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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Sistem dan Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING
Aryanto, I Komang Agus Ady
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Developing a smart system for infant incubators using the internet of things and artificial intelligence Aryanto, I Komang Agus Ady; Maneetham, Dechrit; Triandini, Evi
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp2293-2312

Abstract

This research is developing an incubator system that integrates the internet of things and artificial intelligence to improve care for premature babies. The system workflow starts with sensors that collect data from the incubator. Then, the data is sent in real-time to the internet of things (IoT) broker eclipse mosquito using the message queue telemetry transport (MQTT) protocol version 5.0. After that, the data is stored in a database for analysis using the long short-term memory network (LSTM) method and displayed in a web application using an application programming interface (API) service. Furthermore, the experimental results produce as many as 2,880 rows of data stored in the database. The correlation coefficient between the target attribute and other attributes ranges from 0.23 to 0.48. Next, several experiments were conducted to evaluate the model-predicted value on the test data. The best results are obtained using a two-layer LSTM configuration model, each with 60 neurons and a lookback setting 6. This model produces an R2 value of 0.934, with a root mean square error (RMSE) value of 0.015 and a mean absolute error (MAE) of 0.008. In addition, the R2 value was also evaluated for each attribute used as input, with a result of values between 0.590 and 0.845.
Neobots: an open-source platform for a low-cost neonatal incubator with internet of things approach Aryanto, I Komang Agus Ady; Maneetham, Dechrit; Triandini, Evi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1817-1837

Abstract

A baby incubator implements the internet of things (IoT) with an architectural design combining several scientific fields, such as networks, software, and hardware. Furthermore, this research develops an open-source platform called Neobots, including open-source program code to create a baby incubator. Then an overview of the system includes sending sensor data to the IoT Broker with the message queuing telemetry transport (MQTT) protocol and automatically storing data in the database. The results of the comparison value on each temperature sensor with a temperature sensor at the midpoint with an error of less than 0.7°C. Then testing the fuzzy between the Neobots program and the simulation in MATLAB got an error rate of 0-28.27%. In addition, in less than 10 minutes, the system response can adjust the temperature conditions to a setpoint value of 34°C from 29°C, and the average error value is 0.35°C during 1 hour of the Fuzzy implementation on the incubator. Then transfer data from the incubator to the database in a room without noise and full noise to get results for lost data less than 16.41% and 42.14%, delay rates between 0-6 seconds and 0-7 seconds with testing for 1 hour at every 1 second.
Implementasi Sistem Pengendali Lampu Berbasis Mikrokontroler dan Teknologi Infrared Aryanto, I Komang Agus Ady; I Gusti Ayu Nandia Lestari
Jurnal Sistem dan Informatika (JSI) Vol 18 No 1 (2023): Jurnal Sistem dan Informatika (JSI)
Publisher : Direktorat Penelitian,Pengabdian Masyarakat dan HKI - Institut Teknologi dan Bisnis (ITB) STIKOM Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30864/jsi.v18i1.597

Abstract

Smarthome adalah sistem yang mengatur berbagai objek di dalam rumah. Penelitian ini bertujuan mengembangkan sistem pengaturan lampu di dalam rumah menggunakan teknologi sistem tertanam, yang memungkinkan pengguna untuk mengontrol penerangan dengan mudah menggunakan komponen yang tersedia di pasaran dengan harga terjangkau. Sistem ini dirancang khusus untuk mengatur lampu di dalam rumah, memanfaatkan berbagai komponen elektronik seperti mikrokontroler, relay, infrared, LED, push button, dan power supply. Mikrokontroler berfungsi sebagai inti pemrosesan untuk mengontrol on/off lampu melalui relay. Modul infrared berperan sebagai media komunikasi antara perangkat dengan remote infrared pengguna. Proses kerja sistem terdiri dari dua tahap yaitu, pengaturan identitas relay dan pengontrolan lampu yang terhubung dengan relay. Pada tahap pengaturan identitas relay, pengguna memberikan kode unik ke setiap relay sesuai dengan tombol remote yang digunakan. Sedangkan pada tahap pengontrolan, pengguna memberikan perintah ke relay sesuai dengan alamatnya melalui tombol remote yang telah didaftarkan sebelumnya. Hasil pengujian menunjukkan bahwa perangkat dapat bekerja dengan baik sesuai dengan perintah yang diberikan. Misalnya, tombol remote 1 menyala menyebabkan relay 1 menyala, tombol 2 menyebabkan lampu 2 menyala, tombol 3 menyebabkan lampu 3 menyala, dan tombol 4 menyebabkan lampu 4 menyala. Hal ini mengindikasikan bahwa perangkat sudah berfungsi sesuai dengan target yang diharapkan.
Effectiveness of AdaBoost and XGBoost Algorithms in Sentiment Analysis of Movie Reviews Lestari, I Gusti Ayu Nandia; Dewi, Ni Made Rai Masita; Meiliana, Komang Gita; Aryanto, I Komang Agus Ady
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.9077

Abstract

Currently there are many entertainment platforms that provide various movies, TV shows, games, and other content. These platforms usually offer a variety of features, one of which is reviews. Review data written by viewers plays an important role in influencing public interest in the film. However, the increasing number of reviews makes it difficult to assess the sentiment of the film quickly and accurately. This highlights the need for a system that can analyze reviews based on sentiment, making it easier for viewers to evaluate the film and supporting the entertainment industry in understanding the needs of the audience. Therefore, this study develops a sentiment analysis model to identify whether a review contains positive or negative sentiment using machine learning algorithms. The data used to build the model is obtained from user reviews of a film on the IMDb platform. This dataset is available on Kaggle with 50,000 movie reviews in text format. The characteristics of the data include two columns: review_text and sentiment. The methods used to create the classification model are AdaBoost and XGBoost. The data preprocessing process includes several stages such as text cleaning, tokenization, stopword removal, lemmatization, and vectorization using TF-IDF to convert the review text into numeric form, as well as converting the positive and negative labels into 1 and 0. Based on the results of model training with cross-validation, the accuracy of the XGBoost model is 85% and AdaBoost is 77%. Feature selection showed an improvement in the XGBoost model's accuracy from 85% to 86%, while the AdaBoost model's performance remained stable at 77%. Thus, it can be concluded that the XGBoost model demonstrates better performance than the AdaBoost model in sentiment classification.
Co-Authors Dewi, Ni Made Rai Masita Evi Triandini I Gusti Ayu Nandia Lestari Lestari, I Gusti Ayu Nandia Maneetham, Dechrit Meiliana, Komang Gita