Analisa Fitur Tekstur Nukleus dan Deteksi Sitoplasma pada Citra Pap Smear

Currently the identification of Pap smear cells in the early detection process of cervical cancer is still an important stage of the process. The ease of detecting Pap smear cells will be very helpful in the introduction of cell abnormalities. Pap smear cell images consist of parts of the nucleus and cytoplasm. Proper analysis of parts of the nucleus and cytoplasm will facilitate the process of identifying cell abnormalities. This study presents Pap smear cell texture analysis on the pap smear cell nucleus and segmentation of the cytoplasmic area. Texture analysis was performed on 250 cell images of the nucleus. While cytoplasmic segmentation was performed for 887 cytoplasmic cell images. Senua cell image used has class categories categorized into seven classes. Three classes of them are normal cell image class categories that include: Normal Superficial, Normal Intermediate, and Normal Columnar, and the other four classes are abnormal cell image class categories which include: mild dysplasia, moderate dysplasia, severe dysplasia and carcinoma Di There. The method used for texture analysis using 8 bit grayscale. And using the second sequence of Gray Level Co-occurrence Matrix (GLCM) statistics, with contrast, correlation, energy, homogeneity and entropy features. Cytoplasmic detection uses edge detection and some morphological analyzes. The results showed that the numerical results of all the texture of the nucleus for each class of Pap smear image had slightly different properties. As for the results of cytoplasmic detection showed that the stage of the proposed detection process results in a clean area of the cytoplasm and can be detected wel

ANALISA FITUR TEKSTUR NUKLEUS DAN DETEKSI SITOPLASMA PADA CITRA PAP SMEAR

Currently, the identification of Pap smear cells in the early detection process of cervical cancer is still an important stage of the process. The ease of detecting Pap smear cells will be very helpful in the introduction of cell abnormalities. Pap smear cell images consist of parts of the nucleus and cytoplasm. Proper analysis of parts of the nucleus and cytoplasm will facilitate the process of identifying cell abnormalities. This study presents Pap smear cell texture analysis on the pap smear cell nucleus and segmentation of the cytoplasmic area. Texture analysis was performed on 250 cell images of the nucleus. While cytoplasmic segmentation was performed for 887 cytoplasmic cell images. Senua cell image used has class categories categorized into seven classes. Three classes of them are normal cell image class categories that include: Normal Superficial, Normal Intermediate, and Normal Columnar, and the other four classes are abnormal cell image class categories which include: mild dysplasia, moderate dysplasia, severe dysplasia, and carcinoma Di There. The method used for texture analysis using 8-bit grayscale. And using the second sequence of Gray Level Co-occurrence Matrix (GLCM) statistics, with contrast, correlation, energy, homogeneity, and entropy features. Cytoplasmic detection uses edge detection and some morphological analyzes. The results showed that the numerical results of all the texture of the nucleus for each class of Pap smear image had slightly different properties. As for the results of cytoplasmic detection showed that the stage of the proposed detection process results in a clean area of the cytoplasm and can be detected wel

DIAGNOSIS OF CORONAVIRUS DISEASE 2019 (COVID-19) SURVEILLANCE USING C4.5 ALGORITHM

Coronavirus Disease 2019 (COVID-19) has become a pandemic in Indonesia as a non-natural disaster in the form of disease outbreaks which must be undertaken as a response. The Ministry of Health in the Republic of Indonesia published a guidebook for prevention and control of COVID-19 in its response efforts. This guideline is intended for health officials as a reference in preparing for COVID-19. This handbook contains early detection and response activities to identify conditions of PDP, ODP, OTG, or confirmed cases of COVID-19. The efforts made are adjusted to the world situation progress from COVID-19 which is monitored by the World Health Organization (WHO). From the results of documentation studies that have been carried out on the COVID-19 pandemic in Indonesia, there are several problems that must be resolved from the prevention of the disease outbreak COVID-19. Lack of knowledge and awareness of the general public in the prevention and control of COVID-19 is one of the factors increasing the spread of that virus in Indonesia. Furthermore, there are difficulties in carrying out surveillance, early detection, contact tracing, infection prevention or control, and risk communication or people empowerment. This is due to the lack of implementation and testing on artificial intelligence methods for COVID-19 diagnosis that can be used by the public. The purpose of this research is to make a diagnosis of surveillance classification which includes PDP, ODP, and OTG using the C4.5 algorithm. The results showed that the diagnosis of the COVID-19 surveillance category using the C4.5 algorithm was successfully modeled into a decision tree with PDP, ODP, and OTG classification. The testing process in a confusion matrix with 3 (three) classes produces an accuracy rate of 92.86% which is included in the excellent classification category

