Article Per Year (5 Year)
p-Index From 2021 - 2026
4.359
P-Index
This Author published in this journals
All Journal International Journal of Advances in Applied Sciences PROtek : Jurnal Ilmiah Teknik Elektro Ainet : Jurnal Informatika Indonesian Journal of Data and Science Jurnal Teknik Informatika (JUTIF) Jurnal Algoritma, Logika dan Komputasi Jurnal Informatika Progres Mechatronics Journal in Professional and Entrepreneur (MAPLE) ARUS JURNAL SAINS DAN TEKNOLOGI Journal of Muhammadiyah’s Application Technology
Muhyiddin A.M Hayat
Informatika, Universitas Muhammadiyah Makassar
Humanities 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 Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Physics Social Sciences Other

Published : 34 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 34 Documents
Search

 1   2   3   4 
PENERAPAN ALGORITMA K-NEAREST NEIGHBOR DALAM ANALISIS PEMINJAMAN BARANG PADA DIVISI INVENTARIS TVRI MAKASSAR Risal; Danuputri, Chyquitha; Darniati; 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.v17i2.474

Abstract

Inventory management in the TVRI Makassar Inventory Division is inefficient due to the lack of a predictive system, hampering proactive asset requirement planning. This study aims to apply the K-Nearest Neighbor (KNN) algorithm to analyze historical borrowing patterns, predict demand for goods three months in advance, and evaluate model accuracy. Using a quantitative approach, this study implements a systematic machine learning workflow, including data preprocessing, temporal feature engineering, class imbalance handling using the Synthetic Minority Over-sampling Technique (SMOTE), and hyperparameter optimization using GridSearchCV. The results show that the optimized KNN model achieved an overall accuracy of 80.18%, significantly outperforming the baseline model. Key findings revealed that the model's performance is contextual, with very high reliability (F1-Score > 0.95) on frequently borrowed assets, and is able to identify strong temporal demand patterns. It is concluded that KNN is effective for segmented inventory demand prediction and has the potential to serve as a basis for TVRI Makassar to adopt a proactive, data-driven inventory management strategy, enabling more efficient resource allocation.
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.
Penerapan Machine Learning untuk Mengklasifikasikan Genre Musik Berdasarkan Fitur Audio Abrah, Rezkytullah; Hayat, Muhyiddin A M; Lukman , Lukman
Arus Jurnal Sains dan Teknologi Vol 3 No 2: Oktober (2025)
Publisher : Arden Jaya Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57250/ajst.v3i2.1699

Abstract

Penelitian ini bertujuan untuk mengembangkan sistem klasifikasi genre musik secara otomatis berdasarkan fitur-fitur audio yang diekstraksi dari file lagu. Proses ekstraksi fitur menggunakan pustaka Librosa untuk menghitung Zero Crossing Rate (ZCR), Spectral Centroid, dan 13 koefisien Mel-Frequency Cepstral Coefficients (MFCC). Setelah melalui proses normalisasi, fitur-fitur tersebut digunakan sebagai input dalam pelatihan tiga model klasifikasi: K-Nearest Neighbor (KNN), Random Forest, dan Naïve Bayes. Dataset terdiri dari lima genre musik populer dengan lebih dari 500 lagu. Hasil pengujian menunjukkan bahwa model Random Forest menghasilkan akurasi tertinggi dan mampu mengenali pola audio dengan baik, termasuk pada lagu-lagu yang tidak terdapat dalam data pelatihan. Sistem juga mampu menampilkan hasil ekstraksi fitur dan prediksi dalam bentuk visualisasi, sehingga memberikan transparansi dalam proses klasifikasi. Dengan pendekatan ini, sistem dapat digunakan untuk membantu analisis musik secara otomatis dan efisien.
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.
Game Edukasi Berbasis Android sebagai Media Pembelajaran Matematika untuk Anak Tunarungu Bakti, Rizki Yusliana; Wahyuni, Titin; Hayat, Muhyiddin A M; Ridwang, Ridwang
PROtek : Jurnal Ilmiah Teknik Elektro Vol 8, No 1 (2021): Protek : Jurnal Ilmiah Teknik Elektro
Publisher : Program Studi Teknik Elektro Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/protk.v8i1.2377

