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All Journal SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal Sistem Informasi dan Sistem Komputer Jurnal Minfo Polgan (JMP) Fahma : Jurnal Informatika Komputer, Bisnis dan Manajemen International Journal of Applied Mathematics and Computing. Jurnal Komtika (Komputasi dan Informatika)
Achmad, Refi Riduan
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Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Analisis Perbandingan Performa Algoritma Asimetris Elgamal Dan Merkle-Hellman-Knapsack Pada Pesan Teks Rahmadani, Nurika; Arifin, Zainal; Achmad, Refi Riduan; Hamdani, Hamdani
Jurnal Informatika Komputer, Bisnis dan Manajemen Vol 23 No 1 (2025): Januari 2025
Publisher : LPPM STMIK El Rahma Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61805/fahma.v23i1.158

Abstract

Perkembangan algoritma kriptografi ditandai dengan beragamnya jenis-jenis algoritma kriptografi  yang dapat digunakan dalam enkripsi dan dekripsi, seperti algoritma Elgamal dan Merkle-Hellman-Knapsack. Algoritma Elgamal didasarkan pada logaritma diskrit, sedangkan algoritma Merkle-Hellman-Knapsack didasarkan pada kurva aljabar eliptik. Penelitian ini bertujuan untuk mengetahui perbandingan performa algoritma Elgamal dan Merkle-Hellman-Knapsack dalam melakukan enkripsi dan dekripsi pesan teks. Analisis perbandingan dalam penelitian ini dilakukan dengan mengukur beberapa parameter, yaitu penggunaan memori Resident Set Size (RSS), penggunaan Central Processing Unit (CPU), ukuran ciphertext hasil enkripsi, dan waktu eksekusi. Penelitian dilakukan menggunakan 11 data plaintext dengan ukuran beragam. Hasil pengujian menggunakan ukuran plaintext 10.000 bytes pada metode Elgamal sebesar 51.114 bytes, sedangkan menggunakan metode Merkle Hellman Knapsack hanya sebesar 34.982 bytes.  Sementara itu, plaintext 100.000 bytes menggunakan metode Elgamal menghasilkan ciphertext sebesar 511.216 bytes dan Merkle Hellman Knapsack menghasilkan ciphertext sebesar 348.138 bytes. Hasil penelitian menunjukkan bahwa kedua algoritma dapat digunakan untuk mengamankan pesan teks, namun algoritma Elgamal lebih sesuai digunakan untuk sistem dengan keterbatasan memori dan CPU.
Kuliah kerja nyata sebagai katalis peningkatan budaya Keselamatan dan Kesehatan Kerja (K3) di Perusahaan Achmad, Refi Riduan; Supratman, Agus Ahmad; Hidayatullah, Hidayatullah; De Jesus, Maria Martina; Walhidayat, Mohammad Taufik; Nurhasanah, Nurhasanah; Hadi, Nur
SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Vol 9, No 6 (2025): November (In Progress)
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jpmb.v9i6.34251

