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All Journal JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Kewirausahaan dan Bisnis
Deti Karmanita
Universitas Dehasen Bengkulu
Agriculture, Biological Sciences & Forestry Computer Science & IT Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Social Sciences Other

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Penguatan Pengelolaan Lingkungan dan Pemberdayaan Masyarakat di Kelurahan Sawah Lebar Antonius Antonius; Rita Hamid; Liza Wahyuni; Rahmad Trigono; Yongki Guswandi; Bayu Surya Kencana; Deti Karmanita; Sandi Aprianto
Jurnal Kewirausahaan dan Bisnis Vol. 8 No. 1 (2026): Februari
Publisher : Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jkb.v8i1.1310

Abstract

Environmental and social issues, particularly household waste management, remain major challenges in urban residential areas. Low levels of collective community awareness, limited facilities, and the underutilization of the economic potential of waste have adversely affected environmental quality and public health. This community service article aims to describe and analyze the implementation of the Thematic Community Service Program (KKNT) of Dehasen University Bengkulu in RT 27 RW 06, Sawah Lebar Subdistrict. The method employed was a participatory descriptive approach through observation, direct action, and activity documentation. The results indicate an increase in community awareness of environmental cleanliness, strengthened mutual cooperation, and an initial understanding of waste banks as an alternative economic potential. This program contributes positively to supporting sustainable, environment-based community development.
Plang Edukasi Berapa Lama Sampah Terurai Marsel Deno Palta; Eep Elpres Insagi; Jovandika Anugrah; Aldi Pranata; Ranny Fitri Imran; Deti Karmanita; Karona Cahya Susena
Jurnal Kewirausahaan dan Bisnis Vol. 8 No. 1 (2026): Februari
Publisher : Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jkb.v8i1.1331

Abstract

Household waste remains a major problem in residential areas, including RT 03 RW 01, Penurunan Village, Ratu Samban District, Bengkulu City. Low public awareness of sorting and disposing of waste according to type has resulted in an unclean environment and the potential for pollution. One effort that can be made to overcome this problem is through the installation of waste education signs. Waste education signs serve as a medium of information and a reminder for residents to dispose of and sort waste properly. This activity aims to increase public awareness of the importance of waste management starting from the household level and to create a clean, healthy, and sustainable environment.
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 Aldi Pranata Antonius Antonius Bayu Surya Kencana Dewi Harwini Eep Elpres Insagi Feri Hari Utami Jovandika Anugrah Karona Cahya Susena Liza Wahyuni Marsel Deno Palta Prahasti Rahmad Trigono Ranny Fitri Imran Rita Hamid Sandi Aprianto Yongki Guswandi