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All Journal Hemera Zoa Bulletin of Electrical Engineering and Informatics Jurnal Teknik Hidro AGREGAT Applied Information System and Management Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal Ilmiah Ecosystem Jurnal Abdi Insani Bandar: Journal of Civil Engineering Jurnal Teknik Informatika (JUTIF) Jurnal Algoritma, Logika dan Komputasi Buletin Sistem Informasi dan Teknologi Islam Joutica : Journal of Informatic Unisla Jurnal Informatika Progres Information Technology Education Journal Kohesi: Jurnal Sains dan Teknologi Jurnal INFOTEL ARUS JURNAL SAINS DAN TEKNOLOGI Cosmic Jurnal Teknik Journal of Green Complex Engineering Journal of Muhammadiyah’s Application Technology Journal Techno Entrepreneur Acta
Muhammad Syafaat S. Kuba
Universitas Muhammadiyah Makassar
Agriculture, Biological Sciences & Forestry 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 Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Social Sciences Transportation Other

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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.
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.
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.
Climate-Driven Disease Outbreak Anomaly Detection Using PCA, DBSCAN, and Isolation Forest Titin Wahyuni; Muhammad Faisal; Muhammad Syafa’at S. Kuba; Eny Syatriana; Muh Al Haikal
Joutica Vol 11 No 02 (2026): SEPTEMBER
Publisher : Universitas Islam Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30736/jti.v11i02.1674

Abstract

Climate variability strongly influences dengue transmission, but prediction-focused surveillance often overlooks unusual climate-dengue configurations that emerge before or outside epidemic peaks. This study proposes a fully unsupervised anomaly-oriented framework for national-level dengue surveillance in Brazil. Weekly dengue case data were integrated with NASA POWER climate variables aggregated to the same epidemiological week scale. Seven standardized climate indicators were reduced using Principal Component Analysis (PCA); the first three components retained 93.34% of the total variance. DBSCAN was then used to identify structurally isolated climate regimes, and Isolation Forest was applied to assign anomaly severity scores and examine complementary transitional deviations. The results show that DBSCAN detected 19 epidemiological weeks (5.19%) as structural anomaly candidates, while Isolation Forest highlighted severity-ranked weeks whose case totals were not always the highest in the study period. To support interpretation, detected anomalies are evaluated using temporal identifiers, dengue case counts, climate-variable deviations, and cross-method robustness rather than treated as automatic outbreak declarations. The findings indicate that climate-driven dengue anomalies are episodic and multivariate and may occur outside peak incidence periods. The proposed framework therefore offers an explainable and reproducible screening layer for climate-informed early warning and risk prioritization in public health surveillance
Climate-Driven Disease Outbreak Anomaly Detection Using PCA, DBSCAN, and Isolation Forest Titin Wahyuni; Muhammad Faisal; Muhammad Syafa’at S. Kuba; Eny Syatriana; Muh Al Haikal
Joutica Vol 11 No 02 (2026): SEPTEMBER
Publisher : Universitas Islam Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30736/jti.v11i02.1674

Abstract

Climate variability strongly influences dengue transmission, but prediction-focused surveillance often overlooks unusual climate-dengue configurations that emerge before or outside epidemic peaks. This study proposes a fully unsupervised anomaly-oriented framework for national-level dengue surveillance in Brazil. Weekly dengue case data were integrated with NASA POWER climate variables aggregated to the same epidemiological week scale. Seven standardized climate indicators were reduced using Principal Component Analysis (PCA); the first three components retained 93.34% of the total variance. DBSCAN was then used to identify structurally isolated climate regimes, and Isolation Forest was applied to assign anomaly severity scores and examine complementary transitional deviations. The results show that DBSCAN detected 19 epidemiological weeks (5.19%) as structural anomaly candidates, while Isolation Forest highlighted severity-ranked weeks whose case totals were not always the highest in the study period. To support interpretation, detected anomalies are evaluated using temporal identifiers, dengue case counts, climate-variable deviations, and cross-method robustness rather than treated as automatic outbreak declarations. The findings indicate that climate-driven dengue anomalies are episodic and multivariate and may occur outside peak incidence periods. The proposed framework therefore offers an explainable and reproducible screening layer for climate-informed early warning and risk prioritization in public health surveillance
A Hybrid Random Forest–Support Vector Machine Model for Classifying Cyberattacks Using SSH Honeypot Log Data Firrofiqil A'la Kadram; Muhammad Faisal; Runal Rezkiawan; Aedah Abd Rahman; Andi Makbul Syamsuri; Muhammad Syafaat S. Kuba
Applied Information System and Management (AISM) Vol. 9 No. 2 (2026): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v9i2.51928

