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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Advances in Applied Sciences TEKNIK INFORMATIKA Bulletin of Electrical Engineering and Informatics Harmoni: Jurnal Pendidikan dan Penelitian Seni Budaya Journal of Geoscience, Engineering, Environment, and Technology SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan ILKOM Jurnal Ilmiah Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Ainet : Jurnal Informatika International Journal of Industrial Optimization (IJIO) Jurnal Teknik Informatika (JUTIF) Mattawang: Jurnal Pengabdian Masyarakat Jurnal Indonesia : Manajemen Informatika dan Komunikasi Nusantara Hasana Journal Indonesian Community Journal Aptekmas : Jurnal Pengabdian Kepada Masyarakat Buletin Sistem Informasi dan Teknologi Islam Journal of Practical Computer Science (JPCS) Jurnal Informatika Progres ROUTERS: Jurnal Sistem dan Teknologi Informasi Journal of Embedded Systems, Security and Intelligent Systems Jurnal INFOTEL Jurnal Indonesia : Manajemen Informatika dan Komunikasi Journal of Computer Science and Information Technology ARUS JURNAL SAINS DAN TEKNOLOGI Journal of Muhammadiyah’s Application Technology
Muhammad Faisal
Universitas Muhammadiyah Makassar
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KLASIFIKASI MULTI-CLASS STATUS GIZI BALITA MENGGUNAKAN ARSITEKTUR DEEP NEURAL NETWORK Alizha Nur Arspandy; Desi Anggreani; Muhyiddin A.M Hayat; Muhammad Faisal; Muhammad Syafaat; Indriyanti; Emil Aguslaim Habi Thalib
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.v18i2.464

Abstract

This study aims to develop a classification model for toddler nutritional status using a Deep Neural Network (DNN) with a multi-class classification approach. The research utilizes anthropometric data of toddlers aged 0-60 months obtained from UPTD Puskesmas Cendana Putih, North Luwu Regency, covering the period 2023–2025. The dataset consists of 156 records with features including age, weight, height, and Z-score indicators. Data preprocessing involves validation, normalization, and splitting into training and testing sets with a ratio of 85:15. The DNN model is constructed with multiple hidden layers (128, 64, and 32 neurons) and trained using the Adam optimizer and categorical cross-entropy loss function. The results show that the model achieves an accuracy of 91.67% on the testing data, indicating good performance in classifying nutritional status into categories such as undernutrition, normal, and obesity. Evaluation using confusion matrix and classification metrics (precision, recall, and F1-score) reveals that the model performs well on dominant classes but shows limitations in minority classes due to data imbalance. Overall, the proposed model demonstrates potential as a decision support tool to assist healthcare workers in identifying toddler nutritional status more accurately and efficiently.
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.
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.
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..
PREDIKSI KEBUTUHAN STOK OBAT MENGGUNAKAN METODE HYBRID LONG SHORT-TERM MEMORY (LSTM) DAN CATBOOST Parwati Parwati; 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.v18i2.472

Abstract

Drug inventory planning in primary health facilities requires an accurate forecasting model because fluctuating demand can trigger stockouts or excess inventory. This study develops and evaluates a hybrid Long Short-Term Memory (LSTM) and CatBoost model for predicting the stock requirements of five essential medicines at Puskesmas Pattingalloang. The dataset consists of monthly drug dispensing records from January 2019 to December 2025. LSTM is applied as a temporal feature extractor with a three-month sliding window, while CatBoost functions as the final nonlinear regression estimator. Model performance is assessed using MAE, RMSE, MAPE, and SMAPE, with a single LSTM model used as the baseline comparison. The results show that model suitability depends on the demand pattern of each medicine. The hybrid LSTM-CatBoost model performs better on highly fluctuating medicines, particularly Paracetamol 500 mg with 24.03% SMAPE and Guaifenesin with 37.28% SMAPE. In contrast, the single LSTM model is more efficient for relatively stable demand, especially Blood Supplement Tablets with 14.09% SMAPE. Forecasting for 2026 also provides annual demand estimates that can support data-driven drug requirement planning. These findings indicate that machine learning-based forecasting is useful for pharmaceutical inventory decision support, but model selection must consider the fluctuation characteristics of each drug.
Ensemble Learning for Android Privacy-Risk Flow Pre-Screening Using Permission and Metadata Features Tri Wahyuni; Muhammad Faisal; Titin Wahyuni; Nurnawaty; Rio Prasetyo Lukodono; Titik Khawa Abd Rahman
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 3 (2026): September 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i3.13180

