- Covid-19 pandemic has brought many changes for education, working life and so on. Digitalization is increasingly felt and forced to change from being face-to-face to online, so inevitably world moves faster to accelerate the use of technology. Technology development also applied in industry, including in terms of accounting records. The importance of debriefing for teachers and students of VHS, especially accounting vocational, related to using Accurate accounting software which has been widely used. This training improves self-competence and competitiveness in the face of digitalization. Therefore, Pusat Penelitian dan Pengabdian Masyarakat TSM in collaboration with TSM lecturers help to provide training for teachers and students to recognize and able to operate one of accounting software that has been widely used, especially by SME industry in Indonesia, namely Accurate. The purpose of this training is to provide briefing to teachers and students in understanding and operating various features in Accurate related to accounting transactions which are expected to improve skills in using this software. The training method includes Accurate introduction session, simulation, journal and financial statements discussion. Targets and outcomes to be achieved from this training are increased the understanding and skills of teachers and students in operating Accurate so the students could meet the requirements of world of work in terms of the use of technology.

Penelitian ini bertujuan untuk memodelkan volatilitas return indeks saham perusahaan dividen tertinggi di Indonesia (DIV 20) sebelum dan sesudah pandemi COVID 19. Model keluarga ARCH (Autoregressive Conditional Heteroscedasticity) digunakan dalam hal ini. Periode penelitian diperpanjang dari 18 Mei 2018 hingga 18 Februari 2022. Batas waktu dimulainya pandemi adalah 1 April 2020. Data pengembalian adalah pengembalian mingguan. Hasilnya menunjukkan bahwa sebelum pandemi, GJR-GARCH(1,1) dapat memetakan dan melacak volatilitas dengan sangat baik karena mencetak AIC dan SIC pra-pandemi terendah. Oleh karena itu, penelitian ini menguatkan bukti adanya reaksi asimetris dari partisipasi pasar terhadap kemunculan dan penyebaran berita baik dan buruk di pasar. Setelah pandemi, efek ARCH menjadi kurang jelas. Angka signifikansi menurun meskipun efek ARCH masih signifikan pada 0,15. Performa model ARCH(1) secara signifikan lebih tinggi daripada model lain pasca-pandemi. Hasil tersebut menjadi bukti bahwa pascapandemi ketidakpastian yang dihadapi pelaku pasar sangat tinggi. Hal ini mengakibatkan meningkatnya volatilitas. Model keluarga ARCH menjadi kurang signifikan karena pengembaliannya lebih acak. Analisis lebih lanjut, bagaimanapun, menunjukkan bahwa pengembalian belum mengikuti model random walk meskipun keacakan meningkat. Oleh karena itu, ARCH(1) masih sesuai untuk memodelkan volatilitas setelah Pandemi.   This research aims at modeling the volatility of Indonesian highest paying dividend companies stock index (DIV 20) returns before and after pandemic COVID 19. The ARCH (Autoregressive Conditional Heteroscedasticity) family models were employed in this regard. The research period extended from 18 May 2018 to 18 February 2022. The cutoff for the commencement of pandemic was 1st April 2020. The return data were weekly returns. The results suggested that before pandemic, GJR-GARCH(1,1) could map and trace the volatility very well since it scored the lowest AIC and SIC pre-pandemic. Therefore, this research corroborated the evidence that there existed asymmetric reaction from the market participation toward the emergence and spread of good and bad news in the market. After pandemic, the ARCH effect became less obvious. The significance number was decreasing although the ARCH effect was still significant at 0.15. ARCH(1) model performance was significantly higher than the other models post-pandemic. The result presented evidence that after pandemic the uncertainty facing the market participants was very high. This resulted in the increase of the volatility. The ARCH family model was becoming less significant because the returns were more random. Further analysis, however, showed that the returns did not yet follow the random walk model despite the increasing randomness. Therefore, ARCH(1) was still appropriate to model the volatility after Pandemic.