Article Per Year (5 Year)
p-Index From 2019 - 2024
0.444
P-Index
This Author published in this journals
All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) NARATIF : Jurnal Nasional Riset Aplikasi dan Teknik Informatika
Hadhiwibowo, Ari
Sekolah Tinggi Teknologi Bandung
Computer Science & IT Electrical & Electronics Engineering Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
PENERAPAN KONSEP IOT DALAM BUDIDAYA IKAN Abdurrohman; Ari Hadhiwibowo
Naratif (Jurnal Nasional Riset, Aplikasi Dan Teknik Informatika) Vol 1 No 2 (2019): NARATIF (Jurnal Ilmiah Nasional Riset Aplikasi Dan Teknik Informatika)
Publisher : Sekolah Tinggi Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (418.298 KB) | DOI: 10.53580/naratif.v1i2.62

Abstract

AbstrakPerkembangan jaringan internet yang sudah sangat berkembang di indonesia, ditandai dengan banyaknya pengunaan smartphone atau mobile devices dalam kehidupan sehari-hari masyarakat indonesia dan ditunjang dengan perkembangan infrastruktur telekomunikasi yang sudah sampai ke pelosok-pelosok wilayah indonesia. Kemajuan teknologi informasi tersebut dapat dimanfaatkan untuk meningkatkan taraf hidup manusia. Internet untuk merupakan suatu konsep yang bertujuan untuk memperluas manfaat dari konektivitas internet yang tersambung secara terus menerus [1]. Manfaat utama dari IoT adalah kemampuan berbagi data dan pemantauan jarak jauh terhadap sesuatu objek yang akan kita pantau dengan cara menanamkan sensor ke objek tersebut (contoh, sensor suhu, kelembaman dll). Berdasarkan konsep IoT tersebut di atas, maka penulis akan menerapkan konsep tersebut didalam sistem pemantauan suhu dan PH air untuk digunakan didalam budidaya ikan.Kata kunci: Internet of Things (IoT), Monitoring, Budidaya ikan, Arduino 2560AbstractThe development of the internet network that has been highly developed in Indonesia, is characterized by the many uses of smartphones or mobile devices in the daily lives of Indonesian people and is supported by the development of telecommunications infrastructure that has reached remote areas of Indonesia. Advances in information technology can be utilized to improve the standard of living of humans. The internet for is a concept that aims to expand the benefits of continuously connected internet connectivity [1]. The main benefit of IoT is the ability to share data and remote monitoring of an object that we will monitor by implanting sensors into the object (for example, temperature sensors, inertia etc.). Based on the above IoT concept, the writer will apply the concept in a temperature and water PH monitoring system for use in fish farming.Keywords: Internet of Things (IoT), Monitoring, Fish farming, Arduino 2560
Comparison of Machine Learning Algorithms in Detecting Tea Leaf Diseases Candra Nur Ihsan; Nova Agustina; Muchammad Naseer; Harya Gusdevi; Jack Febrian Rusdi; Ari Hadhiwibowo; Fahmi Abdullah
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 1 (2024): February 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i1.5587

Abstract

Tea is one of the top ten export products sent from Indonesia to foreign countries. However, in recent years, the amount of tea leaf exports from Indonesia has decreased, although the value of the export impacts the country’s economic structure. In addition to market competition, Indonesia must maintain tea leaf production so that the increase in export decline is not significant or even increases tea leaf export production. To improve production quality and reduce production costs, early detection of tea leaf diseases is necessary. This study aims to classify tea leaf images for early detection of tea leaf disease so that appropriate treatment can be carried out early. This study compares machine learning algorithms to determine the best algorithm for detecting tea leaf diseases. The algorithms tested as performance comparisons in classifying tea leaf diseases are random forest (RF), support vector classifier (SVC), extra tree classifier (ETC), decision tree (DT), XGBoost classifier (XGB), and convolutional neural algorithms. Network (CNN). As a result, the average accuracy performance generated by ETC produces a higher value than other algorithms, i.e., getting an average accuracy performance of 77.47%. Another algorithm, SVC, has an average accuracy of 76.57%, RF of 76.12%, DT of 65.31%, XGB of 71.62%, and the lowest is CNN of 59.08%. ETC has been proven to be the most superior machine learning algorithm for detecting tea leaf diseases in this study.
Co-Authors Abdurrohman Candra Nur Ihsan Fahmi Abdullah Harya Gusdevi Jack Febrian Rusdi Muchammad Naseer Nova Agustina