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      "date": "2026-01-21",
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      "abstract": "Kualitas perangkat lunak dipengaruhi oleh kemampuan sistem dalam\r\nmengidentifikasi kesalahan lebih awal selama proses pengembangan. Just-in-Time\r\nDefect Prediction (JIT-DP) adalah metode yang memperkirakan kemungkinan\r\nterjadinya bug saat commit dilakukan dengan memanfaatkan metrik perubahan\r\nkode dan aktivitas pengembang. Tujuan dari penelitian ini adalah untuk\r\nmengembangkan dan menilai model prediksi bug berbasis algoritma Random\r\nForest menggunakan Dataset ApacheJIT yang mencakup metrik tingkat commit\r\ndari berbagai proyek perangkat lunak open-source.  \r\n \r\nLangkah-langkah dalam penelitian ini mencakup pra-pemrosesan data, \r\npembagian data dengan dua metode validasi (Random Split dan Chronological\r\nSplit), pelatihan model, dan penilaian performa menggunakan metrik Recall,\r\nPrecision, F1-score, ROC-AUC, dan MCC. Hasil dari eksperimen menunjukkan\r\nbahwa Random Forest dapat memberikan performa prediksi yang konsisten pada\r\nkedua metode validasi, meskipun Random Split menunjukkan performa yang lebih\r\ntinggi secara statistik. Namun, metode Chronological Split dianggap lebih\r\nrepresentatif karena mencerminkan sifat waktu dalam alur commit dan mengurangi\r\nrisiko kebocoran data. Analisis Feature Importance menunjukkan bahwa ukuran\r\nperubahan kode, terutama jumlah baris yang ditambahkan dan dihapus, adalah\r\nindikator utama dalam mempengaruhi terjadinya commit cacat.  \r\n \r\nTemuan ini memperkuat hasil penelitian sebelumnya yang menekankan \r\npentingnya metrik perubahan dalam metode JIT-DP. Secara keseluruhan, penelitian\r\nini menunjukkan bahwa algoritma Random Forest efektif digunakan dalam tugas\r\nprediksi bug berbasis commit, dengan efisiensi komputasi yang baik dan\r\nkemampuan interpretasi yang cukup. Diharapkan, hasil penelitian ini dapat menjadi\r\nlandasan untuk mengembangkan alat bantu kualitas perangkat lunak yang dapat\r\nmemberi peringatan dini kepada pengembang tentang risiko cacat pada tahap\r\ncommit.",
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      "title": "EKSPERIMEN ALGORITMA RANDOM FOREST UNTUK PREDIKSI\r\nBUG JUST-IN-TIME PADA PERANGKAT LUNAK OPEN-SOURCE\r\nMENGGUNAKAN DATASET APACHEJIT",
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