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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 Depik Jurnal Ilmu-Ilmu Perairan, Pesisir, dan Perikanan Jurnal abimana (Jurnal Pengabdian Kepada Masyarakat Nasional) Jurnal Cendekia Ilmiah Jurnal Penelitian, Pengabdian dan Pemberdayaan Masyarakat
Sutarni, Sutarni
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Journal : SOSCIED

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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 Aris Iwansyah Arishandi , Ninda Putri Armawan, Nyoman Azissah , Danur Azmin, Nurul Bernadhed, Bernadhed Bina Unteawati Bria, Angela Merici Budi Trisnanto, Teguh Bunyamin Bunyamin Cholid Fatih Dayang Berliana 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 Ichsan Setiawan Indaryati, Sri Khanif, Alfin Nazlil Kholilullah, Muhammad Taufan Kurniawan Saputra Kurniawati, Fifin Listiarini Edy Sudiati Lubis, Hiqmah Purnama M. Zaini, M. Ma'anum, Inaatul Mahmud, Ramli Margasaty, Fadila Marlinda Apriyani Muhiddin Palennari Mulia Sulistiyono Munawir Yusuf Noer, Irmayani Noor, Tajuddin Nopitabela , Vreciosa Nuni Anggraini Parida, Parida Permatasari, Ratna Pirmansah, Imam Ainudin Pitaloka, Seroja Elok Poncowati, Neny Rossita Prasetia, Yudi Pratama, Nadia Lediana Pratiwi, Anik Pristiwantoro, Eko Cahyo Purbangga, Hafis Indra Puspita, Putri Fina Ramadhaniaty, Mutia Roni Lukum Santoso, Wahid Hasim Sari , Lara Harnelis Satria, Dhimas Adi Saty, Fadila Marga Sekarningrum, Anggun Anindya Setiawan , Arief Setiawan, Putra Fajar Sislya, Meylda Putri Subektiningsih Subektiningsih Sudiyo, Sudiyo Syabila, Qoriah Syahrul Purnawan Tri Marinda, Siti Trisnanto, Teguh Budi Tuti Rohani, Tuti Unteawati, Bina Widya Kusumaningsih Widyawati, Dewi Kania Yopi Ilhamsyah Yuniarti, Evi