Abstract

Education is a right for every individual. Not only those with normal conditions, but also those with special needs such as the deaf. Limited intellectual ability in deaf children has consequences for their difficulties in attending academic lessons including maths lessons. This research aims to create an application as a learning medium to attract deaf children in developing their intelligence. This application was created as a means to provide convenience to deaf children in helping the learning process of mathematics. This study uses observation data collection methods, interviews / questionnaires and library studies. The design method used is the waterfall and the testing technique used is Integration and System testing. The result of this study is an android-based game application named math games. The test results show that this application is easy to learn and there are media that make childrenhappy.
Penerapan Algoritma Support Vector Machine Untuk Penentuan Konsentrasi Mahasiswa Program Studi Manajemen Universitas Muhammadiyah Makassar RAMLI, ANDI RAODATUL ADAWIYAH; Fahrim Irhmna Rachman; Muhyiddin A.M Hayat
Ainet : Jurnal Informatika Vol. 7 No. 1 (2025): Maret (2025)
Publisher : Universitas Muhammadiyah Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26618/kdxvxa79

Abstract

Pemilihan konsentrasi merupakan aspek penting bagi mahasiswa program studi manajemen di Universitas Muhammadiyah Makassar, terutama bagi mahasiswa semester enam. Program studi ini menawarkan konsentrasi di bidang SDM, pemasaran, dan keuangan. Penelitian ini menggunakan data set mahasiswa angkatan 2018 hingga 2021 dan menerapkan algoritma Support Vector Machine (SVM) untuk menentukan konsentrasi yang tepat. Hasil penelitian menunjukkan bahwa model SVM mencapai akurasi sebesar 70,55% dalam menentukan konsentrasi mahasiswa. Validasi dilakukan dengan membandingkan hasil prediksi SVM dengan perhitungan manual menggunakan Microsoft Excel. Kesamaan hasil antara metode SVM dan perhitungan manual menunjukkan bahwa model ini berhasil mereplikasi keputusan manual dengan baik. Temuan ini mengindikasikan bahwa model SVM dapat menggeneralisasi pola dari data training ke data testing dengan akurasi yang memadai. Sistem ini dinilai andal dan efisien dalam proses pengambilan keputusan tanpa mengorbankan akurasi, menunjukkan bahwa metode ini dapat diandalkan secara konsisten.Kata kunci: Support Vector Machine (SVM), Konsentrasi, Mahasiswa, Klasifikasi.
Analisis Data Kepuasan Mahasiswa Terhadap Sarana Dan Prasarana Dengan Menggunakan Algoritma Naïve Bayes Pada Universitas Muhammadiyah Makassar suriani, suriani; Muhyiddin A.M Hayat; Rizki Yusliana Bakti
Ainet : Jurnal Informatika Vol. 7 No. 2 (2025): September (2025)
Publisher : Universitas Muhammadiyah Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26618/wrccj205

Abstract

SURIANI, Salah satu institusi pendidikan tinggi yang berperan sentral dalam menyediakan pendidikan berkualitas adalah Universitas Muhammadiyah Makassar (Unismuh). Perguruan tinggi sebagai penyedia layanan harus menyediakan mutu pelayanan yang unggul kepada mahasiswa. Salah satu atribut pelayanan adalah hal yang tidak berwujud. Dalam konteks mahasiswa terhadap pelayanan fasilitas, pegawai dan kepuasan staf pengajar perlu memberikan pelayanan berkualitas tinggi kepada mahasiswa sesuai dengan kebutuhan mereka. Mahasiswa telah mengorbankan uang dan waktu untuk pendidikan, sehingga perguruan tinggi harus memberikan layanan yang sebanding dengan pengorbanan ini. Dari perhitungan yang telah dilakukan dengan metode Naïve Bayes, terdapat terdeteksi terdapat 16 mahasiswa mahasiswa yang menyatakan puas dan 427 mahasiswa yang menyatakan tidak puas, dengan perbandingan persentase 4% banding 96%. Dengan nilai presisi, recall, dan f1 score masing-masing bernilai 100%. Artinya pihak kampus perlu melakukan pengembangan dan perbaikan terhadap sarana dan prasarana yang ada pada kampus universitas Muhammadiyah makassar.
OPTIMALISASI DISTRIBUSI PEMILIH TERHADAP TPS MENGGUNAKAN METODE CLUSTERING FUZZY C-MEANS Djalil, Sony Achmad; Muhammad Faisal; Muhyiddin AM Hayat; Titin Wahyuni
Ainet : Jurnal Informatika Vol. 7 No. 2 (2025): September (2025)
Publisher : Universitas Muhammadiyah Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26618/6jc0a759

