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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) JIKO (Jurnal Informatika dan Komputer) JURNAL TEKNOLOGI DAN OPEN SOURCE JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Bumigora Information Technology (BITe) Infotek : Jurnal Informatika dan Teknologi Infotech: Journal of Technology Information Jurnal Teknik Informatika (JUTIF) Information System Journal (INFOS) Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) Jurnal Teknik Informatika Jurnal Nasional Teknologi Komputer Proceeding of International Conference Health, Science And Technology (ICOHETECH) Prosiding Seminar Nasional Teknologi Informasi dan Bisnis Innovative: Journal Of Social Science Research Dedikasi Saintek Jurnal Pengabdian Masyarakat SmartComp CSRID DEDIKASI SAINTEK Jurnal Pengabdian Masyarakat
Irawan, Ridwan Dwi
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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IMPLEMENTASI CHATBOT BERBASIS ARTIFICIAL NEURAL NETWORK UNTUK MENDUKUNG EFISIENSI LAYANAN KONSULTASI DI PT LOKAKITA KREATIF INDONESIA Harist, Muhammad Abdul; Sopingi, Sopingi; Irawan, Ridwan Dwi
Infotech: Journal of Technology Information Vol 11, No 2 (2025): NOVEMBER
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v11i2.449

Abstract

The rapid development of information technology has driven companies to innovate in delivering faster and more efficient services. PT Lokakita Kreatif Indonesia, a company engaged in consulting and mapping services, often receives recurring questions from clients, which were previously handled manually. To address this issue, this study aims to design and develop a Chatbot system based on Artificial Neural Networks (ANN) that can understand user intent and provide automated responses. The system was developed through several stages, including data collection, text preprocessing, ANN model training using Keras, and integration into a web application built with Flask. The dataset was compiled from frequently asked questions submitted by clients and processed into a machine-readable format. Testing results show that the Chatbot can provide accurate answers with an accuracy rate of up to 87% and a relatively fast response time. These findings indicate that the system can assist the company in delivering initial consultation services in a consistent and autonomous manner. The Chatbot not only improves work efficiency but also opens opportunities for further development through integration with other communication platforms.
SISTEM REKOMENDASI PEMILIHAN PAKET INTERNET WIFI MENGGUNAKAN KNOWLEDGE BASED RECOMMENDATION Laksono, Ardean Risqi; Nurmalitasari, Nurmalitasari; Irawan, Ridwan Dwi
Infotech: Journal of Technology Information Vol 11, No 2 (2025): NOVEMBER
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v11i2.437

Abstract

The internet has become a crucial need in modern society, including in rural areas such as Wonokarto. PT. Yasmin Amanah Media, as a local internet service provider, offers a variety of internet packages to meet customer needs. However, the wide range of options often leads to confusion in selecting the most suitable package. This research aims to design a WiFi internet package recommendation system based on Knowledge-Based Recommendation (KBR) that can provide personalized suggestions based on user preferences. The system development method used is the System Development Life Cycle (SDLC), which consists of planning, analysis, design, implementation, testing, and maintenance phases. This study focuses on the system design stage in the form of a web-based application. Data were collected through interviews, observation, and literature study. The designed recommendation system utilizes constraint-based techniques to match user requirement attributes with the characteristics of available internet packages. The results show that the knowledge-based recommendation approach is effective in providing internet package selection suggestions, especially for new users who have no previous interaction history. This system is expected to improve customer satisfaction, internet usage efficiency, and loyalty to the services of PT. Yasmin Amanah Media.
Application of Deep Learning Algorithm to Detect Fraud in Online Transaction Networks Ridwan Dwi Irawan; Agus Fatkhurohman
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 7 No. 2 (2024): Jurnal Teknologi dan Open Source, December 2024
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v7i2.3890

Abstract

Online transaction fraud is a severe problem that may cost businesses and people a lot of money. This paper suggests using deep learning algorithms to detect fraud as a remedy to this issue. These algorithms were chosen based on their ability to handle large amounts of intricate data and identify patterns that are difficult to identify using traditional techniques. Important components of this research include gathering and preprocessing transaction data, creating deep learning models, and assessing model performance. This investigation examines a variety of financial transaction types that may have involved fraud. The deep learning approach uses deep neural network designs, including Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN), to maximize detection accuracy. The study's findings demonstrate that the deep learning models created are excellent at identifying questionable transactions and can lower the false positive rate, which raises the overall effectiveness of fraud detection systems. As a result, deep learning algorithms have demonstrated a high degree of efficacy in identifying fraudulent activity inside internet-based transaction networks, so they play a vital role in fraud prevention.
Co-Authors Abdul Harits, Muhammad Agus Fatkhurohman Akbar Pratama, Hamzah Alva Hendi Muhammad Amelia, Dinda Anjarwati, Pipin Antoni DM, Nikolas Aprilisa Arum Sari Ardiyanto, Marta Artdelia Pingkan Salsabilla Ayu Setyowati, Sintiya Brilliant Firdaus, Azkha Choirul Ananda, Naufal Eko Purwanto Eko Purwanto Ema Utami Fathoni, Syahrul Agung Febrian Fitiraldy Gilang Pramudito, Vincensius Harist, Muhammad Abdul Ihza Yuzar Vianda, Rizqi Ika Putri Pujianti, Ika Putri Pujianti Imanuel, Christian Indra Kristiawan, Yohanes Intan Rahmawati Kresna Agung Yudhianto Laksono, Ardean Risqi Maulana Akbar, Reza Maulindar, Joni Muh Adha Muh Adha Muhamad Paliya Sadana Muhammad Paliya Sadana Nailurrizqi, Adistya Nibras Faiq Muhammad Nugroh, Heri Nur Rohman, Sofyan Nurmalitasari Nurmalitasari Nurmalitasari Oktaviana Az Zahra, Erika Permatasari, Hanifah Pramono Pramono Pratama, Dika Adi Rahmaning Umiadi, Amara Riyadsyah, Deprinico Rizqy Alfiansyah, Rafif Rudi Susanto Satria Yudha, Rafiq Satrio Atmojo, Fattah Setiawan, Stevania Shalikashilla Tiaramadhan, Faiza Sidik Prasetyo, Sidik Sofa Marwati Sopingi Sopingi, Sopingi Stevania Setiawan Suryanti, Ika Syarif , Muhammad Tri Hartanti, Ninik Viery, Andrean Yoga Saputra, Muchammad Zitnaa Dhiaaul Kusnaa Washilatul Arba’ah Zitnaa Dhiaaul Kusnaa Washilatul Arba’ah