Optimization the Naive Bayes Classifier Method to diagnose diabetes Mellitus

World Health Organization (WHO) states that Diabetes Mellitus is the world's top deadly  disease. several studies in the health sector including diabetes mellitus have been carried out to detect diseases early. In this study  optimization of naive bayes classifier using particle swarm optimization was applied to the data of patients with 2 classes namely positive diabetes mellitus and negative diabetes  mellitus and data on patients with 3 classes, those who tested positive for diabetes mellitus type 1, diabetes mellitus type 2 and negative diabetes mellitus. After testing, the algorithm of Naive Bayes Classifier and Naive Bayes Classifier based on Particle Swarm Optimization, the results obtained are the Naive Bayes Classifier method for 2 classes and 3 classes each producing an accuracy value of 78.88% and 68.50%. but after adding Particle Swarm Optimization the value of accuracy increased respectively to 82.58% and 71, 29%. The classification results for 2 classes have an accuracy value higher than 3 classes with a difference of 11.29

K-MEANS SEGMENTATION AND CLASSIFICATION OF SWIETENIA MAHAGONI WOOD DEFECTS

The potential and usefulness of wood to meet the needs of human life are not in doubt. Demands us to continue to maintain the quality. Wood quality is closely related to wood defects. Manual defect checks in the wood industry are unreliable because they are prone to human error, For example, due to acute symptoms of headaches and tired eyes, technology in the form of image processing can help identify wood defects Swietenia Mahagoni. In this case, the method used is Euclidean distance with a ratio of k-means segmentation and thresholding on 42 images of wood defects consisting of 3 types of defects, namely growing skin defects, rotting knots, and healthy knots, every 14 images with data sharing. training for 30 images and testing for 12 images. The results of the k-means segmentation are then extracted on 6 features including metric, eccentricity, contrast, correlation, energy, and homogeneity using the Gray Level Co-occurrence Matrix (GLCM) extractor and classified by calculating the closest distance using the euclidean distance between the results of data feature extraction. testing of the value of feature extraction in the training data which is used as a previous database. It is the smallest value that indicates the type of defect. The success calculation is presented in the confusion matrix calculation and gets a success or accuracy value of 91.67%

Segmentasi K-Means Citra Daun Tin Dengan Klasifikasi Ciri Gray Level Co Occurance Matrix

Tanaman Tin dengan nama latin Ficus Caric adalah sejenis tanaman buah dari sejenis pohon yang banyak tumbuh di kawasan daerah tropis dan subtropis. Tanaman Tin saat ini sudah banyak dibudidayakan di Indonesia. Buah Tin memiliki buah yang berwarna kuning kecoklatan, dengan rasa yang manis. Cerotelium Fici adalah jenis penyakit karat daun yang menyerang pada daun tin, dan menjadi ancaman terbesar terhadap produksi buah tin. Penyakit lain yang menyerang pada tanaman tin adalah kutu kebul dan virus mosaik. Virus mosaik ini pertama kali muncul di California dan menyebar ke sebagaian besar wilayah Indonesia dan Amerika Serikat. Daun tin yang terinfeksi virus ini menjadi bintik-bintik cokelat menyebabkan pertumbuhan tanaman tin menjadi lambat dan cacat pada buah tin. Dalam perkembangan ilmu pengetahuan dan teknologi, cara untuk mendeteksi penyakit pada tanaman tin seperti karat daun, virus mosaik dan kutu kebul dapat dilakukan dengan bantuan pengolahan citra. Untuk itu penelitian ini bertujuan melakukan pengolahan citra berupa segmentasi K-Means pada citra daun tin yang dianalisa dengan ekstrasi fitur GLCM dan mengklasifikasikan Naïve Bayes untuk mendapatkan akurasi terbaik dalam klasifikasi penyakit citra daun tin. Setelah itu, dilakukan analisis tekstur menggunakan metode Grey Level Co-Occurance Matrix (GLCM) dan segmentasi K-Means clustring dalam pengolahan citra daun tin

Improved point center algorithm for K-Means clustering to increase software defect prediction

The k-means is a clustering algorithm that is often and easy to use. This algorithm is susceptible to randomly chosen centroid points so that it cannot produce optimal results. This research aimed to improve the k-means algorithm’s performance by applying a proposed algorithm called point center. The proposed algorithm overcame the random centroid value in k-means and then applied it to predict software defects modules’ errors. The point center algorithm was proposed to determine the initial centroid value for the k-means algorithm optimization. Then, the selection of X and Y variables determined the cluster center members. The ten datasets were used to perform the testing, of which nine datasets were used for predicting software defects. The proposed center point algorithm showed the lowest errors. It also improved the k-means algorithm’s performance by an average of 12.82% cluster errors in the software compared to the centroid value obtained randomly on the simple k-means algorithm. The findings are beneficial and contribute to developing a clustering model to handle data, such as to predict software defect modules more accurately

