Sistem Manajemen Energi Listrik dan Otomatisasi pada Kolam Ikan

Indonesia has abundant fishery resources, including tilapia (Oreochromis niloticus), a leading commodity in freshwater fisheries. With increasing market demand locally and internationally, fish farmers must control water use and production costs more efficiently by intensive cultivation with Recirculating Aquaculture Systems (RAS). However, a significant challenge in RAS systems is the high operational cost, particularly the electrical energy consumption for pumps and aerators. The research aimed to implement energy management in RAS-based tilapia aquaculture by utilizing real-time electricity monitoring technology, ON-OFF control automation, and Variable Speed Drive (VSD) inverters. The researcher conducted tests on two conventional and intensive systems. The intensive system used a control strategy that integrates scheduling on timer outlets and pump speed control through inverters. The test results showed that the intensive system was more efficient than the conventional system, with an initial power consumption of 80.75 kWh, then decreasing to 67.97 kWh. The energy efficiency reached 16%, equivalent to a monthly cost saving of Rp17,279, making the total operational cost of the intensive system only Rp91,895 per month. In addition, the Blynk app-based control system enabled real-time energy monitoring and management, supporting energy savings and improved cost efficiency.

Comparative Analysis of LSTM, GRU and Meta Prophet Stock Forecasting Methods with Var-Es Risk Evaluation

This study compares the performance of Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Prophet models in predicting real estate stock prices on the Indonesia Stock Exchange (2019–2024) and evaluates investment risks using Value at Risk (VaR) and Expected Shortfall (ES). Historical stock data underwent normalization and dataset splitting (ratios of 70:30, 80:20, and 90:10), with time steps of 40, 60, and 100, and three dense layers (25 and 50 neurons). Performance was evaluated using MSE, RMSE, MAE, and MAPE. Results indicate that GRU achieved the highest accuracy, especially for PWON, ASRI, and DILD stocks, with the lowest MSE values (PWON: 120.7436, ASRI: 26.3150, DILD: 28.9713). LSTM showed competitive performance, while Prophet had the lowest accuracy for short-term predictions. Risk analysis revealed Prophet had the lowest historical risk but the highest risk for 150-day forecasts. LSTM demonstrated superior long-term risk mitigation. Comparison with actual prices revealed that LSTM and GRU more accurately captured stock price fluctuations than Prophet, particularly during sharp price changes. GRU provided the closest predictions in the 150-day forecast scenario, making it the most effective model for real estate stock forecasting. This study offers valuable insights for investors and portfolio managers in understanding stock price movements and managing investment risks in the real estate sector

Perancangan User Interface/Experience Aplikasi Penyewaan Motor Listrik Online XYZ Menggunakan Metode Kansei Engineering

This study aims to redesign the interface (UI) and user experience (UX) of the XYZ application with the Kansei Engineering approach to capture the emotional needs of users and translate them into design elements. The research process was carried out through initial usability testing using the System Usability Scale (SUS) method, followed by Kansei word identification, semantic differential questionnaire preparation, and multivariate statistical analysis (CCA, PCA, FA, and PLS). The resulting design draft was then retested using the SUS method. The results showed that the SUS score decreased from 61.25 to 60.13. This decrease indicates that although the Kansei Engineering approach can help compile designs based on user emotional perceptions, the implementation of the design still requires further evaluation to be functionally and emotionally aligned

Analysis of the Efficiency of a Microcontroller-Based Automatic Feeder Prototype in Aquaculture

This research aims to analyze the efficiency of a microcontroller-based automatic fish feeder prototype to support the optimization of aquaculture. The device utilizes an Arduino microcontroller, an LCD for the interface, and a mechanical motor for automatic feed dispensation twice daily, with volumes adjusted according to the fish's age. The experimental method was conducted in a fish farming pond, comparing automatic and manual feeding over 90 days for 100 fish. Results showed that the automatic system reduced average daily feed usage from 200 grams (manual) to 180 grams, and feed efficiency increased from 75% (manual) to 85% (automatic). Average fish weight gain with the automatic system reached 85 grams, 15 grams higher than the manual method (70 grams). The Feed Conversion Ratio (FCR) for the automatic system was 1.91, which is better than the manual FCR of 2.57. Ultimately, the automatic feeding system proved to be more efficient in feed usage and supported better fish growth. Keywords: Automatic Feeder, Efficiency, Aquaculture

Rancang Bangun Aplikasi Untuk Penyedia Kursus Mengemudi Berbasis Web Dengan Incremental Model