Abstract

Purpose - This study develops a privacy-oriented pre-screening framework for identifying Android applications with potential Sensitive Data Exposure by combining lightweight permission and metadata features with ensemble learning. Design/methods/approach - Android applications obtained from the AndroZoo repository were analyzed using FlowDroid to construct reference labels based on sensitive source–sink flows. Privacy-oriented features were derived from permissions, application metadata, source and sink indicators, and interaction patterns. An Ensemble Stacking model integrating Random Forest, Support Vector Machine, and Extreme Gradient Boosting with Logistic Regression as the meta-classifier was evaluated under class imbalance. Additional circularity, ablation, repeated validation, and clean-feature experiments were conducted to assess robustness and deployment feasibility. Findings - The proposed framework demonstrated strong capability in distinguishing applications containing FlowDroid-defined potential privacy-risk flows. FlowDroid-derived source and sink indicators were highly discriminative, while permission-only features were less effective. Importantly, the clean-feature configuration retained strong discriminatory capability without requiring FlowDroid at inference time, supporting its use as a lightweight first-stage screening mechanism before more computationally intensive taint analysis. Research implications/limitations - The framework can support developers, security auditors, and platform administrators in prioritizing applications for deeper privacy inspection. However, the study relies on static analysis, a single application repository, and FlowDroid-derived reference labels. Originality/value - This study contributes a two-stage Android privacy-risk screening framework that combines lightweight deployable features with targeted static taint analysis while explicitly addressing label-feature circularity and inference-time feasibility.
Co-Authors . Darniati Abd Rahman Wahid Abd Rahman, Aedah Abdul Rakhim Nanda Adnan Ahsan Agung, Andi Ahmad Nur Rahman Akbar DB, Andi Muhammad Alizha Nur Arspandy ALRASYID.S, NUR FUAD Alvian Syah Burhani Alvina Felicia Watratan Alvina Felicia Watratan Andi Agung Andi Citra Ayu Lestari Andi Harmin Andi Makbul Syamsuri ANDI MAWADDA TAIBA MAWADDA TAIBA Andi Muhammad Akbar DB Andi Muhammad Nur Hidayat Ari Ahmad Dahril Ashabul Kahfi Azzah Aulia Syarif Baharuddin, Suardi Hi Bakti, Rizki Yusliana Billy Eden William Asrul Burhanuddin, Fathurrahman Chyquitha Danuputri Chyquitha Danuputri Chyquitha Danuputri Danuputri, Chyquitha Darniati Dayang Aisyah Desi Anggreani Djalil, Sony Achmad Emil Agus Salim Habi Talib Erick Yusuf Kotte Erika Yanti Fachrim Irhamna Rachman Fahrim I. Rahman Fahrim Irhamna Rachman Fahrim Irhamna Rachman Fahrim Irhamna Rachman Farida Gaffar Feng, Zhipeng Ferdiansyah Firdaus Fidaus FUAD, NUR FUAD ALRASYID.S Galbi Nadifah Hamdan Gani Hamzah Al Imran Hardita Subanda Herlinah Herlinah Hi Baharuddin, Suardi HS, Hafsah Ida Ida Ida Mulyadi, Ida Indra Aditya Indriyanti Indriyanti Azis Irmawati Irmawati Irnawaty Idrus IRSAN KADIR Jihan Izzathul Mujidah Kusumawardani, Nurul Lisa Fitriani Ishak Lukman Lukman Anas LUKMAN ANAS Lukman Anas Lukman Lukman M Agusalim M Agusalim M. Fikri Haikal Ayatullah Made Widia, I Dewa Majeri Majeri Mardiah Mardiah Mardiah Mardiah Medy Wisnu Prihatmono Medy Wisnu Prihatmono Muh Akram Riyadi Ramadhan Muh Dzikri Alfauzan Nuzul Muh Ilham Akbar Muh Ilham Akbar Muh Khayyir Muh. Amir Zainuddin Muh. Fikri Haekal Muh. Riswan Muhammad Aditya Yudhistira Muhammad Agusalim Muhammad Asygar Faeruddin Muhammad Hasraddin Hasnan Muhammad Khadafi Muhammad Khaiyyir Muhammad Syafaat S. Kuba Muhsin, Muh Arief Muhyiddin A.M Hayat Muhyiddin A.M Hayat Muhyiddin A.M. Hayat Musdalifa Thamrin Musdalifa Thamrin Muthalib, Ade Nirwani Abdurahman Nasir Usman Nasir Usman Nini Apriani Rumata Nur Alam Nur Annisa Syarifuddin Nur Milani Hidayah Nur Rahman, Ahmad Nur Ramadhan Nur Ramadhan, Nur Nurahmad Nurahmad Nurdiansyah Nurdiansyah Nurfadillah Nurfadillah Nurnawaty Nurul Kusumawardani Nurul Qalbi Parwati Parwati Praja, Soemitro Emin Rahmania Rahmat Anbiyah Rasyidi, Muhammad Fachri Rio Prasetyo Lukodono Riswan, Muh. Rizky Yusliana Bakti Rosnani Rosnani Rosnani Rosnani Saharuddin Saharuddin Samsuria, Samsuria Sarina Siti Marwa Sri Wahyuni Suardi Hi Baharuddin Suharmin Djumali Suriani Suriani Swa Lee Lee Syadiah Nor Wan Shamsuddin SYAFAR, A. MUHAMMAD Syahril Akbar Syahrul Hasbir Syahrul Suhardi Titik Khawa Abd Rahman Titik Khawa Abd Rahman Titik Khawa Abd Rahman Titik Khawa Abdul Rahman Titin Wahyuni Tri Wahyuni Try Gustaf Said Wa Nanda Sulystrian Wahid, Abd Rahman Wiwin Fuad Sanjaya