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All Journal Jurnal Media Infotama Riau Journal of Computer Science JurTI (JURNAL TEKNOLOGI INFORMASI) JOURNAL OF SCIENCE AND SOCIAL RESEARCH Indonesian Journal of Engineering Research Jurnal Dehasen Untuk Negeri Jurnal Komputer, Informasi dan Teknologi
Feri Hari Utami
Universitas Dehasen Bengkulu
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Public Health Social Sciences Other

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Analisis Keselamatan, Kesehatan Kerja dan Lingkungan (K3L) Pekerja dalam Membangun SMK 3 Kota Bengkulu Feri Hari Utami; Maria A. Kartawidjaja
Jurnal Media Infotama Vol 21 No 1 (2025): April 2025
Publisher : UNIVED Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmi.v21i1.8216

Abstract

The purpose of identifying post-fire occupational safety is to ensure that a workplace or facility affected by a fire can resume operations safely and effectively. This includes measures to protect workers, prevent further damage, and minimize potential hazards that may arise after a fire. Primary data was obtained by conducting interviews and questionnaires were administered to select project personnel who held supervisory positions. Secondary data refers to information obtained by other individuals from various sources, such as the internet, literature, and previous research, Characteristics of Occupational Safety and Health (OHS) hazards that often arise in construction projects were obtained from literature research and observation, The HIRADC technique has demonstrated efficacy in identifying, evaluating, and categorizing hazards, in addition to ensuring appropriate mitigation strategies.
PENERAPAN BIG DATA ANALYTICS DALAM PREDIKSI TREN E-COMMERCE DI INDONESIA Deti Karmanita; Feri Hari Utami; Prahasti Prahasti; Dewi Harwini
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 8 No. 4 (2025): November 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i4.4587

Abstract

Abstract: The growth of e-commerce in Indonesia has been accelerating, driven by increasing internet penetration and the widespread use of mobile devices. The large, complex, and diverse volume of transaction data requires appropriate analytical methods to produce accurate trend predictions. This study aims to apply Big Data Analytics in analyzing consumer shopping patterns, popular product trends, and factors influencing purchasing decisions. Data were collected from various e-commerce platforms, processed using Hadoop and Spark, and further analyzed through predictive modeling with Machine Learning algorithms. The results indicate that integrating Big Data Analytics can improve trend prediction accuracy by up to 85% compared to conventional methods. These findings are expected to support strategic decision-making in Indonesia’s e-commerce sector. Keywords: Big Data Analytics, E-commerce, Machine Learning, Trend Prediction, Indonesia Abstrak: Pertumbuhan e-commerce di Indonesia semakin pesat, didorong oleh penetrasi internet dan meningkatnya penggunaan perangkat mobile. Data transaksi yang besar, kompleks, dan beragam membutuhkan metode analisis yang tepat untuk menghasilkan prediksi tren yang akurat. Penelitian ini bertujuan untuk menerapkan Big Data Analytics dalam menganalisis pola belanja konsumen, tren produk populer, serta faktor yang memengaruhi keputusan pembelian. Metode yang digunakan mencakup pengumpulan data dari berbagai platform e-commerce, pemrosesan menggunakan Hadoop dan Spark, serta analisis prediktif dengan algoritma Machine Learning. Hasil penelitian menunjukkan bahwa integrasi Big Data Analytics mampu meningkatkan akurasi prediksi tren hingga 85% dibanding metode konvensional, sehingga dapat mendukung strategi bisnis e-commerce di Indonesia. Kata kunci: Big Data Analytics, E-commerce, Machine Learning, Prediksi Tren, Indonesia
Co-Authors Andree Adhi Dharma Asnawai Asnawati Asnawati Asnawati Deti Karmanita Dewi Harwini Eko Suryana Fredricka, Jhoanne Hambali Hambali Irvan Ciantury Jhoanne Fredericka Kanedi, Indra Kanedi Khairil Khairil Leni Natalia Zulita Liza Yulianti Maria A. Kartawidjaja Melinda Apriyani Mirna Mirna Prahasti Ricky Zulfiandry Rimba Putri Yani Rizka Tri Alinse Rupawan Azzahra Saira Asmar Sapri Sapri Venny Novita Sari Yupianti, Yupianti