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Journal : PROtek : Jurnal Ilmiah Teknik Elektro

On-Grid Photovoltaic (PV) - Battery - PLN for Smart Home System Abdurrahman, Tanridio Silviati Delfina; Basalamah, Abdullah; Salmiah, Salmiah; Rahman, Muhammad Natsir
PROtek : Jurnal Ilmiah Teknik Elektro Vol 11, No 2 (2024): Protek : Jurnal Ilmiah Teknik Elektro
Publisher : Program Studi Teknik Elektro Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/protk.v11i2.7089

Abstract

Electricity is one of basic human needs. However, PLN's ability to meet customer demands is hampered by its limitations. On the other hand, the sunny geographical advantage of Makassar city can be utilized as a new renewable and environmentally friendly energy source in a smart home. Smart house is a family residence that is able to synergize electricity usage based on the habits of its residents with the help of smart technology so that comfort, safety and efficiency of using electrical energy are obtained. The utilization of solar cell hybrid power – battery – PLN can be implemented in addition to meeting the needs of electricity load in the smart home, it can also contribute excess energy to fulfill off-grid building load.  Monte Carlo Simulation (MCS) is carried out at the beginning of data processing by randomly generating 24-hour models of solar irradiance and smart home load requirements along with weather conditions. PLN not only takes over fulfilling the needs of the smart home load when there is less and or no sunlight and minimum battery capacity conditions, but also it will charge the battery capacity up to 100% every midnight. On average, the daily load requirement for a smart home is almost half the energy produced by PVs, which are 12,439 kW and 24,509 kW respectively. Furthermore, the smart home hybrid power is capable of producing 8,946 MW of excess energy in a year to serve the off-grid building load needs.
IoT-Based Smart Dustbin Prototype Saad, Andi Muhammad; Jul, Badillah Ode; Basalamah, Abdullah; Sayuti, Saidah
PROtek : Jurnal Ilmiah Teknik Elektro Vol 10, No 2 (2023): Protek : Jurnal Ilmiah Teknik Elektro
Publisher : Program Studi Teknik Elektro Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/protk.v10i2.6035

Abstract

Increasing population and consumption patterns affect the volume, type, and characteristics of waste which vary and increase periodically. This increment must be accompanied by the management of waste transportation so that the accumulation of waste can be prevented. Therefore, it is necessary to design a prototype of a smart dustbin condition monitoring tool that is capable of providing full information and notifying the collector in the form of the location of the IoT-based dustbin. The research method is a prototyping type to obtain a smart dustbin prototype and produces IoT-based smart dustbin hardware and software. Ultrasonic sensors are used to detect objects and measure the height of waste, loadcell sensor measure the weight of waste, and GPS is used to obtain the location of dustbin. NodeMCU ESP8266 processes sensor data and sends it to the user. The results showed that testing of the object detection hardware was able to open and close the dustbin automatically when there was an object at a distance of ≤50 cm. Detectors of height and weight of waste can measure the height and weight of waste with an error of 0.4% and 0.15% respectively. The results of software testing show that the tool succeeds in sending WhatsApp notification data when the waste height reaches 3 cm from the sensor or 4000-gram waste weight, with a Throughput measurement of 327.95 kbps, and takes 2.61 seconds to send a notification message of 69 bytes.
Implementation Of Convolutional Neural Network (Cnn) Based On Mobile Application For Rice Quality Determination Altim, Muhammad Zainal; Basalamah, Abdullah; kasman, kasman
PROtek : Jurnal Ilmiah Teknik Elektro Vol 12, No 1 (2025): Protek : Jurnal Ilmiah Teknik Elektro
Publisher : Program Studi Teknik Elektro Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/protk.v12i1.9396

Abstract

The purpose of this study is to design and build a CNN deep learning program modeling for a mobile application for rice quality classification and analyze the performance of a mobile application-based classification program as a means of halal information in real time. The method applied is an experimental method that utilizes machine learning technology by using many rice images that are used as datasets. The data of these images is classified by their shape, color and background. This image is used as a reference for the training dataset. After the CNN training model is formed, it is then set up in a web editor p5.js then an interface is created to connect to a server such as Google Cloud using FastAPI, which can be accessed using a mobile application or a web server such as Chrome. In the mobile application, create an interface to connect with the camera system and data base on the cloud server. The results of the study were obtained that CNN deep learning modeling can be used in real time. In web browser usage, the data shown is also affected by lighting. The accuracy level of the built model reached above 99.8 percent with a validation accuracy rate of 99.7 percent in the data training process. When testing, the average accuracy of the data was around 99.9 percent. This clearly proves that CNNs can be used to classify objects properly and accurately.
On-Grid Photovoltaic (PV) - Battery - PLN for Smart Home System Abdurrahman, Tanridio Silviati Delfina; Basalamah, Abdullah; Salmiah, Salmiah; Rahman, Muhammad Natsir
PROtek : Jurnal Ilmiah Teknik Elektro Vol 11, No 2 (2024): Protek : Jurnal Ilmiah Teknik Elektro
Publisher : Program Studi Teknik Elektro Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/protk.v11i2.7089

Abstract

Electricity is one of basic human needs. However, PLN's ability to meet customer demands is hampered by its limitations. On the other hand, the sunny geographical advantage of Makassar city can be utilized as a new renewable and environmentally friendly energy source in a smart home. Smart house is a family residence that is able to synergize electricity usage based on the habits of its residents with the help of smart technology so that comfort, safety and efficiency of using electrical energy are obtained. The utilization of solar cell hybrid power – battery – PLN can be implemented in addition to meeting the needs of electricity load in the smart home, it can also contribute excess energy to fulfill off-grid building load.  Monte Carlo Simulation (MCS) is carried out at the beginning of data processing by randomly generating 24-hour models of solar irradiance and smart home load requirements along with weather conditions. PLN not only takes over fulfilling the needs of the smart home load when there is less and or no sunlight and minimum battery capacity conditions, but also it will charge the battery capacity up to 100% every midnight. On average, the daily load requirement for a smart home is almost half the energy produced by PVs, which are 12,439 kW and 24,509 kW respectively. Furthermore, the smart home hybrid power is capable of producing 8,946 MW of excess energy in a year to serve the off-grid building load needs.