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All Journal Jurnal Teknologi Informatika dan Komputer Jurnal SINTA: Sistem Informasi dan Teknologi Komputasi
Firdaus Satrio Utomo
Universitas Sultan Ageng Tirtayasa
Computer Science & IT Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering

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Implementasi Arsitektur Medallion Open-source untuk Analisis Komparatif Tech Layoffs Global Abdulhadi Muntashir; Firdaus Satrio Utomo; Rendy Ilyasa; Gladys Andromeda Bilqis; Mohamad Hilman
Jurnal SINTA: Sistem Informasi dan Teknologi Komputasi Vol. 3 No. 2 (2026): SINTA: APRIL
Publisher : Berkah Tematik Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61124/sinta.v3i2.243

Abstract

Ekosistem startup Indonesia yang bergantung pada pendanaan ventura asing rentan terhadap gelombang tech layoffs global, tetapi analisisnya kerap terhambat inkonsistensi data geospasial dan biaya infrastruktur cloud yang tinggi. Penelitian ini bertujuan menunjukkan bahwa kombinasi open-source PostgreSQL dan Data Build Tool (DBT) berbasis Arsitektur Medallion mampu menghasilkan analisis komparatif PHK teknologi Indonesia dan global secara akurat tanpa platform berbayar. Metode ELT (Extract, Load, Transform) diterapkan dengan novelty berupa strategi compound join key untuk mengeliminasi anomali duplikasi baris (Cartesian product) akibat ketidakseragaman koordinat geospasial. Sistem berhasil mengeksekusi delapan model data dalam 1,19 detik dengan tingkat keberhasilan uji kualitas 100% (24/24 pengujian) dan zero row inflation. Hasil analisis mengungkap karakteristik spesifik PHK di Indonesia berupa dominasi sektor Transportation sebesar 76,81% dari total 2.721 pekerja yang tersentralisasi di Jakarta, berbeda signifikan dari pola global yang terdiversifikasi pada sektor Consumer dan Retail. Penelitian ini menunjukkan bahwa infrastruktur open-source berbasis PostgreSQL dan DBT mampu menghasilkan performa analitik yang andal dan efisien untuk mendukung analisis komparatif PHK teknologi serta mengidentifikasi konsentrasi kerentanan ekosistem digital nasional pada wilayah tertentu.
Evaluating the Accuracy of Minimum Admission Score Threshold Projections Using Linear Regression and Shrinkage-to-Mean on SNBT Data Royan Habibie Sukarna; Yulian Ansori; Fitri Damyati; Firdaus Satrio Utomo; Nisrina Putri Rajwa
Jurnal Teknologi Informatika dan Komputer Vol. 12 No. 2 (2026): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v12i2.3764

Abstract

This study evaluates, out-of-sample, the accuracy of two methods for projecting minimum admission score thresholds for the SNBT selection track: per-series linear regression (program × degree-level combinations) and shrinkage-to-mean, using 2023–2025 historical data tested against actual 2026 values across 47 series. Pure linear regression achieves MAE 39.40, RMSE 52.34, and 71.5% pass/fail classification accuracy, but exhibits optimistic bias (recall for the "fail" class only 0.543) and a systematic failure pattern: all 13 series with monotonic three-year training trends (consistently rising or falling) missed 100% in the mean-reversion direction during the evaluation year. The shrinkage-to-mean method, which blends linear projections with historical averages weighted by in-sample R², improves every metric (MAE 31.91, RMSE 43.58, accuracy 74.5%, "fail" recall 0.585) without additional data, yet does not change the bias direction and shows limitations for series with very short historical records (degenerate R²). This study formulates and compares two training-data-only early risk-detection mechanisms, with an explicit trade-off between discriminative power and recalibration requirements. Main contributions: genuine out-of-sample evaluation, identification of a universally consistent mean-reversion pattern, and concrete recommendations for designing similar trend-based prediction features.
Co-Authors Abdulhadi Muntashir Fitri Damyati Gladys Andromeda Bilqis Mohamad Hilman Nisrina Putri Rajwa Rendy Ilyasa Sukarna, Royan Habibie Yulian Ansori