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All Journal Jurnal Buana Informatika Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Journal of Telematics and Informatics Proceeding of the Electrical Engineering Computer Science and Informatics TRANSISTOR Elektro dan Informatika Indonesian Journal of Community Services Journal of Innovation and Future Technology (IFTECH) Abdimas: Jurnal Pengabdian Masyarakat Universitas Merdeka Malang Indonesian Journal of Data and Science Jurnal Pengabdian kepada Masyarakat Nusantara JAST Populer: Jurnal Penelitian Mahasiswa Prosiding Seminar Riset Mahasiswa International Journal of Health, Engineering and Technology Journal of Computing Theories and Applications Jurnal Rekayasa Sistem Informasi dan Teknologi International Journal of Artificial Intelligence in Medical Issues Technema: Journal of Intelligent Engineering and Computing
Poetro, Bagus Satrio Waluyo
Universitas Islam Sultan Agung
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary Other

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Efficient Few-Shot Classification of Indonesian Academic Correspondence Using SetFit Yudi Aditya Pratama; Bagus Satrio Waluyo Poetro; Rizqi Agung Fauzi
Technema: Journal of Intelligent Engineering and Computing Vol. 1 No. 3 (2026): Technema: Journal of Intelligent Engineering and Computing
Publisher : CV SCRIPTA INTELEKTUAL MANDIRI

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Abstract

Digital transformation in higher education has increased the need for intelligent systems capable of organizing and classifying institutional correspondence efficiently, particularly when labeled training data are limited. This study develops and evaluates an Indonesian academic-correspondence classification system using SetFit within an efficient few-shot learning framework. A quantitative experimental design was applied to 145 official PDF documents collected from five units at Universitas Islam Sultan Agung. Native digital documents were extracted using PyMuPDF, whereas scanned documents were processed using Doctr OCR, followed by correspondence-specific preprocessing, manual ground-truth labeling, and structured dataset preparation. Model training employed intfloat/multilingual-e5-base with incremental N-shot experiments from 1-shot to 6-shot and contrastive representation learning using CosineSimilarityLoss. The 6-shot configuration, using six training documents per class, produced the best reported performance, achieving an accuracy of 85.87% and a weighted F1-score of 0.85. Training-loss and error-rate patterns also indicated stable model adaptation under the reported experimental conditions. The selected model was subsequently integrated into a lightweight Streamlit prototype. The findings demonstrate that SetFit provides an efficient approach for academic-document classification when institution-specific labeled datasets are scarce, while consistent label definition and real-time OCR integration remain important considerations for further system development.  
Development of a Question Answering System Using the Best Matching 25 (BM25) Method to Support Final Project Completion for Informatics Engineering Students at UNISSULA Iqbal Nur Al Sandi; Ndaru Senopati Nusa; Muhammad Farrel Ardan; Bagus Satrio Waluyo Poetro; Moch Taufik
Technema: Journal of Intelligent Engineering and Computing Vol. 1 No. 3 (2026): Technema: Journal of Intelligent Engineering and Computing
Publisher : CV SCRIPTA INTELEKTUAL MANDIRI

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Abstract

This study developed and evaluated a BM25-based question-answering system to support final-project information retrieval for Informatics Engineering students at Universitas Islam Sultan Agung. The system used the official Final Project Guidebook and Supervision Logbook as its knowledge corpus and integrated document reconstruction, heading-aware chunking, paragraph overlap, lexical preprocessing, synonym-based query expansion, heading-weighted indexing, BM25 ranking, and source-grounded answer presentation. Evaluation was conducted using 50 manually mapped ground-truth queries and measured through Recall@1, Recall@3, Recall@5, and Mean Reciprocal Rank. The results showed that 41 queries retrieved the relevant passage at rank 1, eight at rank 2, and one at rank 3. Recall@1 reached 82.00%, while Recall@3 and Recall@5 both achieved 100.00%, with an MRR of 0.9067. These findings indicate that the proposed lexical retrieval architecture can reliably recover authoritative academic information while maintaining transparency, interpretability, and source traceability. The remaining limitation concerns first-rank discrimination among procedurally similar passages, suggesting that future improvements should prioritize reranking or hybrid retrieval without sacrificing complete Top-3 coverage. The approach also provides a reproducible baseline for evaluating more complex semantic retrieval methods in academic information environments.  
Implementation Of Convolutional Neural Network(CNN) Resnet-50 Architecture For Mammography Image Classification In Early Breast Cancer Detection Akbar Muhammad Dzidan; Bagus Satrio Waluyo Poetro; Moh. Taufik Febriansah
International Journal of Health Engineering and Technology Vol. 5 No. 3 (2026): SEPTEMBER 2026
Publisher : CV. AFDIFAL MAJU BERKAH