Abstract

Secure Shell (SSH) is widely used for remote server administration; however, its exposure to public networks makes it a frequent target of cyberattack activities. This article presents an attack classification framework for Cowrie SSH Honeypot logs, using a hybrid Random Forest–Support Vector Machine approach. The raw logs were processed through log auditing, data cleaning, session reconstruction, class labeling, feature construction, feature selection, and model evaluation. The dataset consisted of 38 log files, 213,494 valid records, 29,099 unique sessions, and 1,618 unique source IP addresses. The analyzed attack categories included failed authentication, other suspicious interactions, successful compromise, and command execution. Random Forest ranked ten candidate behavioral features, selected the five highest-ranked features within each rolling fold, and generated time-aware out-of-fold class-probability estimates. The selected features and RF probability outputs were combined, standardized, and passed to a support vector machine with a radial basis function (RBF) kernel as the stage-1 classifier; sessions predicted as successful interactions were then refined into successful compromise or command execution based on observed command activity. Evaluation using eight rolling time-based folds achieved an accuracy of 99.65% ± 0.38%, a balanced accuracy of 97.02% ± 5.77%, and a macro F1-score of 97.84% ± 4.37% for the final four-class classification. The event_count, username_diversity, interarrival_std, and password_diversity features were selected in all folds. Theoretically, RF–SVM provides a stable session-level representation; in practice, it supports honeypot log triage and the prioritization of higher-risk SSH sessions.
Designing Microservices for Small-Scale E-Commerce Systems: An Empirically Evaluated Domain-Driven Refactoring Approach Using Event-Driven Architecture Arina Manasikana; Lukman; Muh Faisal; Emil Agusalim Habi Talib; Muhammad Syafaat S. Kuba; Muhyiddin A.M Hayat; Andi Agung
Information Technology Education Journal Vol. 5, No. 4, November (2026)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v5i4.13430

