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All Journal Jurnal Teknologi Informasi dan Komunikasi
Sobari, Dicky Iskandar
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Computer Science & IT Control & Systems Engineering

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SISTEM MONITORING KUMBUNG JAMUR TIRAM OTOMATIS BERBASIS IoT (Internet of Things) MENGGUNAKAN METODE K-NEAREST NEIGHBOUR Sobari, Dicky Iskandar; Munandar, Haris
Jurnal Teknologi Informasi dan Komunikasi Vol 16 No 1 (2023): April
Publisher : STMIK Subang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47561/a.v16i1.239

Abstract

In response to the growing market demand for oyster mushrooms, effective monitoring of the growth environment becomes crucial. This research aims to develop an automated Internet of Things (IoT)-based monitoring system for oyster mushroom cultivation, utilizing Wemos D1 R1 hardware, DHT22 temperature and humidity sensor, LM35 temperature sensor, and the K-Nearest Neighbour (KNN) method for data analysis. The IoT platform employed in this study is Antares. The system is designed to monitor real-time temperature and humidity within the mushroom cultivation chamber. The DHT22 sensor is employed for simultaneous measurement of air temperature and humidity, while the LM35 sensor gauges the soil temperature in the mushroom growth substrate. Data collected by these sensors are automatically transmitted to the Antares platform through Wemos D1 R1 via WiFi connectivity. The K-Nearest Neighbour (KNN) method is applied to analyze the accumulated temperature and humidity data. KNN provides the capability to identify patterns and trends in oyster mushroom growth based on environmental conditions. The results of this analysis offer valuable insights for oyster mushroom farmers to optimize growth conditions and enhance harvest yields. Through the implementation of this system, it is anticipated that efficiency and productivity in oyster mushroom cultivation will be improved. This IoT-based automated monitoring system provides an effective and practical solution for real-time monitoring of oyster mushroom growth conditions, offering farmers the opportunity to take prompt and informed actions to enhance their harvest yields.
SISTEM INFORMASI TUNJANGAN PENDAPATAN KEPEGAWAI DI KANTOR KECAMATAN MENGGUNAKAN FRAMEWORK CODEIGNITER Selviani, Rima; Haq, Haris Nizhomul; Sobari, Dicky Iskandar
Jurnal Teknologi Informasi dan Komunikasi Vol 17 No 1 (2024): April
Publisher : STMIK Subang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47561/a.v17i1.256

Abstract

In the current era of technological advancements, the demand for information has significantly risen across various facets of life. The Employee Income Allowance Information System (TPP) implemented at the Subang Sub-District Office encompasses comprehensive data, including employee profiles, attendance records, TPP reports, and employee assessments. This system facilitates employees in efficiently accessing and reviewing attendance and TPP report data. The implementation is carried out using CodeIgniter, PHP, and MySQL technologies.
PENGEMBANGAN SISTEM IOT UNTUK PEMANTAUAN KESEHATAN DOMBA DENGAN ALGORITMA C4.5 BERBASIS THINGSPEAK Nugraha, Fahmi; Wijaya, Anderias Eko; Hermawan, Rian; Sobari, Dicky Iskandar
Jurnal Teknologi Informasi dan Komunikasi Vol 17 No 2 (2024): October
Publisher : STMIK Subang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47561/a.v17i2.266

Abstract

Effective livestock health monitoring is one of the primary challenges in modern farming, especially in detecting early health disorders in sheep. This research aims to develop a sheep health monitoring system based on the Internet of Things (IoT) using the C4.5 algorithm and the ThingSpeak platform. The system collects vital sheep data such as body temperature, heart rate, sound, and physical activity in real time through sensors and microphones connected to IoT devices. The data is then transmitted to the ThingSpeak platform for analysis and storage. The C4.5 algorithm is used to build a decision model capable of classifying the health conditions of sheep based on collected parameters such as temperature, heart rate, and respiration. The processed data results are displayed in the form of graphs and warning notifications on the ThingSpeak platform, allowing farmers to monitor livestock health easily and responsively. The accuracy test yielded a 90% accuracy rate using a confusion matrix with a data sampling split of 80% for training data and 20% for testing data. This indicates that the system has a high level of accuracy in detecting sheep health conditions. Consequently, the system has the potential to assist farmers in improving the efficiency of livestock health monitoring automatically and in real time. Moreover, the application of IoT technology and the C4.5 algorithm in the livestock sector is expected to provide innovative solutions to support productivity and animal welfare.
Co-Authors Anderias Eko Wijaya Fahmi Nugraha, Fahmi Haq, Haris Nizhomul Haris Munandar Rian Hermawan Selviani, Rima