Abstract

General elections are a fundamental pillar of modern democratic systems, requiring an implementation that is efficient, fair, and inclusive. One of the key factors influencing the success of an election is the determination of polling station locations, as their placement directly affects voter accessibility, travel distance, and public participation. Inappropriate polling station allocation can lead to service inequality, voter congestion, and a decline in the overall quality of the voting process. At the local administrative level, polling station determination is still largely conducted manually by grouping voters based on neighborhood or administrative boundaries. This conventional approach is often time consuming, prone to administrative errors, and frequently results in an uneven distribution of voters across polling stations. In addition, electoral regulations impose limits on the maximum number of voters per polling station to ensure smooth and orderly voting procedures, which are not always optimally satisfied through manual methods. As voter data complexity and geographic dispersion increase, computational approaches are needed to support more effective decision making. Clustering techniques in unsupervised learning enable objective grouping of voters based on spatial characteristics. The Fuzzy C-Means method represents a suitable approach because it can accommodate data uncertainty and overlapping service areas. The application of this method is expected to produce a more efficient, equitable, and data driven distribution of polling stations, thereby contributing to the improvement of election management quality and democratic integrity
Stacking architecture-endpoint detection: a hybrid multi layered architecture for endpoint threat detection Wahid, Abd Rahman; Anggreani, Desi; Hayat, Muhyiddin A. M.; Abd Rahman, Aedah; Faisal, Muhammad
International Journal of Advances in Applied Sciences Vol 14, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i4.pp1263-1280

Abstract

Modern endpoint threat detection systems face persistent challenges in balancing detection accuracy, resilience against zero-day attacks, and the interpretability of artificial intelligence (AI) models. Although deep learning (DL) approaches often achieve high accuracy on benchmark datasets, they remain vulnerable to adversarial perturbations and operate as opaque “black boxes,” thereby reducing trust and limiting practical adoption in critical infrastructures. This research introduces stacking architecture-endpoint detection (STACK-ED), a hybrid multi-layered architecture for endpoint threat detection. STACK-ED integrates three complementary paradigms: supervised learning for known attack patterns, self-supervised Fgraph-based learning for structural relationships, and unsupervised anomaly detection for emerging or unknown threats. The outputs are consolidated by a meta learner, followed by a post-hoc correction (PHC) mechanism to minimize false negatives. The framework was evaluated on a combined benchmark dataset (CSE-CIC-IDS2018 and UNSW-NB15, hereafter referred to as HIDS-Set). Experimental results demonstrate state-of-the-art performance, achieving an F2-score of 98.89% after hybrid integration and active learning, with the primary optimization objective being the reduction of undetected attacks. Furthermore, the Shapley additive explanations (SHAP) method enhances interpretability by revealing feature contributions, while the PHC successfully recovered 62.64% of missed zero-day candidates. The findings position STACK-ED not only as a highly accurate detection model but also as an adaptive, resilient, and transparent framework, offering practical implications for enterprise-grade endpoint defense and future zero-trust cybersecurity systems.
Implementasi Tanda Tangan Digital dengan Metode AES dan Deflate untuk Verifikasi Surat Permohonan KKP Lukman; Hayat, Muhyiddin A M; Nur, Muhammad Ikhsan
Journal of Muhammadiyah’s Application Technology Vol. 4 No. 3 (2025)
Publisher : Universitas Muhammadiyah Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26618/k6zs6353

