Article Per Year (5 Year)
p-Index From 2021 - 2026
0.751
P-Index
This Author published in this journals
All Journal bit-Tech Journal of Artificial Intelligence and Innovative Applications (JOAIIA) Jurnal Pengabdian Kepada Masyarakat Kalam
Achmad Sehan
Universitas Pamulang
Computer Science & IT Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Social Sciences Other

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : bit-tech

 1 
Multi-Criteria Teacher Performance Evaluation Using the SMART Decision Support Method Achmad Sehan; Rezy Azril Fadillah
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.v8i3.3681

Abstract

Teacher performance evaluation plays a strategic role in sustaining instructional quality, yet existing appraisal systems in educational settings are often designed for conventional schools and lack structured computational frameworks capable of capturing the multidimensional demands of nature-based and spiritually integrated institutions. This study develops a web-based Decision Support System (DSS) integrating the Simple Multi-Attribute Rating Technique (SMART) to operationalize teacher performance assessment at Sekolah Alam Tahfidzpreneur, which combines academic instruction, Quran memorization, entrepreneurship, and outdoor learning. Unlike prior SMART-based implementations that primarily focus on score automation, this study formalizes weight rationalization, explicit benefit–cost criteria structuring, and utility normalization to enhance decision transparency and methodological replicability. Ten performance criteria were defined and weighted through institutional policy alignment, and teacher ratings were transformed into normalized utility scores to generate composite rankings. The system produced consistent performance stratification across eleven teacher alternatives, with top-ranked scores exceeding the institutional evaluation threshold. Efficiency gains and reduced subjectivity were inferred through comparative process mapping against the prior manual interview-based approach, demonstrating shorter evaluation cycles and explicit audit trails of weighting and scoring logic. By externalizing evaluation assumptions and computational procedures, the proposed DSS strengthens accountability and supports evidence-based professional development planning. The findings demonstrate that structured multi-criteria modeling can provide a transparent and replicable governance mechanism for complex hybrid educational environments.
Spice Image Classification Based on Content-Based Image Retrieval Meidy Fajar Wahyu; Lely Panca Andriyanto; Amin Hidayat; Achmad Sehan; Eko Sutono
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.v8i3.4149

Abstract

Indonesia possesses exceptional spice biodiversity, yet public familiarity with the original visual characteristics of many spices is declining because of packaged processing, reduced direct exposure, and changing food-consumption patterns. This study develops RempahID, a web-based spice identification system that integrates Content-Based Image Retrieval (CBIR) with machine-learning classification. The study addresses the limited availability of Indonesian spice recognition systems that simultaneously provide class predictions and visually similar reference images for user verification. The system uses a dataset comprising ten major spice categories, including ginger, turmeric, galangal, aromatic ginger, cinnamon, cloves, nutmeg, coriander, candlenut, and star anise. Each image is preprocessed through resizing, normalization, noise reduction, and Otsu-based segmentation. Visual representation combines 24 Hue-Saturation-Value color histogram features, four Gray-Level Co-occurrence Matrix texture descriptors, and seven Hu Moment shape features, producing a 35-dimensional feature vector. Euclidean Distance is employed to rank visually similar database images, while K-Nearest Neighbors, Support Vector Machine, and Random Forest are compared for classification. Performance is evaluated using accuracy, precision, recall, and F1-score. The Support Vector Machine with a radial basis function kernel achieved the best result, with 92.1% accuracy, 0.91 precision, 0.92 recall, and a 0.91 F1-score. Retrieved reference images also supported transparent visual comparison rather than presenting an isolated predicted label alone. These findings demonstrate that integrating complementary color, texture, and shape descriptors within a CBIR framework provides an effective and interpretable approach for Indonesian spice identification.
Co-Authors Amin Hidayat Eko Sutono Lely Panca Andriyanto Lukmanul Hakim Meidy Fajar Wahyu Muhammad Adli Firjatullah Rezy Azril Fadillah