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Journal : Tsabit

Design and Construction of Automatic Clothes Drying Rack Prototype Based on IoT (Internet of Things) Syahputra, Abdillah; Maulana, Halim
Tsabit Journal of Computer Science Vol. 1 No. 2 (2024): December Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/tsabit31

Abstract

During the dry season, the intense heat of the sun is highly sought after by Indonesian communities for various needs, one of which is drying clothes that are still wet. Therefore, Indonesians prefer using clotheslines as a medium for drying clothes. Essentially, in addressing the issue of clotheslines, an automated control system is needed. Advances in the field of science and technology, particularly in IoT (Internet of Things), will lead to new innovations. One such innovation is an automatic clothesline control system, which helps and simplifies human tasks. The system automatically moves or shifts dried clothes to a place that is not exposed to rain. In this research, an automatic clothesline system is designed to secure clothes during rain or other weather changes using several sensors: rain sensor, Light Dependent Resistor (LDR), and Temperature and Humidity Sensor (DHT), as well as an external fan that functions as additional drying assistance during rain. This system utilizes the ESP8266 microcontroller and is based on the Internet of Things, allowing remote monitoring and control via smartphone. Based on conducted tests, this system effectively responds to weather changes.
Smart Blind Stick Design Using HC-SR04 Sensor and ESP 32 Based Water Level Sensor to Improve the Mobility of Blind Persons Zakhir, Zharfan; Maulana, Halim
Tsabit Journal of Computer Science Vol. 1 No. 2 (2024): December Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/tsabit32

Abstract

Blind people in Indonesia, who are estimated to number around 3.75 million people, face major challenges in their daily mobility. In the era of technology 4.0, various innovations have been developed to help them, including the use of walking aids such as smart blind sticks. This research aims to design and build a smart blind stick based on the ESP32 microcontroller, which is equipped with an HC-SR04 ultrasonic sensor and water level sensor to detect holes and puddles of water, as well as a vibration module and speaker to provide warnings. The research method used is the prototyping method, which involves collecting system requirements, making prototypes, and evaluating users. The research results show that this smart blind stick is effective in providing warnings of obstacles on the road through vibration and sound, as well as making travel easier and increasing the safety of blind people. All main components function as expected, making this device a practical and innovative solution for improving the mobility of blind people.
Comparison of Random Forest and XGBOOST Methods on Weather in North Sumatera Sibuea, Royhan Umri; Maulana, Halim
Tsabit Journal of Computer Science Vol. 2 No. 1 (2025): June Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/tsabit44

Abstract

Accurate weather forecasting is crucial for various sectors, including agriculture, transportation, and disaster management. The weather data used includes variables such as humidity, temperature, and wind speed collected from weather stations across North Sumatra. The Random Forest method is an ensemble algorithm based on decision trees known for its ability to handle overfitting and provide accurate results. On the other hand, XGBoost is a boosting technique that improves model performance through iterative learning, correcting errors made by previous models. Research results show that both methods have their respective advantages in terms of accuracy and prediction speed. The Random Forest method yields a Root Mean Squared Error (RMSE) of 0.753732 and a Coefficient of Determination (R²) of 0.736315. In contrast, XGBoost shows a slightly lower RMSE of 0.737818 and a higher R² of 0.747332. It is concluded that XGBoost performs slightly better in minimizing prediction errors (RMSE) and improving model fit to the data (R²) compared to Random Forest.