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All Journal Jurnal Teknoinfo
Viga Laksa Hardjanto
Computer Science Department, Binus Online Learning, Bina Nusantara University
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Library & Information Science

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Building a Digital Lexical Resource for Banyumasan Javanese: A Low-Resource Language Approach Nisrina Hanifa Setiono; Angga Kurniawan; Viga Laksa Hardjanto
Jurnal Teknoinfo Vol. 20 No. 2 (2026): Period July 2026
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/teknoinfo.v20i2.1705

Abstract

Banyumasan Javanese, widely recognized through the Ngapak dialect, remains culturally significant but is still underrepresented in reusable computational resources. This study develops a digital Banyumasan-Indonesian lexical corpus and frames it as a reusable research artifact rather than a static appendix. The corpus was constructed from a Banyumasan-Indonesian dictionary, normalized into a structured bilingual dataset, and packaged as an installable Python resource so that it can be used directly in computational experiments. The implemented resource supports dataset loading, Banyumasan lookup, Indonesian lookup, simple translation, structured translation analysis, batch translation, and corpus statistics. The resulting corpus contains 2,000 lexical pairs, 1,996 unique Banyumasan forms, 1,444 unique Indonesian equivalents, and 4 duplicated Banyumasan headwords that preserve lexical ambiguity from the source material. To demonstrate practical utility, the study includes a 100-sentence implementation example in which Banyumasan text is translated with the published banyumasan-corpus package and evaluated against Indonesian ground truth using the Indonesian-focused embedding model LazarusNLP/all-indo-e5-small-v4. The average semantic similarity rises from 0.4833 for direct Banyumasan-versus-ground-truth comparison to 0.6427 after translation, producing an absolute gain of 0.1594 and a relative improvement of approximately 33.0% over the baseline. These findings indicate that a structured lexical corpus, when distributed in a directly reusable computational form, can strengthen both resource accessibility and small-scale downstream experimentation for a low-resource regional language.
Feature Optimization for a Content-Based Music Recommendation System on Spotify Viga Laksa Hardjanto; Nisrina Hanifa Setiono
Jurnal Teknoinfo Vol. 20 No. 2 (2026): Period July 2026
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/teknoinfo.v20i2.1731

Abstract

The growth of music streaming services like Spotify has encouraged users to explore millions of songs, making effective recommendation systems essential. This study examines content-based music recommendation systems by comparing feature configurations and similarity metrics. The system uses 11 Spotify audio features, both with and without genre features encoded using one-hot encoding. Similarity is calculated using cosine similarity and Euclidean distance on normalized features (MinMaxScaler) to generate the top 10 recommendations. Recommendations are considered relevant if the recommended song is by the same artist as the searched song. Performance is measured using Precision@10 and Recall@10 on 100 samples. Using audio features alone yields Precision@10 of 3.80–3.90% and Recall@10 of 6.36–6.45%. The addition of genre features improved performance to Precision@10 of 9.00–9.10% and Recall@10 of 9.06–9.89%. These results show that genre features significantly improve the relevance of recommendations, while both similarity metrics perform similarly when the features have been well normalized. This study contributes by demonstrating that feature representation plays a more critical role than similarity metrics in content-based music recommendation.
Co-Authors Angga Kurniawan Nisrina Hanifa Setiono Nisrina Hanifa Setiono