Article Per Year (5 Year)
p-Index From 2021 - 2026
3.525
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Informatics and Communication Technology (IJ-ICT) IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Bulletin of Electrical Engineering and Informatics Jurnal Informatika dan Teknik Elektro Terapan JOIN (Jurnal Online Informatika) BAREKENG: Jurnal Ilmu Matematika dan Terapan Building of Informatics, Technology and Science Indonesian Journal of Electrical Engineering and Computer Science Computer Science and Information Technologies Brahmana : Jurnal Penerapan Kecerdasan Buatan Journal La Multiapp Action Research Literate (ARL) Malcom: Indonesian Journal of Machine Learning and Computer Science Cerdika: Jurnal Ilmiah Indonesia The Indonesian Journal of Computer Science
Zahra, Amalia
Unknown Affiliation
Aerospace Engineering Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation

Published : 23 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 23 Documents
Search

 1   2   3 
PREDIKSI KELULUSAN SISWA SEKOLAH MENENGAH PERTAMA MENGGUNAKAN MACHINE LEARNING Naibaho, Agusti Frananda Alfonsus; Zahra, Amalia
Jurnal Informatika dan Teknik Elektro Terapan Vol. 11 No. 3 (2023)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v11i3.3056

Abstract

In recent years, there has been a number of students who graduated late at Lubuk Alung 1st State Junior Highschool. This statement is supported by graduation data from Lubuk Alung 1st Satet Junior Highschool. Therefore, it is necessary to predict students’ graduation status to identify which factors influence the student’s graduation, which will also consequently help the school to solve problem more easily. To solve this problem, the researchers predict student graduation based on student graduation information. The attributes used are personal data related to students, student academic data, and data related to the work of the student’s parents. This research retrieved data on student graduation from schools that have been recapitulated. The classification algorithms used to predict students’ graduation are decision tree, random forest, and extreme gradient boosting with grid searchCV and k-fold=5. The prediction accuracy using the random forest algorithm outperforms the others with a value of 99.5%.
HUBUNGAN ANTARA CITRA TUBUH DENGAN PERILAKU DIET MAHASISWI DI AKADEMI KEPERAWATAN AL HIKMAH 2 BREBES Karyawati, Tati; Seventina, Healthy; Zahra, Amalia
Cerdika: Jurnal Ilmiah Indonesia Vol. 3 No. 09 (2023): Cerdika : Jurnal Ilmiah Indonesia
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/cerdika.v3i09.667

Abstract

Tujuan dalam penelitian ini untuk mengetahui hubungan antara citra tubuh dengan perilaku diet mahasiswa di Akademi Keperawatan Al Hikmah 2 Brebes. Penelitian ini menggunakan metode kuantitatif dengan desain cross sectional. Penelitian ini dilakukan di Akper Al Hikmah 2 Brebes untuk menganalisis hubungan antara citra tubuh dan perilaku diet pada mahasiswa. Responden sebanyak 59 mahasiswa mengisi kuesioner terkait citra tubuh dan perilaku diet mereka. Hasil penelitian menunjukkan bahwa 50,8% responden memiliki citra tubuh negatif, sedangkan 49,2% memiliki citra tubuh positif. Sebanyak 50,8% responden memiliki perilaku diet sehat, dan 49,2% memiliki perilaku diet tidak sehat. Analisis statistik menggunakan uji chi-square menghasilkan p-value sebesar 0,006, yang lebih kecil dari tingkat signifikansi ? = 0,05. Hal ini mengindikasikan bahwa terdapat hubungan yang signifikan antara citra tubuh dan perilaku diet mahasiswa di Akper Al Hikmah 2 Brebes, sehingga dapat disimpulkan bahwa citra tubuh berpengaruh terhadap perilaku diet mahasiswa.
Javanese and Sundanese speech recognition using Whisper Raharjo, Alim; Zahra, Amalia
Computer Science and Information Technologies Vol 6, No 3: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v6i3.p253-261

Abstract

Automatic speech recognition (ASR) technology is essential for advancing human-computer interaction, particularly in a linguistically diverse country like Indonesia, where approximately 700 native languages are spoken, including widely used languages like Javanese and Sundanese. This study leverages the pre-trained Whisper Small model an end‑to‑end transformer pretrained on 680,000 hours of multilingual speech, fine tuning it specifically to improve ASR performance for these low resource languages. The primary goal is to increase transcription accuracy and reliability for Javanese and Sundanese, which have historically had limited ASR resources. Approximately 100 hours of speech from OpenSLR were selected, covering both reading and conversational prompts, the data exhibited dialectal variation, ambient noise, and incomplete demographic metadata, necessitating normalization and fixed‑length padding. with model evaluation based on the word error rate (WER) metric. Unlike approaches that combine separate acoustic encoders with external language models, Whisper unified architecture streamlines adaptation for low‑resource settings. Evaluated on held‑out test sets, the fine‑tuned models achieved Word Error Rates of 14.97% for Javanese and 2.03% for Sundanese, substantially outperforming baseline systems. These results demonstrate Whisper effectiveness in low‑resource ASR and highlight its potential to enhance transcription accuracy, support language preservation, and broaden digital access for underrepresented speech communities. 
Co-Authors Al Mukarram, Khasyi Cahyadi, Jonathan Caprisiano, Ebenhaiser Jonathan Chandra, Wiryanata David David Fadhil, Muhammad Farhan Fadzlul Rahman, Ferry Faldi, Faldi Farhan Fadhil, Muhammad Fathur Rahman Halim, Rico Harits, Sayekti Hendra Saputra Henry, Michael Himawan, Randy Joko Pranoto, Wawan Karyawati, Tati Kristian Handoko Wijaya Sukardjoh Martulandi, Adipta Maskur, Maimunah Michael, Stefanus Muhammad Manaqib, Muhammad Mukhlas, M. Anang Naibaho, Agusti Frananda Alfonsus Novika, Andien Dwi Raharjo, Alim Ramadhansyah, Muhammad Hafizh Riftiarrasyid, Mohammad Faisal Roesmiatun Purnamadewi, Yasinta Sams, Andrew Steven Setyoko, Andreas Dimas Seventina, Healthy Suma'inna, Suma'inna Vincent Vincent, Vincent William, Ezra