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All Journal MANAJEMEN IKM: Jurnal Manajemen Pengembangan Industri Kecil Menengah Jurnal Manajemen Pendidikan (JMP) Jurnal Penelitian Pertanian Terapan Jurnal Penelitian dan Pengembangan Pendidikan Luar Biasa Mimbar Agribisnis: Jurnal Pemikiran Masyarakat Ilmiah Berwawasan Agribisnis AdBispreneur Biology Teaching and Learning JURNAL ILMIAH MAHASISWA AGROINFO GALUH Jambura Agribusiness Journal Proceeding of National Conference on Accounting & Finance SOSCIED Jurnal Abdi Insani Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Al-Hikmah Journal of Food System and Agribusiness (JoFSA) Jurnal Pengabdian Nasional Jurnal Ekonomi Unram Journal of Community Service (UJCS) Al-MIKRAJ: Jurnal studi Islam dan Humaniora Jurnal Ekonomika Dan Bisnis Indonesian Journal of Information Technology and Computing (IMAGING) Abdimas Indonesian Journal Proceeding Applied Business and Engineering Conference Journal of Economics and Social Sciences Innovative: Journal Of Social Science Research Jurnal AGRISEP: Kajian Masalah Sosial Ekonomi Pertanian dan Agribisnis Jurnal Kewirausahaan dan Bisnis Jurnal abimana (Jurnal Pengabdian Kepada Masyarakat Nasional) J-CEKI
Sutarni, Sutarni
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Journal : SOSCIED

 1 
EVALUASI KINERJA CHATBOT DENGAN INTEGRASI ALGORITMA RNN DAN LSTM DALAM OPTIMALISASI RESPON PERCAKAPAN PADA SISTEM PMB Eko Prasetyo; sutarni, Sutarni; Prasetyo, Eko; Sudiati, Listiarini Edy
SOSCIED Vol 7 No 2 (2024): SOSCIED - November 2024
Publisher : LPPM Politeknik Saint Paul Sorong

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32531/jsoscied.v7i2.826

Abstract

AI-based chatbots are increasingly used in various sectors to improve service efficiency, including in education. This research evaluates the performance of a chatbot on the New Student Admission (PMB) system with the integration of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) algorithms. In its implementation, chatbots play an important role in providing information and answering user questions automatically, but often have difficulty in understanding complex conversational contexts. The use of RNN and LSTM algorithms is expected to overcome the limitations of traditional chatbots in understanding conversational context and providing more relevant responses. The evaluation results show that the integration of RNN and LSTM is able to improve the quality of chatbot responses, both in terms of accuracy of 92.86% and relevance in complex conversation scenarios. The proposed chatbot proved to be effective in understanding user requests and providing faster answer responses compared to the conventional methods used. This implementation provides a more optimal solution in the PMB system, which is expected to be implemented at STMIK AKI Pati.
EVALUASI KINERJA CHATBOT DENGAN INTEGRASI ALGORITMA RNN DAN LSTM DALAM OPTIMALISASI RESPON PERCAKAPAN PADA SISTEM PMB sutarni, Sutarni; Prasetyo, Eko; Sudiati, Listiarini Edy
SOSCIED Vol 7 No 2 (2024): SOSCIED - November 2024
Publisher : LPPM Politeknik Saint Paul Sorong

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32531/jsoscied.v7i2.826

Abstract

AI-based chatbots are increasingly used in various sectors to improve service efficiency, including in education. This research evaluates the performance of a chatbot on the New Student Admission (PMB) system with the integration of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) algorithms. In its implementation, chatbots play an important role in providing information and answering user questions automatically, but often have difficulty in understanding complex conversational contexts. The use of RNN and LSTM algorithms is expected to overcome the limitations of traditional chatbots in understanding conversational context and providing more relevant responses. The evaluation results show that the integration of RNN and LSTM is able to improve the quality of chatbot responses, both in terms of accuracy of 92.86% and relevance in complex conversation scenarios. The proposed chatbot proved to be effective in understanding user requests and providing faster answer responses compared to the conventional methods used. This implementation provides a more optimal solution in the PMB system, which is expected to be implemented at STMIK AKI Pati.
Co-Authors Acihmah Sidauruk Amanda Hardianti, Dwi Analianasari Analianasari Andarwangi, Tunjung Andya Bellapama, Intan Anggrellanggi, Arsy Annisa Fitri Annisa Fitri, Annisa Arishandi , Ninda Putri Armawan, Nyoman Azissah , Danur Berliana, Dayang Bernadhed, Bernadhed Bina Unteawati Bria, Angela Merici Budi Trisnanto, Teguh Bunyamin Bunyamin Cholid Fatih Desfaryani, Rini Dimas, Syifa Shabihah Dita Pratiwi Dwi Maya Sari, Dwi Maya Edy Humaidi Eko Prasetyo Eko Prasetyo Fath, Muqaffa Raga Fitriani Fitriani Hidayat, Muhammad Alfi Hilda Karim Hilintri, Yatri Indaryati, Sri Khanif, Alfin Nazlil Kholilullah, Muhammad Taufan Kurniawan Saputra Listiarini Edy Sudiati Lubis, Hiqmah Purnama M. Zaini, M. Mahmud, Ramli Margasaty, Fadila Marlinda Apriyani Muhiddin Palennari Mulia Sulistiyono Munawir Yusuf Noer, Irmayani Noor, Tajuddin Nopitabela , Vreciosa Nuni Anggraini Parida, Parida Pirmansah, Imam Ainudin Pitaloka, Seroja Elok Poncowati, Neny Rossita Pratiwi, Anik Puspita, Putri Fina Roni Lukum Santoso, Wahid Hasim Sari , Lara Harnelis Satria, Dhimas Adi Saty, Fadila Marga Sekarningrum, Anggun Anindya Setiawan , Arief Sislya, Meylda Putri Subektiningsih Subektiningsih Sudiyo, Sudiyo Syabila, Qoriah Trisnanto, Teguh Budi Tuti Rohani, Tuti Unteawati, Bina Widya Kusumaningsih Widyawati, Dewi Kania Yuniarti, Evi