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All Journal Jurnal Inkofar
Adie Kusna Wibowo
Politeknik META Industri Cikarang

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Optimizing to Predict Purchase Intention in Fashion Thrifting Using Artificial Neural Networks Approach Fadil Abdullah; Manase Sahat H Simarangkir; Adie Kusna Wibowo; Abdullah Rizky Alfatih; Nadiya Maharani
INKOFAR Vol. 10 No. 1 (2026)
Publisher : Politeknik META Industri Cikarang

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Background Thrifting has emerged as a prominent trend within the fashion industry, driven by increasing consumer awareness of sustainability and the demand for affordable fashion alternatives Purpose This study develops an Artificial Neural Network (ANN) model to optimize purchase intention for thrifting fashion products based on trends, online promotions, and brand image Methodology The model uses three node variations (10, 20, 30), two hidden layers, a sigmoid activation function, 10,000 iterations, and a feed-forward propagation algorithm. The 30-node configuration performed best, achieving a determination coefficient of 0.97 during training and 0.98 during testing, indicating high predictive accuracy. Findings The findings confirm that trends, online promotions, and brand image significantly influence purchase intention, demonstrating the model’s potential to optimize marketing strategies. By leveraging ANN, businesses can enhance marketing efficiency, adapt to market dynamics, and improve decision-making Implications This research highlights the effectiveness of AI-driven methodologies in analyzing consumer behavior and supporting targeted marketing efforts. The model’s success also suggests broader AI integration possibilities in strategic planning for the fashion industry Originality this study contributes to the literature by providing deeper insights into purchase intention formation and offers practical implications for improving marketing efficiency and strategic decision-making in sustainable fashion businesses
An Integrated Web-Based Monitoring System For Optimizing The Supervision Of Student Internships And Final Projects Ilyas Ruhiyat; Nita Winda Sari; Adie Kusna Wibowo; Santo Wijaya; Manase Sahat H Simarangkir
INKOFAR Vol. 10 No. 1 (2026)
Publisher : Politeknik META Industri Cikarang

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Abstract

Background : The implementation of Field Work (PKL)/Internships and Final Projects is an important part of the academic process at universities, requiring ongoing monitoring by academic advisors and academic programs. However, at many universities, the monitoring of these two activities is still conducted through separate platforms, resulting in inefficient data management, disjointed information, and difficulties in comprehensively tracking student progress Purpose : This research aims to develop and implement a web-based monitoring platform that integrates the supervision processes for PKL/Internships and Final Projects into a single system, so that all data, advising activities, progress, and academic documents can be managed centrally Methodology : The system was developed using the Waterfall method, which includes requirements analysis, design, implementation, testing, and maintenance, utilizing the Laravel framework, the PHP programming language, and a MySQL database Findings : System validation was performed through Black Box Testing on 10 test scenarios. Test results showed a 100% success rate for system functionality, meaning all key features operate as intended to meet user needs Implications : The integration of the monitoring processes for internships and final projects into a single platform is the primary contribution of this research, as it enables unified progress monitoring, reduces data management duplication, and provides consistent academic information for all stakeholders. Originality : The system’s implementation offers benefits such as improved efficiency in the monitoring process, ease of tracking student progress in real time, accelerated academic administration, and the provision of more accurate information as a basis for evaluation and decision-making.