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All Journal International Journal of Electrical and Computer Engineering Jurnal Gamma TELKOMNIKA (Telecommunication Computing Electronics and Control) JUTI: Jurnal Ilmiah Teknologi Informasi CommIT (Communication & Information Technology) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research IJCIT (Indonesian Journal on Computer and Information Technology) Indonesian Journal of Artificial Intelligence and Data Mining Jiko (Jurnal Informatika dan komputer) Technologia: Jurnal Ilmiah Jurnal Abdimas Madani dan Lestari (JAMALI) bit-Tech ILKOMNIKA: Journal of Computer Science and Applied Informatics Automata TIN: TERAPAN INFORMATIKA NUSANTARA JURNAL PENDIDIKAN, SAINS DAN TEKNOLOGI Journal Research of Social Science, Economics, and Management Jurnal Informatika: Jurnal Pengembangan IT
Arrie Kurniawardhani
Universitas Islam Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other

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Classification of Roasting Maturity Levels of Coffee Beans Using CNN Method Based on Mobilenetv2 Arsyan, Renalda Geriel Rafidan; Kurniawardhani, Arrie; Paputungan, Irving Vitra
Journal Research of Social Science, Economics, and Management Vol. 5 No. 6 (2026): Journal Research of Social Science, Economics, and Management
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jrssem.v5i6.1269

Abstract

Determining the roasting maturity level of coffee beans is an important process to maintain consistency in flavor quality. However, the assessment process, which is still largely manual, tends to be subjective and highly dependent on the experience of farmers. This research develops an automatic classification model for four categories of coffee bean roasting levels—green, light, medium, and dark—using a convolutional neural network (CNN) architecture based on MobileNetV2. The dataset was divided into training, validation, and testing sets with a ratio of 75:15:10. The model was trained in two stages: initial training with a frozen base model, followed by fine-tuning of the last quarter of the layers. The experimental results show that the model achieved an accuracy of 96% with stable performance, as indicated by the loss and accuracy curves. These findings demonstrate that MobileNetV2 can serve as an effective solution for classifying coffee bean roasting levels with efficient computational time and competitive accuracy.
Augmented Reality in Secondary Science: Implementation, Evaluation, and Cognitive Learning Outcomes Adi, Anggito Sulistyo; Kurniawardhani, Arrie
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3730

Abstract

Science learning at the secondary school level remains challenging due to the abstract and complex nature of subjects such as physics, chemistry, and biology, which require advanced spatial and conceptual reasoning. Although Augmented Reality (AR) has increasingly been introduced to enhance visualization and interactivity in science classrooms, empirical evidence remains fragmented across implementation types, evaluation designs, and reported learning outcomes. Prior educational technology reviews rarely provide a focused synthesis explaining how specific AR implementation features relate to cognitive learning outcomes in secondary science education, leaving an important gap in understanding the pedagogical conditions under which AR becomes instructionally effective. This study systematically reviews recent empirical research on AR in secondary science education to identify dominant implementation patterns, examine evaluation approaches, and synthesize reported cognitive learning outcomes. A Systematic Literature Review (SLR) was conducted following Kitchenham’s guidelines and the PRISMA 2020 framework. Searches in ScienceDirect and Taylor & Francis Online identified 15 peer-reviewed studies published between 2020 and 2025 that met the inclusion criteria. A structured comparative synthesis categorized AR trigger mechanisms, media formats, and evaluation strategies to identify patterns linking implementation characteristics with learning outcomes. The results show that marker-based AR integrated with interactive three-dimensional models or simulations is the most common approach and is more consistently associated with positive cognitive outcomes. Studies employing structured pre-post or quasi-experimental designs reported clearer evidence of learning gains than those relying primarily on perception-based assessments. Overall, AR effectiveness appears to depend more on instructional design quality and rigorous evaluation methods than on technological novelty alone.
Implementation of Semi-Supervised Learning with YOLOv11 for On-Shelf Availability Detection of Retail Pandu Avilba; Arrie Kurniawardhani; Dhomas Hatta Fudholi
JIKO (Jurnal Informatika dan Komputer) Vol 8 No 3 (2025)
Publisher : Program Studi Teknik Informatika Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v8i3.10881

