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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Indonesian Journal of Information System
Musa, Rahmat
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Perancangan Chatbot Menggunakan Dialogflow Natural Language Processing (Studi Kasus: Sistem Pemesanan pada Coffee Shop) Chandra, Albert Yakobus; Kurniawan, Didik; Musa, Rahmat
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 4, No 1 (2020): Januari 2020
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v4i1.1505

Abstract

Some cases that are often experienced at a particular institution such as Micro Enterprise are often a staff / employee in providing information services and transactions that are carried out manually to customers related to these business activities. This cycle always repeats from one customer to another. The impact if there are conditions where the queue of customer that is quite crowded than the workload of staff/employees will be higher and the risk of error in transactions will be high too. The development of information technology in artificial intelligence on 4.0 industry era is moving forward. One of them is Machine Learning - Natural Language Processing (NLP) which is one of the sciences that focuses on how computers can understand the human language and response to it. Therefor in this research a chatbot system will be builtin providing information and conducting transaction with the customers. This chatbot will be develop using the Dialogflow tools provided by Google. This Chatbot that was build expected to be an alternative that can be implemented in various bussines to provide better service for customers
Identification of Formaldehyde Bananas using Learning Vector Quantization Musa, Rahmat; Akbar, Mutaqin
Indonesian Journal of Information Systems Vol 3, No 2 (2021): February 2021
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijis.v3i2.4110

Abstract

Bananas that ripen with chemical process or do not ripen naturally usually, this can be recognized by the presence of blackish patches on the surface of the skin. But visual recognition has its drawbacks, which is that it is difficult to recognize similarities between formalin bananas and natural bananas, resulting in a lack of accurate identification. In this study, a system was built that can determined formalin bananas and natural bananas through digital image identification using supervised classification. The image to be identification previously goes through the process of transforming RGB (Red Green Blue) color to Grayscale, and the process of extracting texture features using statically recognizable features through histograms, in the form of average, standard deviation, skewness, kurtosis, energy, entropy and smoothness. The extraction of texture features is classified with LVQ (Learning Vector Quantization) to determine formalin or natural bananas. The test was conducted with 122 banana imagery sample data, 100 imagery as training data consisting of 50 imagery for natural bananas and 50 imagery for bananas formalin, 22 imagery as test data. The test results showed LVQ method has the best percentage at Learning Rate 0.1, Decreased Learning Rate 0.75 and maximum epoch of 1000 with the smallest epoch of 7, obtained accuracy 90.90%, precision 84.61% and recall 100%.
Co-Authors Albert Yakobus Chandra Mutaqin Akbar Pusaka, Semerdanta