Seiring meningkatnya jumlah pemilik kendaraan bermotor roda empat, pihak berwenang, dalam hal ini, Dinas Perhubungan dan Satuan Kepolisian Republik Indonesia menghimbau para pengemudi baru untuk mengikuti Kursus Mengemudi sebelum mulai mengendarai mobil. Jumlah pengelola kursus mengemudi di Surabaya saat ini sudah menyentuh angka ratusan, dengan semakin banyaknya pengemudi-pengemudi baru dan persaingan antar penyedia jasa kursus mengemudi seperti saat ini, dibutuhkan sebuah Sistem Informasi untuk mengatasi potensi masalah tersebut. Dengan melakukan Rekayasa Perangkat Lunak, diharapkan penelitian ini akan menghasilkan aplikasi yang dapat membantu semua pihak yang terlibat dalam proses kursus mengemudi, baik bagi Siswa / Pelajar, Instruktur Kursus, dan Pemilik / Admin Kursus. Pengembangan Perangkat Lunak menggunakan Incremental Model memberikan tim pengembang kemampuan untuk berfokus terhadap kebutuhan setiap pengguna yang sudah disebutkan sebelumnya. Karakteristik iteratif yang dimiliki incremental model diyakini akan menghasilkan aplikasi berkualitas dan mampu menyelesaikan masalah yang dihadapi oleh pihak-pihak dalam rangkaian proses kursus mengemudi. Hasil pengujian berdasarkan standar ISO 25010:2023 menunjukkan bahwa perangkat lunak memperoleh skor sebesar 91,3% untuk faktor ketepatan fungsional, yang dikategorikan sebagai Sangat Tepat. Selain itu, untuk faktor kapabilitas interaksi, perangkat lunak mendapatkan skor sebesar 84,2%, yang diklasifikasikan sebagai Sangat Mudah digunakan

Optimalisasi Manajemen SDM Melalui Pengembangan Aplikasi Mobile Absensi dan Perizinan Digital pada Lembaga Amil Zakat Yatim Mandiri

In the current digital era, manual attendance systems are increasingly being abandoned due to inefficiencies, susceptibility to data manipulation, and time-consuming recap processes—particularly in large-scale organizations with over 500 employees spread across multiple provinces and two different time zones. The main challenges of manual systems include delayed data validation, inconsistency in attendance timing across regions, and difficulties in generating real-time integrated reports. To address these issues, this study developed a location-based (GPS), mobile, and real-time attendance application using the Rapid Application Development (RAD) approach, which emphasizes fast prototyping and active user involvement. The application was built using React Native for cross-platform mobile interfaces (iOS and Android), PHP Laravel for the backend, and PostgreSQL along with Firebase Cloud Messaging for database and notification services. Testing results show that the application achieves a location accuracy rate of 95%, reduces approval processing time from over one day (manual) to an average of 1–2 minutes, and ensures 100% success in real-time notification delivery. Additionally, the application simplifies cross-time-zone attendance recapitulation and achieved a user satisfaction rate of 92% based on a User Acceptance Test (UAT) involving 50 respondents from various branches. These findings indicate that the developed application is effective in enhancing operational efficiency, data accuracy, and transparency in the digital and integrated management of employee attendance

Linking Carbonate Facies to Stylolite Distribution of Middle Jurassic Limestone, Onshore Abu Dhabi Oil Field

This study examines the relationship between facies and stylolitization in the Upper Araej Member carbonates of onshore Abu Dhabi. Analysis of core and thin sections identified four facies: wispy-laminated skeletal wackestone (F-1), peloidal skeletal mud-dominated packstone (F-2), coated-grain skeletal grainstone (F-3), and peloidal skeletal floatstone (F-4), deposited across a shallow carbonate ramp. Stylolites were described and measured for vertical offset amplitude to assess facies dependence. Results show facies-related tendencies in stylolite amplitude and morphology. Mud-supported facies (especially floatstones and wackestones) tend to display higher variability, with floatstones reaching amplitudes of up to 20 mm, whereas grainstones may also contain isolated high-amplitude stylolites (up to 14 mm). Packstones and wackestones, by contrast, rarely exceed 10-13 mm. Boxplots highlight greater variability in mud-rich facies, whereas grainstones exhibit narrower distributions. Statistical testing (ANOVA, p = 0.109; Kruskal–Wallis, H = 3.38, p = 0.34) indicates no statistically significant differences in mean stylolite amplitude across facies, although descriptive data reveal trends in variability and extremity. Jagged stylolites occur in both mud-rich and grain-supported facies, whereas wispy seams are strongly associated with micrite-rich facies and are largely absent in grainstones. Stylolites in these carbonates may act as both vertical barriers and localized porosity enhancers. Their facies-associated occurrence emphasizes the need to integrate stylolitization into reservoir models to better predict connectivity, compartmentalization, and flow behavior in Middle Jurassic carbonates

