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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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IMPLEMENTASI ALGORITMA K-NEAREST NEIGHBOR TERHADAP PENENTUAN RISIKO KREDIT USAHA MIKRO KECIL DAN MENENGAH Ida; Baharuddin, Suardi Hi; Faisal, Muhammad; Ramadhan, Nur; Darniati
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 4 No. 1 (2023): Januari
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v4i1.163

Abstract

This research was carried out in the context of implementing the K-NN algorithm so that a source of information can be produced as a basis for supporting decisions on initial credit applications by customers so that they can help cooperative managers more as knowledge of the progress of credit proposals that are carried out at the Micro, Small and Medium Enterprise Cooperative Service Office ( SMEs) South Sulawesi Province. The K-Nearest Neighbor algorithm is used to classify objects based on attributes and training samples. Among them, from k objects, the k-Nearest Neighbor algorithm uses neighbor classification as the predicted value. The results show that the algorithm produces a classification with a faster calculation time based on the prediction of customer data resulting from the calculation.
Microsoft Copilot Training for Monitoring Student Learning: A Case Study Vocational High School Makassar - Indonesia Dikwan Moeis; Nasir Usman; Muhammad Faisal; Andi Harmin; Ida Mulyadi; Musdalifa Thamrin
I-Com: Indonesian Community Journal Vol 4 No 3 (2024): I-Com: Indonesian Community Journal (September 2024)
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/icom.v4i3.5134

Abstract

Artificial intelligence (AI) has become an increasingly popular technology and brings significant educational benefits. This technology increases the learning process's efficiency and productivity, allowing for the development of students' abilities in a more focused manner. AI is a catalyst in preparing generations to face future challenges. One example of AI's application in education is Microsoft Copilot, an artificial intelligence model developed by Microsoft in collaboration with OpenAI. Microsoft Copilot is designed to understand and support various academic tasks through human-like interactions. Training on using Microsoft Copilot was carried out for students of SMKS Wahyu Makassar. This training aims to support the learning process, increase learning effectiveness, and assist students in doing academic assignments. The evaluation results showed that Microsoft Copilot provided significant benefits, with positive feedback from participants. Most students found this training useful, easy to understand and improved their knowledge.
Weather Prediction for Strawberry Cultivation Using Double Exponential Smoothing and Golden Section Optimization Methods Herlinah, Herlinah; Asrul, Billy Eden William; HS, Hafsah; Faisal, Muhammad; Lee, Swa Lee; Gani, Hamdan; Feng, Zhipeng
ILKOM Jurnal Ilmiah Vol 16, No 3 (2024)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v16i3.2290.305-317

Abstract

Strawberry is one of the fruit commodities that has a high demand so that it is widely cultivated by most people in Bantaeng Regency to meet with the market needs. The high intensity of weather changes is the main challenge in the strawberry production, which is influenced by climate dynamics and the start season time changes. Climate change does not only affect the amount of rainfall, but also causes a shift in the rainy season and dry season start. As a result, in the cultivation of plants such as strawberries, there are often difficulties in adjusting or slow anticipation in the extreme changes of rainfall. This research began with the data collection stage through field observations, interviews, and literature studies. The design tool used a systematically organized UML, which included a use case diagram, then an activity diagram, as well as an elaboration into sequence diagrams, and class diagrams. The system was developed by implementing the PHP programming language on the interface design as well as MySQL as a database processing. The algorithm used to predict the air temperature feature, wind speed feature, and rainfall feature was Double Exponential Smoothing, followed by the optimization of the Golden Section method to select the right smoothing value. Referring to the results of this study, the system can provide planting time recommendations based on prediction of rainfall, air temperature, and wind speed parameters through a web-based platform. Based on the calculation of the accuracy value of the prediction results using the Mean Absolute Percentage Error (MAPE), the obtained forecast error value was of 5.89% for wind speed, 0.63% for air temperature, and 0.69% for rainfall. The Golden Section Optimization in Double Exponential Smoothing provided the best smoothing for prediction.
Weather Prediction for Strawberry Cultivation Using Double Exponential Smoothing and Golden Section Optimization Methods Herlinah, Herlinah; Asrul, Billy Eden William; HS, Hafsah; Faisal, Muhammad; Lee, Swa Lee; Gani, Hamdan; Feng, Zhipeng
ILKOM Jurnal Ilmiah Vol 16, No 3 (2024)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v16i3.2290.305-317

