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All Journal Bulletin of Electrical Engineering and Informatics Jurnal Teknik Hidro Jurnal Environmental Science SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan NUSANTARA : Jurnal Ilmu Pengetahuan Sosial Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Bandar: Journal of Civil Engineering Jurnal Teknik Informatika (JUTIF) Buletin Sistem Informasi dan Teknologi Islam Jurnal Informatika Progres Prosiding Seminar Nasional Teknik Sipil UMS Jurnal Karajata Engineering Jurnal Teknik Sipil Macca Kohesi: Jurnal Sains dan Teknologi ARUS JURNAL SAINS DAN TEKNOLOGI Journal of Green Complex Engineering Journal of Muhammadiyah’s Application Technology Jurnal Rekayasa Infrastruktur Hexagon Jurnal Balireso: Jurnal Pengabdian pada Masyarakat Journal Techno Entrepreneur Acta
Andi Makbul Syamsuri
Universitas Muhammadiyah Makassar
Agriculture, Biological Sciences & Forestry Humanities Astronomy 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 Earth & Planetary Sciences 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 Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

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KLASIFIKASI TINGKAT KEMATANGAN LADA MENGGUNAKAN ENSEMBLE LEARNING BERDASARKAN CITRA WARNA KULIT Jihan Izzathul Mujidah; Rizki Yusliana Bakti; Lukman; Muhammad Faisal; Muhammad Syafaat; Muhyiddin AM Hayat; Andi Makbul Syamsuri
PROGRESS Vol 17 No 2 (2025): September
Publisher : P3M STMIK Profesional Makassar

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Abstract

Pepper fruit (Piper nigrum L.) is an agricultural commodity whose market value strongly depends on its ripeness level at harvest. Ripeness determination, which is still commonly performed through visual observation, tends to be inaccurate and subjective. This study aims to classify the ripeness level of pepper fruit based on skin color using an ensemble learning approach. The dataset consists of 1,996 pepper fruit images categorized into four ripeness levels unripe, semi ripe, ripe, and overripe. Color features were extracted from the HSV color model using color moment statistics including mean, standard deviation, and skewness. Random Forest and XGBoost models were combined using a soft voting method. The results show that the ensemble model achieved 98.25% accuracy, 98.30% precision, 98.27% recall, and 98.26% F1-score. The ensemble approach proved superior to single models by providing more accurate and stable classification of pepper fruit ripeness.
KLASIFIKASI PENYAKIT TANAMAN NILAM BERDASARKAN CITRA DAUN MENGGUNAKAN GLCM DAN SVM Sarina; Rizki Yusliana Bakti; Muhammad Faisal; Muhammad Syafaat; Andi Makbul Syamsuri; Muhyiddin AM Hayat; Andi Lukman Anas
PROGRESS Vol 17 No 2 (2025): September
Publisher : P3M STMIK Profesional Makassar

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Abstract

This study presents a classification model for detecting diseases in patchouli (Pogostemon cablin Benth) leaves using image processing techniques. The method combines Grey Level Co-occurrence Matrix (GLCM) for texture feature extraction and Support Vector Machine (SVM) for classification, optimised using the Particle Swarm Optimisation (PSO) algorithm. A total of 2,080 leaf images were collected and categorized into four classes: healthy, leaf spot, yellowing, and mosaic. Each image was augmented and converted to grayscale to enhance the dataset and reduce computational complexity. Four GLCM features—contrast, correlation, energy, and homogeneity—were extracted to represent leaf textures. The classification model achieved an accuracy of 89.74% using SVM alone, and improved to 97.12% when optimized with PSO. The results indicate that the integration of GLCM, SVM, and PSO provides an effective and accurate solution for early detection of patchouli leaf diseases, potentially supporting farmers in decision-making and improving crop productivity and quality.
IMPLEMENTASI DEEP LEARNING MENGGUNAKAN HYBRID SENTENCE-TRANSFORMERS DAN K-MEANS UNTUK PERBANDINGAN JURNAL Muhammad Asygar Faeruddin; Muhammad Faisal; Rizki Yusliana Bakti; Muhammad Syafaat; Muhyiddin AM Hayat; Andi Makbul Syamsuri; Andi Lukman Anas
PROGRESS Vol 17 No 2 (2025): September
Publisher : P3M STMIK Profesional Makassar

