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All Journal Phi : Jurnal Pendidikan Fisika dan Terapan Cyberspace: Jurnal Pendidikan Teknologi Informasi Jurnal Teknologi Informasi, Komputer, dan Aplikasinya (JTIKA ) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) J-SIGN (Journal Of Informatics, Information System, And Artificial Intelligence)
Maisura, Mira
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Engineering Physics Other

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DUMMY BOOK IoT: PANDUAN VISUAL KONSEP DAN IMPLEMENTASI IoT Maisura, Mira; Yuliana, Cut Putroe; Ridwan, Ridwan; Alifa, Fatin; Maulidza, Chairunnisa
CYBERSPACE: Jurnal Pendidikan Teknologi Informasi Vol 9 No 2 (2025)
Publisher : Universitas Islam Negeri Ar-Raniry Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22373/cj.v10i1.34093

Abstract

The Industrial Revolution 4.0 demands the integration of technological literacy such as the Internet of Things (IoT) into the educational curriculum. However, the main obstacles at the high school/Islamic high school level are the lack of affordable, practical, and context-appropriate IoT training media, limited school infrastructure, and competent resources. This study aims to develop an IoT dummy boom that can be used as a suitable IoT training media for use in learning. The development method applied is a 4D model with stages of define, design, develop, and dissemination. The use of the 4D model ensures quality control of the media. The results of the media feasibility test obtained an average value of 4.414 (on a Likert scale), which indicates that 92.45% of the total respondents agreed that the media is very suitable and feasible to use.
Pengembangan Model Klasifikasi Code Smells Pada Backend Python Menggunakan Algoritma Random Forest (Studi Kasus Proyek Open Source Github) Ar-Hammar, Aqilla; Maisura, Mira
JURNAL FASILKOM Vol. 16 No. 2 (2026): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v16i2.12107

Abstract

Deadline pressure in software development often drives coding shortcuts, leading to internal quality degradation known as code smells. These structural anomalies contribute to technical debt accumulation and complicate system maintenance over time. This study develops an automated classification model to detect code smell contamination in the Python backend ecosystem. The methodology uses the Random Forest ensemble algorithm integrated with the Synthetic Minority Over-sampling Technique (SMOTE) for class balancing. Data mining on GitHub with high-reputation criteria extracted 137,728 code samples from 7 large-scale repositories using the Radon multi-metric tool. To simulate human error in real-world scenarios, 5% random noise was inserted into the labeling data. Testing using the confusion matrix shows the proposed model achieves highly stable and balanced performance, with average precision, recall, and f1-score of 0.95 in both macro and weighted averages. Ablation study analysis proves that SMOTE intervention effectively maintains detection consistency in minority class categories. Feature importance ranking identifies the Logical Lines of Code (LLOC) metric as the most crucial indicator with 37.65% influence weight, followed by LOC and Blank metrics. This research provides an automated quality assurance system for developers to detect code refactoring opportunities at an early stage.
Co-Authors 'Afifah, Nur Alifa, Fatin Ar-Hammar, Aqilla Aulia Syarif Aziz Fahrurrazi, Anjas Firmansyah Firmansyah Hendri Ahmadian Khairan AR Khairunnas, Khairunnas Malahayati Malahayati Maulidza, Chairunnisa Muazzinah Muazzinah Muhammad Sabaruddin, Muhammad Najri, T Piana, Arma Pradana, Nanang Restu Rahmat Musfikar Ridwan, Ridwan Sarini Vita Dewi Yuliana, Cut Putroe