Please use this identifier to cite or link to this item: https://repositori.uma.ac.id/handle/123456789/26311
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dc.contributor.advisorMuliono, Rizki-
dc.contributor.authorSandy, Boy-
dc.date.accessioned2025-01-14T07:14:31Z-
dc.date.available2025-01-14T07:14:31Z-
dc.date.issued2024-10-
dc.identifier.urihttps://repositori.uma.ac.id/handle/123456789/26311-
dc.description10 Halamanen_US
dc.description.abstractDalam persaingan bisnis yang semakin ketat, industri ritel seperti Chatime Binjai Supermall harus beradaptasi dengan cepat. Perubahan tren konsumen, preferensi, dan kemajuan teknologi sangat memengaruhi strategi bisnis. Untuk tetap kompetitif, Chatime Binjai Supermall perlu mengoptimalkan penjualan, pemasaran, dan manajemen persediaan melalui analisis data dan prediksi yang akurat. Random Forest, sebuah algoritma pembelajaran mesin yang kuat, digunakan untuk memproses data historis dan memprediksi penjualan dengan lebih tepat. Penelitian ini mengevaluasi kinerja Random Forest dalam memprediksi penjualan harian, mingguan, dan bulanan. Hasil analisis menunjukkan bahwa produk seperti "Jasmine Green Tea (L)" memiliki permintaan tertinggi secara harian, "PEARL (L)" mendominasi penjualan mingguan, dan ada peningkatan permintaan untuk produk-produk tertentu secara bulanan, seperti "CT RAINBOW JELLY (L)". Implementasi algoritma Random Forest di Chatime Binjai Supermall menunjukkan potensi signifikan dalam meningkatkan efisiensi penjualan dan pengambilan keputusan berbasis data, yang membantu perusahaan tetap relevan serta mampu bersaing di tengah perubahan pasar. In an increasingly competitive business environment, retail industries like Chatime Binjai Supermall must quickly adapt. Changes in consumer trends, preferences, and technological advancements significantly impact business strategies. To stay competitive, Chatime Binjai Supermall needs to optimize sales, marketing, and inventory management through accurate data analysis and prediction. Random Forest, a powerful machine learning algorithm, is used to process historical data and more accurately predict sales. This study evaluates the performance of Random Forest in predicting daily, weekly, and monthly sales. The analysis shows that products like "Jasmine Green Tea (L)" have the highest daily demand, "PEARL (L)" leads weekly sales, and there is an increase in demand for specific products monthly, such as "CT RAINBOW JELLY (L)." The implementation of the Random Forest algorithm at Chatime Binjai Supermall demonstrates significant potential in enhancing sales efficiency and data-driven decision making, helping the company remain relevant and competitive amidst market changes.en_US
dc.language.isoiden_US
dc.publisherUniversitas Medan Areaen_US
dc.relation.ispartofseriesNPM;178160074-
dc.subjectIndustri Ritelen_US
dc.subjectPrediksi Penjualanen_US
dc.subjectRandom Foresten_US
dc.subjectAnalisis Dataen_US
dc.subjectRetail Industryen_US
dc.subjectSales Predictionen_US
dc.subjectRandom Foresten_US
dc.subjectData Analysisen_US
dc.titleImplementasi Metode Random Forest untuk Memprediksi Penjualan (Studi Kasus Chatime Binjai Supermall)en_US
dc.title.alternativeThe Implementation of Random Forest to Predict Sales a Case Study at Chatime Binjai Supermallen_US
dc.typeThesisen_US
Appears in Collections:SP - Informatic Engineering

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