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Abstract

This study addresses the challenge of identifying semantic relatedness between scientific journal articles by developing a classification system based on deep learning. The system applies an unsupervised learning approach using the Sentence-Transformers model and K-Means clustering to generate semantic similarity scores and categorical labels. Abstracts from journal PDFs are extracted and processed to determine similarity levels across four predefined categories. The optimal number of clusters was determined using Elbow Method, Silhouette Score, and Davies-Bouldin Index, resulting in k = 4. The system is implemented as a web-based application that allows users to upload two PDF files, compare them semantically, and receive both a similarity score and an AI-generated narrative explanation. Functional testing showed that all core features performed as expected. This system significantly reduces the time required to assess relatedness between journal articles, offering an efficient tool for academic research navigation.
IMPLEMENTASI K-MEANS DAN ANALISIS SENTIMEN KRITIK SARAN BERBASIS NLP PADA DATA MONEV BBPSDMP KOMINFO MAKASSAR Syahril Akbar; Muhammad Faisal; Rizki Yusliana Bakti; Muhammad Syafaat; Andi Makbul Syamsuri; Muhyiddin AM Hayat; Andi Lukman Anas
PROGRESS Vol 17 No 2 (2025): September
Publisher : P3M STMIK Profesional Makassar

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Abstract

Manual analysis of large-scale and unstructured textual feedback data is often inefficient and subjective, thereby hindering data-driven decision-making. This study aims to design and implement an integrated analytical workflow to automatically filter, cluster, and classify feedback data consisting of criticisms and suggestions. The research employs a hybrid approach that begins with TF-IDF-based data filtering, followed by dimensionality reduction using Latent Semantic Analysis (LSA), and topic clustering through K-Means clustering optimized with the Silhouette Score. The resulting cluster labels are then used as training data to build a Multinomial Naive Bayes classification model. The results show that this workflow successfully identified two main thematic clusters, namely "Criticism and Expectations" and "Suggestions and Compliments", and the classification model achieved an overall accuracy of 91%. Although class imbalance affected the recall of the minority class (47%), the model demonstrated high precision (95%) for that class. It is concluded that this hybrid approach effectively transforms raw data into structured insights, and utilizing clustering results as training data is an efficient strategy for automating feedback categorization, providing a reliable tool for institutional analysis.
PREDIKSI PEMAKAIAN AIR BULANAN DI PDAM KECAMATAN TAMALATE MENGGUNAKAN METODE AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) Nur Annisa Syarifuddin; Titin Wahyuni; Muhammad Faisal; Muhammad Syafaat; Andi Makbul Syamsuri; Muhyiddin AM Hayat; Andi Lukman Anas
PROGRESS Vol 17 No 2 (2025): September
Publisher : P3M STMIK Profesional Makassar

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Abstract

Water consumption forecasting is a crucial aspect of efficient water resource management, particularly in urban areas with increasing demand. This study aims to predict the monthly water usage volume at the PDAM of Tamalate District using the Autoregressive Integrated Moving Average (ARIMA) method. The dataset consists of historical water usage data from January 2022 to December 2024, totaling 36 monthly observations. The analysis process includes stationarity testing using the Augmented DickeyFuller (ADF) test, model parameter identification through ACF and PACF plots, and performance evaluation using MAE, RMSE, and MAPE metrics. The results show that the best-performing model is ARIMA, which demonstrates high prediction accuracy, with a MAE of 26,049.80 m³, RMSE of 37,459.00 m³, and MAPE of 4.12%. This model is capable of generating predictions close to actual values and can be relied upon as a basis for PDAM’s water distribution planning. It is expected that this research will contribute to data-driven decision-making and support digital transformation in the public service sector.
IMPLEMENTASI HYBRID LEXICON-BASED DAN SVM UNTUK KLASIFIKASI ANALISIS SENTIMEN TERHADAP PELATIHAN BBPSDMP KOMINFO MAKASSAR Nur Alam; Muhammad Faisal; Rizki Yusliana Bakti; Muhammad Syafaat; Andi Makbul Syamsuri; Muhyiddin AM Hayat; Andi Lukman Anas
PROGRESS Vol 17 No 2 (2025): September
Publisher : P3M STMIK Profesional Makassar