Abstract

Strawberry is one of the fruit commodities that has a high demand so that it is widely cultivated by most people in Bantaeng Regency to meet with the market needs. The high intensity of weather changes is the main challenge in the strawberry production, which is influenced by climate dynamics and the start season time changes. Climate change does not only affect the amount of rainfall, but also causes a shift in the rainy season and dry season start. As a result, in the cultivation of plants such as strawberries, there are often difficulties in adjusting or slow anticipation in the extreme changes of rainfall. This research began with the data collection stage through field observations, interviews, and literature studies. The design tool used a systematically organized UML, which included a use case diagram, then an activity diagram, as well as an elaboration into sequence diagrams, and class diagrams. The system was developed by implementing the PHP programming language on the interface design as well as MySQL as a database processing. The algorithm used to predict the air temperature feature, wind speed feature, and rainfall feature was Double Exponential Smoothing, followed by the optimization of the Golden Section method to select the right smoothing value. Referring to the results of this study, the system can provide planting time recommendations based on prediction of rainfall, air temperature, and wind speed parameters through a web-based platform. Based on the calculation of the accuracy value of the prediction results using the Mean Absolute Percentage Error (MAPE), the obtained forecast error value was of 5.89% for wind speed, 0.63% for air temperature, and 0.69% for rainfall. The Golden Section Optimization in Double Exponential Smoothing provided the best smoothing for prediction.
COMPARISON OF THE PERFORMANCE OF REGRESSION-SPECIFIC AND MULTI-PURPOSE ALGORITHMS Usman, Nasir; Darniati, Darniati; Rosnani, Rosnani; Musdalifa Thamrin; Nurahmad, Nurahmad; Nurdiansyah, Nurdiansyah; Faisal, Muhammad
Nusantara Hasana Journal Vol. 4 No. 8 (2025): Nusantara Hasana Journal, January 2025
Publisher : Yayasan Nusantara Hasana Berdikari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59003/nhj.v4i8.1274

Abstract

Regression is a data science method for evaluating the relationship between independent and dependent variables. This study compares the performance of various regression algorithms using the Boston Housing Dataset, which consists of 506 samples divided into 80% for training and 20% for testing. Performance evaluation was conducted using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the Coefficient of Determination (R²). All algorithms were implemented with default hyperparameter settings provided by the Scikit-learn library to ensure fair comparison. The results showed that versatile algorithms, particularly Gradient Boosting Machines (GBM) and Random Forest, achieved the best performance with R² values of 0.92 and 0.89, respectively, and lower errors. Conversely, regression-specific algorithms, such as Linear Regression and Ridge Regression, recorded R² values of approximately 0.67, while the k-Nearest Neighbors algorithm had the lowest performance with an R² of 0.65. Versatile algorithms proved to be more effective for datasets with complex non-linear patterns, while regression-specific algorithms were better suited for linear data patterns. These findings provide guidance for practitioners in selecting algorithms based on data characteristics and analysis objectives.
COMPARISON OF THE PERFORMANCE OF REGRESSION-SPECIFIC AND MULTI-PURPOSE ALGORITHMS Usman, Nasir; Darniati, Darniati; Rosnani, Rosnani; Musdalifa Thamrin; Nurahmad, Nurahmad; Nurdiansyah, Nurdiansyah; Faisal, Muhammad
Nusantara Hasana Journal Vol. 4 No. 8 (2025): Nusantara Hasana Journal, January 2025
Publisher : Yayasan Nusantara Hasana Berdikari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59003/nhj.v4i8.1274

Abstract

Regression is a data science method for evaluating the relationship between independent and dependent variables. This study compares the performance of various regression algorithms using the Boston Housing Dataset, which consists of 506 samples divided into 80% for training and 20% for testing. Performance evaluation was conducted using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the Coefficient of Determination (R²). All algorithms were implemented with default hyperparameter settings provided by the Scikit-learn library to ensure fair comparison. The results showed that versatile algorithms, particularly Gradient Boosting Machines (GBM) and Random Forest, achieved the best performance with R² values of 0.92 and 0.89, respectively, and lower errors. Conversely, regression-specific algorithms, such as Linear Regression and Ridge Regression, recorded R² values of approximately 0.67, while the k-Nearest Neighbors algorithm had the lowest performance with an R² of 0.65. Versatile algorithms proved to be more effective for datasets with complex non-linear patterns, while regression-specific algorithms were better suited for linear data patterns. These findings provide guidance for practitioners in selecting algorithms based on data characteristics and analysis objectives.
Machine learning for global trade analysis: a hybrid clustering approach using DBSCAN, elbow, and SOM Thamrin, Musdalifa; Mulyadi, Ida; Made Widia, I Dewa; Faisal, Muhammad; Hi Baharuddin, Suardi; Prihatmono, Medy Wismu; Nurdiansyah, Nurdiansyah; Usman, Nasir
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3033-3046

