Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Jurnal Informatika dan Rekayasa Elektronik Software Engineering in Computing Systems
Brian Damastu Ridho Hutama
Universitas Teknologi Bandung
Computer Science & IT

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
BIG DATA ANALYTICS DAN MACHINE LEARNING UNTUK MEMPREDIKSI PERILAKU KONSUMEN DI E-COMMERCE Djihadul Mubarok; Kannisa Adjani; Brian Damastu Ridho Hutama; Muhamad Malik Mutoffar; Rina Indrayani
Jurnal Informatika dan Rekayasa Elektronik Vol. 8 No. 1 (2025): JIRE APRIL 2025
Publisher : LPPM STMIK Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36595/jire.v8i1.1561

Abstract

Dalam era digital yang berkembang pesat, marketplace digital menjadi salah satu platform utama bagi konsumen dalam melakukan transaksi online. Pemanfaatan Big Data Analytics dalam menganalisis aktivitas konsumen dapat memberikan pengetahuan mendalam untuk pelaku usaha dalam meningkatkan strategi pemasaran dan layanan pelanggan. Penelitian ini bertujuan untuk mengeksplorasi penerapan Big Data Analytics dalam memahami pola pembelian konsumen serta faktor yang mempengaruhi loyalitas pelanggan di marketplace digital. Metode penelitian yang digunakan mencakup pengumpulan data primer melalui survei terhadap 1.000 responden serta data sekunder yang diperoleh dari web scraping dan teknik data mining. Data yang dikumpulkan dianalisis menggunakan teknik analisis Big Data dan algoritma Machine Learning untuk mengidentifikasi tren perilaku konsumen. Hasil penelitian menunjukan bahwa faktor harga, kecepatan pengiriman, serta pengalaman pengguna memiliki pengaruh signifikan terhadap loyalitas pelanggan. Selain itu, penerapan analisis prediktif berbasis Machine Learning mampu meningkatkan akurasi prediksi perilaku konsumen hingga 85%. Pada temuan ini, dapat memberikan pengetahuan bagi pelaku bisnis dalam menentukan ploda dan strategi pemasaran lebih efektif dalam meningkatkan kepuasan pelanggan. Penelitian ini dapat menjadikan peluang bagi penelitian berikutnya mengenai optimalisasi algoritma Machine Learning dalam segmentasi pelanggan untuk personalisasi pengalaman belanja.
Global Trends and Framework Development of AI and IoT Integrated Waste Automation for Emerging Economies Sri Erina Damayanti; Brian Damastu Ridho Hutama; Popon Dauni; Jack Febrian Rusli
Software Engineering in Computing Systems Vol. 1 No. 2 (2026): May: Software Engineering in Computing Systems
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/secons.v1i2.444

Abstract

Waste management in Indonesia faces extreme regional disparities, ranging from critical waste accumulation zones and circular economy transition zones to specific material deficit zones. The primary problem lies in the inability of conventional systems to process heterogeneous waste efficiently, which leads to the failure of sustainable environmental conservation. An intelligent solution is required to integrate physical technology with an adaptive policy evaluation system. This research develops a systematic framework for the development and evaluation of waste processing automation technology. The research stages begin with a Bibliometric Analysis and Systematic Literature Review (SLR) using metadata from Scopus and Web of Science to identify global trends via VOSviewer. Furthermore, this study integrates AI and IoT as primary instruments for nature conservation. Through the processing of large data volumes (Big Data) from IoT sensors, AI (such as Multi-Criteria Decision Making) performs predictive analysis to automatically evaluate three regional conditions. AI plays a crucial role in determining corrective actions, including optimizing the use of oxy-hydrogen (HHO) fuel in incinerators to suppress emissions and managing cross-regional waste logistics, thereby ensuring natural resources are preserved through precise and low-pollution waste elimination processes. This research generates intelligent governance patterns and actionable insights to guide system users, particularly local governments and industrial managers, in implementing appropriate waste processing technologies. This solution provides automated operational guidance that ensures energy efficiency and economic sustainability while maintaining ecosystem preservation through standardized waste processing based on the specific regional characteristics in Indonesia.
Co-Authors DJIHADUL MUBAROK Jack Febrian Rusli Kannisa Adjani Muhamad Malik Mutoffar Popon Dauni Rina Indrayani Sri Erina Damayanti