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All Journal Jurnal Simetris TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Teknoinfo Jurnal Teknologi dan Sistem Informasi Journal Social Science And Technology For Community Service

Agus Ambarwari

Politeknik Negeri Lampung

Author-ID : 2454249

Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Environmental Science Languange, Linguistic, Communication & Media Social Sciences

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Analisis Pengaruh Data Scaling Terhadap Performa Algoritma Machine Learning untuk Identifikasi Tanaman Agus Ambarwari; Qadhli Jafar Adrian; Yeni Herdiyeni
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 1 (2020): Februari 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Data scaling has an important role in preprocessing data that has an impact on the performance of machine learning algorithms. This study aims to analyze the effect of min-max normalization techniques and standardization (zero-mean normalization) on the performance of machine learning algorithms. The stages carried out in this study included data normalization on the data of leaf venation features. The results of the normalized dataset, then tested to four machine learning algorithms include KNN, Naïve Bayesian, ANN, SVM with RBF kernels and linear kernels. The analysis was carried out on the results of model evaluations using 10-fold cross-validation, and validation using test data. The results obtained show that Naïve Bayesian has the most stable performance against the use of min-max normalization techniques as well as standardization. The KNN algorithm is quite stable compared to SVM and ANN. However, the combination of the min-max normalization technique with SVM that uses the RBF kernel can provide the best performance results. On the other hand, SVM with a linear kernel, the best performance is obtained when applying standardization techniques (zero-mean normalization). While the ANN algorithm, it is necessary to do a number of trials to find out the best data normalization techniques that match the algorithm.

Sistem Pemantau Kondisi Lingkungan Pertanian Tanaman Pangan dengan NodeMCU ESP8266 dan Raspberry Pi Berbasis IoT Agus Ambarwari; Dewi Kania Widyawati; Anung Wahyudi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 3 (2021): Juni 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

The increasing need for food is not in line with the clearing of agricultural land for food crops. So that the effort to increase the productivity of agricultural products is by applying precision agriculture. However, in reality, precision agriculture is difficult to apply to conventional processes, where farmers come to the farm, collect data, then carry out maintenance. This method will make production results not optimal because maintenance is not done accurately. This study introduces a monitoring system for environmental conditions based on the Internet of Things (IoT) for agricultural land, where trials are carried out in a greenhouse. The system that has been developed consists of several sensors designed to collect information related to agricultural environmental conditions, including DHT22 sensor (temperature and humidity), DS18B20 sensor (soil temperature), soil moisture sensor (moisture content in the soil), and BH1750 sensor (light intensity). Based on the Message Queuing Telemetry Transport (MQTT) protocol, the data is sent to a gateway (Raspberry Pi) and a local server via a wireless network to be stored in a database. By using the Node-RED Dashboard, the received sensor data is then displayed on the browser every time the sensor sends data. In addition, the local server also publishes sensor data to the public MQTT broker so that sensor data can be accessed through the MQTT Dashboard application on a smartphone. The results of testing for 25 days of the system running obtained an average success of the system in storing data of 99.64%.

Co-Authors Anung Wahyudi Dewi Kania Widyawati Emir Mauludi Husni Imam Ahmad Iqbal Yasin Irman Hermadi Muhammad Adam Putra Darma Muharman Lubis Neneng Neneng Qadhli Jafar Adrian Rohmat Indra Borman Yusmaida Yusmaida Yusra Fernando

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Journal : Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)