelitian adalah data persediaan dan penjualan obat di Apotek Silangkitang yang terdiri dari 50 data obat selama periode Februari hingga Juni, dengan variabel stok awal, stok akhir, dan penjualan bulanan sebagai atribut utama. Penelitian menggunakan pendekatan kuantitatif dengan tahapan pengumpulan data, preprocessing melalui transformasi dan normalisasi Min-Max, pembagian data menjadi 80% data latih dan 20% data uji, pembangunan model menggunakan aplikasi Orange Data Mining, serta evaluasi model menggunakan Confusion Matrix berdasarkan nilai akurasi, presisi, recall, dan F1-score. Penerapan metode SVM diharapkan mampu menghasilkan model klasifikasi kebutuhan stok obat ke dalam kategori Low Demand, Medium Demand, dan High Demand secara baik sehingga dapat membantu pengelola apotek dalam pengambilan keputusan pengadaan persediaan yang lebih efektif, efisien, serta mengurangi risiko overstock dan understock. Kata Kunci: Inventory Obat, Support Vector Machine, Orange Data Mining, Klasifikasi Stok Obat, Machine Learning ============================================= Medication inventory management at Silangkitang Pharmacy faces challenges in accurately determining stock requirements, potentially leading to either overstock or understock situations. This study aims to apply the Support Vector Machine (SVM) method to classify medication stock requirements based on historical sales data. The study utilizes inventory and sales data for 50 medications at Silangkitang Pharmacy covering the period from February to June, with initial stock, final stock, and monthly sales serving as the primary attributes. A quantitative approach was employed, comprising data collection, preprocessing (via transformation and Min-Max normalization), data splitting (80% training and 20% testing), model development using Orange Data Mining software, and model evaluation via a confusion matrix based on accuracy, precision, recall, and F1-score. The application of the SVM method is expected to generate a classification model that effectively categorizes medication stock requirements into Low Demand, Medium Demand, and High Demand, thereby assisting pharmacy management in making more effective and efficient procurement decisions while reducing the risks of overstocking and understocking. Keywords: Medication Inventory, Support Vector Machine, Orange Data Mining, Medication Stock Classification, Machine Learning
| Item Type: | Thesis (Skripsi) |
|---|---|
| Uncontrolled Keywords: | Inventory Obat, Support Vector Machine, Orange Data Mining, Klasifikasi Stok Obat, Machine Learning===============Medication Inventory, Support Vector Machine, Orange Data Mining, Medication Stock Classification, Machine Learning |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General) Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4050 Electronic information resources Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4450 Databases |
| Divisions: | Fakultas Sains Dan Teknologi > Sistem Informasi |
| Depositing User: | Unnamed user with email repository@ulb.ac.id |
| Date Deposited: | 17 Sep 2026 08:23 |
| Last Modified: | 17 Sep 2026 08:23 |
| URI: | http://repository.ulb.ac.id/id/eprint/2826 |
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