The complexity of brain tissue requires skillful technicians and expert medical doctors to manually analyze and diagnose Glioma brain tumors using multiple Magnetic Resonance (MR) images with multiple modalities. Unfortunately, manual diagnosis suffers from its lengthy process, as well as elevated cost. With this type of cancerous disease, early detection will increase the chances of suitable medical procedures leading to either a full recovery or the prolongation of the patient’s life. This has increased the efforts to automate the detection and diagnosis process without human intervention, allowing the detection of multiple types of tumors from MR images. This research paper proposes a multi-class Glioma tumor classification technique using the proposed deep-learning-based features with the Support Vector Machine (SVM) classifier. A deep convolution neural network is used to extract features of the MR images, which are then fed to an SVM classifier. With the proposed technique, a 96.19% accuracy was achieved for the HGG Glioma type while considering the FLAIR modality and a 95.46% for the LGG Glioma tumor type while considering the T2 modality for the classification of four Glioma classes (Edema, Necrosis, Enhancing, and Non-enhancing). The accuracies achieved using the proposed method were higher than those reported by similar methods in the extant literature using the same BraTS dataset. In addition, the accuracy results obtained in this work are better than those achieved by the GoogleNet and LeNet pre-trained models on the same dataset.

Abstract. Use of XML offers a structured approach for representing information while maintaining separation of form and content. XML information retrieval is different from standard text retrieval in two aspects: the XML structure may be of interest as part of the query; and the information does not have to be text. In this paper, we describe an investigation of approaches to retrieve text and images from a large collection of XML documents, performed in the course of our participation in the INEX 2006 Ad Hoc and Multimedia tracks. We evaluate three information retrieval similarity measures: Pivoted Cosine, Okapi BM25 and Dirichlet. We show that on the INEX 2006 Ad Hoc queries Okapi BM25 is the most effective among the three similarity measures used for retrieving text only, while Dirichlet is more suitable when retrieving heterogeneous (text and image) data.

Penelitian ini bertujuan untuk mengetahui sejauh mana peningkatan hasil belajar siswa kelas IX.A pada materi report text. Penelitian dilakukan pada Kelas IX.A di SMP Negeri 1 Sape semester 2 Tahun Pelajaran 2020-2021. Subjek penelitian sebayank 33 siswa. Metode yang digunakan dalam penelitian ini adalah metode penelitian tindakan kelas yang terdiri dari dua tindakan siklus yaitu I dan siklus II. Penelitian ini menggunakan metode penelitian kuantitatif berupa tes tulis dan metode penelitian kualitatif menggunakan lembar pengamatan dan lembar refleksi diri. Hasil penelitian menunjukkan bahwa pembelajaran berdiferensiasi dapat meningkatkan hasil belajar pada materi report text dengan pencapaian ketutasan belajar dari kondisi awal pra siklus diperoleh 36,36% menjadi 66,67% pada siklus I dan pada siklus II mencapai 90,91%.

Classification of brain tumor is one of the most vital tasks within medical image processing. Classification of images greatly depends on the features extracted from the image, and thus, feature extraction plays a great role in the correct classification of images. In this paper, an enhanced method is presented for glioma MR images classification using hybrid statistical and wavelet features. In the proposed method, 52 features are extracted using the first-order and second-order statistical features (based on the four MRI modalities: Flair, T1, T1c, and T2) in addition to the discrete wavelet transform producing a total of 152 features. The extracted features are applied to the multilayer perceptron (MLP) classifier. The results using the MLP were compared with various known classifiers. The method was tested on the dataset MICCAI BraTS 2015 which is a standard dataset used for research purposes. The proposed hybrid statistical and wavelet features produced 96.72% accuracy for high-grade glioma and 96.04% accuracy for low-grade glioma, which are relatively better compared to the existing studies. INDEX TERMS MRI classification, glioma tumor, hybrid statistical features, multilayer perceptron.

Abstract-With the advent of more powerful computing devices, system automation plays a pivotal role. In the medical industry, automated image classification and segmentation is an important task for decision making about a particular disease. In this research, a new technique is presented for classification and segmentation of low-grade and high-grade glioma tumors in Multimodal Magnetic Resonance (MR) images. In the proposed system, each multi modal MR image is divided into small blocks and features of each block are extracted using three Dimensional Discrete Wavelet Transform (3D DWT). Random Forest classifier is used for the classification of multiple Glioma tumor classes, then segmentation is performed by reconstructing the MR image based on the classified blocks. MIC CA I BraTS dataset is used for testing the proposed technique and experiments are performed for Low Grade Glioma (LGG) and High Grade Glioma (HGG) datasets. The results are compared with different classifiers e.g. multi layer perceptron, radial basis function, NaIve Bayes, etc., After careful analysis, Random Forest classifier provided better precision by securing average accuracy of 89.75% and 86.87% is obtained for HGG and LGG respectively.