Abstract

On-Shelf Availability (OSA) is a critical aspect of retail operations that affects customer satisfaction and potential sales. Computer vision–based systems have emerged as a promising solution to monitor product availability on store shelves. However, their implementation faces the challenge of limited labeled data, which requires time-consuming manual annotation with precise bounding boxes. This study proposes a semi-supervised learning approach based on pseudo-labeling using the YOLOv11n architecture to address the scarcity of labeled data. We utilized a dataset of 918 retail product images with 174 classes, divided into four proportions of labeled data (20%, 40%, 60%, and 80%). The research stages included training a teacher model, generating pseudo-labels with a confidence threshold of 0.5, and training a student model using a combination of labeled and pseudo-labeled data. Experimental results show that this approach effectively improves detection performance. With 60% labeled data, the model achieved an mAP50 of 0.931 and an mAP50-95 of 0.864, along with high-quality pseudo-labels (F1-Score 0.727; IoU 0.819). This significant improvement indicates that pseudo-labels can enrich data variation without introducing excessive noise. The study demonstrates that semi-supervised learning can reduce dependence on large labeled datasets while offering a practical and efficient solution for OSA detection systems in retail environments
Automated Brain Tumor Classification and Segmentation Using Standard U-Net on Balanced Multi-Class MRI Dataset Aljunaid, Wajeehaldeen Ahmed Qasem; Kurniawardhani, Arrie
AUTOMATA Vol. 7 No. 1 (2026)
Publisher : AUTOMATA

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Brain tumor classification and segmentation are essential tasks for medical diagnosis and treatment planning. This study presents a comprehensive approach for simultaneous brain tumor classification and segmentation using a standard U-Net architecture applied to 2D brain MRI scans. The primary contribution of this work is the development of a novel balanced multi-class dataset comprising 6,380 MRI images created by merging and balancing two publicly available datasets, ensuring equal representation across four classes: no tumor, glioma, meningioma, and pituitary tumors (1,595 images per class). Our unified framework performs both pixel-level segmentation and image-level classification in a single forward pass, where classification is derived from segmentation outputs through spatial probability analysis. The standard U-Net model achieved robust performance with test accuracy of 99.62\%, Dice coefficient of 0.8423, and IoU of 0.9913. Image-level classification demonstrated precision and recall values ranging from 0.89 to 0.97 across all tumor classes. The perfectly balanced dataset eliminates class imbalance issues commonly encountered in medical imaging, enabling fair model evaluation and robust performance across all tumor types. This work provides a strong baseline for brain tumor analysis and demonstrates the effectiveness of proper dataset curation combined with classical deep learning architectures for medical image analysis applications.
Co-Authors Abidatul Izzah Adi, Anggito Sulistyo Afdhari, Fahrezi Bintang Agus Eko Minarno Agus Zainal Arifin Ahmad Faris Akbar Alamsyah, Juan Parez Mangku Alfaz, Revansha Zulfikar Alhanafi, Ahmad Azzam Aljunaid, Wajeehaldeen Ahmed Qasem Andaru, Gabriel Imam Arsyan, Renalda Geriel Rafidan Ayu Septya Maulani Aziz, Achmad Noer Catur Singgih Daniel Swanjaya Dhomas Hatta Fudholi Diana Purwitasari Dimas Setyawan Ramadhansyah Fathurrahman, Ihya Fayruz Rahma Ferdian Nursulistio Fitri Bimantoro Fudholi, Dhomas Hatta Huda, Sheila Nurul Ihya Fathurrahman Fathurrahman Indra Lukmana Indrayanti, Indrayanti Indrayanti, Indrayanti Irving V. Paputungan Isye Arieshanti Izzati Muhimmah Khoirunnisa Shofwatul Laili Mufti Leriansyah Muhammad Rizqi Az Zayyad Muhimmah, Izzati mukhamad masrur Najmudin, Nabil Nanik Suciati Nur Jati Lantang Marfu'ah Pandu Avilba Paputungan, Irving Vitra Rahadian Kurniawan Rasyadi, M Javier Rio Galang Jati Respati Salsabila Zahirah Pranida Santoso, Muhammad Lucky Lazuardi Setya Winarno Siti Mutrofin Sri Kusumadewi Yuda Munarko