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                <text>Kemajuan dalam bidang teknologi informasi dan komunikasi telah memberikan pengaruh besar terhadap berbagai bagian dari aspek yang ada di kehidupan, salah satunya email. Email atau electronic mail salah satu bentuk komunikasi digital yang paling banyak digunakan dalam kehidupan sehari-hari, telah menjadi bagian penting dalam kehidupan modern. Namun dengan semakin meningkatnya penggunaan email,  berbagai permasalahan pun bermunculan, termasuk spam. Oleh karena itu, mendeteksi dan mengklasifikasikan email spam  sangat penting untuk meningkatkan keamanan dan kenyamanan pengguna email. Untuk mengatasi masalah ini, berbagai metode klasifikasi telah dikembangkan untuk mendeteksi dan memfilter email spam.  Di antara metode tersebut, Algoritma Long Short Term Memory (LSTM), Naïve Bayes, dan Support Vector Machine (SVM) adalah beberapa teknik yang umum digunakan. Penelitian ini diharapkan dapat meningkatkan pemahaman tentang teknik klasifikasi dalam bidang pengolahan data, dan dapat mengetahui Algoritma mana yang lebih baik dalam mendeteksi email spam. Berdasarkan hasil pengujian sistem yang telah dilakukan, Naive Bayes menunjukkan performa terbaik dengan accuracy 93,3%, precision 90,91%, recall 96,77%, dan F1-score 93,75%. SVM juga memiliki performa cukup baik dengan accuracy 76,6%, precision 84%, recall 67,74%, dan F1-score 75%. Sementara itu, LSTM mencatat performa terendah dengan accuracy 70%, precision 84,21%, recall 51,61%, dan F1-score 63,91%.</text>
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                <text>Rambu lalu lintas berperan penting dalam menjaga ketertiban dan keselamatan di jalan raya.  Namun, anak-anak sering kali mengalami kesulitan dalam memahami makna rambu lalu lintas karena &#13;
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                <text>Sri Wahyuni</text>
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                <text>ISSN : 3030-9735</text>
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                <text>Penerapan Jaringan Syaraf Tiruan Backpropagation pada Peramalan Curah Hujan di Kabupaten Muna</text>
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                <text>Peramalan curah hujan merupakan langkah penting dalam mendukung pengambilan keputusan di sektor pertanian dan mitigasi bencana. Penelitian ini bertujuan untuk menerapkan model Jaringan Syaraf Tiruan (JST) dengan algoritma backpropagation berbasis Long Short-Term Memory (LSTM) dalam memprediksi curah hujan di Kabupaten Muna. Data yang digunakan mencakup curah hujan, kelembapan udara, tekanan udara, dan suhu dalam rentang waktu bulanan. Seluruh data diolah melalui proses normalisasi menggunakan StandardScaler dan dibentuk dalam format deret waktu menggunakan pendekatan sliding window selama tiga bulan untuk memprediksi curah hujan bulan keempat.</text>
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                <text>Wa Ode Nuriil Rizqy Annisa, Adha Mashur Sajiah</text>
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                <text>Sri Wahyuni</text>
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                <text>ISSN : 3030-9735</text>
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