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All Journal International Journal of Advances in Applied Sciences Jurnal Teknologi Informasi dan Ilmu Komputer JTERA (Jurnal Teknologi Rekayasa) Jurnal SOLMA Pendas : Jurnah Ilmiah Pendidikan Dasar ILKOM Jurnal Ilmiah Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Mobile and Forensics Zonasi: Jurnal Sistem Informasi Indonesian Journal of Data and Science Jurnal Algoritma, Logika dan Komputasi Jurnal Teknologi Sistem Informasi Buletin Sistem Informasi dan Teknologi Islam Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Jurnal Informatika Progres Internet of Things and Artificial Intelligence Journal Journal of Computer Science and Information Technology ARUS JURNAL SAINS DAN TEKNOLOGI
Desi Anggreani
Universitas Muhammadiyah Makassar
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Neuroscience Physics Social Sciences Other

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ESTIMASI RENCANA ANGGARAN BIAYA PROYEK TELEKOMUNIKASI BERBASIS ALGORITMA CATBOOST DAN DATA BOQ Galbi Nadifah; Desi Anggreani; Rizki Yusliana Bakti; Muhammad Faisal; Lukman Anas
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.v18i2.465

Abstract

Estimasi Rencana Anggaran Biaya (RAB) yang akurat merupakan elemen krusial dalam keberhasilan proyek infrastruktur telekomunikasi. Penentuan harga satuan pada dokumen Bill of Quantities (BOQ) secara konvensional sering kali tidak efisien dan subjektif. Tantangan utama otomatisasi estimasi ini adalah tingginya kardinalitas fitur kategorikal berupa teks deskriptif. Penelitian ini mengusulkan penerapan algoritma CatBoost untuk memprediksi harga satuan pekerjaan berbasis data BOQ. Tahapan penelitian meliputi pembersihan data historis sebanyak 20.611 item, transformasi logaritma natural pada variabel target, serta pelatihan model dengan pembagian data 80% latih dan 20% uji. Hasil eksperimen menunjukkan CatBoost mampu menghasilkan Coefficient of Determination (R2) sebesar 97,66% dan Mean Absolute Error (MAE) sebesar Rp 13.289. Kinerja ini unggul dibandingkan algoritma pembanding XGBoost (R2 85,93% dan MAE Rp 23.614). Validasi manual mengonfirmasi rasio kesalahan prediksi hanya 0,05 dari total nilai proyek, yang membuktikan kelayakan model untuk otomatisasi RAB secara presisi.
KLASIFIKASI PENYAKIT PNEUMONIA MENGGUNAKAN MODEL HYBRID CNN-TRANSFORMER BERBASIS CITRA X-RAY PARU-PARU Nur Milani Hidayah; Muhammad Faisal; Desi Anggreani; Nurnawaty; Andi Makbul Syamsuri; M. Agusalim
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.v18i2.471

Abstract

This study aims to apply a Hybrid CNN-Transformer model based on Medical Vision Transformer (MedViT) for pneumonia classification using chest X-Ray images. The dataset consisted of 450 images, including 150 pneumonia images, 150 non-pneumonia images, and 150 random images as a control class to test system robustness. The data were obtained from Labuang Baji Hospital, Makassar, during the 2023 to 2025 period. The research stages included data collection, preprocessing, augmentation, dataset splitting, model implementation, training, and performance evaluation. The tested models consisted of CNN, Vision Transformer (ViT), and Hybrid CNN-Transformer. The evaluation used accuracy, precision, recall, F1-score, AUC, confusion matrix, ROC curve, and Grad-CAM visualization. The results showed that the Hybrid CNN-Transformer model achieved the best performance with an accuracy of 95.59%, precision of 96.12%, recall of 95.59%, F1-score of 95.58%, and AUC of 0.9968. The model improved accuracy by 8.83% compared with CNN and produced fewer classification errors. The Grad-CAM visualization also indicated that the model focused on relevant lung areas. These findings indicate that combining CNN local feature extraction with Transformer global context can improve pneumonia classification based on medical images..
Co-Authors . Darniati A M Hayat, Muhyddin Abd Rahman Wahid Abd Rahman, Aedah Abd. Rasyid Syamsuri Abdul Rakhim Nanda Aedah Abd Rahman Aedah Abd Rahman Ahmad Faisal Aji Prasetya Wibawa Akbar, Muh Ilham Alizha Nur Arspandy Andi Citra Ayu Lestari Andi Inayah Auliyah Andi Makbul Syamsuri Annisa Alfrini Ashabul Kahfi Bakti, Rizki Yusliana Chyquitha Danuputri Chyquitha Danuputri Danuputri, Chyquitha Danuputri, Chyquitha Danuputri, Chyquitha Darniati Dedi Setiawan Dedi Setiawan Dewi MJ, Wanda Tyrana Emil Agus Salim Habi Talib Fachrim Irhamna Rachman Faturohman, Agung Galbi Nadifah Hamdani Herman Herman Ida Mulyadi, Ida Ikha Andriani Indriyanti Irnawaty Idrus Ismawati Ismawati Ismawati Khadijah Kusumawardani, Nurul Lukman Lukman Lukman Anas Lukman Lukman Lukman Lukman Lukman, Lukman LUKMAN, LUKMAN M Agusalim M. Fikri Haikal Ayatullah Muh Akram Riyadi Ramadhan Muh Dzikri Alfauzan Nuzul Muh Ilham Akbar Muhammad Faisal Muhammad Syafaat S. Kuba Muhyiddin A M Hayat Muhyiddin A.M Hayat Muhyiddin A.M Hayat Nur Hidayah Nur Milani Hidayah Nurmisba Nurmisba Nurmisba Nurmisba Nurmisba Nurmisba, Nurmisba Nurnawaty Nurul Kusumawardani Praja, Soemitro Emin Purnawansyah Purnawansyah Putri, Desy Pratiwi Ika Rahman, Aedah Abd Tenri Batari, Andi Firqatun Najiah Titik Khawa Abd Rahman Titin Wahyuni Try Gustaf Said Wahid, Abd Rahman Widia Wahyuni Widiyanti, Widiyanti Wiwik Handayani