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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JTT (Jurnal Teknologi Terpadu) JSAI (Journal Scientific and Applied Informatics) International Journal of Informatics and Computation REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer Jurnal E-Komtek Jurnal Teknologi Informatika dan Komputer Jurnal Restikom : Riset Teknik Informatika dan Komputer Jurnal Ilmiah Wahana Pendidikan Journal of Technology and Informatics (JoTI) Bulletin of Information Technology (BIT) Pelita Teknologi : Jurnal Ilmiah Informatika, Arsitektur dan Lingkungan Jurnal Ilmiah SIGMA: Informatics Engineering Journal of UPB Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Pengabdian Pelitabangsa JIM: Jurnal Ilmiah Mahasiswa Pendidikan Sejarah VIDHEAS: Jurnal Nasional Abdimas Multidisiplin SAINTEK International Journal of Integrated Science and Technology Jurnal Ekonomi Manajemen Dan Bisnis (JEMB) Akademika Jurnal Teknologi Pendidikan
Ahmad Turmudi Zy
UNiversitas Pelita Bangsa
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering Public Health Social Sciences Other

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Journal : JSAI (Journal Scientific and Applied Informatics)

 1 
Implementasi Algoritma K-Nearest Neighbors Untuk Klasifikasi Spam Email Diani Putri Kusumaningrum; Ahmad Turmudi Zy; Suprapto
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 1 (2025): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i1.7531

Abstract

In modern life, internet access has become essential for communication. Email is one of many communication tools. Cyberattacks such as ransomware, phishing, and cryptojacking continue to evolve and are difficult to detect by security systems as technology rapidly advances. Therefore, this study uses email spam as the subject of research. The aim of this study is to implement and calculate the accuracy of the K-Nearest Neighbors (KNN) algorithm in classifying spam emails with ham and spam labels. An accuracy of 85%, precision of 87%, recall of 93%, and F1-score of 90% were obtained from tests conducted with an 80% training data and 20% testing data ratio. The results show that the K-Nearest Neighbors algorithm can effectively classify spam emails.
Implementasi Retrieval Augmented Generation (RAG) Dalam Perancangan Chatbot Kesehatan Pencernaan Gufranaka Samudra; Ahmad Turmudi Zy; Ermanto
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 1 (2025): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i1.7678

Abstract

The development of artificial intelligence technology, especially in the development of chatbots, has brought significant progress, especially in the health sector. However, the main challenge in using large language models (LLM) is the potential for bias and lack of accuracy in providing information, especially on critical topics such as digestive health. This study aims to implement Retrieval-Augmented Generation (RAG) in designing a digestive health chatbot to improve the accuracy and relevance of the information delivered. The RAG method integrates a generative model with a document-based retrieval system to provide more reliable and evidence-based answers. The research process involves collecting digestive health datasets through data scraping from Alodokter, as well as data processing through the preprocessing stage, embedding using the Indonesian language model (firqaaa/indo-sentence-bert-base), and data processing using a vector database with the HNSW index. The Llama 3.1:8b model is used to generate generative responses. The results of the study show that the application of RAG can reduce model bias and improve the quality of chatbot responses. Evaluation using metrics such as Mean Reciprocal Rank (MRR) 93%, Faithfullness 62%, Answer Relevancy 57%, and Semantic Similarity 81% showed good performance in providing accurate and relevant answers according to context. With this approach, chatbots are able to provide more accurate and contextual information according to user needs, and can reduce the risk of hallucinations in the information provided. This research contributes to the development of more reliable health chatbot technology, especially in the digestive health domain, and opens up opportunities for further application in other health fields
Co-Authors Abdul Ghofir Agung Nugroho Ahmad Rivaldi Anggi Muhammad Rifa'i Anggi Muhammad Rifai Antika Zahrotul Kamalia Aprilia Nur Sa'adah Ary Fahrizal Aswan S Sunge Aswan S Sunge Aswan S. Sunge Aswan Supriyadi Sunge Bagas Pamungkas Butsianto, Sufajar Danang Nurcahyo Daspar Daspar Diani Putri Kusumaningrum Donny Maulana Edora Edora Edora Edora Edy Widodo Elkin Rilvani Endrik Ermanto Faiza Muhammad Julianto Faza Ardan Kusuma Gufranaka Samudra Irfan Afriantoro Irma Nurmaulida* Isarianto Juan Fakhri Lutfi Adji Ardiansyah M.Makmun Effendi Maha Putra Miftahuljannah Mudakir Muhamad David Ali Muhamad Fatchan Muhammad Adipa Muhammad Fatchan Muhammad Makmun Effendi Muhtajuddin Danny Nadya Khairunnisa Nugroho, Agung Putri Nurhafenda Harun Qori yumansyah Qori Ray Fathur Rizky Rhendy Diki Nugraha Riani Riani Rismawati Rismawati Rosa Noviyanti Sunita Dasman SUPRAPTO Suriyanti Suriyanti Wira Syuhada Wiyanto Wiyanto Zaenur Rozikin Zaky Ali Husaeni