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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) eProceedings of Engineering Jurnal Epistemic : Jurnal Ilmiah Pendidikan
Andi Saputra, Wahyu
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Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Pengaruh Penerapan Maqomah terhadap Pengembangan Materi Pembelajaran Al-Qur'an dan Hadits di Madrasah Aliyah Andi Saputra, Wahyu
Epistemic: Jurnal Ilmiah Pendidikan Vol. 3 No. 3 (2024): September 2024
Publisher : Perkumpulan Peneliti dan Pegiat Literasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70287/epistemic.v3i3.161

Abstract

This study addresses the stagnation in the teaching methods of Al-Qur'an and Hadith at Madrasah Aliyyah Al-Falah Nagreg, emphasizing the need for innovation. The objective is to analyze the implementation of the Maqomah method, a systematic approach aimed at enhancing the learning materials of Al-Qur'an and Hadith. Utilizing a qualitative descriptive methodology, the research was conducted through literature reviews, classroom observations, interviews with teachers and students, and document analysis. The design follows a stepwise exploration of the Maqomah method's application and its impact on students’ comprehension. Key findings reveal that the Maqomah method significantly improves students' understanding and internalization of Islamic teachings, contributing to the overall quality of religious education. However, limitations such as time constraints and the specific context of Madrasah Aliyyah Al-Falah may influence the generalizability of the results. Future research should focus on broader applications of the method across different educational institutions and investigate its integration into national Islamic education curricula. The findings of this research provide valuable insights for educators seeking to innovate religious teaching methods in madrasahs and highlight the potential of Maqomah in fostering a deeper engagement with Islamic texts.
Rancang Bangun Website E-Catering Dengan Metode Prototype (Studi Kasus: Langgeng Catering) Sudarwanto, Eko; Andi Saputra, Wahyu
eProceedings of Engineering Vol. 12 No. 2 (2025): April 2025
Publisher : eProceedings of Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Kemajuan pesat dalam teknologi informasi dan komunikasi telah memberikan pengaruh yang signifikan di berbagai sektor, termasuk industri kuliner., termasuk dalam industri kuliner. Langgeng Catering merupakan salah satu usaha jasa makanan yang menerima berbagai pesanan, seperti jajanan pasar, donat, roti, nasi kotak, dan nasi tumpeng. Namun, hingga saat ini, pengelolaan pemesanan masih dilakukan secara konvensional melalui pencatatan manual dan komunikasi via telepon atau WhatsApp. Metode ini memiliki beberapa kelemahan, seperti risiko kehilangan data dan kesalahan pencatatan yang dapat berdampak pada kepuasan pelanggan. Oleh karena itu, diperlukan solusi berupa sistem e catering untuk meningkatkan efisiensi pemesanan dan pengelolaan pesanan. Penelitian ini berhasil membangun website e-catering menggunakan metode Prototype dengan Framework Flask sebagai backend dan MongoDB sebagai basis data. Pengembangan meliputi identifikasi masalah, pengumpulan data, perancangan dengan UML dan wireframe, serta implementasi sistem. Fitur yang dikembangkan mencakup kategori dinamis, perhitungan ongkos kirim berbasis jarak, notifikasi pengiriman, dan pengelolaan pesanan. Berdasarkan pengujian black-box testing terhadap 46 fitur, seluruhnya berhasil dengan tingkat keberhasilan 100%, menunjukkan bahwa sistem telah memenuhi kriteria pengujian dan layak digunakan.Kata kunci— e-catering, prototype, flask, industri kuliner, mongodb, python
Classifier model for lecturer evaluation by students using speech emotion recognition and deep learning approaches Diah Rosita, Yesy; Andi Saputra, Wahyu
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp5157-5171

Abstract

Lecturers play a crucial role in higher education, with their teaching behavior directly impacting learning and teaching quality. Lecturer evaluation by students (LES) is a common method for assessing lecturer performance, though it often relies on subjective perceptions. As a more objective alternative, speech emotion recognition (SER) uses speech technology to analyze emotions in the speech of lecturers during classes. This study proposes using deep learning-based SER, including convolutional neural network (CNN) and bidirectional long short-term memory (Bi-LSTM), to evaluate teaching quality by analyzing displayed emotions. Removing silence from audio signals is crucial for enhancing feature analysis, such as energy, zero-crossing rate (ZCR), and mel-frequency cepstral coefficients (MFCC). This method removes inactive segments, emphasizing significant segments, and improving accuracy in detecting voice and emotions. Results show that the 1D CNN model with Bi-LSTM, using MFCC with 13 coefficients, energy, and ZCR, performs excellently in emotion detection, achieving a validation accuracy of over 0.851 with an accuracy gap of 0.002. This small gap indicates good generalization and reduces the risk of overfitting, making teaching evaluations more objective and valuable for improving practices.
Co-Authors Sudarwanto, Eko Yesy Diah Rosita