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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.

The set of timers in using phototherapy is major problem which has to be resolved to get a good performance of rhinitis phototherapy. This research aims to develop a prototype of phototherapy for allergic rhinitis, incorporating a timer based on light energy. The prototype utilizes a laser diode as a visible light source, specifically with a wavelength of 650 nm. The recommended safe and effective dose of light energy ranges from 1 to 10 Joules, which has been converted into minutes. Measurement tests indicate an average wavelength of 652.40 nm for the right laser, with a measurement uncertainty of ±0.11, and 653.23 nm for the left laser, with a measurement uncertainty of ±0.05. The laser diode source has an average voltage of 1.91 volts and an average current of 1.89 milliamperes, with a measurement uncertainty of ±0.00 and ±0.01, respectively. Additionally, the average discrepancy in the timer is 0.082 minutes for the 10-minute setting and 0.082 minutes for the 20-minute setting. These results confirm the effectiveness and suitability of the developed tool for practical use. The proposed method was useful for rhinitis therapy by using light energy. [ABSTRACT FROM AUTHOR]

Since its emergence in the early 20th century, Malaria has been confirmed as a deadly disease that has spread throughout the world with very high mortality and morbidity. This is in accordance with the WHO report in 2018 which stated that worldwide there have been more than 220 million cases of malaria with a death rate of nearly 500 thousand cases. However, Malaria is actually a disease that can be cured and prevented if treatment initiatives are implemented early and effectively. Unfortunately, this disease is often ignored because it is considered the common cold and is only diagnosed when it has reached a critical phase. This research is expected to be an alternative for early diagnosis of malaria. Hence, confirming the presence of the malaria parasite earlier will make the treatment of this disease more effective in reducing mortality. This research is expected to produce website-based computer-assisted disease diagnosis (CAD) software enriched with deep learning algorithms to become an alternative for early diagnosis of malaria. This CAD system has the potential to provide fast and reliable malaria diagnosis and avoid detection errors by experts due to human error. This research uses various pre-trained CNN architectures that have been proven to have the best performance in extracting features and recognizing image patterns such as MobileNetV2, EfficientNetBO, RestNet50, InceptionV3 and Xception. This architecture was then modified by adding several additional layers to improve its performance. To be concluded, this research succeeded in exceeding previous studies by obtaining an accuracy value above 97%. Moreover, this developed CAD software is also equipped with various features to make it easier to use. [ABSTRACT FROM AUTHOR]

Malaria is categorised as a dangerous disease that can cause fatal in many countries. Therefore, early detection of malaria is essential to get rapid treatment. The malaria detection process is usually carried out with a 100x magnification of thin blood smear using microscope observation. However, the microbiologist required a long time to identify malaria types before applying any proper treatment to the patient. It also has difficulty to differentiate the species in trophozoite stages because of similar characteristics between species. To overcome these problems, a computer-aided diagnosis system is proposed to classify trophozoite stages of Plasmodium Knowlesi (PK), Plasmodium Falciparum (PF) and Plasmodium Vivax (PV) as early species identification. The process begins with image acquisition, image processing and classification. The image processing involved contrast enhancement using histogram equalisation (HE), segmentation procedure using a combination of hue, saturation and value (HSV) color model, Otsu method and range of each red, green and blue (RGB) color selections, and feature extraction. The features consist of the size of infected red blood cell (RBC), brown pigment in the parasite, and texture using Gray Level Co-occurrence Matrix (GLCM) parts. Finally, the classification method using Multilayer Perceptron (MLP) trained by Bayesian Rules (BR) show the highest accuracy of 98.95%, rather than Levenberg Marquardt (LM) and Conjugate Gradient Backpropagation (CGP) training algorithms. [ABSTRACT FROM AUTHOR]

