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All Journal Jurnal Riset Rumpun Ilmu Teknik (JURRITEK) Ocean Engineering: Jurnal Ilmu Teknik dan Teknologi Maritim Jurnal Sistem Informasi dan Ilmu Komputer International Journal of Electrical Engineering, Mathematics and Computer Science
Husna Gemasih
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Engineering Mathematics Mechanical Engineering

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Peramalan Harga Tomat Menggunakan Metode High Order Fuzzy Times Series Multifactors Darmawansyah Darmawansyah; Rayuwati; Husna Gemasih
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 1 No. 3 (2023): Agustus: Jurnal Sistem Informasi dan Ilmu Komputer
Publisher : Universitas Katolik Widya Karya Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59581/jusiik-widyakarya.v1i3.1217

Abstract

The daily needs of the people of Central Aceh cannot be separated from agricultural commodities such as tomatoes, shallots, garlic, and others. Some of these agricultural commodities have sharp price fluctuations, such as tomatoes. When the supply of tomatoes in the market is reduced, the price can be much higher than the normal price. Conversely, when the supply of tomatoes is excessive, the price will fall far below the normal price. This is influenced by various factors such as the harvest season, the amount of production, the amount of public consumption and others. Based on these problems, we need a method to be able to estimate the price of tomatoes so that it can be used to support decision making related to price issues. Forecasting is one of the solutions to be able to estimate the movement of tomato commodity prices. The method used for forecasting tomato prices is High Order Fuzzy Times Series Multifactors. In this method, subinterval formation is carried out using Fuzzy C–means. To calculate the error rate of forecasting results in this study using the Mean Square Error (MSE). Based on the results of the tests carried out, the large values ​​of the training and order data used in forecasting do not guarantee a low error rate.
RANCANG BANGUN SISTEM PAKAR PENYAKIT TANAMAN CABAI MENGGUNAKAN METODE NAÏVE BAYES BERBASIS WEB Khairunnas Khairunnas; Husna Gemasih; Hendri Syahputra
Ocean Engineering : Jurnal Ilmu Teknik dan Teknologi Maritim Vol. 1 No. 3 (2022): September : Jurnal Ilmu Teknik dan Teknologi Maritim
Publisher : Fakultas Teknik Universitas Maritim AMNI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1212.86 KB) | DOI: 10.58192/ocean.v1i3.374

Abstract

In this study a web-based expert system was designed using the Naïve bayes method which was intended to help farmers diagnose chili plants. The system development method used is extreme programming which consists of planning, design, coding, testing. A web-based chilli plant disease diagnostic expert system was developed using the PHP and MySQL programming languages. This expert system is capable of diagnosing chili plants by submitting disease symptoms at the time of inspection. Based on the selected symptoms, this system will provide diagnostic results and then display the disease and solutions for the chili plant disease. The results in this study concluded that there was compatibility between the results of the expert system diagnosis using the Naïve bayes method with experts.
Comparison of Multiple Linear Regression, Backpropagation and Fuzzy Mamdani Methods in Predicting the Revenue of PLN Takengon Unit Richasanty Septima; Hendri Syahputra; Husna Gemasih
International Journal of Electrical Engineering, Mathematics and Computer Science Vol. 2 No. 2 (2025): June : International Journal of Electrical Engineering, Mathematics and Compute
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijeemcs.v2i2.263

Abstract

The performance of data mining techniques has been proven accurate in many studies, but each method in data mining techniques has different accuracy depending on the type of data that is the object of research. Methods in data mining techniques are divided into several functions, namely: clustering, association, classification, and prediction, where each data mining technique objective has a superior method. Therefore, in this case the author will compare the performance of the multiple linear regression method, and neural networks with fuzzy mamdani in predicting the income of PLN Unit Takengon. In several studies, the Backpropagation method shows the highest accuracy compared to other methods. Then the prediction model with multiple linear regression also has the highest accuracy as well as the Fuzzy Mamdani method has high accuracy too. Therefore, the purpose of this study is to compare the three methods, so that it can be determined which method has a higher accuracy value. The results of this study indicate that the Back propagation method has the highest accuracy and the lowest average error, namely a MAPE value of 5.9% with an accuracy of 94.1% and an RMSE of 14398.14, followed by the multiple linear regression method obtaining a MAPE value of 6.9% with an accuracy of 93.1% and an RMSE of 15527.41, then for Fuzzy Mamdani obtaining a MAPE value of 7% with an accuracy of 93% and an RMSE of 16077.76.
IMPLEMENTASI AlGORITMA NAIVE BAYES UNTUK MEMPREDIKSI TINGKAT PENYEBARAN COVID: IMPLEMENTATION OF NAIVE BAYES ALGORITHM FOR PREDICTING THE RATE OF THE SPREAD OF COVID Rayuwati; Husna Gemasih; Irma Nizar
JURNAL RISET RUMPUN ILMU TEKNIK Vol. 1 No. 1 (2022): April : Jurnal Riset Rumpun Ilmu Teknik
Publisher : Pusat riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1105.787 KB) | DOI: 10.55606/jurritek.v1i1.127

Abstract

The development of Information Technology (IT) is now very rapid and has been used in various aspects of life both in the field of government, banking, socio- cultural, industrial, education, and even health. One type of disease that gets attention for the application of IT is corona virus or better known as Covid 19 because the spread is quite widespread throughout the country, especially in the territory of Indonesia. Corona virus disease development in Indonesia is growing, based on WHO data as of today on August 30, 2020 positive cases have reached 172,053 people, cases died 7,343 people and recovered 124,185 people and the number of cases is increasing every day. Based on these conditions, Central Aceh is in a state of alert against the threat of corona virus. then a form of prevention of the widespread spread of the virus can be done by breaking the chain of transmission by doing social distancing. In this study, a system will be designed to anticipate the Covid-19 pandemic by predicting the rate of spread of covid-19, especially in central Aceh districts using the Naive Bayes Classifier method. The accuracy level of this system is a positive case of 60%.
Co-Authors Darmawansyah Darmawansyah Hendri Syahputra Irma Nizar Khairunnas Khairunnas Rayuwati Richasanty Septima