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All Journal EDUMATIC: Jurnal Pendidikan Informatika
Wirarama Wedashwara
Program Studi Teknik Informatika, Universitas Mataram
Computer Science & IT Education

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Rancang Bangun WSN berbasis nRF24L01 dan SIM800l bertenaga Surya untuk Implementasi IoT secara Outdoor Wirarama Wedashwara; Budi Irmawati; Andy Hidayat Jatmika; Ariyan Zubaidi
Jurnal Pendidikan Informatika (EDUMATIC) Vol 5, No 2 (2021): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v5i2.4164

Abstract

Internet of things (IoT) requires an internet network for data communication between machines. Wifi is not always available outdoors and requires more portable data communication. This study aims to design a prototype Wireless Sensor Network (WSN) based on nRF24L01 and solar-powered SIM800l for outdoor IoT implementation. The study used a total of five IoT devices with four nodes with nRF24L01 and one node with nRF24L01 and SIM800l. Each device uses an Arduino nano, TP4056, 6WP solar panel, and a 900mAh 18650 battery. The evaluation of the system includes a comparative QoS analysis, namely packet delivery ratio (PDR), throughput, and delay in star and bus topology through data collection of observation methods by sensors. The evaluation results show that for unidirectional data communication the star topology has better results with PDR 99,099%, throughput 99.393%, and delay 0.0095s. While the bus topology produces a slight difference in PDR 98.766%, throughput 98.461%, and delay 0.017s. Evaluation of energy availability shows that during the day with an average voltage of 3.703v and at night 2.976v, there is no significant difference. During the day it produces 99.301% PDR, 99.653% throughput, and 0.001s delay, while at night it produces 94.221% PDR, 99.881% throughput, and 0.027s delay.
Klasifikasi Teks menggunakan Genetic Programming dengan Implementasi Web Scraping dan Map Reduce Wirarama Wedashwara; Andy Hidayat; Budi Irmawati; Ariyan Zubaidi
Jurnal Pendidikan Informatika (EDUMATIC) Vol 6, No 1 (2022): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v6i1.5274

Abstract

Classification of text documents on online media is a big data problem and requires automation. Research has developed a text classification system with pre-processing using map-reduce and web scraping data collection. This study aims to evaluate text classification performance by combining genetic programming algorithms, map-reduce and web scraping for processing large data in the form of text. Data collection was carried out by observing web-based scraping. Data was collected by reducing 8126 duplicates. Map-reduce has tokenized and stopped-word removal with 28507 terms with 4306 unique terms and 24201 duplication terms. Text classification evaluation shows that a single tree produces better accuracy (0.7072) than a decision tree (0.6874), and the lowest is a multi-tree (0.6726). For the acquisition of genetic programming support values with the multi-tree, the highest average support is 0.3854, followed by the decision tree with 0.3584 and the smallest single tree with 0.3494. In general, the amount of support is not in line with the accuracy value achieved.
Co-Authors Andy Hidayat Ariyan Zubaidi Budi Irmawati Jatmika, Andy Hidayat