Application of Point Counting Petrography for Provenance Determination; Implication for Tectonic Development from the Semilir Formation, Gunung Kidul

The provenance sediments have been analyzed to reveal tectonic development during the Semilir Formation deposition in the Southern Mountain of Yogyakarta area, using essential samples from field observation, petrography analysis, and the point counting method. Outcrop and sampling at two observation points revealed distinct lithological features, including sandstone and siltstone with mudclast structures and slump structures interbedded with siderite. Petrographic analysis using the point-counting method determined the mineral composition of four samples: 1A, 1B, 2A, and 2B. Samples 1A and 1B were categorized as Lithic Wacke, 2A Feldspathic Wacke, and 2B Lithic Wacke. Provenance analysis, crucial for understanding the sedimentary history and reconstructing the geological events preceding sediment deposition, identified that the sandstones in the Ngoro-oro region predominantly fall into the magmatic arc category. Hence, based on our analysis, the tectonic development during Semilir Fm deposition is linked with the convergence event of the first subduction on southern Java, which had huge volcanic influences and slope morphology common in volcanic areas. The findings of this study contribute to a deeper understanding of the tectonosedimentary processes and geological history of the Ngoro-oro region. The integrated approach of petrographic and provenance analyses provides a comprehensive view of the sedimentary rocks formation and evolution, enriching the geological knowledge of the area

Optimasi Parameter Operasional Mini Pembangkit Listrik Tenaga Angin Berbasis Machine Learning untuk Meningkatkan Output Daya

The utilization of renewable energy is experiencing significant growth, with wind turbines emerging as a key solution for generating environmentally friendly electricity. However, the efficiency of wind turbines is highly dependent on their operational parameters, such as wind speed, blade size, angular velocity, and torque. This research aims to optimize the operational parameters of small-scale wind turbines using an XGBoost-based Machine Learning model and an L-BFGS-B algorithm-based optimization method. A simulation dataset was generated based on the physical equations of wind turbine power and a MATLAB Simulink model, incorporating added noise to approximate real-world conditions. The XGBoost model was trained to predict the turbine's output power based on its operational parameters. Subsequently, an optimization method was employed to identify the parameter combination that yields maximum power. The experimental results demonstrate that the model exhibits strong performance, characterized by a low Mean Squared Error (MSE) and a high R-squared score. The optimization process successfully achieved a significant increase in power output compared to the initial configuration. Through this approach, wind turbine systems can operate more efficiently and generate optimal electrical power. This study contributes to the advancement of artificial intelligence-based optimization strategies for renewable energy systems

Penyelesaian Traveling Salesman Problem Dengan Algoritma Ant Colony Menggunakan Multi Processing

Traveling Salesman Problem (TSP) adalah tantangan utama dalam optimasi kombinatorial, di mana menemukan rute terpendek menjadi sulit dengan bertambahnya skala rute. Ant Colony Optimization (ACO) adalah metode populer berbasis perilaku semut untuk menjelajahi solusi optimal. Namun, ACO sering membutuhkan waktu komputasi tinggi, terutama untuk masalah berskala besar. Penelitian ini mengusulkan penggabungan pemrograman paralel melalui multiprocessing dan multithreading untuk mempercepat eksekusi tanpa mengurangi kualitas solusi. Hasil implementasi menunjukkan bahwa multithreading memberikan waktu eksekusi tercepat dengan nilai fitness konsisten, yaitu 0.0030 pada iterasi 100 dan 0.0022–0.0024 pada iterasi 1000. Pada iterasi 100, multithreading mencatat waktu 0.0023–0.0037 detik, sementara multiprocessing membutuhkan 0.1889–0.2041 detik. Pada iterasi 1000, multithreading mencapai 0.0032–0.0042 detik, sedangkan multiprocessing berkisar antara 0.1919–0.2266 detik. Multithreading meningkatkan efisiensi waktu eksekusi hingga sekitar 99,83% dibandingkan dengan ACO standar pada iterasi 100, dan sekitar 99,80% pada iterasi 1000, tanpa mengorbankan kualitas solusi. Penelitian ini menegaskan bahwa multithreading lebih efisien dibandingkan multiprocessing untuk menyelesaikan TSP menggunakan ACO