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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Journal of Electrical Engineering and Computer (JEECOM) Dirasah : Jurnal Pemikiran dan Pendidikan Dasar Islam J-SIGN (Journal Of Informatics, Information System, And Artificial Intelligence)
Afdhaluzzikri, Afdhaluzzikri
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Religion Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Languange, Linguistic, Communication & Media Mathematics Other

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Perfomance analysis of Naive Bayes method with data weighting Afdhaluzzikri, Afdhaluzzikri; Mawengkang, Herman; Sitompul, Opim Salim
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 3 (2022): Article Research Volume 6 Number 3, July 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i3.11516

Abstract

Classification using naive bayes algorithm for air quality dataset has an accuracy rate of 39.97%. This result is considered not good and by using all existing data attributes. By doing pre-processing, namely feature selection using the gain ratio algorithm, the accuracy of the Naive Bayes algorithm increases to 61.76%. This proves that the gain ratio algorithm can improve the performance of the naive bayes algorithm for air quality dataset classification. Classification using naive bayes algorithm for air quality dataset. While the Water Quality dataset has an accuracy rate of 93.18%. These results are considered good and by using all the existing data attributes. By doing pre-processing, namely feature selection using the gain ratio algorithm, the accuracy of the Naive Bayes algorithm increases to 95.73%. This proves that the gain ratio algorithm can improve the performance of the naive bayes algorithm for air quality dataset classification. Classification using Naive Bayes algorithm for Water Quality dataset. Based on the tests that have been carried out on all data, it can be seen that the Weight nave Bayes classification model can provide better accuracy values ​​because there is a change in the weighting of the attribute values ​​in the dataset used. The value of the weighted Gain ratio is used to calculate the probability in Nave Bayes, which is a parameter to see the relationship between each attribute in the data, and is used as the basis for the weighting of each attribute of the dataset. The higher the Gain ratio of an attribute, the greater the relationship to the data class. So that the accuracy value increases than the accuracy value generated by the Naïve Bayes classification model. The increase in accuracy in the Naïve Bayes classification model is due to the number of weights from the attribute selection in the Gain ratio.
IMPLEMENTASI BAHAN AJAR PENDIDIKAN AGAMA ISLAM BERBENTUK VIDEO PADA MODEL PEMBELAJARAN PROBLEM SOLVING TERHADAP KEMAMPUAN BERFIKIR KRITIS SISWA DI SDIT ISKANDAR MUDA ACEH UTARA Fathira, Elia; Athailah, Farhan; Afdhaluzzikri, Afdhaluzzikri; Bahri, Saiful
Dirasah: Jurnal Pemikiran dan Pendidikan Dasar Islam Vol 7 No 2 (2024): Dirasah: Jurnal Pemikiran dan Pendidikan Dasar Islam
Publisher : Sekolah Tinggi Agama Islam Binamadani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51476/dirasah.v7i2.660

Abstract

Penggunaan bahan ajar pendidikan agama Islam dengan bentuk video pada model pembelajaran mengambil peran penting dalam pencapaian pendidikan. Penerapan media video pada model problem solving merupakan hal yang penting karena dapat memudahkan guru dan siswa dalam menanamkan kemampuan berpikir kritis. Tujuan dari penelitian ini adalah untuk mengetahui pengaruh penggunaan media video pada model problem solving terhadap kemampuan berfikir kritis pada mata pelajaran pendidikan agama dan budi pekerti. Subjek penelitian adalah siswa kelas V SDIT Iskandar Muda Aceh Utara. Jenis penelitian ini adalah penelitian kualitatif dan pendekatan deskriptif sedangkan pengumpulan data menggunakan beberapa teknik atau metode, yaitu observasi, wawancara, dokumentasi. Dari hasil penelitian dapat dikatakan bahwa penggunaan media video pada model problem solving berdampak terhadap kemampuan berfikir kritis siswa. Penggunaan media video dan model problem solving berhasil digunakan karena dalam pembelajaran siswa diarahkan dalam memenuhi kebutuhan-kebutuhannya yang maju di dalam belajar.
Enhancing Medical Data Privacy: Neural Network Inference with Fully Homomorphic Encryption Maulyanda, Maulyanda; Deviani, Rini; Afdhaluzzikri, Afdhaluzzikri
Journal of Electrical Engineering and Computer (JEECOM) Vol 7, No 1 (2025)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v7i1.10875

Abstract

Protecting the privacy of medical data while enabling sophisticated data analysis is a critical challenge in modern healthcare. Fully Homomorphic Encryption (FHE) emerges as a powerful solution, enabling computations to be performed directly on encrypted data without exposing sensitive information. This study delves into the use of FHE for neural network inference in medical applications, investigating its role in safeguarding patient confidentiality while ensuring computational accuracy and efficiency. Experimental findings confirm the practicality of using FHE for medical data classification, demonstrating that data security can be preserved without significant loss of performance. Furthermore, the research explores the balance between computational overhead and model precision, shedding light on the complexities of deploying FHE in real-world healthcare AI systems. By emphasizing the significance of privacy-preserving machine learning, this work contributes to the development of secure, ethical, and effective AI-driven medical solutions.
WorkoutLife: Penerapan Algoritma K-Nearest Neighbor (KNN) untuk Rekomendasi Workout Berdasarkan Data Gaya Hidup Maulyanda, Maulyanda; Deviani, Rini; Sabrina, Fathia; Afdhaluzzikri, Afdhaluzzikri
J-SIGN (Journal of Informatics, Information System, and Artificial Intelligence) Vol 3, No 2 (2025): November
Publisher : Department of Informatics, Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/j-sign.v3i2.50115

Abstract

Current physical activity is no longer viewed merely as a bodily exercise but has become an essential part of the lifestyle. However, the diversity of available workout types often makes it difficult for individuals to determine the most suitable form of exercise based on their personal needs and lifestyle habits. This issue serves as the foundation of this research, which aims to develop a workout recommendation system based on lifestyle data using the K-Nearest Neighbor (KNN) algorithm. The results indicate that the KNN algorithm, with an optimal K value of 107, achieves an accuracy rate of 90% in recommending workout types. The High Intensity Interval Training (HIIT) and Yoga categories were identified as the most accurately recognized exercises by the model, with an F1-score of 95%. These findings demonstrate that the KNN method is effective in identifying lifestyle patterns and providing personalized workout recommendations. Therefore, the KNN-based recommendation system is expected to serve as an adaptive and intelligent solution to assist individuals in selecting workout types that best fit their lifestyles.
Co-Authors Athailah, Farhan Deviani, Rini Fathira, Elia Herman Mawengkang Maulyanda, Maulyanda Opim Salim Sitompul Sabrina, Fathia Saiful Bahri