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All Journal Jurnal Teknik Hidro NUSANTARA : Jurnal Ilmu Pengetahuan Sosial Bandar: Journal of Civil Engineering Buletin Sistem Informasi dan Teknologi Islam Jurnal Informatika Progres Jurnal Karajata Engineering Jurnal Teknik Sipil Macca Kohesi: Jurnal Sains dan Teknologi ARUS JURNAL SAINS DAN TEKNOLOGI Cosmic Jurnal Teknik Journal of Green Complex Engineering Journal of Muhammadiyah’s Application Technology Jurnal Balireso: Jurnal Pengabdian pada Masyarakat Journal Techno Entrepreneur Acta
M Agusalim
Universitas Muhammmadiyah Makassar
Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Materials Science & Nanotechnology Mechanical Engineering Social Sciences Other

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Optimalisasi Automated Unit Testing pada Pemrograman Berbasis Web Menggunakan Pendekatan Reinforcement Learning Muh Akram Riyadi Ramadhan; Muhammad Faisal; Lukman Lukman; Desy Anggreani; M. Agusalim; Irnawaty Idrus; Soemitro Emin Praja
Buletin Sistem Informasi dan Teknologi Islam (BUSITI) Vol 7, No 3 (2026)
Publisher : Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/busiti.v7i3.3507

Abstract

Pengujian perangkat lunak penting dalam pengembangan aplikasi web, namun automated unit testing konvensional berbasis aturan statis terbatas menghadapi kompleksitas sistem modern. Penelitian ini memformulasikan prioritisasi kasus uji sebagai Markov Decision Process: agen Reinforcement Learning mengamati state, memilih kasus uji, dan menerima reward multiobjektif yang memadukan cakupan kode dan deteksi kesalahan dikurangi biaya eksekusi. Tiga algoritma (DQN, DDQN, PPO) dibandingkan terhadap baseline Random dan Greedy menggunakan lima seed dan uji Mann–Whitney U. Pada System Under Test berbasis Flask, DDQN memperoleh performa terbaik dengan APFD 0,8504, NAPFD 0,8447, dan F1 0,4183, mengungguli baseline secara signifikan (nilai p terkecil 7,15 × 10⁻¹⁴⁴) dengan latensi keputusan 3,0033 ms, kurang dari separuh PPO. Greedy mencapai cakupan tertinggi namun deteksi terendah, menegaskan cakupan bukan indikator memadai. Validasi eksternal pada BugsInPy (82 bug web) mengonfirmasi arah keunggulan serupa dengan margin lebih kecil. Reinforcement Learning merupakan pendekatan menjanjikan dan dapat direproduksi untuk automated unit testing adaptif pada aplikasi web.
A cross-validated discharge–grain-size model for maximum bed scour in a mobile-bed open channel Soemitro Emin Praja; Muhammad Nurhidayat; M. Agusalim; Asran
Journal of Green Complex Engineering Vol. 4 No. 1 (2026): August
Publisher : Gio Architect

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59810/greenplexresearch.v4i1.294

Abstract

This study develops a compact empirical model for maximum bed scour in an unobstructed mobile-bed open channel. Experiments were conducted in a 9 m flume using a 3×3 matrix that combined discharges of 0.0026, 0.0034, and 0.0045 m³ s⁻¹ with nominal grain sizes of 2.36, 1.18, and 0.85 mm. Final bed change was measured at 20 sections and seven transverse points for each condition, providing 1,260 spatial observations. Maximum scour increased from 0.8 to 4.5 cm, while the proportion of scoured observation points rose from 19.3% to 100%. The additive relation dₛ(cm)=1.272+0.883Q*−1.037d* explained 93.5% of the fitted variance and retained R²=0.843 under leave-one-condition-out cross-validation, with RMSE=0.394 cm. A particle-densimetric-Froude formulation produced a very strong fit on the normalized response scale but only a marginal reduction in cross-validated error after back-transformation. Discharge and grain size therefore acted as comparably strong controls in opposite directions within the tested range. The proposed relation is intended for interpolation within this experimental domain rather than as a universal design equation.
OPTIMASI KLASIFIKASI JENIS INDUSTRI KECIL MENENGAH (IKM) MENGGUNAKAN DEEP NEURAL NETWORK BERBASI SHAP ANALYSIS M. Fikri Haikal Ayatullah; Desi Anggreani; Rizki Yusliana Bakti; Muhammad Faisal; Muhammad Syafaat; 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.v18i2.463

