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All Journal IJID (International Journal on Informatics for Development) The Indonesian Journal of Computer Science
Ndungi, Rebeccah
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Computer Science & IT Electrical & Electronics Engineering Engineering

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A Review of Automated Reasoning and Its Applications in the 21st Century. Ndungi, Rebeccah; Uyun , Shofwatul
The Indonesian Journal of Computer Science Vol. 12 No. 2 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i2.3175

Abstract

This article takes a look at the progress and advancement of automated reasoning and its applications in the 21st century. Reasoning refers to the method of reaching logical conclusions. The construction of computing systems that automate this process over some knowledge bases is the focus of automatic reasoning. Automated Reasoning is frequently regarded as a subfield of machine learning. It is also studied in theoretical computer science and philosophy. Some of the applications of automated reasoning include but not limited to Tableau-style systems, Automatic Theorem Proving, Superposition and Saturation, benchmarks and Classical First-Order Logic. The development of formal led to the development of artificial intelligence, which was essential in the development of artificial intelligence for reasoning.
A Sign Language Prediction Model using Convolution Neural Network. Ndungi, Rebeccah; Karuga, Samuel
IJID (International Journal on Informatics for Development) Vol. 10 No. 2 (2021): IJID December
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2021.3284

Abstract

The barrier between the hearing and the deaf communities in Kenya is a major challenge leading to a major gap in the communication sector where the deaf community is left out leading to inequality. The study used primary and secondary data sources to obtain information about this problem, which included online books, articles, conference materials, research reports, and journals on sign language and hand gesture recognition systems. To tackle the problem, CNN was used. Naturally captured hand gesture images were converted into grayscale and used to train a classification model that is able to identify the English alphabets from A-Z.  Then identified letters are used to construct sentences. This will be the first step into breaking the communication barrier and the inequality.  A sign language recognition model will assist in bridging the exchange of information between the deaf and hearing people in Kenya. The model was trained and tested on various matrices where we achieved an accuracy score of a 99% value when run on epoch of 10, the log loss metric returning a value of 0 meaning that it predicts the actual hand gesture images. The AUC and ROC curves achieved a 0.99 value which is excellent.
Price Forecasting of Chili Variant Commodities Using Radial Basis Function Neural Network Ramadhan, Ade Umar; Siregar, Maria Ulfah; Nafisah, Syifaun; Anshari, Muhammad; Ndungi, Rebeccah; Mulyawan, Rizki; Nurochman, Nurochman; Gunawan, Eko Hadi
IJID (International Journal on Informatics for Development) Vol. 12 No. 1 (2023): IJID June
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2023.5129

Abstract

This study addresses the challenge of price instability in chili markets, which can lead to economic losses and inflation. To mitigate this issue, we propose a machine learning model using Radial Basis Function Neural Networks (RBFNN) to predict prices of various chili variants. Our quantitative approach involves a comprehensive data preparation process, including preprocessing and normalization of time series data collected from 2018 to 2022. The RBFNN model is constructed with K-Means clustering for optimal hidden layer configurations and evaluated using Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). The results demonstrate promising accuracy, with MAPE error rates below 20% and relatively low RMSE values for large red chili (10.37%, 4484) and curly red chili (14.77%, 5590). Our findings indicate the potential for creating a reliable forecast model for predicting chili prices over 7 days, enabling better supply and demand management. The study's results also suggest that increased training data enhances forecasting accuracy. This research contributes to the development of effective price forecasting models, providing valuable insights for policymakers and stakeholders in the chili industry.
Co-Authors Gunawan, Eko Hadi Karuga, Samuel Maria Ulfah Siregar Muhammad Anshari, Muhammad Mulyawan, Rizki Nurochman Nurochman, Nurochman Ramadhan, Ade Umar Syifaun Nafisah Uyun , Shofwatul