A Rewriting-Logic-Based Technique for Modeling Thermal Systems

This paper presents a rewriting-logic-based modeling and analysis technique for physical systems, with focus on thermal systems. The contributions of this paper can be summarized as follows: (i) providing a framework for modeling and executing physical systems, where both the physical components and their physical interactions are treated as first-class citizens; (ii) showing how heat transfer problems in thermal systems can be modeled in Real-Time Maude; (iii) giving the implementation in Real-Time Maude of a basic numerical technique for executing continuous behaviors in object-oriented hybrid systems; and (iv) illustrating these techniques with a set of incremental case studies using realistic physical parameters, with examples of simulation and model checking analyses.Comment: In Proceedings RTRTS 2010, arXiv:1009.398

Sistem Pengenalan Ayat Al Qur'an Pada Surah Al Qari'ah Menggunakan Metode Hidden Markov Model (HMM)

Al-Qur’an merupakan kitab suci umat Islam yang berisi firman Allah yang diturunkan kepada Nabi Muhammad Saw, dengan perantara malaikat Jibril untuk dibaca, dipahami dan diamalkan sebagai petunjuk atau pedoman hidup bagi umat manusia. Dalam proses pembacaan ayat Al Qur’an terkadang kita lupa terhadap ayat yang dibacakan. Maka dari itu untuk meminimalisir keadaan penulis membangun sebuah sistem yang mampu mengenali seluruh ayat yang ada pada Al Qur’an menggunakan pengolahan citra. Untuk dapat melakukan proses pengenalan ayat . Penelitian ini dilakukan selama enam bulan. Pada penelitian tugas akhir ini penulis membuat suatu sistem pengenalan ayat Al Quran surah Al Qari’ah menggunakan metode Hidden Markov Model. Hidden Markov Model (HMM) adalah peluasan dari rantai Markov di mana statenya tidak dapat diamati secara langsung (tersembunyi), tetapi hanya dapat diobservasi melalui suatu himpunan pengamatan lain

Forecasting Palawija Harvest Results In North Aceh Using Multiple Linear Regression Method

The agricultural sector is one sector that is very dominant in people's income in Indonesia because most Indonesians work as farmers. One of the plants that play the most crucial role is the palawija plant. An increase in the amount of production and the harvested area will also increase the amount of harvest produced; therefore, to find out which areas have the potential to become producers of productive secondary crops in North Aceh, one of them is by predicting crop yields using the multiple linear regression method. The dataset used for this research is data on the development of palawija crop intensification in North Aceh Regency from 2017 to 2021, taken from the Department of Agriculture and Food Crops, North Aceh Regency. The multiple linear regression method is implemented by entering actual data. Then the data will be calculated in various stages, starting from determining the value of constants and coefficients using a table helper. After getting the result value, then converted into matrix form A and H to find the determinants A1, A2, A3, and A4. After that, enter it into a linear regression pattern and produce a predictive value of crop yield data for the coming year. Calculations in the linear regression method are taken on soybean yields in the Tanah Jambu Aye sub-district in 2022, ranging from 61.00 tons