Abstract

Global trade constitutes a highly complex and interdependent system influenced by diverse economic, geographic, and political factors. This study proposes a hybrid clustering framework that integrates density-based spatial clustering of applications with noise (DBSCAN), elbow, and self-organizing maps (SOM) methods to uncover latent structures in international trade patterns. Utilizing averaged trade data from 25 countries spanning the period from 2013 to 2023, the framework identifies distinct clusters based on export-import characteristics. The DBSCAN is employed to detect dense trade hubs and outlier behaviors, the elbow method determines the optimal number of clusters, and SOM facilitates the visualization of non-linear, high-dimensional trade relationships. The analysis reveals three prominent trade clusters: Global Trade Leaders, Emerging Trade Powers, and Niche Exporters, each reflecting varying degrees of trade diversification and dependency. These empirical findings align with established economic theories, including the Heckscher Ohlin model and dependency theory, and provide actionable insights for policymakers seeking to enhance trade competitiveness and regional integration strategies.
IMPLEMENTASI HYBRID CNN, FACIAL LANDMARK DAN LIVENESS DETECTION PADA SISTEM ABSENSI WAJAH Akbar DB, Andi Muhammad; Faisal, Muhammad; AM Hayat, Muhyiddin
PROGRESS Vol 17 No 2 (2025): September
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56708/progres.v17i2.483

Abstract

This paper presents the implementation of a hybrid approach for face recognition attendance systems, combining Convolutional Neural Network (CNN), facial landmark detection, and liveness detection. The CNN model extracts facial features for identity recognition, while facial landmark detection captures dynamic movements such as eye blinking and mouth motion. Liveness detection ensures system robustness against spoofing attempts including photo and video replay. The system was developed using Python with OpenCV, MediaPipe, and TensorFlow, and tested under multiple spoofing scenarios. Results show a detection accuracy of 95.5%, with real-time performance and resilience against common spoofing threats.
ANALYSIS OF DETERMINING PERMANENT EMPLOYEES USING OCRA (OPERATIONAL COMPETITIVENESS RATING ANALYSIS) METHODOLOGY Darniati; Nurahmad, Nurahmad; Mulyadi, Ida Mulyadi; Musdalifa Thamrin; Samsuria, Samsuria; Muhammad Faisal
Nusantara Hasana Journal Vol. 4 No. 1 (2024): Nusantara Hasana Journal, Juny 2024
Publisher : Yayasan Nusantara Hasana Berdikari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59003/nhj.v4i1.1143

Abstract

The determination of permanent employees is a crucial strategic decision for every organization. This decision affects productivity, efficiency, and work culture within the company. This study aims to analyze and determine permanent employees using the OCRA (Operational Competitiveness Rating Analysis) Method. OCRA is a method that assesses employee performance based on indicators of Length of Work, Work Loyalty, Age, Last Education, Work Performance, Communication Skills, and Teamwork Skills that are relevant to the operations of a company. In this study, employee performance data is collected and analyzed using a quantitative approach. Each employee is assessed based on a number of criteria that include productivity, quality of work, compliance with procedures, and contribution to the team and the company as a whole. The results of the OCRA analysis are then used to identify employees who have superior performance and deserve to be considered as permanent employees. The results of the study show that the OCRA Method is able to provide an objective and measurable assessment in determining permanent employees. A3-employees with the highest OCRA scores are proven to have consistent performance and a significant contribution to the achievement of company goals. This study concludes that the use of the OCRA Method in determining permanent employees can increase transparency and accuracy in human resource management decision-making.
A Bluetooth-Based Attendance System for Educational Administration at SMA Muhammadiyah: Cross-Platform Development and Usability Validation Hayat, Muhyddin A.M.; Rasyidi, Muhammad Fachri; Faisal, Muhammad; Bakti, Rizki Yusliana; Syamsuri, Andi Makbul
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.4873

Abstract

The transformation of educational administration through technology has accelerated significantly, particularly in attendance systems, which have traditionally relied on manual roll calls. These conventional methods are time-consuming, error-prone, and susceptible to manipulation. This study presents a novel Bluetooth-based attendance system that contributes to the field by demonstrating passive MAC address detection for automated attendance recording, eliminating the need for additional software installations on student devices. The system was developed using React Native for cross-platform compatibility, with PostgreSQL for data management and NestJS for backend processing. The software engineering process followed Rapid Application Development (RAD) methodology, combined with comprehensive system validation through experimental testing. Usability evaluation with 133 participants using the System Usability Scale (SUS) yielded a score of 79.85, categorizing the system within the "Good to Excellent" usability range. The findings demonstrate significant improvements in efficiency and a reduction in attendance fraud compared to conventional methods. However, hardware quality and device proximity remain key limitations. Future research should explore the integration of Bluetooth Low Energy (BLE) technology, the implementation of machine learning algorithms for anomaly detection, or the development of hybrid validation models that combine multiple authentication factors. This system demonstrates the potential to modernize educational administration through seamless, device-level integration while maintaining high user acceptance.
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