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Abstract

The evaluation of government training programs is often hindered by manual analysis of unstructured qualitative feedback, making the process inefficient and subjective. This study aims to implement and evaluate a sentiment classification model using a hybrid Lexicon-Based and Support Vector Machine approach to analyze participants’ perceptions of the Vocational School Graduate Academy training organized by BBPSDMP Kominfo Makassar, as well as to compare the performance of a standard SVM model with a model optimized using Particle Swarm Optimization. This quantitative research employs 2,313 unstructured review data, which undergo text preprocessing, initial lexicon-based labeling, and TF-IDF feature extraction before being classified using an SVM with an RBF kernel. The results show that the SVM model optimized with PSO consistently outperforms the standard model across all four evaluation aspects, with the most significant accuracy improvement observed in the instructor category from 84.71% to 89.02% and in the assessor category reaching 91.46%. PSO optimization has proven effective in enhancing the model’s ability to identify negative sentiments, which represent the minority class. The hybrid approach with PSO optimization is capable of producing a more accurate and balanced classification system, with practical implications as an objective automated evaluation tool.
A Hybrid Salp Swarm Optimization and Behavioral Nudge Framework for Optimizing Software Developer Task Allocation Ashabul Kahfi; Muhammad Faisal; Titin Wahyuni; Desi Anggreani; Darniati Darniati; Muhammad Syafaat S Kuba; Andi Makbul Syamsuri; Ida Mulyadi
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 15 No. 2 (2026)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v15i2.106920

Abstract

Effective task allocation is critical in Agile software development, yet most optimization-based approaches treat it as a purely technical scheduling problem and disregard behavioral factors such as motivation, fairness, and engagement. This study proposes a Hybrid Salp Swarm Optimization–Behavioral Nudge Framework (HSSO–BNF) for developer–task allocation that integrates technical constraints with human-centered cues. The model formulates allocation as a multi-objective function combining workload balance, skill mismatch, deadline penalties, and a motivation score derived from three nudge components: Motivational Cue (MC), Social Comparison (SC), and Effort–Reward Feedback (ERF). These behavioral signals are embedded directly into the SSO position update and fitness evaluation, enabling the swarm to adapt simultaneously to performance and motivational states. Experiments on real developer–task records collected from GitHub compare HSSO–BNF against GA, PSO, and standard SSO using convergence behavior, allocation cost, fairness, satisfaction, and motivation dynamics. The results show that HSSO–BNF achieves faster and more stable convergence, reduces allocation cost by approximately 32% compared with GA and SSO and about 25% compared with PSO, and improves workload fairness and developer satisfaction while preserving psychologically sustainable specialization patterns. Heatmap visualizations and motivation trends further confirm that the behavioral layer produces more coherent and interpretable task assignments, indicating that behavior-aware metaheuristics are a promising direction for intelligent, human-centered task allocation in Agile teams.
Analisis Kontribusi Deteksi Tangan dan Ekstraksi Landmark untuk Pengenalan Alfabet BISINDO Wa Nanda Sulystrian; Muhammad Faisal; Fahrim Irhamna Rachman; Abd Rakhim Nanda; Rizki Yusliana Bakti; Muhammad Syafaat S. Kuba; Andi Makbul Syamsuri
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.v7i3.3506