Abstract

Abstrak Program Kuliah Kerja Nyata di PT. Karunia Armada Indonesia, Kecamatan Tabang, Kabupaten Kutai Kartanegara, dilaksanakan untuk meningkatkan kesadaran dan kepatuhan karyawan terhadap aspek keselamatan dan kesehatan kerja. Kegiatan dilakukan melalui sosialisasi K3LH dan pendampingan praktik penerapan Lock Out Tag Out (LOTO) di area workshop. Metode pelaksanaan meliputi pelatihan interaktif, demonstrasi penggunaan APD, dan observasi perilaku keselamatan sebelum dan sesudah kegiatan. Program diikuti oleh 22 karyawan, dengan 14 orang diantaranya mengikuti pendampingan praktik LOTO secara langsung. Hasil evaluasi menunjukkan peningkatan kepatuhan penggunaan APD dari sekitar 60% menjadi 85%, serta peningkatan penerapan prosedur LOTO dari 30% menjadi 70% pada aktivitas perawatan alat. Perubahan ini mengindikasikan bahwa program KKN efektif dalam memperkuat pemahaman dan budaya keselamatan kerja karyawan, serta berkontribusi terhadap penerapan standar K3LH yang lebih konsisten di lingkungan industri. Kata kunci: Kuliah Kerja Nyata; K3LH; LOTO; keselamatan kerja; pengabdian masyarakat. AbstractThe Community Service Program (Kuliah Kerja Nyata—KKN) conducted at PT. Karunia Armada Indonesia, located in Tabang District, Kutai Kartanegara Regency, aimed to enhance workers’ awareness and compliance with occupational health and safety practices. The program was implemented through K3LH (Occupational Health, Safety, and Environment) socialization and hands-on mentoring on the application of Lock Out Tag Out (LOTO) procedures in the workshop area. The activities involved interactive training sessions, demonstrations of proper personal protective equipment (PPE) usage, and observational assessments conducted before and after the intervention. A total of 22 employees participated in the program, with 14 individuals taking part in direct LOTO practice mentoring. The evaluation results indicated an increase in PPE compliance from approximately 60% to 85%, as well as an improvement in LOTO implementation from 30% to 70% across maintenance activities. These findings demonstrate that the KKN program effectively strengthened workers’ understanding and safety culture, contributing to more consistent adherence to K3LH standards within the industrial environment. Keywords: community service program; K3LH; LOTO; workplace safety; industrial engagement.
Optimasi Gradient Boosted Trees dalam Memprediksi Minat Nasabah untuk Berlangganan Pinjaman Berjangka Achmad, Refi Riduan; Zulfariansyah, Muhammad
Jurnal Komtika (Komputasi dan Informatika) Vol 9 No 2 (2025)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v9i2.15259

Abstract

This research focuses on optimizing the design parameters of Gradient Boosted Trees (GBT) to predict customer interest in subscribing to term loans. The study highlights the importance of tuning parameters such as the number of trees, tree depth, and learning rate to enhance the predictive accuracy of GBT. Through this optimization, the model aims to provide more precise insights into customer behavior, aiding financial institutions in making informed decisions and improving operational efficiency. The research compares GBT with other algorithms like Decision Trees and Random Forests, utilizing metrics such as accuracy, precision, recall, and AUC. The results indicate that GBT, with optimal parameter settings, outperforms the other models in predicting customer interest. The study concludes that GBT is an effective tool for market segmentation and can significantly contribute to more accurate predictions in financial services, ultimately helping companies develop better-targeted marketing strategies.
Pengembangan Sistem Informasi Terpadu Untuk Pengelolaan Aset, IT dan Laboratorium di Sekolah Kejuruan Achmad, Refi Riduan; Hermawanto, Dwi Fiqkri; Agung , Rafi
Jurnal Sistem Informasi dan Sistem Komputer Vol 11 No 1 (2026): Vol 11 No 1 - 2026
Publisher : STIMIK Bina Bangsa Kendari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51717/simkom.v11i1.1224

Abstract

Pengelolaan sarana prasarana, teknologi informasi, dan laboratorium di sekolah kejuruan memerlukan sistem yang mampu mengelola data secara terintegrasi dan terstruktur. Pada praktiknya, pengelolaan aset di sekolah masih banyak dilakukan menggunakan beberapa file dan aplikasi terpisah, sehingga berpotensi menimbulkan duplikasi data, kesalahan pencatatan, dan kesulitan dalam monitoring. Penelitian ini bertujuan untuk mengembangkan sistem informasi terpadu berbasis web yang mendukung pengelolaan aset, teknologi informasi, dan laboratorium sebagai studi kasus di SMK Tamansiswa Kota Mojokerto. Metode pengembangan sistem yang digunakan adalah Rational Unified Process (RUP) karena bersifat iteratif dan sistematis. Sistem dikembangkan menggunakan framework CodeIgniter 4 dengan arsitektur Model–View–Controller (MVC) serta basis data MySQL. Pengujian sistem dilakukan melalui Black Box Testing untuk memastikan fungsi sistem berjalan sesuai kebutuhan, serta User Acceptance Testing (UAT) untuk mengetahui tingkat penerimaan pengguna. Hasil pengujian menunjukkan bahwa seluruh fungsi utama sistem dapat berjalan dengan baik dan UAT memperoleh tingkat kepuasan pengguna sebesar 92% dengan kategori Sangat Baik. Dengan demikian, sistem yang dikembangkan mampu mendukung pengelolaan aset sekolah secara terintegrasi dan diterima dengan baik oleh pengguna.
Komparasi Kinerja dan Toleransi Kesalahan antara Cloud dan Fog Computing dalam Aplikasi IoT Terdistribusi Achmad, Refi Riduan; Abil, Muhammad; Fadhilah, Muhammad Raihan; Sandi, Sandi
Jurnal Minfo Polgan Vol. 14 No. 2 (2025): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v14i2.15576

