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All Journal Techno.Com: Jurnal Teknologi Informasi Perfecting a Video Game with Game Metrics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ILKOMNIKA: Journal of Computer Science and Applied Informatics ABDI MASSA: Jurnal Pengabdian Nasional
Rizka Wakhidatus Sholikah
Institut Teknologi Sepuluh Nopember
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Environmental Science Industrial & Manufacturing Engineering Public Health Other

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Journal : Techno.Com: Jurnal Teknologi Informasi

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A Comparative Study of Multi-Label Classification for Document Labeling in Ethical Protocol Review Rizka Wakhidatus Sholikah; Diana Purwitasari; Mohammad Zaenuddin Hamidi
Techno.Com Vol 21, No 2 (2022): Mei 2022
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/tc.v21i2.5994

Abstract

An ethical clearance document ensures that the research will protect the subject in accordance with existing ethical principles. The ethical clearance is issued by the Research Ethics Commission (KEP). KEP will conduct a review of the proposed ethical protocol based on the seven standards contained in a protocol. The review process is done manually by KEP. This process often creates bottlenecks in research due to the large number of protocols that must be reviewed, so that the process to get ethical clearance takes a long time. This can affect the setback in the schedule of the research process. Therefore, in this research, a comparative study was conducted on the problem of multi-label classification to automate the ethical protocol review process. Automation of the labeling process can increase the effectiveness of the review process because it can provide an overview to the reviewer regarding the label of a document before conducting a more in-depth review process. The experiment results show that the use of the traditional machine learning approach produces better performance than the deep learning approach. The machine learning method with the best results is Naïve Bayes+BoW with precision, recall, and F-score values of 0.76, 0.80, and 0.78, respectively.
Visual Question Answering Bahasa Indonesia Berbasis Deep Learning untuk Pembelajaran Visual Anak TK Asiyah Hanifah; Rizka Wakhidatus Sholikah; R.V. Hari Ginardi
Techno.Com Vol 23, No 1 (2024): Februari 2024
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v23i1.9694

Abstract

Indonesia semakin gencar melakukan persiapan transformasi digital dalam berbagai sektor, termasuk dalam bidang pendidikan. Salah satu upaya yang dilakukan pemerintah adalah dengan mengimplementasikan platform e-learning dalam kegiatan belajar mengajar hingga ke jenjang taman kanak-kanak. Metode pembelajaran visual pada taman kanak-kanak dapat diimplementasikan ke dalam e-learning yang lebih interaktif dan menarik dengan sistem Visual Question Answering (VQA). Sistem VQA dapat memberikan pertanyaan terkait dengan gambar yang ditampilkan dan mengecek kesesuaian jawaban dari siswa secara otomatis. Pada penelitian ini dibangun sistem VQA yang dapat menerima pertanyaan berbahasa Indonesia dan mengoreksi jawaban dalam bahasa Indonesia. Sistem dibangun dengan menggunakan model Bootstrapping Language-Image Pre-training (BLIP) untuk VQA dan model No Language Left Behind (NLLB) untuk penerjemahan. Uji coba dilakukan pada enam jenis jawaban yaitu ya/tidak, kata benda, kata kerja, kata sifat, kata keterangan, dan numeral. Hasil pengujian menunjukkan bahwa sistem dapat menjawab dengan nilai ketepatan 100 untuk jawaban ya/tidak, kata benda, kata kerja, dan numeral. Sementara untuk kata sifat dan kata keterangan masing-masing memiliki nilai ketepatan 62,5 dan 87,5.
Co-Authors Agus Zainal Arifin Aminul Wahib Asiyah Hanifah Chastine Fatichah Desepta Isna Ulumi Diana Purwitasari Dimas Fanny Hebrasianto Permadi Dini Adni Navastara, Dini Adni Hanoraga, Tony Juniarun Fathurrohman Khakim Ghozali Moch Zawaruddin Abdullah Mohammad Zaenuddin Hamidi Muhammad Mirza Muttaqi Narandha Arya Ranggianto R.V. Hari Ginardi Ridho Rahman Hariadi Rosyadi, Ahmad Wahyu