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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika EDUMATIC: Jurnal Pendidikan Informatika
Abiyyi, Ryandhika Bintang
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Computer Science & IT Education

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Journal : EDUMATIC: Jurnal Pendidikan Informatika

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Implementasi BERT dan Cosine Similarity untuk Rekomendasi Dosen Pembimbing berdasarkan Judul Tugas Akhir Sabilillah, Ferris Tita; Winarno, Sri; Abiyyi, Ryandhika Bintang
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 2 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i2.27791

Abstract

Challenges in completing final projects, which often contribute to delays in student graduation, are frequently due to a mismatch between students' research topics and the expertise of their supervisors. Therefore, a method is needed to address this misalignment in the final project process. This study aims to implement a Bidirectional Encoder Representations from Transformers (BERT) model and cosine similarity to recommend supervisors based on students' final project titles. The research dataset includes 3,723 research titles collected through web scraping from Google Scholar and ResearchGate, representing the expertise of 63 lecturers in the Informatics Engineering Program at Universitas Dian Nuswantoro. Data processing includes preprocessing to generate embedding vectors from lecturers' research titles, which are then compared with students' final project titles. Our findings indicate that the developed recommendation model achieves an accuracy of 90% in identifying relevant supervisors based on topic alignment between students' final project titles and lecturers' areas of expertise, as reflected in their publications. This result can make a significant contribution to supporting students in completing their final projects more efficiently and improving the quality of academic supervision by facilitating more appropriate supervisor selection.
Centing: Aplikasi Cegah Stunting Anak berbasis Android menggunakan TensorFlow Lite Abiyyi, Ryandhika Bintang; Subhiyakto, Egia Rosi; Sabilillah, Ferris Tita
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 2 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i2.27875

Abstract

Stunting is a serious health problem that affects children's growth and development, especially in areas with limited access to early detection. This research aims to develop a TensorFlow Lite-based “CENTING” Android application to detect stunting risk quickly and accurately. The prototyping method is used with the stages of identifying user needs, making initial prototypes, testing, and refinement based on the feedback of health workers and parents, until the application is ready to be implemented optimally. The dataset contains 121,000 child growth data from public sources, with variables such as age, gender, height, and nutritional status to detect stunting traits early. The data was processed and split 80:20 for training and testing, resulting in a detection accuracy of 98%. The selection of TensorFlow Lite is based on its advantage in response speed on mobile devices. The results showed that the CENTING application functioned optimally with a user acceptance score of 89.5%. The app supports self-detection, prevention education, and offline access, relevant for network-limited areas. These findings accelerate stunting intervention efforts and support government programs in reducing stunting prevalence.
Co-Authors Ajib Susanto Christy Atika Sari Egia Rosi Subhiyakto, Egia Rosi Sabilillah, Ferris Tita Sri Winarno