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55227/ijhet.v5i3.1196

Abstract

Breast cancer is one of the leading causes of death among women. Early detection through mammography screening is crucial to reduce mortality rates. However, manual image interpretation by radiologists is susceptible to subjectivity and visual fatigue, which can lead to misdiagnoses (False Positives and False Negatives). This study aims to design and implement a deep learning model using a Convolutional Neural Network (CNN) with the ResNet-50 architecture to classify mammography images into three tissue conditions: Benign, Malignant, and Normal. The model was developed using a Transfer Learning approach based on ImageNet weights, with specific modifications made to the classifier head layer. The secondary dataset was sourced from Kaggle, resulting in a final total of 23,939 images after undergoing preprocessing stages that included background removal (Auto-Crop), dimensional resizing (224x224 pixels), normalization, and geometric data augmentation. Model training was optimized using a Two-Stage Training strategy consisting of Feature Extraction and Fine-Tuning—to overcome potential performance degradation caused by an imbalanced data distribution (class imbalance) and to maximize the recognition of pathological features. Testing results conducted on 4,542 images in the testing data demonstrated that the proposed model achieved an Overall Accuracy of 98%. The evaluation of the F1-Score metric recorded a value of 0.98 for the Benign and Malignant classes, and a perfect 1.00 for the Normal class. Furthermore, the model exhibited excellent sensitivity (Recall) with zero False Negative predictions for malignant cases detected as Normal. Overall, this modified ResNet-50 architecture is proven to be reliable, precise, and holds significant potential to be implemented as a Computer-Aided Diagnosis (CAD) system to support the efficiency of medical professionals' clinical decisions.
Implementasi Vision Transformer (VIT) dan K-Means Clustering dalam Klasifikasi Gaya Visual Poster untuk Otomatisasi Ekstraksi Skema Warna Digital Muhammadun Hanif; Bagus Satrio Waluyo Poetro; Ghufron Ghufron
Populer: Jurnal Penelitian Mahasiswa Vol. 5 No. 3 (2026): September : Populer: Jurnal Penelitian Mahasiswa
Publisher : Universitas Maritim AMNI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58192/populer.v5i3.5516

Abstract

The purpose of this study is to implement the Vision Transformer (ViT) architecture and K-Means Clustering algorithm to automate fine-grained visual style classification of posters and the extraction of digital color schemes. MSMEs and beginner digital creators often face challenges in determining harmonious color schemes for promotional poster designs, leading to a time-consuming trial-and-error design process. The research utilized a custom dataset of 1,011 poster images scraped from the Behance platform, evenly distributed into three visual style classes: Bauhaus, Swiss Style, and Minimalist. The data preprocessing employed a dual-pipeline approach, where color extraction was optimized through SLIC Superpixel segmentation within the CIELab* perceptual color space to significantly reduce computational complexity without degrading the boundaries of original visual elements. The evaluation results demonstrated that the ViT model performed exceptionally well, achieving an accuracy of 79% and a Macro F1-Score of 0.80. In the color extraction module, the K-Means algorithm successfully formed five highly separated dominant palette clusters with an average Silhouette Score of 0.7698. The perceptual color accuracy validation recorded an average  of 4.7648, proving that the extracted hexadecimal (HEX) palette codes operate well within the acceptable limits of human visual deviation.
Co-Authors Ahmat Yusuf Akbar Muhammad Dzidan Alda Cendekia Siregar Andika, Ardhi Dwi Angeli Khairunissa Aulia Rahma Angriani, Husni Arief Budiman Aris Puji Widodo Aris Wahyu Murdiyanto Ayudhawara, Ahmad Dedy Kurniadi Dian Hafidh Zulfikar Dian Hafidh Zulfikar Effan Najwaini Fadhila Tangguh Admojo Ghufron Ghufron Hamada Zein I Made Sunia Raharja Imam Much Ibnu Subroto Iqbal Nur Al Sandi Iven Aurananta Damaivila izmy alwiah musdar Jiwani, Fatwa Akbar Kusworo Adi M Maulana Fathul Muin Maria, ⁠⁠Eny Medi Taruk Meldyantono, Anggara Putra Moch Taufik Moh. Taufik Febriansah Muhammad Farrel Ardan Muhammad Fikri Maulana Muhammad Giza Aditya Nurdarmawan Muhammad Qomaruddin Muhammadun Hanif Ndaru Senopati Nusa Nicodemus Mardanus Setiohardjo Nur Muhammad Syaifuddin Nurul Hidayah Nuzulia Khoiriyah Putra, Allief Suryatama Jaya Riansyah, Andi Rizqi Agung Fauzi Saifurrohman, Ahmad Sam Farisa Chaerul Haviana Sinra, A. Sri Mulyono Sri Mulyono Suryani Alifah Vani Aulia Pramesti Widhya Nugroho Satrioajie Yudi Aditya Pratama