Iris recognition has very high recognition accuracy in comparison with many other biometric features. The iris pattern is not the same even right and left eye of the same person. It is different and unique. This paper proposes an algorithm to recognize people based on iris images. The algorithm consists of three stages. In the first stage, the segmentation process is using circular Hough transforms to find the region of interest (ROI) of given eye images. After that, a proposed normalization algorithm is to generate the polar images than to enhance the polar images using a modified Daugman's Rubber sheet model. The last step of the proposed algorithm is to divide the enhance the polar image to be 16 divisions of the iris region. The normalized image is 16 small constant dimensions. The Gray-Level Co-occurrence Matrices (GLCM) technique calculates and extracts the normalized image's texture feature. Here, the features extracted are contrast, correlation, energy, and homogeneity of the iris. In the last stage, a classification technique, discriminant analysis (DA), is employed for analysis of the proposed normalization algorithm. We have compared the proposed normalization algorithm to the other nine normalization algorithms. The DA technique produces an excellent classification performance with 100% accuracy. We also compare our results with previous results and find out that the proposed iris recognition algorithm is an effective system to detect and recognize person digitally, thus it can be used for security in the building, airports, and other automation in many applications. [ABSTRACT FROM AUTHOR]

Corrosion of steel reinforcement is a major cause of structural damage that requires repair or replacement. Early detection of steel corrosion can limit the extent of necessary repairs or replacements and costs associated with the rehabilitation works. The ground penetrating radar (GPR) method has been found to be a useful method for evaluating reinforcement corrosion in existing concrete structures. In this paper, GPR was utilized to assess corrosion of steel reinforcement in a concrete slab. A technique for accelerating reinforcement bar corrosion using direct current (DC) power supply with 5% sodium chloride (NaCl) solution was used to induce corrosion to embedded reinforcement bars (rebars) in this concrete slab. A 2 GHz GPR was used to assess the corrosion of the rebars. The analysis of the results of the GPR data obtained shows that corrosion of the rebars could be effectively localized and assessed. [ABSTRACT FROM AUTHOR]

This study develops a novel cervical precancerous detection system by using texture analysis of field emission scanning electron microscopy (FE-SEM) images. The processing scheme adopted in the proposed system focused on two steps. The first step was to enhance cervical cell FE-SEM images in order to show the precancerous characterization indicator. A problem arises from the question of how to extract features which characterize cervical precancerous cells. For the first step, a preprocessing technique called intensity transformation and morphological operation (ITMO) algorithm used to enhance the quality of images was proposed. The algorithm consisted of contrast stretching and morphological opening operations. The second step was to characterize the cervical cells to three classes, namely normal, low grade intra-epithelial squamous lesion (LSIL), and high grade intra-epithelial squamous lesion (HSIL). To differentiate between normal and precancerous cells of the cervical cell FE-SEM images, human papillomavirus (HPV) contained in the surface of cells were used as indicators. In this paper, we investigated the use of texture as a tool in determining precancerous cell images based on the observation that cell images have a distinct visual texture. Gray level co-occurrences matrix (GLCM) technique was used to extract the texture features. To confirm the system's performance, the system was tested using 150 cervical cell FE-SEM images. The results showed that the accuracy, sensitivity and specificity of the proposed system are 95.7%, 95.7% and 95.8%, respectively. [ABSTRACT FROM AUTHOR]

Fourier transform infrared (FTIR) spectroscopy technique can detect the abnormality of a cervical cell that occurs before the morphological change could be observed under the light microscope as employed in conventional techniques. This paper presents developed features extraction for an automated screening system for cervical precancerous cell based on the FTIR spectroscopy as a second opinion to pathologists. The automated system generally consists of the developed features extraction and classification stages. Signal processing techniques are used in the features extraction stage. Then, discriminant analysis and principal component analysis are employed to select dominant features for the classification process. The datasets of the cervical precancerous cells obtained from the feature selection process are classified using a hybrid multilayered perceptron network. The proposed system achieved 92% accuracy. [ABSTRACT FROM AUTHOR]