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      "keywords": "E-commerce, Perilaku belanja konsumen, Machine learning,        \r\nKlasifikasi, Prediksi, Consumer shopping behavior, Classification, Prediction.",
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      "abstract": "Perkembangan pesat e-commerce menghasilkan data transaksi dalam\r\njumlah besar yang mencerminkan perilaku belanja konsumen, namun kompleksitas\r\ndata serta ketidakseimbangan kelas antara konsumen yang berlangganan dan tidak\r\nberlangganan menjadi permasalahan dalam proses analisis dan pemodelan         \r\nklasifikasi. Kondisi ini dapat menyebabkan model cenderung bias terhadap kelas\r\nmayoritas sehingga menurunkan kemampuan dalam mengenali karakteristik             \r\nkonsumen secara menyeluruh dan berdampak pada kurang optimalnya strategi\r\npemasaran berbasis data. Penelitian ini bertujuan untuk memprediksi status               \r\nberlangganan konsumen e-commerce serta membandingkan kinerja beberapa             \r\nalgoritma machine learning dalam mengklasifikasikan perilaku belanja konsumen.\r\nDataset yang digunakan terdiri dari 3.900 data dengan 18 atribut yang                       \r\nmerepresentasikan karakteristik transaksi dan perilaku belanja. Metode penelitian\r\nmeliputi Exploratory Data Analysis (EDA), pembersihan data, penanganan                  \r\nOutlie rmenggunakan metode Outlier capping berbasis Interquartile Range (IQR),\r\nencoding fitur kategorikal menggunakan One-hot encoding, pembagian data\r\ndengan metode stratified Train-test split, Feature scaling menggunakan                    \r\nStandardScaler, serta penyeimbangan data latih menggunakan metode SMOTE. \r\nSelanjutnya, proses pemodelan dilakukan menggunakan algoritma Naive Bayes,      \r\nLogistic regression, dan Random forest dengan evaluasi kinerja berdasarkan metrik\r\naccuracy, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa \r\nketiga algoritma mampu memprediksi status berlangganan konsumen dengan                \r\nperforma yang baik, di mana Naive Bayes menghasilkan kinerja terbaik                           \r\nberdasarkan nilai F1-score. Hasil penelitian ini dapat dimanfaatkan oleh pelaku              \r\ne-commerce sebagai dasar pengambilan keputusan strategis dalam pengelolaan\r\npelanggan dan penyusunan strategi pemasaran berbasis data.",
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      "title": "ANALISIS KOMPARATIF ALGORITMA MACHINE \r\nLEARNING UNTUK KLASIFIKASI PERILAKU BELANJA\r\nKONSUMEN PADA E-COMMERCE",
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