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Application of Artificial Neural Networks to Predict Exports of Four-Wheeled Vehicles by Destination Country Ema Meyliza; Eka Irawan; Dedi Suhendro
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 1 No. 3 (2022): September
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (450.585 KB) | DOI: 10.55123/jomlai.v1i3.914

Abstract

Motorized vehicles are vehicles that are energized through machines and used for land transportation,, as well as movement through technical equipment in the form of electric motors and other tools that have the function of converting an energy source into power to drive the vehicle. This study aims to determine the results of the predicted number of four-wheeled vehicle exports by destination country in the years to come. The research data used is data on exports of four-wheeled motor vehicles by destination country for 2012-2020. The algorithm used in this research is an artificial neural network with Back-propagation method. The best architectural model used in this research is architect 4-4-1 with an accuracy rate of 82% epoch of 2261 iterations and MSE of 0.0081876. So it can be concluded that the model can be used to predict export data for four-wheeled vehicles by destination country.
Implementation of One-step Secant Algorithm for Forecasting Open Unemployment by Highest Educational Graduate Ismi Azhami; Eka Irawan; Dedi Suhendro
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 1 No. 3 (2022): September
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (601.346 KB) | DOI: 10.55123/jomlai.v1i3.946

Abstract

Based on data, the open unemployment rate according to the highest education graduate in Indonesia shows the number of semester unemployment which has an unstable value, sometimes up and sometimes down. This study aims to implement the ability and performance of one of the training functions on the backpropagation algorithm, namely one-step secant, which can later be used as a reference in terms of data forecasting. The one-step secant algorithm is an algorithm that is able to train any network as long as the input, weight and transfer functions have derivative functions and this algorithm is able to make training more efficient because it does not require a very long time. The data used in this study is open unemployment data according to the highest education completed in Indonesia in 2006-2021 based on semester, which is sourced from the Indonesian Central Statistics Agency. Based on this data, a network architecture model will be formed and determined using the One-step secant method, including 14-13-2, 14-16-2, 14-19-2, 14-55-2, and 14-77- 2. From these 5 models, after training and testing, the results show that the best architectural model is 14-19-2 (14 is the input layer, 19 is the number of neurons in the hidden layer and 2 is the output layer). The accuracy level of the architectural model for semester 1 and semester 2 is 75% with MSE values of 0.00130797 and 0.00388535.
Classification Techniques in Predicting New Student Admission Using the Naïve Bayes Method Suwayudhi Suwayudhi; Eka Irawan; Bahrudi Efendi Damanik
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 1 No. 3 (2022): September
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (394.074 KB) | DOI: 10.55123/jomlai.v1i3.963

Abstract

Admission of new students is the registration process for new students entering school and the initial gate through which students enter the object of Education; this activity is the starting point for determining the smoothness of the tasks of a school, assisted by teaching staff and equipped with optimal facilities and infrastructure in teaching and learning activities, producing skilled and broad-minded students. However, the uncertainty of the number of registrants also influences the policies that will be taken in the future. Therefore, it is necessary to forecast or predict to estimate the number of students who are likely to register so that the school can prepare everything. In this study, the prediction process for new students will use a classification technique using the Naïve Bayes method. This study aims to predict the rise and fall of the number of students who register using the Naïve Bayes method. The research data was obtained by distributing questionnaires randomly to 200 respondents (students) who were about to enter high school. The data is accumulated using the help of Microsoft excel. The results obtained are that the prediction of high-class precision is 100%, while the prediction of low-class precision is 94.23%. The conclusion is that the extracurricular, cost and distance criteria need attention and improvement. This is because disinterest and low prediction are higher than interest with high prediction results.
PEMETAAN HASIL PRODUKSI BUAH-BUAHAN DENGAN TEKNIK DATA MINING K-MEDOIDS Ira Audita; Irfan Sudahri Damanik; EKA IRAWAN
Jurnal Teknik Mesin, Industri, Elektro dan Informatika Vol. 1 No. 3 (2022): September : JURNAL TEKNIK MESIN, INDUSTRI, ELEKTRO DAN INFORMATIKA
Publisher : POLITEKNIK PRATAMA PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1102.947 KB) | DOI: 10.55606/jtmei.v1i3.535

Abstract

Buah-buahan merupakan salah satu komoditas hortikultura yang memegang peranan penting bagi pembangunan pertanian di Indonesia. Secara garis besar, produksi buah-buahan di Provinsi Sumatera Utara selama periode 2018-2020 mengalami penurunan. Penurunan jumlah produksi buah-buahan dapat mengakibatkan harga buah menjadi mahal, dan stok buah-buahan menjadi langkah. Penelitian ini bertujuan untuk mengetahui hasil dari pengelompokkan tanaman buah-buahan menggunakan metode K-Medoids yang merupakan bagian dari Data Mining. Metode K-Medoids ini merupakan metode clustering yang dapat memecahkan dataset menjadi beberapa kelompok. Pada penelitian ini data yang digunakan bersumber dari Badan Pusat Statistik pada tahun 2017-2021. Hasil dari penelitian ini diperoleh sebanyak 21 komoditas yang tergolong cluster rendah dan 2 komoditas yang tergolong dalam cluster tinggi. Penelitian ini diharapkan dapat Membantu Pihak Dinas Pertanian Provinsi Sumatera Utara dalam mengupayakan meningkatkan hasil produksi tanaman buah-buahan yang ada di Provinsi Sumatera Utara.
Application of Data Mining on Patterns of Sales of Goods in Minimarkets Using the Apriori Algorithm Siti Hadija; Eka Irawan; Irfan Sudahri Damanik; Jaya Tata Hardinata
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 1 No. 4 (2022): December
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (561.502 KB) | DOI: 10.55123/jomlai.v1i4.1668

Abstract

Minimarket is a shop that sells goods for daily needs. Each minimarket generates a lot of sales data every day. Sales transaction data can only be stored without further analysis. Based on this description, research was conducted to assist minimarket managers in making it easy to solve sales pattern problems at minimarkets using the Apriori algorithm. The Apriori algorithm is an algorithm that searches for item set frequencies using the association rule technique. The final result of using data mining using the Apriori association method is proven to be able to find out the results of the analysis that appear simultaneously based on sales data at the Mawar Simp.Tangsi Balimbingan Minimarket with a minimum amount of support of 30% and 80% confidence resulting in 8 association rules that are formed.