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All Journal Compiler Malcom: Indonesian Journal of Machine Learning and Computer Science
Teddy Hidayat
Universitas Kebangsaan Republik Indonesia
Computer Science & IT

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Monitoring System and Hydroponic Plant Automation Using Microcontroller Internet of Things Based (IoT) Nopi Ramsari; Teddy Hidayat
Compiler Vol 11, No 2 (2022): November
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1952.843 KB) | DOI: 10.28989/compiler.v11i2.1365

Abstract

Hydroponics is a technique of cultivating plants without using soil media, but using water as a planting medium. Important factors that must be considered in hydroponic plants are plant nutrition, water, temperature, and light intensity. These factors are necessary for hydroponic plants for the growth and reproduction of hydroponic plants. Due to the nutritional needs of hydroponic plants, farmers must take the time to check the concentration of nutrients in hydroponic plants so that the plants can grow well. Therefore, it is necessary to develop new methods of hydroponic farming so that they can grow and develop properly, which can be controlled and monitored automatically. Thus, farmers no longer need to come to agricultural locations. One of the technologies used is technology with the Internet of Things (IoT). This study uses a prototype methodology. For the plant sample used is the lettuce plant. The microcontroller is used as the main controller of all IoT components while the sensors used are the TDS sensor, temperature sensor and light intensity sensor. The research that has been done is IoT technology on hydroponic plants that can monitor and automate plant nutrition settings that are personalized to the needs of hydroponic plants. From the results of testing the IoT tool by reading data from the light intensity and temperature sensors, there is a link between the UV light intensity produced and the water temperature in the hydroponic plant reservoir, that is, the higher the UV light intensity, the higher the water temperature and vice versa. In addition, the other factors are weather and climate conditions around hydroponic plants. IoT data is stored in cloud storage which requires a rental fee.
Sistem Pakar Diagnosa Awal Penyakit Ginjal Menggunakan Metode Forward Chaining: Expert System for Early Diagnosis of Kidney Disease Using Forward Chaining Method Teddy Hidayat; Nopi Ramsari
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 3 No. 2 (2023): MALCOM October 2023
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v3i2.887

Abstract

Ginjal merupakan organ penting dari sistem metabolisme dalam tubuh. Sebagian besar masyarakat masih kurang memahami bagaimana pentingnya peranan ginjal di tubuh manusia, terlebih lagi berbagai macam penyakit yang dapat muncul di area ginjal. Masyarakat kurang memahami gejala-gejala apa saja yang terdapat pada penyakit ginjal. Penyakit ginjal membutuhkan dokter spesialis untuk mendiagnosanya dan membutuhkan biaya yang relatif besar disamping jumlah dokter spesialis ahli ginjal di Indonesia masih belum banyak. Karena adanya keterbatasan pengetahuan mengenai penyakit ginjal dan jumlah dokter spesialis ginjal sehingga menyulitkan masyarakat untuk mendiagnosa awal dalam penyakit ginjal. Metodologi pengembangan sistem yang digunakan adalah metodologi Extreme Programming (XP). Penelitian ini membangun aplikasi sistem pakar untuk diagnosis awal penyakit ginjal dengan menggunakan metode inferensi yaitu metode forward chaining. Dilakukan survey terhadap 20 responden yang dipilih secara sampling untuk menguji desain dan kemudahan penggunaan dari aplikasi yang dibangun Setelah dilakukan pengujian UAT (user acceptance task) maka hasil jawab oleh responden rata-rata persentase UAT sebesar 84,4 % untuk menilai desain dan kemudahan penggunaan aplikasi sistem pakar penyakit ginjal.
Co-Authors Nopi Ramsari Nopi Ramsari