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Abdul Rahman
Universitas Negeri Makassar, Indonesia
Aerospace Engineering Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics

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Siri’ To Mate : Tedong Sebagai Harga Diri Pada Rambu Solo’ di Toraja Sammuel Moris; Abdul Rahman
Jurnal Syntax Admiration Vol. 3 No. 1 (2022): Jurnal Syntax Admiration
Publisher : Ridwan Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46799/jsa.v3i1.368

Abstract

Tulisan ini memaparkan suatu degradasi tradisi dalam stuktur masyarakat di suku Toraja khususnya Toraja Utara, suatu pergeseran makna budaya yang dihayati oleh mayoritas masyarakat Toraja Utara, Provinsi Sulawesi Selatan. Dimana orang Toraja dalam melakukan prosesi kematian (rambu solo’), menjadi hal yang mutlak untuk mengurbankan kerbau sebagai suatu nilai kasih sayang kepada mendiang yang meninggalkan segenap keluarga, namun lama kelamaan terjadi miss-intepretasi dimana tedong digunakan sebagai sarana unjuk diri dengan lebih menitik beratkan gengsi pribadi dan keluarga
The Evaluation Problem in Cryptocurrency Price Forecasting with Machine Learning and Deep Learning: A Problem-Centric Systematic Review of 48 Studies (2018–2025) Ansari Saleh Ahmar; Abdul Rahman
Journal of Economics, Entrepreneurship, Management Business and Accounting Vol 4 No 1 (2026): Volume 4, Issue 1, January 2026
Publisher : CV. Sakura Digital Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61255/jeemba.v4i1.1307

Abstract

Purpose – Cryptocurrency price forecasting with machine learning (ML) and deep learning (DL) has produced 48 Scopus-indexed journal articles since 2018, yet the same LSTM architecture applied to Bitcoin daily closing prices yields mean absolute percentage errors ranging from 1.7% to 4.8% across papers in this corpus. This review examines why the literature fails to accumulate knowledge despite growing output and identifies the evaluation practices responsible for that failure. Design/methodology/approach – A PRISMA 2020 compliant search of Scopus retrieved 48 peer-reviewed English-language articles on ML and DL applications to cryptocurrency price prediction published between 2018 and 2025. All articles were retained after dual-reviewer screening (κ = 0.86) and Mixed Methods Appraisal Tool quality appraisal at the ≥10/16 threshold. Structured data extraction covered architecture type, target coin, forecast horizon, evaluation metric, and train/test split specification. Finding/Results – Five evaluation failure modes affect 39 of 48 articles: calendar concealment (47.9%), split inconsistency (37.5%), normalisation silence (33.3%), baseline heterogeneity (25.0%), and single-regime evaluation (100%). CNN-LSTM hybrids outperform standalone LSTM in 9 of 12 studies that test both, yet neither this finding nor the 6× Transformer growth ratio can be verified across studies because evaluation conditions are not shared. Originality/Value – This is the first PRISMA 2020 compliant systematic review of cryptocurrency ML forecasting. It introduces a five-mode evaluation failure taxonomy and proposes a regime-stratified evaluation design prescribing three mandatory calendar-anchored test periods — the 2021 bull run, the 2022 FTX collapse, and the 2024 institutional entry period — as the minimum standard for deployment-relevant performance claims.
Optimizing Fraud Detection in Indonesia via Rare-Event Logit Approach: A Simulation Study on Large-Scale Agung Tri Utomo; Abdul Rahman
Journal of Economics, Entrepreneurship, Management Business and Accounting Vol 4 No 1 (2026): Volume 4, Issue 1, January 2026
Publisher : CV. Sakura Digital Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61255/jeemba.v4i1.1309

Abstract

Purpose: This study examines the use of the Rare-Event Logit approach to improve fraud detection under conditions of extreme class imbalance. The topic is important because fraud cases usually represent only a very small proportion of total financial transactions, which may reduce the accuracy of conventional classification models. Design/methodology/approach: This study uses a simulation-based quantitative design to evaluate fraud detection performance in large-scale imbalanced data settings. The analysis compares standard logistic regression and Rare-Event Logit with bias-corrected estimation, including Firth’s penalized likelihood approach. Model performance is assessed using the Area Under the Precision-Recall Curve and F1-Score. Findings/Results: The results show that standard logit and Rare-Event Logit perform similarly under moderate imbalance conditions. However, Rare-Event Logit provides a stronger theoretical advantage in handling rare-event bias and stabilizing parameter estimation as data sparsity increases. This indicates that bias-corrected probabilistic models are more suitable for fraud detection in highly imbalanced environments. Originality/Value: This study highlights the value of Rare-Event Logit as an alternative approach for fraud detection in rare-event settings. The findings imply that financial institutions can improve fraud risk identification by adopting bias-corrected models that are more robust to class imbalance.
Co-Authors Agung Tri Utomo Ansari Saleh Ahmar Sammuel Moris