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All Journal Pelita - Jurnal Penelitian Mahasiswa UNY METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi
Doni Bowo Nugroho
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management

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PORTEBLE ARTICULATION MIRROR (PAM) AS A MEDIA TO IMPROVE THE THE DEAF CHILDREN’S ABILITY TO USE FACIAL EXPRESSIONS Erbi Bunyanuddin; Doni Bowo N; Rahayu Rizky P; Rizky Junianto; Muh. Nur Huda
Pelita - Jurnal Penelitian Mahasiswa UNY Volume VIII, Nomor 1, April 2013
Publisher : Pelita - Jurnal Penelitian Mahasiswa UNY

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The purpose of this reaserch is (1) Portable media design and articulationMirror; (2) Mirror articulation tes Portable know the results of the ability of languageexpression deaf children.This type of research is the development of research. A research process: PortablePembuatanrangka articulation Mirror (PAM), making a series of amplifiers, makinga series balancer, merging, testing tools such as pre-test and post-test in children withhearing and ending with evaluation. The instrument used was a questionnaire forusers who include teachers and parents as well as guide books use PAM.The results of this research is to design articulation Portable Mirror (PAM) consistsof power switch, power indicator, Mirror, audio level indicator, Knop treble,volume Knop, Speaker, Knop balance, bass Knop, battery charge indicator, batterycharger Terminal, Port microphone, microphone, and has dimensions of 35 x 21.5 x4.2 cm. Mechanism of action of the PAM PAM works with activated using the powerbutton. To adjust the sound output is set by using the knob-knob (bass, balance,treble, volume). PAM then placed with a distance of 35-55 cm in front of deaf childrenand companion (users).'s Meant to allow users and deaf children to be see each other.PAM can also increase the expression deaf children language skills.
FEW-SHOT LEARNING FOR AML CELL CLASSIFICATION USING PROTOTYPICAL I Gde Eka Dirgayussa; Kevin Elfancyus Herman; Doni Bowo Nugroho; Sekar Asri Tresnaningtyas; Meita Mahardianti; Nurul Maulidiyah; Rafli Filano; Rudi Setiawan; Muhammad Artha Jabatsudewa Maras; Yohanssen Pratama
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 11 No. 2 (2025): Volume 11 Nomor 2 Tahun 2025
Publisher : Universitas Methodist Indonesia

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Accurate blood cell classification is crucial for diagnosing Acute Myeloid Leukemia (AML) but limited medical data poses challenges for traditional machine learning models. This study presents a Few-Shot Learning (FSL) framework utilizing a Prototypical Network architecture with a ResNet-34 backbone to classify AML blood cell types from microscopic images. In this study, we utilize datasets consisting of 15 morphologically distinct cell classes. A 15-way, 5-shot, 5-query episodic setup was adopted to simulate data-scarce conditions. Evaluation via 5-fold cross-validation yielded strong performance, with an average accuracy of 97.76%, precision of 98.78%, recall of 96.55%, and F1-score of 97.76%. FSL training times were consistent (4.22–4.26 minutes per fold), and t-SNE along with confusion matrices confirmed the model’s ability to distinguish similar cell types. To validate the approach, its performance was compared with a conventional supervised CNN using the same ResNet-34 backbone. The FSL model outperformed the CNN across all metrics such as accuracy (98.32% vs. 77.25%), precision (98.55% vs. 76.87%), recall (98.31% vs. 78.66%), and F1-score (98.33% vs. 75.26%), while also requiring far less training time (~4.24 min/fold vs. ~420 min total). These results highlight the promise of FSL based methods for accurate, efficient, and scalable hematologic diagnostics in data limited settings.
Co-Authors Erbi Bunyanuddin I Gde Eka Dirgayussa Kevin Elfancyus Herman Meita Mahardianti Muh. Nur Huda Muhammad Artha Jabatsudewa Maras Nurul Maulidiyah Rafli Filano Rahayu Rizky P Rizky Junianto Rudi Setiawan Sekar Asri Tresnaningtyas Yohanssen Pratama