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All Journal Faktor Exacta Indonesian Journal of Electrical Engineering and Computer Science KOMMAS: Jurnal Pengabdian Kepada Masyarakat
Kahfi Heryandi Suradiradja
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Algoritme Machine Learning Multi-Layer Perceptron dan Recurrent Neural Network untuk Prediksi Harga Cabai Merah Besar di Kota Tangerang Kahfi Heryandi Suradiradja
Faktor Exacta Vol 14, No 4 (2021)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v14i4.10376

Abstract

Chilli consumption keeps increasing along with the annual population increase in Indonesia. Meanwhile, chilli prices also fluctuate due to rainfall, affecting production and inflation. In the industrial era 4.0, IT support is crucial in various fields including in agriculture such as chilli planting to help stakeholders, both in the economy and agriculture sectors, make decisions based on accurate predictive data support. The study aims to compare the accuracy of two machine learning algorithm models, i.e., Multilayer Perceptron (MLP) and Recurrent Neural Network (RNN), for time-series regression implementable to predict chilli prices in Tangerang City. The experimental method stages include business understanding, data understanding, data preparation, modelling, evaluation, and deployment stages. The required dataset attributes include red chilli prices, date, inflation, and rainfall. This research is expected to contribute to machine learning algorithms to assist stakeholders and to be implemented by information system developers. The research result indicates that the MLP algorithm with the rmsprop optimizer performs better than the RNN with the metric measurement of Loss = 0.0038, MSE = 10271959,0 and MAPE = 3.79%. Suggestions for further research include the urgency to innovate architectural models, either for activation functions, optimizers, or other regression algorithms for better metric measurement results.
PELATIHAN PEMBUATAN WEBSITES MENGGUNAKAN GOOGLE SITES DI SMK AVICIENNA MANDIRI RANCABUNGUR BOGOR Kahfi Heryandi Suradiradja; Dede Sahrul Bahri; Yulies Herni
KOMMAS: Jurnal Pengabdian Kepada Masyarakat Vol 3, No 3 (2022): KOMMAS: JURNAL PENGABDIAN KEPADA MASYARAKAT
Publisher : KOMMAS: Jurnal Pengabdian Kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (148.46 KB)

Abstract

Perkembangan teknologi yang begitu cepat banyak melahirkan berbagai aplikasi-aplikasi yang dapat digunakan oleh masyarakat secara luas, tak terkecuali pelajar. Sebagai motor perkembangan teknologi informasi google merupakan salah satu mesin pencarian yang banyak digunakan di seluruh dunia. Google merupakan perusahaan mesin pencarian yang didirikan tahun 1998 oleh Sergey Brin dan Larry page. 70 % pencarian secara online ditangani oleh google. Google merupakan salah satu situs paling sukses di Internet selain mesin pencarinya google juga banyak menawarkan berbagai produk online lainnya seperti, akun email, broser web, perangkat lunak produktifitas, berbagi video, e-book, ponsel dan aplikasi-aplikasi. Diantara begitu banyak aplikasi yang dihadirkan/diluncurkan oleh google salah satunya adalah Google Sites. Google Sites merupakan layanan produk google untuk mempermudah pembuatan situs web dengan mudah. Salah satu aplikasi produk google yang dapat digunakan sebagai tools membuat website yang dibuat secara gratis juga dapat dibuat secara mudah dan dikelola dengan mudah oleh pengelola adalah Google Sites. Dengan mempelajari Google Sites para siswa SMK Aviciena dapat membuat situs yang dijadikan sebagai media informasi ataupun media pembelajaran. Antusias siswa terlihat begitu besar sehingga pelatihan ini mampu mendongkrak minat siswa dalam mempelajari aplikasi-aplikasi produk google.
Estimation of biomass of forage sorghum (sorghum bicolor) Cv. Samurai-2 using support vector regression Kahfi Heryandi Suradiradja; Imas Sukaesih Sitanggang; Luki Abdullah; Irman Hermadi
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i3.pp1786-1794

Abstract

One alternative to improve feed quality is to combine the main feed with forages which are more economical in cost but contain high protein sources, such as sorghum. Production estimation is essential because it will determine the sustainability of the feed. This study aimed to estimate the amount of sorghum production using support vector regression (SVR). Several stages of this research are collecting data, preprocessing, modelling, and evaluation. The dataset used and the input for this SVR algorithm model is field observation data. The kernels used in the SVR algorithm modelling are linear, Polynomial, and RBF. Sorghum production estimation using SVR has a performance evaluation value that refers to the root mean square error (RMSE). The result of this research is that the model obtained from the SVR algorithm can estimate sorghum production with performance evaluation values using R2, mean absolute error (MAE), mean absolute percentage error (MAPE), and RMSE. The best results on the Polynomial kernel are R2=0.7841, MAE=0.0681, MAPE=0.46641, and RMSE=0.1006. This study shows that the classification model obtained from the SVR algorithm with Kernel Polynomial is the best model for estimating sorghum production.
Co-Authors Dede Sahrul Bahri Irman Hermadi Luki Abdullah Sitanggang, Imas S. Yulies Herni