Abstract

Perkembangan Internet of Things (IoT) menuntut sistem komputasi yang mampu memproses data secara cepat dan andal. Komputasi awan (Cloud Computing) selama ini menjadi solusi utama dalam pengelolaan data IoT, namun arsitekturnya yang terpusat menyebabkan latensi tinggi dan ketergantungan pada jaringan. Untuk mengatasi hal tersebut, komputasi kabut (Fog Computing) dikembangkan dengan mendistribusikan proses lebih dekat ke sumber data. Penelitian ini bertujuan untuk membandingkan kinerja dan toleransi kesalahan antara arsitektur Cloud Computing dan Fog Computing dalam lingkungan IoT terdistribusi. Pengujian dilakukan dengan mensimulasikan sistem publisher–broker–consumer menggunakan protokol MQTT di lingkungan Docker. Parameter yang diuji meliputi rata-rata latency, maksimum, minimum, jitter, serta toleransi kesalahan saat terjadi gangguan broker. Hasil pengujian menunjukkan bahwa Fog Computing memiliki rata-rata latency sebesar 0.0019 detik, lebih rendah dibandingkan Cloud Computing sebesar 0.0022 detik. Namun, pada uji toleransi kesalahan, Cloud Computing menunjukkan stabilitas yang sedikit lebih tinggi dengan tingkat kehilangan pesan 55% dibanding Fog sebesar 56.67%. Temuan ini mengindikasikan bahwa Fog Computing lebih unggul untuk aplikasi IoT real-time karena latensi rendah dan pemulihan cepat, sedangkan Cloud lebih sesuai untuk pengolahan data berskala besar dan berkelanjutan. Penelitian selanjutnya dapat memperluas pengujian terhadap parameter bandwidth dan konsumsi energi untuk mendapatkan gambaran performa sistem yang lebih komprehensif.
Comparative Evaluation of YOLOv5, YOLOv7, and YOLOv8 for Outdoor Traffic Object Detection Achmad, Refi Riduan; Abil, Muhammad; Fadhilah, Muhammad Raihan; Sandi
International Journal of Applied Mathematics and Computing Vol. 3 No. 2 (2026): April: International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v3i2.291

Abstract

Object detection plays a crucial role in intelligent transportation systems, particularly for outdoor traffic monitoring applications that require accurate and real-time performance under limited computational resources. Recent developments in YOLO-based architectures have introduced multiple model variants; however, their practical performance under constrained training conditions remains insufficiently explored. This study presents a comparative evaluation of YOLOv5, YOLOv7, and YOLOv8 for outdoor traffic object detection using a real-world dataset and identical experimental settings. The main objective of this research is to analyze the robustness and detection quality of different YOLO variants when trained with a limited number of epochs, reflecting practical deployment scenarios. All models were trained and evaluated using the same dataset, preprocessing pipeline, and hardware configuration to ensure a fair comparison. Performance evaluation was conducted using multiple metrics, including precision, recall, mAP@50, Precision–Recall curves, area under the curve (AUC), and peak F1-score. Experimental results indicate that YOLOv5 outperformed YOLOv7 and YOLOv8 in terms of overall detection stability and robustness. The merged Precision–Recall analysis shows that YOLOv5 achieved a higher effective AUC and superior mAP@50, reflecting better global detection performance. In addition, YOLOv5 exhibited a higher peak F1-score, indicating a more balanced trade-off between precision and recall. In contrast, YOLOv7 and YOLOv8 showed performance degradation under limited training conditions despite their more advanced architectures. These findings suggest that YOLOv5 remains a reliable and efficient solution for outdoor traffic object detection, particularly in resource-constrained environments. The study highlights the importance of comprehensive evaluation metrics and practical experimental settings when selecting object detection models for real-world applications.
QoS-Based Assessment and Classification of Network Conditions Using OSPF and BGP Routing Protocols Achmad, Refi Riduan; Reza, Muhammad Ali
International Journal of Applied Mathematics and Computing Vol. 3 No. 2 (2026): April: International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v3i2.293

