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All Journal Jurnal Pengabdian UntukMu NegeRI JURTEKSI PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Computer Science and Information Technology (CoSciTech) Jurnal Pendidikan Jasmani Khatulistiwa Jurnal Sistem Informasi dan Teknik Informasi Filantropi: Jurnal Manajemen Zakat dan Wakaf Journal Sport Academy (JSA) TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Journal of Software Engineering and Information System (SEIS) Journal Sport Academy
Rahmad Firdaus
Fakultas Kedokteran Universitas Sumatera Utara
Religion Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Health Professions Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Physics Public Health Social Sciences Other

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Survei Event Quality pada Kejuaran Proliga Bolavoli di Pontianak Berdasarkan Persepsi Penonton Firdaus, Rahmad; Andinata; Muhammad Suhairi,
Journal Sport Academy Vol. 3 No. 1 (2024): June 2024
Publisher : Universitas PGRI Pontianak

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31571/jsa.v3i1.107

Abstract

This study aims to examine and understand sports spectators' perceptions of service quality at the Proliga Volleyball event in Pontianak. The research uses a descriptive method with both qualitative and quantitative approaches. The survey method was employed, with data collected through questionnaires and analyzed using SPSS, utilizing percentage formulas from the survey data. The instrument used was a questionnaire. The study population included men and women who had attended the Proliga event, with a sample of 170 respondents located in Pontianak. The data analysis results showed that the estimated values for all variable relationships were greater than 0.5, with a value of 1.000. The indicators with average distribution ratings are: (1) game quality with sub-indicators: Skill Performance at 86.9% and Operating Time at 91.5%; (2) augmented service quality with sub-indicators: Information at 93.5%, Entertainment at 89.5%, and Concessions at 79.5%; (3) interaction quality with sub-indicators: Committee Interaction at 83.3% and Fan Interaction at 81.8%; (4) outcome quality with sub-indicators: Sociability at 83.4% and Valence at 92.7%; (5) physical environment quality with sub-indicators: Ambience at 92.7%, Design at 93.2%, and Signage at 88.5%. The analysis and discussion results indicate that the satisfaction of spectators' perceptions regarding the quality of the Proliga event was rated as good. The model test showed that all Event Quality criteria were met and satisfactory. Based on the analysis results, it is concluded that game quality, augmented service quality, interaction quality, outcome quality, and physical environment quality can be considered dimensions of service quality for the event.
Kombinasi Algoritma Gaussian Naïve Bayes Dan Adaboost Untuk Meningkatkan Akurasi Dalam Klasifikasi Penyakit Diabetes Handayani, Fitri; Firdaus, Rahmad; Wahyudi, Ashari; Fu'adah Amran, Hasanatul; Medikawati Taufiq, Reny
JURNAL FASILKOM Vol. 15 No. 2 (2025): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v15i2.9279

Abstract

Diabetes mellitus adalah penyakit metabolik kronis yang dapat menyebabkan komplikasi serius jika tidak terdeteksi dini. Penelitian ini bertujuan untuk meningkatkan akurasi klasifikasi diabetes dengan menggabungkan algoritma Gausian Naïve Bayes dan Adaboost menggunakan teknik ensemble learning. Ensemble learning adalah metode dalam pembelajaran mesin yang meningkatkan akurasi model dengan menggabungkan prediksi dari beberapa model yang berbeda. Teknik ini mengintegrasikan model-model yang mungkin memiliki performa kurang optimal secara individu untuk membentuk model yang lebih unggul. Adaboost memberikan bobot lebih besar pada sampel yang sulit diklasifikasikan, sehingga efektif dalam menangani data yang kompleks dan tidak seimbang. Dataset yang digunakan berasal dari Sylhet Diabetes Hospital, Bangladesh, yang berisi data kuesioner yang telah diverifikasi oleh dokter. Evaluasi menggunakan Confusion Matrix menunjukkan bahwa kombinasi Gausian Naïve Bayes dan Adaboost meningkatkan akurasi klasifikasi diabetes secara signifikan. Model ini mencapai akurasi 96.1% pada pembagian data 80:20, lebih tinggi dibandingkan Naïve Bayes tunggal (87.69%). Precision tertinggi (100%) tercatat pada pembagian data 80:20, dengan recall stabil pada 93.7%–94%, dan F1-Score tertinggi sebesar 96.7%. Hasil ini menunjukkan bahwa kombinasi kedua algoritma melalui teknik ensemble learning dapat saling melengkapi dan meningkatkan performa klasifikasi, menjadikannya lebih efektif dalam identifikasi diabetes
Sistem Penunjang Keputusan PNS Berprestasi dan Teladan Dilingkungan Dinas Kominfo Kabupaten Tanah Datar Menggunakan Metode SAW Firdaus, Rahmad
Jurnal SANTI - Sistem Informasi dan Teknik Informasi Vol. 1 No. 1 (2021)
Publisher : Yayasan Rahmatan Fiddunya Wal Akhirah