Abstract

Komunitas tunarungu menghadapi hambatan dalam aspek bahasa dan komunikasi sehingga membutuhkan dukungan teknologi yang sesuai dengan karakteristik visual mereka. Kondisi tersebut menunjukkan pentingnya pengembangan teknologi untuk mendukung akses komunikasi yang lebih inklusif melalui pengenalan Bahasa Isyarat Indonesia (BISINDO). Penelitian ini menganalisis kontribusi deteksi tangan dan ekstraksi landmark terhadap performa pengenalan alfabet BISINDO menggunakan pendekatan berbasis computer vision. Sistem yang diusulkan mengintegrasikan YOLOv8 untuk deteksi tangan, MediaPipe Hands untuk ekstraksi dua puluh satu landmark, normalisasi landmark, serta Multi-Layer Perceptron sebagai model klasifikasi. Evaluasi dilakukan menggunakan dataset berisi 1066 citra alfabet BISINDO dari 26 kelas melalui tiga skenario eksperimen. Hasil penelitian menunjukkan bahwa kombinasi deteksi tangan dan normalisasi landmark menghasilkan performa terbaik dengan nilai accuracy 0.915, precision 0.913, recall 0.915, dan F1-score 0.896, serta meningkatkan accuracy 14.8% dibandingkan pendekatan tanpa deteksi tangan. Temuan ini menunjukkan bahwa deteksi tangan dan representasi landmark berkontribusi penting terhadap peningkatan akurasi sistem. Pendekatan yang diusulkan berpotensi diterapkan pada aplikasi penerjemah alfabet BISINDO berbasis kamera secara real-time untuk mendukung komunikasi yang lebih inklusif bagi komunitas tunarungu di Indonesia.
Web-Based Expert System for Human Skin Disease Diagnosis Using Forward Chaining and Mamdani Fuzzy Inference Muhammad Khadafi; Muhammad Faisal; Muhyiddin AM Hayat; Muh. Arief Muhsin; Muhammad Syafaat S.Kuba; Lukman Anas; Andi Makbul Syamsuri
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 4 (2026): JUTIF Volume 7, Number 4, August 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.4.5676

Abstract

This paper aims to develop a web-based expert system aimed at assisting the general public and parties involved in the health sector, particularly in the field of human skin diseases. This system uses a combination of forward chaining and Mamdani fuzzy inference methods. The knowledge base is created based on observations and interviews with a dermatologist at Bima Regional General Hospital, covering 13 symptoms and 6 types of skin diseases, each equipped with expert confidence weights. Disease candidates are inferred from symptoms selected by the user and rules using forward chaining, while symptom weights are managed through fuzzy logic using the Laravel framework with the PHP language. From 20 test data, a comparison between the results from doctors and the system in an operational web application model shows a compatibility level of 95%. This system is suitable for use by doctors with further review. With this application, it is hoped that it can solve problems experienced by the community, such as limited access to dermatologists and economic problems, which are some of the main reasons why this application was designed and developed.
Integrasi Principal Component Analysis dan DBSCAN untuk Klasterisasi Tingkat Keparahan Stunting pada Balita Azzah Aulia Syarif; Rizki Yusliana Bakti; Muhammad Faisal; Nini Apriani Rumata; Titik Khawa Abd Rahman; Andi Makbul Syamsuri; M. Agusalim
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.v7i3.3512

