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All Journal Jurnal Informatika Progres
Fahrim Irhamna Rachman
Informatika, Universitas Muhammadiyah Makassar
Computer Science & IT

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PENERAPAN ALGORITMA HIDDEN MARKOV MODEL PADA PREDIKSI EKSPOR KOMODITI BIJI KOPI Wiwin Fuad Sanjaya; Fahrim Irhamna Rachman; Emil Agusalim Habi Talib; Muhammad Faisal; Lukman Anas; Muhammad Syafaat S. Kuba; Indriyanti Azis
PROGRESS Vol 18 No 2 (2026): September
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56708/progres.v18i2.466

Abstract

Fluktuasi volume dan nilai ekspor komoditas biji kopi Indonesia dipengaruhi oleh faktor dinamis yang tidak dapat diamati secara langsung, seperti pergeseran kondisi pasar internasional dan dinamika iklim. Penelitian ini bertujuan untuk menerapkan algoritma Hidden Markov Model (HMM) dalam memodelkan tren tersembunyi (hidden states) serta memprediksi tren ekspor biji kopi. Dataset yang digunakan merupakan data historis bulanan ekspor biji kopi periode 2020–2024. Estimasi parameter dilakukan menggunakan algoritma Baum-Welch, sedangkan performa model dievaluasi menggunakan metrik Mean Absolute Error (MAE) dan Root Mean Square Error (RMSE) serta dibandingkan dengan metode Seasonal Naïve (S-Naïve). Hasil penelitian menunjukkan bahwa HMM mampu mengidentifikasi tiga state utama (Naik, Stabil, dan Turun) dengan nilai MAE sebesar 20,81 dan RMSE sebesar 28,45. Performa HMM melampaui metode S-Naïve yang memiliki MAE sebesar 35,12. Dengan demikian, pendekatan HMM terbukti adaptif dalam menangkap volatilitas dan transisi kondisi pasar pada komoditas biji kopi.
KLASIFIKASI RISIKO INFEKSI SALURAN PERNAPASAN AKUT MENGGUNAKAN ENSEMBLE SOFT VOTING BERBASIS REKAM MEDIS Alvian Syah Burhani; Muhammad Faisal; Fahrim Irhamna Rachman; Darniati; Titin Wahyuni; Muhammad Syafaat S. Kuba; Farida Gaffar
PROGRESS Vol 18 No 2 (2026): September
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56708/progres.v18i2.467

Abstract

This study proposes a risk classification model for Acute Respiratory Infections using a Soft Voting-based Ensemble Learning approach, which integrates prediction probabilities from the Random Forest and Extreme Gradient Boosting algorithms. The research utilized 456 patient medical record data from RSUD Latemmamala spanning January 2020 to December 2025. Comparative evaluation results show that the Random Forest model achieved an accuracy of 94.57%, Extreme Gradient Boosting reached 95.65%, and the Soft Voting Ensemble model delivered the best performance with an accuracy of 96.74%, precision of 96.97%, recall of 96.88%, and an F1-Score of 96.77%. Furthermore, the Soft Voting Ensemble model successfully achieved a perfect recall score for the Severe Acute Respiratory Infection category, ensuring that no high-risk patients went undetected. In conclusion, the Soft Voting Ensemble model serves as a reliable decision-support tool to assist medical professionals in triaging Acute Respiratory Infection patients quickly, objectively, and accurately.
REDUKSI DATA BERLABEL PADA DETEKSI TUBERKULOSIS BERBASIS CITRA X-RAY MENGGUNAKAN FRAMEWORK SIMCLR Majeri Majeri; Fahrim Irhamna Rachman; Muhammad Faisal
PROGRESS Vol 18 No 2 (2026): September
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56708/progres.v18i2.469

Abstract

The development of deep learning-based Computer-Aided Diagnosis (CAD) for tuberculosis (TB) detection faces a fundamental challenge: high reliance on massive annotated data, which requires scarce radiological expertise, considerable time, and high costs. This study proposes a Self-Supervised Learning (SSL) approach via the SimCLR framework as a strategy to reduce labeled data requirements in X-Ray-based TB classification. The model utilizes a ResNet-50 encoder trained contrastively on unlabeled data using the NT-Xent Loss, followed by downstream adaptation via linear probing (SimCLR-LP) and fine-tuning (SimCLR-FT). Utilizing datasets from UPF BBKPM Makassar, evaluations were conducted across four labeled data fractions (10%, 25%, 50%, 100%). Results demonstrated that at the 10% fraction, SimCLR-LP achieved 85.50% accuracy and an AUC of 0.9091, significantly outperforming the Baseline model (62.60% accuracy) which suffered from degenerate prediction. The SimCLR-LP variant achieved ≥80% accuracy using only 60 labeled images, whereas the Baseline required 303 images to reach a comparable threshold, demonstrating a fivefold labeling efficiency. Grad-CAM analysis confirmed that SimCLR-FT yielded localized activations in the perihilar and lower lung lobes, unlike the Baseline's scattered activations without anatomical focus.
Co-Authors Alvian Syah Burhani Darniati Emil Agus Salim Habi Talib Farida Gaffar Indriyanti Azis LUKMAN ANAS Majeri Majeri Muhammad Faisal Muhammad Syafaat S. Kuba Titin Wahyuni Wiwin Fuad Sanjaya