Abstract

Object detection plays a crucial role in intelligent transportation systems, particularly for outdoor traffic monitoring applications that require accurate and real-time performance under limited computational resources. Recent developments in YOLO-based architectures have introduced multiple model variants; however, their practical performance under constrained training conditions remains insufficiently explored. This study presents a comparative evaluation of YOLOv5, YOLOv7, and YOLOv8 for outdoor traffic object detection using a real-world dataset and identical experimental settings. The main objective of this research is to analyze the robustness and detection quality of different YOLO variants when trained with a limited number of epochs, reflecting practical deployment scenarios. All models were trained and evaluated using the same dataset, preprocessing pipeline, and hardware configuration to ensure a fair comparison. Performance evaluation was conducted using multiple metrics, including precision, recall, mAP@50, Precision–Recall curves, area under the curve (AUC), and peak F1-score. Experimental results indicate that YOLOv5 outperformed YOLOv7 and YOLOv8 in terms of overall detection stability and robustness. The merged Precision–Recall analysis shows that YOLOv5 achieved a higher effective AUC and superior mAP@50, reflecting better global detection performance. In addition, YOLOv5 exhibited a higher peak F1-score, indicating a more balanced trade-off between precision and recall. In contrast, YOLOv7 and YOLOv8 showed performance degradation under limited training conditions despite their more advanced architectures. These findings suggest that YOLOv5 remains a reliable and efficient solution for outdoor traffic object detection, particularly in resource-constrained environments. The study highlights the importance of comprehensive evaluation metrics and practical experimental settings when selecting object detection models for real-world applications.
Analisis Perbandingan Performa Algoritma Asimetris Elgamal Dan Merkle-Hellman-Knapsack Pada Pesan Teks Rahmadani, Nurika; Arifin, Zainal; Achmad, Refi Riduan; Hamdani, Hamdani
Jurnal Informatika Komputer, Bisnis dan Manajemen Vol 23 No 1 (2025): Januari 2025
Publisher : LPPM STMIK El Rahma Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61805/fahma.v23i1.158

Abstract

Perkembangan algoritma kriptografi ditandai dengan beragamnya jenis-jenis algoritma kriptografi  yang dapat digunakan dalam enkripsi dan dekripsi, seperti algoritma Elgamal dan Merkle-Hellman-Knapsack. Algoritma Elgamal didasarkan pada logaritma diskrit, sedangkan algoritma Merkle-Hellman-Knapsack didasarkan pada kurva aljabar eliptik. Penelitian ini bertujuan untuk mengetahui perbandingan performa algoritma Elgamal dan Merkle-Hellman-Knapsack dalam melakukan enkripsi dan dekripsi pesan teks. Analisis perbandingan dalam penelitian ini dilakukan dengan mengukur beberapa parameter, yaitu penggunaan memori Resident Set Size (RSS), penggunaan Central Processing Unit (CPU), ukuran ciphertext hasil enkripsi, dan waktu eksekusi. Penelitian dilakukan menggunakan 11 data plaintext dengan ukuran beragam. Hasil pengujian menggunakan ukuran plaintext 10.000 bytes pada metode Elgamal sebesar 51.114 bytes, sedangkan menggunakan metode Merkle Hellman Knapsack hanya sebesar 34.982 bytes.  Sementara itu, plaintext 100.000 bytes menggunakan metode Elgamal menghasilkan ciphertext sebesar 511.216 bytes dan Merkle Hellman Knapsack menghasilkan ciphertext sebesar 348.138 bytes. Hasil penelitian menunjukkan bahwa kedua algoritma dapat digunakan untuk mengamankan pesan teks, namun algoritma Elgamal lebih sesuai digunakan untuk sistem dengan keterbatasan memori dan CPU.
Co-Authors Abil, Muhammad Agung , Rafi De Jesus, Maria Martina Fadhilah, Muhammad Raihan Hamdani Hamdani Hermawanto, Dwi Fiqkri hidayatullah, Hidayatullah hidayatullah Nur Hadi Nurhasanah Nurhasanah Rahmadani, Nurika Reza, Muhammad Ali Sandi Sandi Sandi Supratman, Agus Ahmad Walhidayat, Mohammad Taufik Zainal Arifin Zulfariansyah, Muhammad