Abstract

Small and Medium Industries (SMEs) in Makassar City face a significant gap between high labor absorption (66.25%) and low GDP contribution (20%), often due to conventional and experience-based determination of industry types. This study implements a Deep Neural Network (DNN) model to classify four categories of SMEs, Bread and Cake, Processed Food, Vehicle Repair, and Textiles and Clothing to facilitate data-driven decisions. Using a supervised learning approach on 31,824 data samples for the 2022-2024 period, this model was developed through feedforward and backpropagation mechanisms. The results showed superior performance with overall accuracy of (93.99%) and balanced accuracy (96.70%), which signified an increase of (15.88%) compared to the Naïve Bayes baseline model. All F1-scores above (90%) indicate strong performance stability in each class. Furthermore, SHAP's analysis revealed that textual features (87.1%) were the dominant factors, followed by the type of business entity and investment value. This study confirms that DNN is effective in modeling complex non-linear interactions, providing objective tools for classification and strategic economic planning in Makassar City.
PENERAPAN RESNET50 DAN SWIN TRANSFORMER PADA IDENTIFIKASI CITRA PENYAKIT DAUN KELAPA SAWIT Siti Marwa; Muhammad Faisal; Muhyiddin A.M Hayat; 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.v18i2.470

Abstract

This study aims to implement and compare the performance of ResNet50 and Swin Transformer models in classifying palm oil leaf diseases. The decline in palm oil productivity is often caused by disease infections such as Curvularia (leaf spot) and Leaf Rust, necessitating a fast and precise automated identification system. This experimental computational research used a primary dataset of 600 digital images proportionally divided into training, validation, and testing sets. The preprocessing stage included resolution adjustment (resizing), data augmentation to prevent overfitting, and normalization. Model performance evaluation was conducted quantitatively through Confusion Matrix calculations and validated qualitatively through heatmap visualization using the Gradient-weighted Class Activation Mapping (Grad-CAM) method. The test results proved that the ResNet50 architecture outperformed the Swin Transformer with an accuracy of 98.00%, precision of 98.01%, recall of 98.00%, and F1-score of 98.00%, compared to the Swin Transformer's accuracy of 96.00%. Grad-CAM analysis also confirmed that ResNet50 is sharper in specifically localizing local infection areas. Overall, it is concluded that the ResNet50 model is more optimal, stable, and recommended for the palm oil leaf disease classification system in this dataset domain.
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 A.Aswar Saputra Abdul Hakim Abdul Khair Kurani Abdul Rakhim Nanda Agus, Fauziah Akbar Fepria Anwar Al Imran, Hamzah Ali, Muh Yunus Alma Yulianti Amril Habibudin Ananta, Ari Azyari Andi Bunga Tongeng Andi Gunawan Andi Makbul Syamsuri Andi Rahmat Asran Azzah Aulia Syarif Bahtiar, Elbu Bakti, Rizki Yusliana Burhanuddin, Fathurrahman Desi Anggreani Faraouk Maricar Fauzan Hamdi Firman Hamzah Al Imran Indriyanti Indriyanti Indriyanti Irnawaty Idrus Irwan Ismail S Ismail Syaputra Ismi Annisa, Nurul Kasmawati Kasmawati Khafifa Khafifa Lukman Lukman M. Fikri Haikal Ayatullah Mahmuddin Mahmuddin Mahmuddin Malkam, Andi Muflih Margie Dayana, Lucke Ayurindra Maricar, Farouk Mi’raj Hilal, Muh. Mi’raj Hilal, Muh. Muh Akram Riyadi Ramadhan Muh Jamir Muh. Amir Zainuddin Muh. Fathir Mz. Muh. Ishaq Haq Muh. Nurul Fitrah S Muhammad Akbar Muhammad Faisal Muhammad Nurhidayat Muhammad Syafaat S. Kuba Muhyiddin A.M Hayat Mutmainna Syarifuddin Nenny, Nenny Nini Apriani Rumata Nur Milani Hidayah Nurhidayat, M. Nurlette, Sri Nurhaini Nurnawaty Praja, Soemitro Emin Putra, M. Sandi Rajib Mahmud Ranum Indah Putri Ratna Sari Ria Sari Rifandi, Arif Risman, Andi Muh. Rusti Sahril Sarjun Siti Marwa Sri Nirwana Sari Supardi Supardi Syah, Erwin Taufik Al Hidayat Taufiq, Muh Titik Khawa Abd Rahman Toha Andi Lala Umar, Nuralfirah Yusril Yusril Zulfikar Arsyad