Analisisrasio pt. g udang garam
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In the energy sector, the use of temporal data stands as a pivotal topic. At GRDF, we have developed several methods to effectively handle such data. This presentation will specifically delve into our approaches for anomaly detection and data imputation within time series, leveraging transformers and adversarial training techniques.Anomaly detection and data imputation within time series
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Analisisrasio pt. g udang garam
- 1. 1. ProfitMargin = 𝑁𝑒𝑡 𝐼𝑛𝑐𝑜𝑚𝑒 𝑆𝑎𝑙𝑒𝑠 = 891.358 15.056.347 = 5,9% 2. Returnon Assets(investment) = 𝑁𝑒𝑡 𝐼𝑛𝑐𝑜𝑚𝑒 𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠 = 891.358 24.904.022 = 4% 3. Returnon Equity= 𝑁𝑒𝑡 𝐼𝑛𝑐𝑜𝑚𝑒 𝑆𝑡𝑜𝑐𝑘ℎ𝑜𝑙𝑑𝑒𝑟𝑠 𝑒𝑞𝑢𝑖𝑡 𝑦 = 891.358 14.530.132 = 6% 4. Receivablesturnover= 𝑆𝑎𝑙𝑒𝑠 (𝑐𝑟𝑒𝑑𝑖𝑡) 𝑅𝑒𝑐𝑒𝑖𝑣𝑎𝑏𝑙𝑒𝑠 = 15.056.347 2.960.3440 = 5,1 5. Average Collectionperiod= 𝐴𝑐𝑐𝑜𝑢𝑛𝑡 𝑅𝑒𝑐𝑒𝑖𝑣𝑎𝑏𝑙𝑒 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑑𝑎𝑖𝑙 𝑦 𝐶𝑟𝑒𝑑𝑖𝑡 𝑆𝑎𝑙𝑒𝑠 = 2.960.344 𝑥 365 15.056.347 = 72 6. InventoryTurnover= 𝑆𝑎𝑙𝑒𝑠 𝐼𝑛𝑣𝑒𝑛𝑡𝑜𝑟𝑦 = 15.056.347 14.016.039 = 1,1 7. FixedAssetTurnover= 𝑆𝑎𝑙𝑒𝑠 𝐹𝑖𝑥𝑒𝑑 𝐴𝑠𝑠𝑒𝑡𝑠 = 15.056.347 6.948.177 = 2,2 8. Total AssetTurnover= 𝑆𝑎𝑙𝑒𝑠 𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠 = 15.056.347 24.904.022 = 0,6 9. Currentratio= 𝐶𝑢𝑟𝑟𝑒𝑛𝑡 𝐴𝑠𝑠𝑒𝑡 𝐶𝑢𝑟𝑟𝑒𝑛𝑡 𝐿𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑖𝑒𝑠 = 17.955.845 9.437.259 = 2 10. Quickratio = 𝐶𝑢𝑟𝑟𝑒𝑛𝑡 𝐴𝑠𝑠𝑒𝑡−𝑖𝑛𝑣𝑒𝑛𝑡𝑜𝑟𝑦 𝐶𝑢𝑟𝑟𝑒𝑛𝑡 𝐿𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑖𝑒𝑠 = 17.955.845−14.016.039 9.437.259 = 0,4 11. Debtto total assets= 𝑇𝑜𝑡𝑎𝑙 𝐷𝑒𝑏𝑡 𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠 = 10.359.076 24.904.022 = 42% 12. TimesInterestEarned= 𝐼𝑛𝑐𝑜𝑚𝑒 𝐵𝑒𝑓𝑜𝑟𝑒 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡 𝑎𝑛𝑑 𝑇𝑎𝑥𝑒𝑑 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡 = 1.527.107 219.328 = 6,9 13. FixedCharge Coverage = 𝐼𝑛𝑐𝑜𝑚𝑒 𝐵𝑒𝑓𝑜𝑟𝑒 𝐹𝑖𝑥𝑒𝑑 𝐶ℎ𝑎𝑟𝑔𝑒 𝑎𝑛𝑑 𝑡𝑎𝑥𝑒𝑑 𝐹𝑖𝑥𝑒𝑑 𝐶ℎ𝑎𝑟𝑔𝑒 = 1.747.431 220324 = 7,9