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1101.98 KB) | DOI: 10.58794/santi.v1i1.10

Abstract

Sistem Penunjang Keputusan merupakan sebuah sistem yang dibuat untuk menunjang seseorang membuat keputusan yang akurat dan tepat. Menurut data yang didapat dari Dinas Komunikasi dan Informatika Kabupaten Tanah Datar saat ini pelaksanaan kegiatan PNS berprestasi dan teladan menerapkan sistem pencatatan manual yang datanya masih direkap menggunakan Microsoft excel sehingga hal tersebut masih dibilang tidak efektif dan efisien dalam pengolahan data serta pengambilan keputusan yang masih terbilang subjektif tanpa adanya data pendukung dan nilai yang pasti terhadap sebuah keputusan sehingga menimbulkan pertanyaan dan menjadi buah bibir oleh Pegawai Negeri Sipil yang lain. Untuk menyelesaikan permasalahan tersebut dibutuhkan aplikasi SPK berbasis web dengan menggunakan metode Simple Additive Weight (SAW) untuk menunjang pelaksanaan kegiatan PNS berprestasi dan teladan. Aplikasi tersebut dikembangkan dengan kerangka website HTML, dengan bahasa pemrograman PHP dan MySQL sebagai database. Hasil akhir dari penelitian ini adalah terciptanya aplikasi Sistem Penunjang Keputusan PNS berprestasi dan teladan yang dapat menunjang pelakasanaan kegiatan.
ANALISIS PERILAKU MASYARAKAT DALAM MENUNAIKAN ZAKAT PERTANIAN Firdaus, Rahmad; Sartika, Cici
Filantropi : Jurnal Manajemen Zakat dan Wakaf Vol. 3 No. 2 (2022): Filantropi
Publisher : Program Studi Manajemen Zakat dan Wakaf

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22515/finalmazawa.v3i2.5894

Abstract

As for the results of the research, the authors found that the behavior of muzakki in paying agricultural zakat was carried out by means of self-calculation, namely by using the method of calculating agricultural zakat 71.4% and 28.6% by using calculations that did not follow sharia rules, namely in accordance with sincerity. Most of the muzakki pay zakat according to haul 92.9% and those who pay during Ramadan 7.1%. While the place to pay zakat, most of the muzakki pay directly to mustahik 92.9%, namely mosques, neighbors or families who are less fortunate and 7.1% pay through BAZNAS institutions. As well as the forms of agricultural zakat distributed by muzakki there are in two forms, namely in the form of cash and rice. 71.4% of the interviewees paid in cash and 28.6% paid zakat in the form of rice or paddy. Zakat adalah pengeluaran harta yang wajib dikeluarkan oleh orang muslim dan diberikan membantu program pemerintah dan mengentaskan kemiskinan. Maka dari itu membayar zakat dapat berguna juga untuk kemajuan suatu negara. peran zakat adalah sebagai pengendali keuangan di dalam negara yang dikenal dengan kebijakan moneter. Karena zakat termasuk dalam pendapatan negara yang disalurkan untuk mensejahterakan masyarakat di bidang ekonomi seperti, kesempatan peluang kerja, keadilan distribusi pendapatan dan kekayaan, serta stabilitas nilai uang.kepada golongan yang berhak menerimanya untuk memenuhi kebutuhan serta meningkatkan pemerataan pertumbuhan ekonomi. Membayar zakat sangat memberi manfaat untuk kehidupan karena dapat meningkatkan iman seseorang pada Allah SWT. Selain itu, zakat juga ikut.
Analisis Infrastruktur Teknologi Informasi untuk Mendukung Kelangsungan Bisnis pada Platform E-Commerce Firdaus, Rahmad; Zidan, Abiyyu Al Thoriq; Rajaguguk, Rifaldi Febriansyah; Suwanto, Fredy; Yeremia, Deryl Andeya; Pratama, Dicky
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 5 No 1 (2025): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol5No1.pp35-45