AbstrakPenelitian ini bertujuan untuk memperoleh gambaran objektif dan komprehensif tentang proses perekrutan pengawas pendidikan dan peran pengawas pendidikan, serta mengetahui faktor-faktor penghambat dan pendukung peran pengawas untuk meningkatkan mutu pendidikan SMP di Kabupaten Bima Provinsi Nusa Tenggara Barat. Penelitian ini menggunakan pendekatan penelitian kualitatif dengan metode studi kasus. Data dikumpulkan melalui wawancara, observasi dan studi dokumen. Tahapan analisis data menggunakan model interaktif dan analisis komponensial. Hasil penelitian menemukan: (1) proses perekrutan pengawas pendidikan belum sepenuhnya sesuai dengan peraturan pemerintah dan undang-undang. (2) pemantauan pelaksanaan program sekolah yang dilakukan pengawas pendidikan belum terlaksana dengan optimal. (3) supervisi yang dilakukan pengawas pendidikan belum terlaksana dengan optimal. (4) evaluasi program kerja sekolah yang dilakukan pengawas pendidikan sudah terlaksana dengan baik. (5) pembuatan laporan hasil pemantauan, supervisi, dan evaluasi yang dilakukan pengawas pendidikan terlaksana dengan baik. (6) Tindak lanjut yang dilakukan pengawas pendidikan belum optimal. Selanjutnya yang menjadi (7) faktor penghambat adalah: (a) letak geografis, (b) akses jalan, (c) fasilitas, (d) penguasaan teknologi, (e) sumber daya manusia; dan (8) faktor pendukung adalah: (a) dana operasional tambahan dari Pemerintah Daerah, (b) ketersediaan motor dinas, (d) pelatihan, (e) keterlibatan masyarakat, (f) tempat domisili pengawas pendidikan, (g) siswa, (h) semangat dalam diri pengawas pendidikan. Kata kunci: pengawas pendidikan, mutu pendidikan, Kabupaten Bima THE ROLE OF EDUCATIONAL SUPERVISOR IN IMPROVING EDUCATION QUALITY OF SECONDARY SCHOOLS IN BIMA, WEST NUSA TENGGARA PROVINCE AbstractThis study aims to analyze and obtain comprehensive and objective description of recruiting process of educational supervisor, the role of educational supervisor, also to know the obstacles and supporting factors of educational supervisor roles to improve the education quality of secondary schools in Bima, West Nusa Tenggara. This is a qualitative approach with case study method. The data were collected through interviews, observation and document study. The phase of data analysis used by interactive model and componential analysis. The results showed: 1) the recruitment process of educational supervisor in Bima was not based on goverment rules and law. 2) monitoring of scholl programmes implementation by educational supervisor ran less optimal. 3) supervising by educational supervisor ran less optimal. 4) evaluating of school programmes undertaken by educational supervisor has ran well. 5) reporting about the results of monitoring, supervising, and evaluating, was done well. 6) the follow up of the results of supervision was not optimal. 7) some obstacles factors are: a) geographical area, b) highway access, c) facilities, d) mastery of information and technology, e) human resources. Then, some

Dampak yang terjadi saat pandemi covid 19 bukan hanya pada sector ekonomi, namun juga dalam pendidikan, salah satunya adalah mengakibatkan pembelajaran daring, banyak dampak yang di rasakan saat pembelajaran daring, dampak negative lebih menonjol dari pada dampak positif saat menjalani pembelajaran daring. Tujuan penelitian ini adalah untuk mengetahui dampak pembelajaran daring terhadap siswa usia 5-8 tahun saat pandemi covid 19. Metode penelitian ini adalah menggunakan metode kualitatif fenomenologis, data di peroleh melalui angket, subjek dari penelitian ini adalah orang tua yang memiliki anak usia 5-8 tahun guru PAUD dan Guru SD tingkat rendah Kecamatan Darma Kabupaten Kuningan. Hasil penelitian ini adalah bahwa pembelajaran daring memiliki beberapa dampak pada siswa yaitu siswa menjadi kurang bersosialisasi, siswa mengalami kekerasan verbal, kurangnya kedisiplinan dalam pembelajaran di rumah, fasilitas pembelajaran yang tidak memadai, dan tidak tercapai tujuan pembelajaran pada siswa

-Traditionally, computer programming assignments are graded manually by educators. As this task is tedious, timeconsuming and prone to bias, the need for automated grading tool is necessary to reduce the educators' burden and avoid inconsistency and favoritism. Recent researches have claimed that Latent Semantic Analysis (LSA) has the ability to represent human cognitive knowledge to assess essays, retrieving information, classification of documents and indexing. In this paper, we adapt LSA technique to grade computer programming assignments and observe how far it can be applied as an alternative approach to traditional grading methods by human. The grades of the assignments are generated from the cosine similarity that shows how close students' assignments to the model answers in the latent semantic vector space. The results show that LSA is not able to detect orders of computer programming and symbols; however, LSA is able to grade assignments faster and consistently, which avoid bias and reduces the time spent by human.