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All Journal International Journal of Computer and Information System (IJCIS) Brilliance: Research of Artificial Intelligence
Sri Lestari, Ninik
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Mathematics Other

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Electronic Door App Development Using Machine Learning And Face ID Hidayat, Wisnu; Sri Lestari, Ninik; Gammanr, Dzulfikri Gammanr
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.6816

Abstract

Face recognition technology (Face ID) has emerged as a highly secure solution by leveraging the distinctive attributes of each individual.  The application of Face ID in electronic door access systems significantly enhances security across residential, commercial, and public facilities.  This research's urgency can markedly enhance access security relative to traditional approaches and intelligent, secure, and efficient security solutions.  The objective of the research is to create a Face ID-based door security system, enhancing efficiency and reliability in access security.  The research employs the experimental technique alongside system development utilising the waterfall model.  The process involves analysing requirements to facilitate research, followed by system design, system testing, and concluding with system implementation.  Face ID employs artificial intelligence and machine learning technology to identify registered faces.  The precision of facial detection with a high-resolution camera and its connection with a smart lock may be managed via the application. A biometric authentication system utilizing facial recognition can serve as a substitute for traditional door lock mechanisms. The main components of the electronic door include biometric recognition based on Face ID. Its mechanism uses a solenoid lock to automatically control the door lock through electromagnetic action. The user interface is equipped with an LCD screen, which displays comprehensive information about the status of the electronic door.  The testing findings indicate that the input and output hardware of the system, specifically the camera and servo motor, function effectively.
Smart Monitoring System For Hybrid Solar Power Plants Using Iot Technology Wirjawan, Agung; Safarudin, Andi; Ramdani, Rika; Sri Lestari, Ninik; Ramadi, Givy Devira; Sukirno, Sukirno
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.6865

Abstract

Solar Power Plants (PLTS) have become one of the most widely used sources of renewable energy across various sectors, including both commercial industries and households. A hybrid solar power system combines solar energy with other sources, such as generators or the electrical grid.  The urgency of this research lies in the development of an Internet of Things (IoT)-based monitoring system for hybrid solar power plants, which can analyze data in real time, facilitate problem detection, optimize performance, and design preventive maintenance, as well as serve as an alternative source of electrical energy.The objective of this study is to develop a monitoring system for hybrid solar power plants using IoT. This technology is essential for enhancing the efficiency and sustainability of hybrid solar power generation. The method used in this research is the experimental method.Solar panels function as energy collectors that convert sunlight into electrical energy. The solar charge controller regulates the amount of electrical energy from the panels used to charge the battery. The voltage, current, and power generated by the plant are detected by two sensors: a DC voltage sensor and an ACS712 current sensor. The energy stored in the battery is used to power lamps. IoT offers an efficient solution for integrating components of a solar power plant, such as solar panels, inverters, batteries, and others. A hybrid solar power monitoring system can ensure maximum energy utilization and reduce long-term operational costs.
Development Of A Solar Powered IoT Based Landslide Detection System Dwiyanto, Dwiyanto; Hidayat, Iman; Alamsyah, Rizky; Sri Lestari, Ninik; Ramadi, Givy Devira; Sukirno, Sukirno
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.6887

Abstract

The surfaces of three major tectonic plates the Eurasian Plate, the Indo Australian Plate, and the Pacific Plate intersect in Indonesia. This condition creates a high risk of earthquakes and landslides in areas located at these plate boundaries. The urgency of this research lies in developing a landslide detection device to help communities living in landslide-prone areas remain alert to disasters that may occur at any time. The objective of this study is to develop an Internet of Things (IoT)-based landslide detection device powered by solar energy. This device does not rely on external power sources but utilizes solar energy as its primary power supply. The research employs an experimental method. The steps include problem identification, literature review, system development methodology, design and application development, testing, and analysis of test results.Landslide detection is carried out using vibration sensors to detect ground tremors that may indicate a landslide, tilt sensors to monitor changes in ground inclination, and soil moisture sensors to measure soil humidity. An Arduino microcontroller processes data from the sensors and transmits signals to the warning system, while solar panels generate electrical energy from sunlight. The use of solar cells is optimized by calculating the required energy capacity to operate the sensors, Arduino board, and early warning system.
Swarm Intelligence Framework using Hybrid ACO–PSO for Lecture Scheduling in Higher Education Hidayat, Rahmad; Sri Lestari, Ninik; Sukirno, Sukirno; Rosmalina, Rosmalina; YS, Herawati; Ramady, Givy Devira; Suhana, Asep; Willa Permatasari, Raden; Sukandi, Ganjar Kurniawan; Afiyah, Salamatul; Aca, Rukman; Subawi, Handoko
International Journal of Computer and Information System (IJCIS) Vol 6, No 3 (2025): IJCIS : Vol 6 - Issue 3 - 2025
Publisher : Institut Teknologi Bisnis AAS Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/ijcis.v6i3.252

Abstract

Complex combinatorial optimization problems that must meet various hard constraints and soft constraints occur in lecture scheduling. A feasible and high-quality schedule in limited computing time is often difficult to produce using conventional methods. In this study, a hybrid optimization model is proposed that combines Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO), the aim of which is to improve solution quality and convergence speed. In this model, ACO builds solutions based on pheromone intensity and heuristic information, while PSO is used to dynamically adjust ACO parameters through learning from individual and global search experiences. The model is implemented using MATLAB R2023b and tested on real data involving 10 courses, 4 classrooms, and 6 time slots per day. The ACO+PSO approach is significantly able to reduce the penalty value. This approach reflects better fulfillment of constraints and is the result of experiments obtained. Compared to pure ACO, the hybrid method shows more consistent and stable performance in various trials. Visualization of parameter convergence also strengthens the effectiveness of this hybrid approach in finding the optimal parameter configuration. This research contributes to the development of an intelligent lecture scheduling system that is adaptive and aligned with institutional policies.
Co-Authors Aca, Rukman Alamsyah, Rizky Dwiyanto Dwiyanto, Dwiyanto Gammanr, Dzulfikri Gammanr Givy Devira Ramady Hidayat, Wisnu Iman Hidayat Ramadi, Givy Devira Ramdani, Rika ROSMALINA ROSMALINA, ROSMALINA Safarudin, Andi Salamatul Afiyah Subawi, Handoko Suhana, Asep Sukandi, Ganjar Kurniawan Sukirno Sukirno Willa Permatasari, Raden Wirjawan, Agung Ys, Herawati