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Journal : Journal of Dinda : Data Science, Information Technology, and Data Analytics

Hangout Places Recommendation System Using Content-Based Filtering and Cosine Similarity Methods Abdul Raihan; Ahmad Ibrahim A.M; Alfian Akbar Gozali
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 4 No 2 (2024): August
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v4i2.1464

Abstract

Coffee shops are becoming the new normal for friends and coworkers to hang out. Selecting the ideal location to hang out can be exceedingly difficult. There are too many choices, and it can be difficult to know where to begin. Based on this problem, a web application that responds to the growing need for an easy method of finding local hangouts is named Nongkies. With a focus on social interaction and exploration, this platform uses a recommender system to find cafes, restaurants, and entertainment venues easily. Key features include location-based search, category, and details places. Extensive testing has confirmed the reliability of Nongkies, offering user-friendly and accurate search results. This system is a website app that suggests places to users based on their preferences. This application was developed using the cosine similarity method, which is a systematic approach that uses a similar method based on cosine angles. Content that is less alike gets lower rankings, while more similar content gets the highest rankings in recommendations. Moreover, this app helps users find local hangouts and directions to those locations, especially university students, and the selection of places to socialize has a significant effect on students' learning experiences.
Mobile Assistant Application for Street Food Consumers in Bandung Julius Angger Satrio Wicaksono; Kadek David Kurniawan; Alfian Akbar Gozali
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 4 No 2 (2024): August
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v4i2.1470

Abstract

In the dynamic city of Bandung, the lively street food scene has captured the fascination of tourists, offering a diverse selection of tempting dishes. Nevertheless, a persistent challenge arises from the lack of comprehensive details about these street foods, presenting a hurdle for consumers in making well-informed and health-conscious choices. This predicament underscores the necessity for a solution, leading to the introduction of the Mobile Assistant Application for Street Food Consumers in Bandung. Harnessing cutting-edge computer vision technology, this application seeks to provide a solution by furnishing users with an intuitive and effective tool for accessing in-depth information regarding street foods. The outcomes of thorough experimentation highlight the application's success in precisely identifying a wide array of street foods in Bandung. Users benefit from accurate information on ingredients and nutritional values, empowering them to make informed dietary decisions and elevating the overall street food experience in Bandung. This inventive solution not only addresses the prevailing information gap but also contributes to the well-being of consumers, ushering in a healthier and more enlightened food culture in Bandung at the tip of one's finger.
Development of Palm Oil Production and Sales Monitoring System Based On Android Chikal Fachdiana; Rafie Novianto Sudrajat; Alfian Akbar Gozali
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 4 No 2 (2024): August
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v4i2.1473

Abstract

Palm oil is one of the most widely used vegetable oils in the world. It is used as a raw material for the economic area and contributes to foreign exchange earnings. The palm oil enterprise performs a critical position in Indonesia's economic development, lowering poverty and creating different businesses supporting the enterprise. This paper aims to assist in improving forecasting, essential factor identification, early caution structures, overall performance monitoring, and decision help for bunches of palm production. in this paper, a machine based totally on system learning is created and applied in order to estimate palm production using models with algorithm decision tree and timeseries.
The Utilizing GPT-4o Mini in Designing a WhatsApp Chatbot to Support the New Student Admission Process at Telkom University Ruhallah, Muhammad Lutfi; Pratami, Rahmat; Gozali, Alfian Akbar
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 5 No 2 (2025): August
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v5i2.1963

Abstract

The rapid adoption of Artificial Intelligence (AI) in higher education has revolutionized student support services, yet delivering scalable, real-time assistance through familiar platforms remains a challenge. This study presents the design, implementation, and evaluation of a WhatsApp-based chatbot powered by a fine-tuned GPT-4o Mini model to streamline the new student admission process at Telkom University. A specialized dataset comprising frequently asked questions and admission-related dialogues was curated and preprocessed for model fine-tuning over 288 epochs. The chatbot system integrates the WhatsApp Business API, a Webhook interface, and the n8n automation platform, all deployed on a Virtual Private Server (VPS) to ensure reliability and low-latency communication. Functional and performance testing involved manual scenario-based assessments and quantitative measurements of response accuracy and latency. Results indicate that the chatbot consistently delivers contextually relevant answers—achieving an average accuracy above 85%—and reduces average response time to under 3 seconds. User interaction studies with prospective and current students revealed high satisfaction levels, highlighting improvements in accessibility and reduction of administrative workload. Challenges identified include occasional misinterpretation of complex queries and the need for enhanced scalability under peak loads. Future work will focus on periodic dataset updates, advanced prompt engineering, scalability stress testing, and the integration of multimodal features such as voice and image recognition. By aligning AI-driven conversational interfaces with users’ existing digital habits, this chatbot demonstrates a viable approach for enhancing admission services and operational efficiency in Indonesian higher education institutions.