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Oil palm plants are essential as they produce palm fruit that can be processed into edible oil—an essential human need. However, these plants are often infected with diseases, negatively impacting crop productivity and the quality of the oil produced. These diseases are caused by mushrooms, bacteria, viruses, and pests that can spread rapidly and damage the leaves. Therefore, early detection of oil palm leaf disease plays a crucial role in reducing the negative impact on crops and significant economic losses. This study aims to design a system to classify the types of leaf diseases of oil palm plants using texture feature extraction (Haar Wavelet Algorithm) and machine learning-based classification algorithms (Cubic SVM, Medium Gaussian SVM, Quadratic SVM, Cosine KNN, Fine KNN, and Weighted KNN). Cubic SVM yielded the highest training result with an averages accuracy of 81.54% and an average time of 48.135 seconds. However, Medium Gaussian SVM outperformed other models during testing, producing an accuracy of 87%, precision of 81%, recall of 81 %, specificity of 90%, and F-score of 81%.

Pepper (Piper nigrum L.) is a high-value cash crop and plays a significant role in Indonesia's agricultural sector. However, pepper production is often hindered by diseases that affect the plant's leaves. This study aims to develop a pepper leaf disease detection model based on image analysis using a Radial Basis Function Neural Network (RBFNN). Conventional methods relying on expert visual assessment are often inefficient, especially on a large scale. In this research, image preprocessing was performed by transforming the images into the CIELAB color space and using K-Means Clustering for feature extraction. Texture feature extraction using the Gray Level Co-occurrence Matrix (GLCM) provides rich information about patterns and intensity distribution in the images, which is effective for distinguishing disease classes. The RBFNN algorithm is then used to identify diseases by capturing the complex non-linearities in the data. Based on the testing results, this model achieved an accuracy rate of 91.67%, demonstrating excellent performance.

Plants are incredibly beneficial to human survival in various ways. Leaves are part of plants widely used as medicine. They are similar in shape but have different advantages. Leaf types can only be identified by experts. This study aims to create a classification system for herbal leaves based on the Haar wavelet transform and machine learning. The study was carried out to assist ordinary people in recognizing herbal leaves. The results revealed that Haar wavelet level 1 was better suited to the leaf data. The Quadratic SVM model yielded the highest result with an accuracy of 77%, a precision of 83%, a recall of 83%, a specificity of 82%, and an F-score of 73%.

The palm oil industry, particularly in Southeast Asia, relies heavily on accurate ripeness classification of oil palm fruit bunches to ensure high-quality oil production. Despite advances in palm oil classification, distinguishing between different ripeness levels remains challenging due to subjective human judgment and labor-intensive traditional methods. This study proposes an intelligent classifier using color-based features to classify oil palm fruit bunches into three categories: ripe, half-ripe, and unripe. This framework involved capturing images of oil palm fruit bunches at Felda Chuping 2 using commercial camera, followed by image pre-processing such as resizing and cropping. Color-based features by means HSV-, RGB- and YCbCr-based features were extracted and used as significant features. The mean and standard deviation of colour-based features were then subjected to k-Nearest Neigbour (kNN) and Support Vector Machine (SVM) classifier utilizing two different strategies of hold-out and 10-fold cross-validation. Based on the results obtain, the YCbCr based features using kNN classifier achieved 97.40% (hold-out) and YCbCr based features using SVM classifier gives the highest recognition which is 100% (10-fold). The results shows that the use of colour space features able in distinguishing the ripeness levels of oil palm fruit bunches, thus considered as promising approach to be implemented in real-time application.

In recent years, IoT has become a reliable technology in the agricultural sector to optimize cultivation results. In the Marsudi Luhur breeder group, the harvest results were not optimal because many catfish died and gave off a bad smell. According to several references, to ensure healthy growth of catfish, continuous irrigation of the pond is required even though the flow rate is low. The location of the catfish pond is quite far from residential areas so it is not practical to monitor irrigation performance by frequently visiting the pond area. Therefore, this project aims to create an IoT-based smart farming system using the Blynk application. The system has been successfully implemented to help monitor and control solenoid performance and water flow remotely. The results show that the IoT-based smart system has performed well and has the potential to increase efficiency, comfort and productivity in managing catfish ponds.

