Fish farmers often face the problem of difficulty monitoring water quality from time to time. One method that can make it easier for fish farmers is to develop a system using the forward chaining method. This research aims to make it easier for fish farmers to monitor and control water quality automatically. This system can monitor and control automatically based on facts collected. Things to pay attention to in the system are the suitability level of temperature, pH, and turbidity in the water. The monitoring and control system using the forward chaining method functions 100% by the rules that have been fully implemented

Fish is one of the foods that contain protein and has a high nutrient content, including minerals, vitamins, and unsaturated fats. The protein content in fish is relatively high. However, fish also contains adverse effects if it has undergone spoilage. Therefore, it is necessary to select fresh fish and non-fresh fish. Currently, fish freshness selection is still done manually by fishermen. This research builds a system that can identify freshness in mackerel which is expected to be an alternative solution in the process of selecting freshness of fish manually which takes a long time. By utilizing the TCS3200 sensor to identify the color of the fish and MQ135 to detect the smell of ammonia gas in the fish. This system uses Arduino Uno as the controller of the whole system. The results obtained from the selection process are in the form of fresh and not fresh fish that have been separated based on color and odor indicators, the data will be displayed on the LCD in the form of the accumulated weight of each fish that has been selected. The results of 30 times testing the entire system as much as 30 data obtained an accuracy value of 86.7%.

Smarthome adalah sebuah sistem yang telah direncanakan dan dikembangkan dengan tujuan menyederhanakan beragam proses yang terjadi di dalam rumah. Dalam konteks ini, salah satu aspek utama yang diterapkan di rumah pintar adalah pemantauan pencahayaan ruangan. Pencahayaan merupakan faktor kunci dalam kenyamanan dan fungsionalitas kehidupan sehari-hari. Oleh karena itu, peran lampu dalam lingkungan rumah sangat dibutuhkan. Seringkali penghuni rumah cenderung lalai dalam mematikan lampu ketika tidak digunakan, hal ini dapat menyebabkan peningkatan konsumsi energi yang tidak efisien dan pemborosan sumber daya. Untuk mengatasi masalah ini, dibutuhkan teknologi yang mampu memantau dan mengendalikan pencahayaan secara otomatis. Pada penelitian ini, sebuah sistem pemantauan dan klasifikasi kondisi lampu dikembangkan dengan menerapkan metode naïve bayes. Dataset yang digunakan dalam penelitian ini terdiri dari 672 data latih dan 168 data uji. Pengujian dilakukan, dengan confusionmatrix, menghasilkan tingkat accuracy sebesar 98.71%, precision sebesar 98.16%, recall sebesar 98.42%, dan F-1 score sevesar 98.29%. Hasil penelitian ini menunjukkan bahwa metode naïve bayes sangat efektif dalam sistem pemantauan dan klasifikasi kondisi lampu lampu pada ruangan.