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All Journal MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Algor
Putri, Maysha Permata
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Computer Science & IT

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ANALISIS DAN PERANCANGAN SISTEM INFORMASI PEDAGANG PASAR LAMA TANGERANG BERBASIS WEB Arijanto, Rudy; Fandini, Leona; Angga Mewo, Marcelino; Ando, Ando; Chintia Angel Sirait, Gesima; Putri, Maysha Permata; Rahul Ramadika, Irvan
ALGOR Vol. 6 No. 2 (2025): Informatics Innovation
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/algor.v6i2.3433

Abstract

The need for marketing development is very much needed for traditional markets, this is in accordance with the development of online marketing which continues to erode traditional markets. The Old Market traders in Tangerang city were also affected by this. They only rely on traditional sales directly with their buyers. This research offers a solution for how Tangerang Old Market traders can also compete and develop their marketing online with a website built to manage the buying and selling transactions they carry out.
Talent Development Center Recommendation System Using Content-Based Filtering Putri, Maysha Permata; Daniawan, Benny
MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Vol 18, No 1 (2026): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v18i1.32816

Abstract

The development of digital technology has led to increased gadget usage among children, often resulting in a decline in interest in productive physical and social activities. The Indonesian Child Protection Commission reported that over 71.3% of school-age children using gadgets daily. This condition highlights the need for efforts to redirect children's attention toward more beneficial activities, one of which is talent development. Early talent development is crucial for supporting personal potential and future career paths. However, limited information often becomes an obstacle in choosing the right place for talent development that suits an individual's needs and interests. This study aims to design a system that can provide talent development center recommendations for seekers. By implementing the Content-Based Filtering (CBF) method, the system matches user preferences—such as interests, skills, and preferred types of activities expressed in keywords—with the descriptions of available talent development center. Weighting is carried out using the Term Frequency–Inverse Document Frequency (TF-IDF) algorithm to enhance the relevance of the recommendations by calculating the similarity level between talent development center descriptions based on keyword weights. This approach allows the system to provide more personalized recommendations without relying on other users' data. The testing conducted in this study, using 7 sample talent development places, resulted in 5 recommendations with the top recommendation being Chic’s Musik, which had the highest TF-IDF value of 1.9029.Index Terms— Content-Based Filtering, Recommendation System, TF-IDF, Talent Development
Co-Authors Ando, Ando Angga Mewo, Marcelino Arijanto, Rudy Chintia Angel Sirait, Gesima Daniawan, Benny Fandini, Leona Rahul Ramadika, Irvan