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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.

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.