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Penerapan Merger Retriever pada Question Answering System Hadits Saputra, Rozi; Harahap, Nazaruddin Safaat; Novriyanto; Affandes, Muhammad
SATIN - Sains dan Teknologi Informasi Vol 10 No 1 (2024): SATIN - Sains dan Teknologi Informasi
Publisher : STMIK Amik Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33372/stn.v10i1.1117

In today's digital world, information technology has had a significant impact on various aspects of life, including the study and exploration of Hadiths. This research aims to develop a Hadith question answering system that enables users to obtain quick and accurate answers to Hadith-related questions. By utilizing LangChain and Gemini-pro models, and implementing the merger retriever method, this system provides an innovative solution for searching Hadiths from the nine main books (kutubut tis’ah). LangChain and Gemini-pro ensure that the generated answers are not only relevant but also accurate, utilizing Large Language Model (LLM) technology. Additionally, the implementation of the merger retriever enhances the system's performance in finding the most suitable answers from various Hadith sources simultaneously. This research involves a series of stages ranging from collecting Hadith data, system analysis and design, implementation, to system testing. The test results show that the Hadith question answering system can produce relevant and accurate answers, with expert Hadith respondents evaluating the quality of answers averaging 88.5% satisfaction with the "Strongly Agree" rating interval, and LangChain evaluator scoring averaging 91% within the "Strongly Agree" category interval. This emphasizes the significant potential of applying information technology in the study of Hadiths, and opens opportunities for further development in this field. This research is expected to make a significant contribution to the ease of access to accurate and fast Hadith information for the Muslim community.

Klasifikasi Sentimen Pada Dataset yang Terbatas Menggunakan Algoritma Convolutional Neural Network Saputra, M Ridho; Surya Agustian; Jasril; Novriyanto
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.613

This study aims to analyze public responses to the appointment of Kaesang Pangarep as the Chairman of the Indonesian Solidarity Party (PSI) using a sentiment classification approach based on the Convolutional Neural Network (CNN) algorithm. The primary dataset consists of 300 Indonesian-language tweets categorized into three sentiment classes: positive, negative, and neutral. The limited size of the training data presents a major challenge, as it can hinder the model's ability to generalize. To address this issue, data augmentation was carried out by incorporating external datasets with Covid-19 and Open Topic themes. The preprocessing stages include text cleaning, normalization, and tokenization. The developed CNN model utilizes a layered architecture and applies regularization techniques such as L2 and dropout to reduce the risk of overfitting. Accuracy, F1-score, precision, and recall were used as performance evaluation metrics. Experimental results show that the best performance was achieved when the Kaesang and Covid-19 datasets were combined, yielding an F1-score of 0.62 on the validation set and 0.51 on the test set. These findings indicate that adding external data can improve classification accuracy, even under limited data conditions. This study contributes to the development of deep learning-based sentiment classification methods for Indonesian-language texts.

PENERAPAN RETRIEVAL AUGEMENTED GENERATION MENGGUNAKAN LANGCHAIN DALAM PENGEMBANGAN SISTEM TANYA JAWAB HADIS BERBASIS WEB Muhammad Irfan Syah; Nazruddin Safaat Harahap; Novriyanto; Suwanto Sanjaya
ZONAsi: Jurnal Sistem Informasi Vol. 6 No. 2 (2024): Publikasi Artikel ZONAsi: Periode Mei 2024
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/zn.v6i2.19940

Hadis ajaran kedua setelah al-Qur'an yang menjadi panduan bagi umat Islam. Pencarian hadis saat ini kurang interaktif dalam menjawaban pertanyaan, dimana hanya menampilkan dokumen relevan. Penelitian ini bertujuan untuk mengembangkan sistem tanya jawab hadis berbasis web dengan menerapkan Retrieval Augmented Generation menggunakan framework LangChain yang diintegrasikan dengan Large Language Model GPT-4-1106-preview dari OpenAI. Sistem ini dirancang untuk membantu pengguna dalam mencari jawaban yang sesuai dengan 9 kitab hadis. Hasil penelitian menunjukkan bahwa model dapat bekerja sesuai dengan instruksi dan data dengan menyertakan sumber dari hadis terkait. Pengujian dilakukan dengan menguji 10 pertanyaan seputar hadis dengan framework BERTScore dan uji Evaluasi kualitas jawaban dengan mahasiwa ushulludin. Pada pengujian BERTScore rata-rata f1 score sebesar 0,7962, yang menunjukkan kemiripan antara jawaban sistem dengan referensi, pengujian pada Evaluasi kualitas jawaban mencapai persentase akurasi 89,4% yang menunjukkan bahwa responden ”Sangat Setuju” terhadap jawaban yang dihasilkan oleh sistem.

Penerapan Merger Retriever pada Question Answering System Hadits Saputra, Rozi; Harahap, Nazaruddin Safaat; Novriyanto; Affandes, Muhammad
SATIN - Sains dan Teknologi Informasi Vol 10 No 1 (2024): SATIN - Sains dan Teknologi Informasi
Publisher : STMIK Amik Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33372/stn.v10i1.1117

In today's digital world, information technology has had a significant impact on various aspects of life, including the study and exploration of Hadiths. This research aims to develop a Hadith question answering system that enables users to obtain quick and accurate answers to Hadith-related questions. By utilizing LangChain and Gemini-pro models, and implementing the merger retriever method, this system provides an innovative solution for searching Hadiths from the nine main books (kutubut tis’ah). LangChain and Gemini-pro ensure that the generated answers are not only relevant but also accurate, utilizing Large Language Model (LLM) technology. Additionally, the implementation of the merger retriever enhances the system's performance in finding the most suitable answers from various Hadith sources simultaneously. This research involves a series of stages ranging from collecting Hadith data, system analysis and design, implementation, to system testing. The test results show that the Hadith question answering system can produce relevant and accurate answers, with expert Hadith respondents evaluating the quality of answers averaging 88.5% satisfaction with the "Strongly Agree" rating interval, and LangChain evaluator scoring averaging 91% within the "Strongly Agree" category interval. This emphasizes the significant potential of applying information technology in the study of Hadiths, and opens opportunities for further development in this field. This research is expected to make a significant contribution to the ease of access to accurate and fast Hadith information for the Muslim community.

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