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All Journal JEECS (Journal of Electrical Engineering and Computer Sciences) Dike
Armiyanti, Siti
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering

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Penerapan Data Mining untuk Prediksi Penjualan Produk Skincare Menggunakan Metode KNN (K-Nearest Neighbors): Studi Kasus Klinik Ayume Beauty Care Amanda, Putri Yulia; Damayanti, Berlian Aisya; Choirun, Alisya Akbar; Sari, Selvi Novita; Armiyanti, Siti; Hidayat, M. Mahaputra
Dike Vol. 3 No. 1 (2025): Dike Edisi Februari
Publisher : CV. Ro Bema

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69688/dike.v3i1.122

Abstract

Abstrakāˆ’Prediksi penjualan produk skincare menjadi tantangan penting dalam industri kecantikan, terutama untuk membantu klinik seperti Ayume Beauty Care dalam menyusun strategi penjualan yang efektif dan mengoptimalkan manajemen stok. Penelitian ini bertujuan menerapkan data mining dengan metode K-Nearest Neighbors (KNN) untuk memprediksi penjualan produk skincare, dengan mengandalkan data historis penjualan, demografi pelanggan, serta informasi promosi dan musiman. Langkah awal meliputi pengumpulan data, pembersihan, dan transformasi data agar sesuai dengan persyaratan algoritma KNN. Model KNN kemudian dilatih menggunakan data historis untuk mengidentifikasi pola dan tren penjualan. Evaluasi dilakukan menggunakan metrik akurasi, precision, dan recall untuk mengukur performa prediksi. Hasil penelitian menunjukkan bahwa metode KNN dapat memberikan prediksi yang cukup akurat, sehingga memungkinkan klinik untuk merencanakan strategi penjualan yang lebih tepat sasaran dan efisien. Penelitian ini juga memberikan rekomendasi praktis bagi klinik dalam menentukan waktu dan jenis promosi serta menjaga ketersediaan stok produk sesuai permintaan yang diprediksi. Penerapan prediksi berbasis data ini diharapkan dapat meningkatkan efektivitas bisnis Klinik Ayume Beauty Care dan memberikan kepuasan lebih kepada pelanggan.
Comparative Study of Obesity Levels Classification Al Malaky, Syahrazad Syaukat; Nisa, Alisya Akbar Choirun; Armiyanti, Siti; Setyawan, Rizky Syahputra
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 10 No. 1 (2025): JEECS (Journal of Electrical Engineering and Computer Sciences)
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v10i1.8

Abstract

Obesity is a growing global health problem, requiring accurate data analysis to understand and address contributing factors. The level of obesity can be identified based on eating habits and physical conditions, which consist of several parameters. However, the performance of widely used machine learning methods has not provided satisfactory results. Therefore, this study analyzes obesity data using pre-processing methods to improve data quality before classifying data. The dataset used is 2111 data and includes 17 variables/features. The classification methods are Random Forest Classifier, Light Gradient Boosting Machine (LGBM) Classifier, Decision Tree Classifier, and Extra Tree Classifier. The process of data pre-processing involves data integration, data labeling, data transformation, normalization, and data cleansing. After pre-processing the data, four algorithms were used to identify patterns in the obesity data. The Random Forest Classifier is used for its ability to handle unbalanced data and reduce the risk of overfitting. The LGBM Classifier is used for a probabilistic approach to classification. The Decision Tree Classifier is applied for straightforward interpretation and clear understanding of patterns, while the Extra Tree Classifier is applied to improve the variety and accuracy of classification. The experimental results showed that a good data pre-processing method significantly improved the performance of the classification. Among the four algorithms tested, the Random Forest Classifier and Extra Tree Classifier performed best in accuracy and generalizability. Combining appropriate data pre-processing with powerful classification algorithms can provide deep insights to address obesity problems and formulate effective public health interventions.
Co-Authors Al Malaky, Syahrazad Syaukat Amanda, Putri Yulia Choirun, Alisya Akbar Damayanti, Berlian Aisya M Mahaputra Hidayat Nisa, Alisya Akbar Choirun Sari, Selvi Novita Setyawan, Rizky Syahputra