Abstract

ABSTRAKTransformasi digital di lingkungan akademik menuntut adanya sistem verifikasi dokumen yang aman, efisien, dan mudah diimplementasikan. Penelitian ini bertujuan untuk mengimplementasikan tanda tangan digital dalam proses verifikasi Surat Permohonan Kuliah Kerja Profesi (KKP) dengan mengombinasikan algoritma Advanced Encryption Standard (AES) dan metode kompresi Deflate. Sistem dirancang melalui beberapa tahapan, meliputi preprocessing data surat untuk menggabungkan seluruh informasi penting ke dalam satu format string, kompresi data menggunakan metode Deflate, serta enkripsi menggunakan algoritma AES-128 dengan mode Cipher Block Chaining (CBC). Data terenkripsi selanjutnya dikonversi ke dalam bentuk QR Code yang berfungsi sebagai representasi tanda tangan digital. Proses verifikasi dilakukan dengan memindai QR Code menggunakan aplikasi client, kemudian dilakukan tahap dekripsi dan dekompresi untuk mengembalikan data ke bentuk aslinya. Hasil pengujian menunjukkan bahwa sistem mampu menjaga kerahasiaan dan integritas data, mempercepat proses verifikasi administrasi, serta meminimalkan potensi kesalahan manusia dalam pengelolaan dokumen. Dengan demikian, integrasi algoritma AES dan metode Deflate dapat diterapkan secara efektif sebagai solusi verifikasi dokumen digital di lingkungan akademik. Kata Kunci: Tanda Tangan Digital, AES, Deflate, Enkripsi, Dokumen Akademik   ABSTRACTDigital transformation in academic environments requires a document verification system that is secure, efficient, and easy to implement. This study aims to implement a digital signature system for verifying Internship Application Letters (Kuliah Kerja Profesi/KKP) by combining the Advanced Encryption Standard (AES) algorithm and the Deflate compression method. The system is designed through several stages, including preprocessing of letter data to merge essential information into a single string format, data compression using the Deflate method, and encryption using the AES-128 algorithm in Cipher Block Chaining (CBC) mode. The encrypted data is then converted into a QR Code, which serves as a digital signature representation. The verification process is carried out by scanning the QR Code using a client application, followed by decryption and decompression to restore the original data. The testing results indicate that the proposed system is capable of maintaining data confidentiality and integrity, accelerating administrative verification processes, and reducing human error in document management. Therefore, the integration of AES and Deflate proves to be an effective solution for digital document verification in academic institutions. Keyworsds: Digital Signature, AES, Deflate, Encryption, Academic Document
Co-Authors . Darniati Abd Rahman, Aedah Abrah, Rezkytullah Ahmad Faisal Akbar DB, Andi Muhammad Akbar, Syahril Anggraeni , Desi Anggreani, Desi Bakti, Rizki Yusliana Basyar, Muhammad Yusuf Danuputri, Chyquitha Danuputri, Chyquitha Dedi Setiawan Desi Anggreani Djalil, Sony Achmad Dzakki Adam, Ahmad Wildan Fachrim Irhamna Rachman Faeruddin, Muhammad Asygar Fahrim Irhmna Rachman Fatimah Azzahra H, chalidah_azzahra00 Hadawina Hadawina Haidul, Haidul Hermanto, Chalidah Azzahrah Khadijah khairat, arikal Lukman LUKMAN ANAS Lukman Lukman Lukman Lukman Muh Nur Aqsal Aminullah Muhammad Faisal Mujidah, Jihan Izzathul Nisha, Khairun Nur Alam Nur, Muhammad Ikhsan Rahman, Fahrim Irhamna RAHMANIA Rahmania RAMLI, ANDI RAODATUL ADAWIYAH Rasyidi, Muhammad Fachri Reski Abbas Ridwang Ridwang Ridwang Ridwang Ridwang, Ridwang Risal S. Kuba, Muhammad Syafa'at Salsabila, Ismi Sarina Sunarti Suriani Suriani Syamsuri, Andi Makbul Syarifuddin, Nur Annisa Titin Wahyuni Usman, Ansharullah Patiroi Utama, Prengki Putra Wahid, Abd Rahman