Jurnal Analisis Dampak Penerapan PSAK 72 Terhadap Kinerja Keuangan Perusahaan Telekomunikasi Di Masa Pandemi Covid-19
Bibliographic record
Abstract
Abstract \nThis study aims to determine how the impact of the application of PSAK 72 on the \nfinancial performance of telecommunications companies during the Covid-19 \npandemic. This research was conducted in the telecommunications sector, which was \naffected by the issuance of PSAK 72. This research uses descriptive quantitative \nanalysis techniques. The sampling technique uses non-probability purposive sampling \ntechnique, namely telecommunication companies listed on the Indonesia Stock \nExchange that meet the predetermined criteria. The following three telecommunication \ncompanies which become the research sample, namely PT. Telekomunikasi Indonesia \nTbk, PT. INDOSAT Tbk AND PT. XL AXIATA Tbk. The results of this study indicate \nthat the application of PSAK 72 resulted in the financial performance of the three \ncompanies not being a little better when compared to the previous standard. The \ndifference in the revenue recognition provisions based on PSAK 72 and the previous \nstandard caused a slight change in the value of revenue from contracts with customers \nin the third quarter of 2020, so that the revenue value was smaller when compared to \nusing the previous standard. On the other hand, based on the assessment of the three \ncompanies, the Covid-19 pandemic has not had a significant adverse impact on the \nbusiness continuity of PT Telkomsel Indonesia Tbk, PT. INDOSAT Tbk and PT. XL \nAXIATA Tbk. \nKeywords: PSAK 72, Financial Performance, Telecommunication Companies, Covid- \n19 Pandemic. \nAbstrak \nPenelitian ini memiliki tujuan untuk mengetahui bagaimana dampak penerapan \nPSAK 72 terhadap kinerja keuangan perusahaan telekomunikasi di masa pandemi \nCovid-19. Penelitian ini dilakukan pada sektor telekomunikasi yang terdampak atas \nterbitnya PSAK 72. Penelitian ini menggunakan teknik analisis deskriptif kuantitatif. \nTeknik pengambilan sampel menggunakan teknik non probability purposive sampling \nyakni perusahaan telekomunikasi yang terdaftar di Bursa Efek Indonesia yang \nmemenuhi kriteria yang telah ditentukan, berikut tiga perusahaan telekomunikasi yang \nmenjadi sampel penelitian yaitu PT. Telekomunikasi Indonesia Tbk, PT. INDOSAT \nTbk DAN PT. XL AXIATA Tbk. Hasil penelitian ini menyatakan bahwa penerapan PSAK 72 mengakibatkan kinerja keuangan ketiga perusahaan tersebut sedikit tidak \nlebih baik apabila dibandingkan dengan menggunakan standar sebelumnya. Perbedaan \nketentuan pengakuan pendapatan berdasarkan PSAK 72 dan standar sebelumnya \nmenyebabkan sedikit perubahan nilai pendapatan dari kontrak dengan pelanggan pada \nkuartal III tahun 2020, sehingga nilai pendapatan menjadi lebih kecil apabila \ndibandingkan dengan menggunakan standar sebelumnya. Disisi lain berdasarkan \npenilaian ketiga perusahaan tersebut, pandemi Covid-19 tidak memberikan dampak \nburuk secara signifikan terhadap kelangsungan bisnis PT.Telkomsel Indonesia Tbk, PT. \nINDOSAT Tbk dan PT. XL AXIATA Tbk. \nKata kunci: PSAK 72, kinerja keuangan, Perusahaan Telekomunikasi, Pandemi Covid- \n19
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.076 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".