Agriculture and plantations are vital for sustaining a significant portion of Indonesia’s population. However, the agricultural sector faces considerable challenges, particularly due to its dependence on weather conditions, leading to fluctuations in production and market volatility. Effective water management is crucial for plant growth, and the specific water requirements of various crops, including melon plants, necessitate careful irrigation. The advancement of Internet of Things (IoT) technology presents substantial benefits for agriculture by optimizing plant growth, deterring pests, and enhancing irrigation systems. This research focuses on developing an automatic irrigation system specifically for melon farming, utilizing IoT technology. A capillary irrigation system controlled by water level sensors is implemented to ensure precise water management, reducing waste while improving plant health and yield. By enhancing agricultural productivity and promoting water sustainability, this system offers an efficient and reliable solution for automating irrigation, making it a suitable option for both household gardens and small-scale melon farms. The success of similar agricultural technologies, such as hydroponics and aquaponics, as demonstrated by Studio Tani Kalisuci in Gunung Kidul, highlights the potential of innovative farming practices in overcoming challenging environmental conditions.

Indonesia has many cultural riches in the form of traditional fabrics, one of which is woven fabrics. Woven fabrics from each region showcase distinctive motifs, manifesting the local community’s daily life, culture, natural conditions, and beliefs. The diverse weaving motifs pose a challenge in determining the origin of the woven fabrics. It highlights the necessity of a system to detect and identify woven fabrics. Texture analysis was performed using the Gray Level Co-occurrence Matrix (GLCM). A classification method based on a Support Vector Machine (SVM) consisting of four models: Linear SVM, Quadratic SVM, Cubic SVM, and Fine Gaussian SVM was developed in this research. The images of woven fabrics came from three regions in Indonesia: Sumatra, Kalimantan, and Nusa Tenggara. This research utilized 240 training images and 12 testing images. The testing results unveiled that the Cubic SVM model, which achieved a 100% accuracy rate in 1.0835s, was the optimum SVM model for the weaving classification.

The process of hatching chicken eggs by farmers still uses manual methods, in the market itself there are hatching machine tools but not fully automated. So that the chicken breeding process is less effective and efficient to meet high market needs. In this study, an automatic chicken egg hatching incubator device based on the DHT 11 sensor was designed. The purpose of this design is to help farmers to hatch chicken eggs automatically. The way this tool works is to hatch chicken eggs automatically with a DHT 11 sensor that can read temperature and humidity so that temperature and humidity can be stable according to the needs obtained from the heat of incandescent lamps, fans, and water in the incubator. Then for the process of turning chicken eggs, farmers do not need to do it manually because they already use automatic racks regulated by RTC (Real Time Clock). For the egg turning schedule so that it will move the dynamo motor and the egg rack will shift to turn the eggs. As a result of this study, the well-designed device works and can produce 91% egg hatching with stable temperature and humidity and scheduled egg turning.

SD Muhammadiyah Sangonan 1 has implemented fingerprint attendance, where teachers and staff record their attendance when they arrive and leave. Attendance records are still manually recorded by administrative personnel, and attendance reporting is limited to attendance recapitulation. In short, the efficiency of managing teacher and staff attendance administration is still low. Therefore, this research aims to improve the efficiency of teacher and staff administration through the implementation of image processing technology for fingerprint attendance. The planned stages of the research are planning, fingerprint attendance system development, administration system training, and program evaluation. In its implementation, this program has proven to be effective in improving the effectiveness of SD Muhammadiyah Sangonan 1 Godean’s school administration in terms of easier and faster attendance data processing. The outputs achieved include mass media news and videos.