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INDONESIA
Faktor Exacta
Published by Universitas Indraprasta PGRI
ISSN : 1979276X     EISSN : 2502339X     DOI : -
Core Subject : Science, Engineering,
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering
Faktor Exacta is a peer review journal in the field of informatics. This journal was published in March (March, June, September, December) by Institute for Research and Community Service, University of Indraprasta PGRI, Indonesia. All newspapers will be read blind. Accepted papers will be available online (free access) and print version.
Arjuna Subject : -
Articles 6 Documents
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Search results for , issue "Vol 14, No 4 (2021)" : 6 Documents clear
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.
Penerapan Fuzzy Sugeno Orde Satu dalam Prediksi Pembelian Devi Fitrianah; Wawan Gunawan; Anggi Puspita Sari
Faktor Exacta Vol 14, No 4 (2021)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v14i4.11268

Abstract

Given the rapid advancement of information technology has a great influence in the fields of industry and services. This brings changes in competition between companies, so that company players must always create various techniques to survive. This study aims to assist SMEs in making purchases of the products they sell so that there is no excess stock. This research is calculated using the Fuzzy Sugeno algorithm with a system inference method that can be applied to determine the prediction of the number of purchases of goods. The prediction generated for the test data at week 30 is 60 pcs and this is less when compared to the real data, namely 70 pcs so that it can avoid overstock. Furthermore, the prediction results from the test data at week 21 to week 30 are tested to determine the error rate using the MAPE method, so that the result is 31.67%, and that means that the test is considered reasonable (reasonable).
Mikro-Irigasi Cerdas dengan Sprinkler Menggunakan Fuzzy Logic Pada Lahan Terbatas Untuk Pertanian 4.0 Abdul Haris; Hengki Sikumbang; L.M Syahrul Anwar
Faktor Exacta Vol 14, No 4 (2021)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v14i4.10742

Abstract

Most irrigation systems in Indonesia use surface irrigation systems or conventional irrigation, which are still heavily influenced by Earth's gravity, making it very difficult to manage and monitor. while current technological developments are almost evenly distributed throughout Indonesia with an already very good internet network, so that it can be used to support agricultural systems 4.0. In this study, researchers used intelligent computing technology on micro irrigation with Fuzzy Logic algorithm and Sugeno inference to decide when irrigation water is distributed to sprinkler irrigation systems based on a predetermined range value, then the results are evaluated to see the accuracy of the model that has been made before testing in the actual environment. The purpose of this research is to produce smart micro irrigation technology that can be used on limited land and lack of water so that it can help facilitate the work of farmers.
Prediksi Penjualan Kendaraan Niaga Berdasarkan Kinerja Purnajual dan Pertumbuhan Pasar Novika Ginanto; Setia Wirawan
Faktor Exacta Vol 14, No 4 (2021)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v14i4.9447

Abstract

Indonesia is one of the largest automotive market in South East Asia with highly demand of passenger and commercial vehicle. Commercial vehicle is used to distribute product to customers, then commercial vehicle strongly related with business growth. Gaikindo said that automotive business growth went down as 10.6%, it would effect to automotive company performance, especially vehicle stock ratio. Vehicle stock ratio can affect to financial and resources planning. Therefore, the forecasting was to be important to predict the market demand in future.  Basically, commercial vehicle would be used in along day due to business value, therefore aftersales services was critical point. In this case, sales forecasting of commercial vehicle (dependent variable) was approached by trend of aftersales performance and market growth (independent variable). Aftersales performance consist of aftersales revenue and unit served volume, then market growth using SAMSAT data. Prediction method used multiple linear regression due to forecasting capability with many variables. And the result using SPSS application was confirmed that independent variable affect to commercial vehicle sales volume and not multicollinearity. The result error of MAD was 3.80.  So that, sales forecasting of commercial vehicle can be predicted based on aftersales performance and market growth using multiple linear regression. Indonesia is one of the largest automotive market in South East Asia with highly demand of passenger and commercial vehicle. Commercial vehicle is used to distribute product to customers, then commercial vehicle strongly related with business growth. Gaikindo said that automotive business growth went down as 10.6%, it would effect to automotive company performance, especially vehicle stock ratio. Vehicle stock ratio can affect to financial and resources planning. Therefore, the forecasting was to be important to predict the market demand in future.  Basically, commercial vehicle would be used in along day due to business value, therefore aftersales services was critical point. In this case, sales forecasting of commercial vehicle (dependent variable) was approached by trend of aftersales performance and market growth (independent variable). Aftersales performance consist of aftersales revenue and unit served volume, then market growth using SAMSAT data. Prediction method used multiple linear regression due to forecasting capability with many variables. And the result using SPSS application was confirmed that independent variable affect to commercial vehicle sales volume and not multicollinearity. The result error of MAD was 3.80.  So that, sales forecasting of commercial vehicle can be predicted based on aftersales performance and market growth using multiple linear regression.  
PENGENDALI MONITORING PENYIRAMAN TAMAN BERBASIS ARDUINO MELALUI PARAMETER APRS (AUTOMATIC POSITION REPORTING SYSTEM ) abdu rahman; Fiqih Ismawan
Faktor Exacta Vol 14, No 4 (2021)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v14i4.10526