Deteksi Defect Coffee Pada Citra Tunggal Green Beans Menggunakan Metode Ensamble Decision Tree

Kopi merupakan salah satu komoditas minuman unggulan, sehingga permintaan biji kopi meningkat dari tahun ke tahun. Permintaan biji kopi didasarkan pada kualitas. Terdapat bebarapa faktor yang mempengaruhi kualitas antara lain bagaimana kopi ditanam dan dipanen, adapun kurangnya nutrisi dan perlindungan tanaman yang tidak memadai, maka akan menghasilkan kopi yang berkualitas rendah. Biji kopi berkualitas rendah sering kali disebut defects. Identifikasi defects coffee sangat penting khususnya bagi para petani dan pengusaha kopi agar dapat memilih biji kopi yang berkualitas tinggi sehingga meningkatkan nilai jual biji kopi. Pada beberapa industri kopi maupun makanan, teknik untuk mengidentifikasi cacat biji kopi biasa dengan cara seleksi manual dan mekanik, yang mana membutuhkan waktu yang lama dan dapat merusak biji kopi. Oleh karena itu diperlukan suatu pendekatan yang lebih modern dalam mengidentifikasi cacat biji kopi  seperti pengolahan citra. Untuk itu penelitian ini bertujuan melakukan pengolahan citra berupa segmentasi pada citra green beans coffee menggunakan metode thresholding. Setelah itu dilakukan analisis tekstur menggunakan GLCM (Grey Level Co-occurence Matrix) dan dilanjutkan dengan pemodelan klasifikasi menggunakan algoritma C4.5 dengan bagging. Dari hasil penelitian yang diperoleh, akurasi dari penggunaan algoritma C4.5 dengan bagging sebesar 94%

Implementasi Bantuan Sosial Tunai Bagi Masyarakat Yang Terdampak Pandemi Covid-19

Penelitian ini bertujuan untuk mendapatkan informasi yang berkaitan dengan pelaksanaan penyaluran program bantuan sosial tunai bagi masyarakat yang terdampak COVID-19 melalui PT Pos Indonesia. Penelitian ini dilatarbelakangi oleh keingintahuan penulis terhadap tingkat kepuasan dan harapan dari Keluarga Penerima Manfaat (KPM) program bantuan sosial tunai yang disalurkan melalui PT Pos Indonesia. Jumlah sampel yang terdapat pada penelitian ini yaitu sebanyak 2.131 responden yang tersebar di 69 Kabupaten/Kota atau sekitar 21 Provinsi yang telah berpartisipasi dalam kuisioner yang telah dibuat, dari hasil analisis data yang sudah dikumpulkan didapatkan hasil bahwa  masih terdapat kesenjangan yang terjadi antara kinerja yang dihasilkan dengan harapan yang seharusnya diperoleh penerima manfaat, seperti: 1) PT Pos Indonesia memiliki peralatan yang canggih dalam melayani KPM (Q4 = -0,12), 2) Pegawai PT  Pos Indonesia tidak memperhatikan dengan baik dalam melayani KPM (Q8 = -0,34), 3) Pegawai PT  Pos Indonesia tidak perduli terhadap KPM (Q14 = -0,41), 4) Pegawai PT  Pos Indonesia sangat lambat dalam melayani KPM (Q19 = -0,38) serta 5) KPM merasa kesulitan dalam mengambil bantuan di Kantor Pos (Q23 = -0,41). Kesimpulan dari kegiatan ini bahwa secara umum program penyaluran bantuan sosial tunai bagi masyarakat yang terdampak COVID-19 melalui PT Pos Indonesia sudah cukup baik, efektif dan efisien serta sangat potensial dalam memberdayakan keluarga penerima manfaat secara berkelanjutan, namun masih ada hal-hal yang harus diperbaiki seperti: pelayanan pegawai PT Pos Indonesia kepada para keluarga penerima manfaat harus lebih ditingkatkan serta kewajiban dari PT Pos Indonesia sendiri yang seharusnya mengirimkan dana bantuan tersebut kepada KPM itu sendiri (jasa antar)

Combination of Technology Acceptance Model and Decision-making Process to Study Retentive Consumer Behavior on Online Shopping

During the spread of the Covid-19 virus, generally the Indonesian people began to switch from conventional markets to buying and selling goods and services online with various features and conveniences offered to users. The purpose of this study is to find out the extent to which indicators of satisfaction and trust influence consumer attitudes and behavior when deciding to make transactions at online shops. The study method uses a combination of TAM (Theory Acceptance Model) and DMP (Decision Making Process) models using a sampling of 110 student respondents and the public who have made transactions in online shops. Data analysis using SEM (Structural Equation Modeling) theory. The results showed that satisfaction and trust will influence consumers in shaping