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All Journal Techno.Com: Jurnal Teknologi Informasi JOURNAL OF APPLIED INFORMATICS AND COMPUTING FATAWA: Jurnal Pendidikan Agama Islam
Noorizki, Adisaputra Zidha
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Religion Computer Science & IT Education Engineering

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Pengaruh Agama Dalam Pembentukan Tanggung Jawab, Moral, Dan Etika Sosial Masyarakat Noorizki, Adisaputra Zidha; Fauziyah, Ananda; Aktafarid, Ardhana Wahyu
FATAWA: Jurnal Pendidikan Agama Islam Vol. 3 No. 1 (2022): Desember
Publisher : Program Studi Pendidikan Agama Islam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37812/6b1qgy70

Abstract

“The Global God Divide” survey conducted by the Pew Research Center in 2020 revealed that Indonesia has the highest ranking as a highly religious country. As many as 98% of respondents stated that religion plays an important role in their lives. In the face of the complexity of modern-day challenges, a deep understanding of how religion influences individual views and behaviors is crucial. This research adopts the literature study method to analyze existing scholarly writings, focusing on the role of religion and its impact on the formation of people's social responsibility, morals, and ethics. Faith-based character education can be a powerful tool in shaping individual character and fostering social responsibility. The integration of religious values in education through programs such as religious ethics discussions, interactive understanding tools, religious values competitions, and religious values-based community projects can help in understanding and internalizing moral principles in life.
Klasifikasi Emosional Ulasan Pelanggan dengan Pendekatan NLP menggunakan Metode Ensemble dan ROS Noorizki, Adisaputra Zidha; Pratikno, Heri; Kusumawati, Weny Indah
Techno.Com Vol. 23 No. 4 (2024): November 2024
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v23i4.11559

Abstract

Konsep Orientasi Pelanggan sangat penting bagi perusahaan untuk berkembang di era saat ini, dengan memanfaatkan teknologi untuk mendapatkan wawasan yang mendalam tentang perilaku pelanggan mereka. Salah satu alat teknologi tersebut adalah pembelajaran mesin, khususnya yang menggunakan pendekatan pemrosesan bahasa alami (NLP). Penelitian ini menggunakan lima algoritma yang berbeda dan menggabungkan berbagai metode untuk meningkatkan kinerja model machine learning. Melalui penerapan teknik-teknik seperti random over-sampling (ROS) dan ensemble learning, akurasi prediksi keseluruhan untuk kelas minoritas meningkat secara signifikan. Model ensemble yang diintegrasikan dengan ROS mencapai akurasi 0,90 dan mean square error 0,91, mengungguli algoritma lain yang diuji dalam penelitian ini. Pendekatan yang dioptimalkan ini tidak hanya menunjukkan keefektifan pemanfaatan teknologi untuk sebuah perusahaan dapat menerapkan strategi yang berpusat pada pelanggan, tetapi juga menyoroti pentingnya peningkatan metodologi dalam pemodelan prediktif untuk keberlanjutan bisnis.   Kata kunci: Klasifikasi Emosi, Pembelajaran Mesin, Pemrosesan Bahasa Alami, Hard Voting, Random Over Sampling.
Ensemble Voting Method for Phonocardiogram Heart Signal Classification Using FFT Features Noorizki, Adisaputra Zidha; Pratikno, Heri; Kusumawati, Weny Indah
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8704

Abstract

Heart disease is still one of the leading causes of death worldwide, hence the need for effective diagnostic tools. Phonocardiogram (PCG) signals have been explored as a complementary approach to electrocardiogram (ECG) to detect cardiac abnormalities. This research investigates the classification of PCG signals using Fast Fourier Transform (FFT) features and deep learning models, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Temporal Convolutional Network (TCN). Hyperparameter tuning, particularly learning rate adjustment, is applied to optimize the performance of the models. The results show that the GRU and TCN models outperform the LSTM, achieving up to 92% accuracy at a learning rate of 0.0001. Ensemble learning with soft voting was also applied to combine the strengths of each model. Although the ensemble model showed strong performance with 92% accuracy and ROC AUC of 0.9636, it did not provide significant improvement over the base model. This finding highlights the importance of hyperparameter tuning in model optimization, with GRU and TCN showing slightly better performance in the time series classification task. This study concludes that ensemble learning offers stability but does not significantly improve classification accuracy beyond a well-tuned base model.
Co-Authors Aktafarid, Ardhana Wahyu Fauziyah, Ananda Heri Pratikno Weny Indah Kusumawati