Abstract

Purpose: This study proposes a practical, domain-driven design (DDD)-based refactoring approach to guide small-scale e-commerce systems transitioning from a monolithic to an event-driven microservices architecture. Design/methodology/approach – The research combines an applied case study with a controlled experimental evaluation using k6 load testing under a low-traffic, single virtual user scenario. The approach is built around clearly identified bounded contexts, a database-per-service pattern, independently deployable services, and asynchronous communication via Apache Kafka. Structural maintainability was assessed using a Change Impact Scope (CIS) metric, while latency and deployment time were measured for the monolithic baseline and refactored architecture, centred on the Order and Payment services. Findings: The refactored architecture reduced the estimated change impact scope from 100% to 50%, indicating a narrower potential scope of change. Under low-traffic conditions, the microservices architecture showed modestly higher latency than the monolithic baseline, at 10.81 ms against 9.21 ms for the order operation, converging at the 95th percentile, while the initial deployment time differed by more than an order of magnitude. Therefore, maintainability gains are not automatically accompanied by better performance or lower operational costs at low concurrency. Research limitations/implications – The evaluation covered only Order and Payment under a single-user load; fault isolation and reliability were not measured and remain open for future work under concurrent workloads. Originality/value – Unlike most literature, which focuses on enterprise-scale or cloud-native environments and assumes uniform performance gains, this study reports both the structural benefits and performance trade-offs of a DDD-based migration in a small-scale case study.
Co-Authors . Darniati Abdul Rakhim Nanda Ade Irfan Aedah Abd Rahman Agus, Fauziah Agusalim, Agusalim Ahmad Nur Rahman Ahmad Syafi'i Zulmi Akrar Syah Al Imran, Hamzah Ali, Muhammad Yunus Alizha Nur Arspandy Alvian Syah Burhani Amal, Citra Amalia Amrullah Anas, Andi Bunga Tongeng Andi Agung Andi Bunga Tongeng Anas Andi Makbul Syamsuri ANDI MAWADDA TAIBA MAWADDA TAIBA Andi Rahmat Anis Dandi Juandani Antaria, Sukmasari Arina Manasikana Arman, Muayyanah Arsyad, Zulfikar Ashabul Kahfi Asnita Virlayani, Asnita Bakti, Rizki Yusliana Berni Satria Gemilang Chyquitha Danuputri Danuputri, Chyquitha Danuputri, Chyquitha Darniati Dayang Aisyah Desi Anggreani Dince Danga Dupa Djunur, Lutfi Hair E. Sulistiyowati . Emil Agus Salim Habi Talib Emil Agusalim Habi Talib Erdi Katanga Yani Ersita Hambajawa Eunike Mita Ga Fachrim Irhamna Rachman Fahrim I. Rahman Fahrim Irhamna Rachman Fahrim Irhamna Rachman Farida Gaffar Fausiah Latief Fauzan Hamdi Firrofiqil A'la Kadram Fithriyah Arief Wangsa Gaffar, Farida Gemilang, Berni Satria Hamzah Al Imran Hasanuddin, Novianingsih Hasbir, Syahrul Hasmi . Ida Mulyadi, Ida Indriyanti Indriyanti Azis Irma Suryana Irwan Irwan, Muhammad Ahlil Khairi Ismail Pawara Jesynta Landu Natar Jihan Izzathul Mujidah Juandani, Anis Dandi Juliandro Juliandro Juliandro, Juliandro Karim, Nenny Kasmawati Kato, Muh Alvin Achmad Kusumawardani, Nurul Lantara, Andi Bintang Latief, Fausiah Leonard Rihi Lisnawati Lisnawati Lukman Lukman Lukman Anas LUKMAN ANAS Lukman, Lukman Lutfi Hair Djunur M Agusalim M. Fikri Haikal Ayatullah Ma'rupah, Ma'rupah Mahmud, Rajib Mahmuddin Mahmuddin Mahmuddin Mohamad Munawir Muh Alvin Achmad Kato Muh Faisal Muh Ilham Akbar Muhammad Aminuddin, Muhammad Muhammad Asygar Faeruddin Muhammad Faisal Muhammad Hasraddin Hasnan Muhammad Khadafi Muhsin, Muh Arief Muhyiddin A.M Hayat Muhyiddin A.M Hayat Munawir, Mohamad Nenny Nenny Nenny Nenny, Nenny Nini Apriani Rumata Nur Alam Nur Annisa Syarifuddin Nur Rahman, Ahmad Nurdiansah, Nurdiansah Nurdiansyah Nurdiansyah Nurnawaty Nurul Kusumawardani Panguriseng, Darwis Pawara, Ismail Putri, Adriani Rahmasari, St. Rajib Mahmud Risman, Andi Muh. Riswal Karamma Runal Rezkiawan S.R. Setyaningsih . Sahril Sandi, Andi Muhammad Sari, Reski Anugrah Saribanong . Sarina Siba, Ikhsan St. Narwiyani . St.S. Yasin . Suhardi, Syahrul Suriani Suriani Suriati . Swa Lee Lee Syadiah Nor Wan Shamsuddin Syah, Akrar Syahril Akbar Syahrul Hasbir Syahrul Suhardi Syahrul, Syahrulrahman Syamsuri, Andi Maqbul Syatriana, Eny T Karim, Nenny Taufiq, Muh Teto Putri Asgetri Titik Khawa Abd Rahman Titik Khawa Abdul Rahman Titin Wahyuni Toha Andi Lala Try Gustaf Said Ulil Amri Umbu Leha Kulandima Usman, Sucipto W. Suharni . Wa Nanda Sulystrian Wangsa, Fithriyah Arief Wiwin Fuad Sanjaya Yesinta Pangga Limu Yosua Pebryanto Blegur Yundari, Yundari Zulfikar Arsyad Zulhaidir DJ, Muhammad Zulmi, Ahmad Syafi'i