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All Journal DEVICE STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer Clean Energy and Smart Technology TECHNOMEDIA : Informatics and Computer Science Servis : Jurnal Pengabdian dan Layanan kepada Masyarakat
muhamad Fuat Asnawi
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Religion Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Mechanical Engineering Other

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Journal : TECHNOMEDIA : Informatics and Computer Science

 1 
Analisis Model Prediksi Kinerja Karyawan Pada Sebuah Perusahaan Garmen Menggunakan Machine Learning Naf'ul Huja, Wisnu; Muslim Hidayat; Muhamad Fuat Asnawi
Tekompedia : Jurnal Ilmiah Ilmu Komputer Vol 2 No 1 (2025): Januari
Publisher : CV Nature Creative Innovation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58641/technomedia.v2i1.123

Abstract

Penelitian ini bertujuan untuk menganalisis dan memprediksi tingkat efektivitas buruh di pabrik garmen menggunakan metode regresi linier dan teknik machine learning. Efektivitas buruh dipengaruhi oleh faktor-faktor seperti keterampilan, pengalaman, motivasi, kondisi fisik, dan penggunaan teknologi. Model regresi linier, bersama dengan algoritma machine learning, digunakan untuk mengidentifikasi hubungan antara variabel-variabel tersebut dengan tingkat efektivitas buruh. Hasil analisis ini diharapkan dapat membantu manajemen dalam meningkatkan efisiensi operasional, mengurangi biaya produksi, serta melakukan intervensi yang tepat waktu untuk mengatasi penurunan produktivitas. Dengan demikian, perusahaan dapat lebih efektif dalam meningkatkan kinerja buruh dan pengambilan keputusan berbasis data.
IMPLEMENTASI BIG DATA ANALYTICS DALAM KLASIFIKASI KUALITAS UDARA MENGGUNAKAN ALGORITMA GRADIENT-BOOSTED TREE CLASSIFIER PADA PYSPARK Muhamad Fuat Asnawi; Nur Fitriyanto; M. Agoeng Pamoengkas
Tekompedia : Jurnal Ilmiah Ilmu Komputer Vol 2 No 1 (2025): Januari
Publisher : CV Nature Creative Innovation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58641/technomedia.v2i1.124

Abstract

This study aims to classify air quality based on PM1.0, PM2.5, and PM10 parameters using a Big Data Analytics approach with the Gradient-Boosted Tree Classifier (GBT) algorithm implemented on the PySpark framework. The dataset used was downloaded from OpenAQ, covering the period from April 14, 2021, to April 16, 2023, with a total of 1,048,154 entries, representing a large and complex volume of data. The research process includes data preprocessing to address data imbalance, dataset splitting for training and testing, and hyperparameter tuning using grid search and cross-validation to optimize model performance. By leveraging PySpark’s advantage in parallel processing of large data, the GBT model achieved an accuracy of 98.87%, precision of 99.00%, recall of 98.87%, and an F1-Score of 98.90%. This study demonstrates how Big Data Analytics can enhance efficiency and accuracy in air quality classification, contributing significantly to the development of real-time monitoring systems that support air pollution mitigation and data-driven policy-making.
Co-Authors Afra Nurul Fadhila Agus Triyadi Ahmad Afif Ahmad Irfa’i Al Ashar, Ilham Ariawan Alif Muhammad Nurman Maulana Alya Faradisa Dani Sifa Abdillah dian asmarajati Dwi Murni Oktaviani Farah Dina Alifah Fuad Najib Gusti Ilman Prayoga HERMAWAN Hindun Choirun Nisa’ Ilham, Ilham Ariawan Al Ashar Jenny Febrina Andini Lutfi Ziddan Fahriza M. Agoeng Pamoengkas Mora Arief Nasucha Muhammad Bondan Abhyoso Munaya Putri Khaura Anjali Muslim Hidayat Mutiara Cahyaningrum Rangkuti Naf'ul Huja, Wisnu Najwa Millah Hanifa Nur Fitriyanto NUR HASANAH Rina Mahmudati Sukowiyono Suwondo, Adi Wahyu Aji Bayu Pemungkas Warohmatika Rofikhoh