The COVID-19 epidemic has become a significant global obstacle as it has impacted people's lives in various sectors, including social, economic, and education. To respond to the shock caused to education systems, massive efforts—such as conducting formal education through online classes—have been made. This study has employed Structural Equation Modeling (SEM) to examine this arena during the COVID-19 pandemic and has elaborated on how effectively the education system responded, especially through online lecturing. The Technology Acceptance Model (TAM) was implemented as this study's theoretical framework. Partial Least Squares Structural Equation Modeling was employed to measure and assess the proposed model. This study was conducted through a survey with 112 student participants in a postgraduate program between January and December 2021. The findings showed that (1) the TAM-based proposed variables have been successfully explained during the pandemic through factors predicting the use by an online class of postgraduate students, (2) significant effects were reported from perceived ease of use and perceived usefulness toward actual system use through behavioral intentions to use, (3) there were no significant results to show an indirect effect from perceived ease of use and perceived usefulness toward actual system use through behavioral intentions to use.

Pulau panjang merupakan salah satu wilayah di perairan Kabupaten Jepara yang memiliki keanekaragamanan ekosistem perairan, antara lain adalah ekosistem lamun yang merupakan tempat hidup bagi biota-biota perairan yang salah satunya adalah epifauna. Secara ekologi, padang lamun mempunyai beberapa fungsi penting di daerah pesisir yang salah satunya yaitu berfungsi menstabilkan dasar-dasar lunak di mana kebanyakan spesies tumbuh, terutama dengan sistem akar yang padat dan saling menyilang. Penelitian ini bertujuan untuk mengetahui kelimpahan epifauna yang berasosiasi pada kerapatan lamun yang berbeda dan mengetahui hubungan kelimpahan epifauna yang berasosiasi pada kerapatan lamun yang berbeda diperairan pantai pulau panjang jepara. Penelitian ini dilaksanakan pada bulan Mei-Juni 2012, di perairan pantai Pulau Panjang, Kabupaten Jepara.  Metoda sampling yang digunakan adalah metoda pemetaan sebaran lamun. Metode yang digunakan dalam penelitian ini adalah metode survey yang bersifat deskriptif. Tingkat kerapatan lamun dibagi menjadi 3 stasiun dengan kerapatan yang berbeda yaitu kerapatan jarang, kerapatan sedang dan kerapatan padat, dengan luasan yang sama (5 m x 5 m). Pengambilan sampel epifauna dilakukan pada 9 titik sampling dengan cara pengambilan permukaan substrat yang berbeda didalam kuadran transek dengan menggunakan cetok dan disaring dengan menggunakan saringan ukuran 1 mm dan diberi formalin 4 %. Sampel disortir di laboratorium dan diidentifikasi. Dari hasil pengamatan diketahui terdapat 5 spesies lamun pada ketiga stasiun dengan jumlah yang berbeda. Jenis lamun yang ditemukan adalah jenis Thalassia sp, Cymodocea sp, Enhallus sp, Halodule sp dan Syringodium sp. Kerapatan lamun yang jarang dengan jumlah individu 15.923 individu, kerapatan lamun sedang berjumlah 36.546 individu dan kerapatan lamun padat dengan berjumlah 53.182 individu. Kelimpahan epifauna yang ditemukan di daerah kerapatan lamun jarang yaitu 118 individu/m2 dari 17 spesies, sedangkan pada daerah kerapatan lamun sedang didapatkan 149 individu/m2 dari 15 spesies dan untuk kerapatan padat didapatkan 170 individu/m2 dari 19 spesies. uji korelasi  pearson didapatkan (nilai Sig (2-tailed pada output SPSS) sebesar 0, 698 ( ≥ 0,05), dengan kesimpulan H0 diterima dan H1 ditolak  yaitu tidak ada perbedaan yang signifikan antara struktur hewan epifauna pada kerapatan lamun yang berbeda di pulau Panjang Jepara. Selain itu, didapatkan nilai korelasi antara hewan epifauna dengan kerapatan lamun sebesar -0, 457. menunjukkan tidak adanya hubungan yang erat antara hewan epifauna dengan kerapatan lamun di Pulau Panjang.

Metagenome studies are an important step in taxonomic grouping. Taxonomic grouping can be done using the binning method. Binning is a process to determine the contigs of each group of phylogenetic species. In this study, Binning was carried out using the Supervise Learning approach. We use the Naïve Bayes Classifier method and Certainty Factor. The classification process is carried out on phylum taxon levels. many of the organisms used were 50 organisms and the length of the fragments used was 500 bp and many readings were 1000 readings. The accuracy results obtained by the Naive Bayes method are 62.5%. While the accuracy obtained in the Certainty Factor method is 54.45%. From the results of the two methods of testing, it can be concluded that Naive Bayes is the best method of classification compared to Certainty Factor.

Many research findings in the field of Education Management cannot be implemented effectively. The problem derived from the fact that many research findings generated recommendation which still focus on normative and theoretical orientation. Despite that, the organizations, where research took place, mostly had limited resources that became constraints for implementing too many recommendations from research findings. This paper elaborated the development of an optimization model for effective implementation of the recommendations from research findings. The basic idea of this model coming from the core-concept of optimization that is starting and focusing to analyse the recommendations from research findings, more specially to identify indicators of research variables which have priority to be improved or to be maintained. The identification process of those indicators using the concept of "Scientific Identification Theory to Conduct Operation Research in Education Management (SITOREM)". The concept of SITOREM consist of assessment process using some criteria to rank indicators of the variables from the highest to the lowest level in term of priority. The criteria used are in terms of Costs, Benefits, Urgent and Importance (CBUI Criteria). It means that indicators which met the criteria of Cost, Benefits, Urgent and Important will have high priority to be optimized when they were implemented in organizational setting.