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INDONESIA
JSIKTI (Jurnal Sistem Informasi dan Komputer Terapan Indonesia)
Published by Infoteks
ISSN : 26552183     EISSN : 26557290     DOI : 10.33173
Core Subject : Science,
Computer Science & IT
data analysis, natural language processing, artificial intelligence, neural networks, pattern recognition, image processing, genetic algorithm, bioinformatics/biomedical applications, biometrical application, content-based multimedia retrievals, augmented reality, virtual reality, information system, game mobile, dan IT bussiness incubation
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 5 Documents
 1 
Search results for , issue "Vol 5 No 1 (2022): September" : 5 Documents clear
Analisis Prediksi Penjualan Lampu Dengan Metode Svm Pada PT. Terang Abadi Raya Sariayu, Vilomena; Sugiartawan, Putu
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 5 No 1 (2022): September
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.172

Abstract

Predictive analytics is a type of data analysis technique used to make predictions about future events. In the Forecasting analysis of companies that buy and sell goods, it is necessary to facilitate the company's sales planning. In this study, an analysis of the Lamp Sales Forecast was carried out at PT. Great Eternal Light. Purpose The purpose of this study is to analyze the 2022 lamp sales forecast using the Support Vector Machine method. The results of the evaluation are carried out using the MAPE method to find out how much capacity the model has used to see the difference between the predicted and actual values. Haili test With MAPE 21.44 it can be said that the forecast model is quite good.
Klasifikasi Data Penjualan Dengan Metode K-Nearest Neighbor Pada Pt. Terang Abadi Raya Novitadewi, Ni Made Ary; Sugiartawan, Putu; Fittryani, Yuri Prima
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 5 No 1 (2022): September
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.173

Abstract

PT. Terang Abadi Raya is a lighting company engaged in trading, with the many types of products to be sold the company has difficulty determining which product sells the most on the market. Making it difficult for the marketing department to offer products to be sold. PT. Terang Abadi Raya has various types of lighting products based on sales data for the last 1 year, using the K-Nearest Neighbor (K-NN) prediction to make it easier for companies to plan sales. To find out the best-selling sales using sales data classification and the K-Nearest Neighbor (K-NN) method, of the 19,290 items classified, the graphic results obtained were 12,420 categorized as best-selling labels, and 6,870 categorized as not-selling labels.
Prediksi Luas Sebaran Hama Wareng pada Tanaman Padi dengan RNN Time Series Wulandari, Selvi Wulandari
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 5 No 1 (2022): September
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.174

Abstract

Rice is a crucial crop in Indonesia as it serves as a staple food. However, rice production is frequently hindered by pests, particularly the brown planthopper (BPH), which poses a serious threat to agricultural productivity in Gianyar District. To minimize crop failures and enhance productivity, predicting the spread of BPH on rice plants is crucial. In this study, a time series dataset consisting of 120 data points on BPH distribution from 2012 to 2021 was utilized. The data was split into 90% training data and 10% testing data. By employing the Recurrent Neural Network (RNN) architecture, the best-performing model achieved a minimal Root Mean Square Error (RMSE) value of 10.0503, with 500 epochs, a learning rate of 0.007, 5 neurons in the input layer, and 80 neurons in the hidden layer. This model also achieved a Mean Absolute Percentage Error (MAPE) value of 16.64%, indicating good predictive performance. The predictive results can be used by laboratories as decision support for rice productivity improvement strategies
Prediksi Sebaran Hama Tikus Pada Tanaman Padi Menggunakan Metode Backpropagation Neural Network Arimawarni, Rafika; Sugiartawan, Putu; Murpratiwi, Santi Ika
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 5 No 1 (2022): September
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.175

Abstract

OPT (Plant Pest Organisms) is any activity or activities that damage and kill plants, one of which is caused by pests, diseases, viruses, etc. In Bali, especially in Tabanan Regency, OPT cases are still very high. OPT in rice plants caused by rats is a problem faced by farmers and in the future, it must be prevented by knowing the spread of rats. Therefore, the purpose of this research is to help farmers prevent pest attacks so that rice productivity can be increased. In this study, the backpropagation neural network method was used to predict the distribution of rat pests on rice plants. This method uses previous data, namely from 2012-2021 when the data is processed and calculated until the smallest error value is obtained. In this study, data were obtained from calculating the distribution of pests in hectares which showed a percentage difference in accuracy error of 16.2%, which means that the prediction of this calculation is good enough to be used as a reference for further research
Prediksi Sebaran Hama Padi Dengan Metode LSTM Pada Pertanian Padi Di Buleleng Negara, I Gede Sunia; Sugiartawan, Putu; Murpratiwi, Santi Ika
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 5 No 1 (2022): September
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.176

Abstract

Prediction is a systematic process of estimating future values based on patterns contained in data that has been converted into numerical form. In this study, the aim was to predict the distribution of rice borer in Buleleng district which could endanger the productivity of the rice agricultural sector. One of the methods used in this research is Long Short Term Memory (LSTM), a form of development of Recurrent Neural Network (RNN) which is suitable for processing and predicting time series data. The data used in this study is rice borer attack data for the last ten years, from 2012 to 2021. The results show that the LSTM model has an MAE data testing of 16.8149 and MAPE data testing of 2.356%, and MAE data training of 16.8149 and MAPE data training of 2,356%. These values measure the prediction error with the MAE and MAPE techniques. With these results, the agricultural service can recognize the pattern of distribution of rice borer attacks in the region and take appropriate action to overcome them.

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