Abstract

Kondisi cuaca yang kurang menentu menyebabkan beberapa dampak, diantaranya adalah kadang kadar kelembaban tanah kurang atau terlalu tinggi. Kondisi kelembaban tanah sangat mempengaruhi kondisi kesehatan tanaman yang ada di wilayah daerah tersebut. Kesehatan tanaman yang buruk dapat mempengaruhi keindahan taman. Kurangnya keindahan taman dapat menyebabkan berkurang pengunjung baik yang bermukim didekat wilayah taman maupun yang jauh dari taman tersebut. Apabila kondisi kelembaban rendah perlu adanya penyiraman secara langsung untuk menaikan kelembaban. Kondisi tersebut karena penyiraman secara manual menggunakan fungsi manusia perlu untuk meluangkan waktu dan kesabaran. Masalah waktu yang lebih sulit dihindari, hal tersebut karena kesibukan manusia dan lebih terasa cepatnya waktu saat ini. Tujuan penelitian merancang sistem irigasi yang berjalan secara otomatis dengan memonitor kelembaban tanah untuk perawatan taman. Metode penelitian yang akan di gunakan dengan melakukan desain konsep alat pengendali otomatis penyiraman tanaman, observasi berdasarkan pengukuran data sistem aprs internet system, serta mengintegrasikan hasil pantauan data dengan konsep alat pengendali otomatis penyiraman taman. Hasil penelitian yaitu memadukan konsep teknologi otomatis yang berkembang saat ini dengan menggunakan Arduino sebagai alat pengendali penyiraman air dan data dari APRS (Automatic Packet Reporting System), sebagai data pendukung dalam kondisi kelembaban tanah di wilayah taman yang memerlukan sistem otomatis berupa data yang real time.
Implementasi Algoritma Naïve Bayes Classifier untuk Mendeteksi Berita Palsu pada Sosial Media Nova Agustina; Adrian Adrian; Mercy Hermawati
Faktor Exacta Vol 14, No 4 (2021)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v14i4.11259

Abstract

Hoax news (lie) on the internet has become a global problem that causes turmoil in society. Its presence can disrupt democratic order, the stability of social, cultural, political and economic life. The results of the research of the Indonesian Telematics Society showed that as many as 44.3% of respondents said they received fake news or misinformation every day. According to information released by Kominfo until August 11, 2021, there were 1848 hoax reports regarding the Covid-19 pandemic, 290 hoax reports regarding the Covid-19 Vaccine. Naïve Bayes Classifier is a classification method based on Bayes theorem, which in this paper is used to detect fake news on social media. The analysis was carried out using the Naïve Bayes Classifier algorithm, in this study using the CRISP-DM (Cross-Industry Standard Process for Data Mining) model. Training data sourced from the Kumparan site as much as 300 Data. In the process carried out using the python library for NLP, namely "satrawi". In testing the model using the confusion matrix method which consists of the number of rows of test data that are predicted to be true and false by the classification model used. At the deployment stage the model is pushed to Heroku so that users can predict news directly through the provided User Interface.

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