Abstract

Stunting masih menjadi masalah kesehatan masyarakat yang serius. Variasi karakteristik antropometri pada balita stunting membuat satu kategori klasterisasi tidak cukup merepresentasikan kondisi pertumbuhan secara menyeluruh. Penelitian ini mengusulkan integrasi Principal Component Analysis (PCA) dan DBSCAN untuk mengidentifikasi subkelompok keparahan stunting pada balita di Puskesmas Turikale (Agustus 2025; n = 406). Setelah pembersihan dan standardisasi usia, PCA diterapkan pada HAZ, WAZ, WHZ, dan usia saat  pengukuran, menghasilkan tiga komponen utama (PC1–PC3) yang menjelaskan 99.35% variasi data. DBSCAN pada ruang PCA (ε = 0.60; MinPts = 5; Euclidean) menghasilkan tiga klaster dengan 12.56% noise yang direalokasi menggunakan nearest-centroid agar seluruh sampel berlabel. Hasil menunjukkan tiga subkelompok: stunting sedang dengan underweight pada usia lebih tua (n = 357), stunting sedang disertai risiko gizi lebih pada usia lebih muda (n = 24), dan stunting berat pada usia dini (n = 25). Dibandingkan K-medoids dan Fuzzy C-Means, PCA–DBSCAN memberikan validasi internal terbaik (Silhouette = 0.277; DBI = 1.062; Dunn = 0.0765). Pendekatan ini menghasilkan profil antropometri yang dapat digunakan sebagai alat bantu penentuan prioritas pemantauan dan penilaian gizi di layanan kesehatan primer, tanpa menggantikan klasifikasi WHO.
Co-Authors Abdul Rakhim Nanda Afdaliah, Athira Agusalim, Agusalim Ahdania, Andi Ahmad Yani ahmad yani Aksan Ali Al Hidayat, Taufik Al Imran, Hamzah Aldi Alfiah, Nur Aisyah Ali, Muhammad Yunus Andi Ahdania Andi Fatimah Andi Ibrahim Andi yusnandar Pratama Anto, Sahril Ashabul Kahfi Asnita Virlayani Asnita Virlayani, Asnita Azzah Aulia Syarif Bakti, Rizki Yusliana Chairatul Anam Darniati Darwis, Muh Fakhruddin Dayana, Lucke Ayurindra Margie Desi Anggreani Erwin Toding Fadly, Feri Fahrim Irhamna Rachman Fajar, Muh Faraouk Maricar Fauzan Hamdi Fauzan Hamdi, Fauzan Firman Fithriyah Arief Wangsa Haerul, Muh. Farhan Hamzah Al Imran Hamzah Al Imran Hardiansyah Hardiansyah Hidriansyah Idhan Akbar Ida Mulyadi, Ida Ikhwal Lukman, Muh Ikram Ilfan Muis Imran, Hamzah Al Imran, Hamzah Al Imran Indriyanti Israil Israil Israil Jalil, Fikri Haikal Jamir, Muh Jihan Izzathul Mujidah Karim, Nenny Kasmawati Kasmawati Kasmawati Kasmawati Lilis Suganda Lisda Lisa Lukman LUKMAN ANAS Lukman Anas M Agusalim M Agusalim M Yusran S Mappatoba, Andi Maricar, Faraouk Maulana, Muhammad Wijdan Maulana Muh Jamir Muh. Syahrul A Muhammad Alqadri Aras Putra Muhammad Asygar Faeruddin Muhammad Faisal Muhammad Khadafi MUHAMMAD SABIR Muhammad Sabir Muhammad Scyroth Arianto Muhammad Syafaat S. Kuba Muhsin, Muh Arief Muhyiddin A.M Hayat Muhyiddin A.M Hayat Munawir, Ical Mustazim Mutiah, Ulfa Nenny Nenny Karim Nenny, Nenny Nini Apriani Rumata Novi Julianti Nur Alam Nur Annisa Syarifuddin Nur Miftahul Janna Nur Milani Hidayah Nurfadilah Nurhidayat, M. Nurnawaty Nurul Ilma Parwati Parwati Rasyidi, Muhammad Fachri Risal Risal Sahabuddin Latif SANDI Sandi Sandi, Andi Muhammad Sarina Sarjun Seftiawan N, Muh Jusuf Siti Marwa Suriamihardja, Dadang Syahril Akbar Taufik Al Hidayat Taufiqur Rachman Thaha, Arsyad Titik Khawa Abd Rahman Titik Khawa Abdul Rahman Titin Wahyuni Toding, Erwin Ulfa Mutiah Usman, Sucipto Wa Nanda Sulystrian Yahya, Fardiyansyah Yahya Zulharnah Zulharnah