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All Journal Jurnal Pseudocode SMARTICS Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING KACANEGARA Jurnal Pengabdian pada Masyarakat Building of Informatics, Technology and Science Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal Abdi Insani Information System Journal (INFOS)
Arif Akbarul Huda
Universitas Amikom Yogyakarta
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics

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Journal : Building of Informatics, Technology and Science

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Sistem Rekomendasi Content-based Filtering Menggunakan TF-IDF Vector Similarity Untuk Rekomendasi Artikel Berita Huda, Arif Akbarul; Fajarudin, Rohmad; Hadinegoro, Arifiyanto
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2511

Abstract

The population of active students in the Informatics Bachelor Program, Universitas Amikom Yogyakarta, in the odd semester of 2021 is 3,870. Efforts to track interest in the three concentration options were carried out early on through article literacy recommendations. Various articles are produced continuously and provided on an ongoing basis to students. However, the many articles offered daily make students overwhelmed and tend to choose articles that do not match what they want. To help solve this problem, recommender system is developed. A recommender system helps to estimate the prediction value or relevancy of an article and create a ranking according to user's taste. Content-based Filtering technique is used in this research. Using the dataset from Kabar Informatika news portal of University of Amikom Yogyakarta, the developed Content-based Filtering Recommendation System is able to produce Recall@5 score at around 73% and Recall@10 at around 80%.
Penerapan Logits Processing Pada Teknologi Transformer untuk Penciptaan Melodi Berbentuk Notasi ABC dalam Pengembangan Game Indie Dhiaulhaq, Muhammad Faishal Ali; Huda, Arif Akbarul; Hadinegoro, Arifiyanto
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6642

Abstract

Generative Artificial Intelligence (Gen AI) technology is increasingly being used by creative professionals, including musicians and game developers. Many game developers now turn to open or paid music assets, but the variety of options is usually quite limited. This research aims to assist game developers in generating music assets in ABC notation format. The research methods include data collection in the form of ABC notation, data processing, model development, and metric evaluation. The data was collected by extracting ABC notation along with the characteristic musical components of each item. Data processing involved handling missing values and feature selection, while data preparation included labeling and tokenization. The model used was GPT-2 based on the Transformer architecture, pretrained on a general dataset. Integration of the model with ABC notation data was enhanced using Logits Processing to improve output control. The evaluation results show that Transformer technology can generate pitch patterns consistent with the validation data, with the EMD values concentrated in the range of 1.0–1.5 and an average of 1.60. Although there are some outliers and differences in pitch distribution between the validation data and generated results, the Horror genre with a Joyful mood and Excitement emotion achieved the highest combined fitness score of 0.528. The model still requires further refinement to produce more consistent pitch distributions. This research demonstrates the potential of Transformer technology in generating music assets for games, but further studies are needed to improve accuracy and consistency in the results.
Co-Authors Adri Priadana Agusta, Rivga Arifiyanto Hadinegoro, Arifiyanto Arya Yudistira Bayu Setiaji Dedy Hariyadi Dhiaulhaq, Muhammad Faishal Ali Fajar Rosyid Hidayat Fajarudin, Rohmad Heru Kristanto Kharisma LILIS DWI FARIDA Lubis, Quiin Latifah Almatin Nazaruddin Ahmad Saputra, Agel Sukamta Sukamta WINDHA MEGA PRADNYA DHUHITA Wiyana, Satria Hadi