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All Journal Journal of Artificial Intelligence and Innovative Applications (JOAIIA) JRIIN :Jurnal Riset Informatika dan Inovasi
Tiara Ulfiah
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Computer Science & IT Decision Sciences, Operations Research & Management

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Perancangan Aplikasi Informasi Data Perpustakaan Menggunakan Metode Rapid Application Development Tiara Ulfiah; Dafrosa Desi Udut; Aries Saifudin
Jurnal Riset Informatika dan Inovasi Vol 1 No 12 (2024): JRIIN : Jurnal Riset Informatika
Publisher : shofanah Media Berkah

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Abstract

Penelitian ini dilakukan karena ada masalah dalam mengelola informasi di perpustakaan, seperti tidak ada data buku, tidak ada informasi anggota perpustakaan, tidak ada catatan dalam peminjaman dan pengembalian buku, dan tidak ada catatan pengembalian buku jika sudah selesai digunakan oleh anggota perpustakaan. Dengan menggunakan metode RAD, aplikasi perpustakaan dibangun dengan cara yang lebih cepat, interaktif, dan mudah disesuaikan. Hasil dari penelitian ini adalah aplikasi perpustakaan yang membantu dalam menjalankan operasinya secara keseluruhan dan membantu anggota perpustakaan mudah untuk mengakses informasi.
ANALISIS DAN IMPLEMENTASI MACHINE LEARNING DALAM MEMPREDIKSI KERUSAKAN LAPTOP MENGGUNAKAN METODE RANDOM FOREST (STUDI KASUS: BARAKA SERVICE DEPOK) Tiara Ulfiah; Anis Mirza
Journal of Artificial Intelligence and Innovative Applications (JOAIIA) Vol. 6 No. 4 (2025): November
Publisher : Journal of Artificial Intelligence and Innovative Applications (JOAIIA)

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Abstract

Laptops are essential electronic devices widely used in academic and professional activities; however, they are often prone to damage due to intensive usage. Manual damage identification by technicians is time-consuming and prone to human error. Thisresearch aims to develop a laptop damage prediction system based on machine learning using the Random Forest method. Thedataset was obtained from Baraka Service Depok, containing information such as year of purchase, daily crash frequency,overheat frequency, symptoms, and types of damage. The research process includes data preprocessing, model training, and webbased system implementation. The results show that the Random Forest model achieved an accuracy of 95% with stableperformance in recognizing various types of laptop damage. The developed system proved effective in assisting technicians byaccelerating the diagnostic process and improving work efficiency. Keywords: Machine Learning, Random Forest, Laptop Damage Prediction, Baraka Service Depok
Co-Authors Anis Mirza Aries Saifudin Dafrosa Desi Udut