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Relationship Between Cleft Lip and Palate Followed by Osteogenesis Imperfecta Accompanied by Tetralogy of Fallot in Children with a Maternal History of Catatonic Schizophrenia with Valproic Acid Therapy and Diabetes Mellitus R. Mohamad Javier; Abdillah Budi Ksatria; Andisa Fadhila Rialdi; Syarif Syamsi Ahyandi; Revita Widya Prasanti; Himawan Wicaksono; Budi Prakoso; Maisuri; Bambang Widiwanto; A. Rusli Budi Ansyah
Jurnal Multidisiplin Madani Vol. 3 No. 5 (2023): May, 2023
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/mudima.v3i5.4404

Abstract

Researchers believe that most cases of cleft lip and cleft palate are caused by genetic and environmental factors. Nevertheless, the exact cause was not found. Nevertheless there are allegations that the condition of the cleft lip and palate has something to do with osteogenesis imperfecta with the heterozygous mutation COL1A2 and the formation of triple helix DNA accompanied by Tetralogy of Fallot in children with a maternal history of catatonic schizophrenia followed by the use of valproic acid and type II diabetes mellitus. Then, it is also found in babies with cleft lip and sky conditions usually accompanied by other congenital anomaly, but not always. This then further gives rise to speculation that there are links between the above variables and the cleft lip and palate. Therefore. Knowing the Relationship Between Cleft Lip and Palate Events with osteogenesis imperfecta with heterozygous mutation COL1A2 and triple helix DNA formation accompanied by Tetralogy of Fallot in children with a maternal history of catatonic schizophrenia followed by the use of valproic acid and type II diabetes mellitus. This research is a Systematic Review using the Preferred Reporting Items for Systematic Reviews and Meta-analyses method or commonly called PRISMA, this method is carried out systematically by following the correct research stages or protocols. The source was taken from the Google Scholar site with journals published in 2017-2023 and then screened the results of 1,870 journals. Journal clustering was carried out and the number of Q1 indexed journals was obtained in 4 journals, Q3 in 1 journal, and Q4 in 1 journal so that there were 6 journals extracted. The entire journal does not directly discuss the relationship between the research variables, but only provides an overview of the increased risk of celestial cleft conditions in babies if the expectant mother has type 2 diabetes mellitus conditions, osteogenesis imperfect, and the relationship of the two health conditions to the possibility of birth of the baby with the Tetralogy of Fallot condition, as well as the relationship of the baby to the condition of the cleft lip and palate and tetralogy of fallot
Peninigkatan Akurasi Prediksi Pembayaran Pajak Kendaraan menggunakan Algoritma Random Forest dengan Pendekatan Cyclical Encoding dan Lagged Variables Himawan Wicaksono; Eka Ardhianto
Jurnal Penelitian Teknologi Informasi dan Sains Vol. 4 No. 2 (2026): Juni: JURNAL PENELITIAN TEKNOLOGI INFORMASI DAN SAINS (JPTIS)
Publisher : Institut Teknologi dan Bisnis (ITB) Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54066/jptis.v4i2.3998

Abstract

Motor Vehicle Tax (PKB) is a key pillar of Regional Original Revenue (PAD) that supports development funding. However, seasonal fluctuations in payment realization create uncertainties in local budget planning. This study aims to address the limitations of the standard Random Forestalgorithm, which suffers from extreme prediction failures on time-series data due to its inability to capture temporal transitions between months. The proposed solution implements feature engineering using a Cyclical Encoding approach (Sine and Cosine transformations) and Lagged Variables. The dataset comprises historical records of motor vehicle tax potential and realization from January 2021 to November 2025. The baseline model evaluation without feature engineering yields highly inaccurate predictions with a Mean Absolute Percentage Error (MAPE) of 203.47% (accuracy of -103.47%). Conversely, after integrating Cyclical Encoding and Lagged Variables, the proposed model's performance improves drastically, achieving a MAPE of 14.40% (an accuracy rate of 85.60%), an MAE of 9,317 units, and an RMSE of 12,638 units. Feature Importance analysis confirms that the cyclically encoded month feature contributes the highest weight to the model's decisions with a score of 0.5031, followed by the potential feature at 0.1798. This study demonstrates that time-based feature engineering effectively optimizes Random Forestfor precise tax revenue forecasting.