Abstract

This research analyzes the implementation of information technology infrastructure in supporting business continuity of e-commerce platforms, focusing on risk management, security, and performance optimization aspects. Using a qualitative methodology with a case study approach, this research integrates COBIT and ITIL frameworks to evaluate IT infrastructure management effectiveness. The results show that the e-commerce platform has achieved 99.95% availability with an average response time of 180ms for standard transactions. The implementation of hybrid cloud-based microservices architecture provides optimal flexibility and scalability in handling demand fluctuations up to 400% from baseline load. Security analysis reveals the effectiveness of zero-trust architecture implementation with a threat detection rate of 98.5%. The integration of COBIT and ITIL frameworks resulted in a 47% increase in IT governance effectiveness, with the DSS domain achieving the highest maturity score of 4.5. This research recommends strategic development including migration to cloud-native architecture, implementation of AI/ML for resource optimization, and strengthening security capabilities through advanced threat detection.
TRAFFIC FLOW DETECTION USING YOLOV4 AND DEEPSORT ON NVIDIA JETSON NANO Taufiq, Reny Medikawati; Syahril, Syahril; Rafdi, Faris Abi; Firdaus, Rahmad; Sunanto, Sunanto; Muarif, Putri Fadhilla
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 11 No. 3 (2025): Juni 2025
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v11i3.3871

Abstract

Abstract: This study aims to develop a Deep Learning-based Traffic Flow Detector to automatically and accurately observe traffic flow. Conventional traffic observation is often conducted manually or via CCTV, but it is prone to human error and difficult to use for real-time trend analysis. In this study, the YOLOv4 method is used to detect four types of vehicles (cars, motorcycles, buses, trucks). To continuously track vehicle movement and address occlusion issues, the Deep SORT algorithm is implemented. The YOLOv4 model used is a pre-trained model and was tested on seven CCTV video recordings obtained from the official website of the Pekanbaru City Transportation Department. The system was implemented on a limited device, the Nvidia Jetson Nano, as a simulation of direct CCTV integration. Test results showed a highest precision of 98%, but the maximum accuracy achieved was only 26%. This low accuracy is influenced by several factors, including video resolution, detection model quality, and lighting conditions. Nevertheless, the system demonstrates potential to support future traffic management and engineering decisions but still requires further optimization, including improving video resolution and quality, retraining the model with a more representative local dataset, using lighter and more accurate detection models, and optimizing the tracking algorithm. Keywords: deep learning; deepsort; NVIDIA Jetson NANO; traffic flow; YOLOv4  Abstrak: Penelitian ini bertujuan mengembangkan Traffic Flow Detector berbasis Deep Learning untuk mengobservasi arus lalu lintas secara otomatis dan akurat. Observasi lalu lintas konvensional sering dilakukan secara manual atau melalui CCTV, namun rentan terhadap human error dan sulit digunakan untuk menganalisis tren secara real-time. Pada penelitian ini digunakan metode YOLOv4 untuk mendeteksi empat jenis kendaraan (mobil, motor, bus, truk). Untuk melacak pergerakan kendaraan secara berkelanjutan dan mengatasi masalah occlusion, digunakan algoritma Deep SORT. Model YOLOv4 yang digunakan merupakan pre-trained model dan diujikan pada tujuh rekaman video CCTV yang diambil dari situs resmi Dinas Perhubungan Kota Pekanbaru. Sistem ini diimplementasikan pada perangkat terbatas Nvidia Jetson Nano sebagai simulasi penerapan langsung pada CCTV. Hasil pengujian menunjukkan presisi tertinggi mencapai 98%, namun akurasi tertingginya hanya sebesar 26%. Rendahnya akurasi dipengaruhi oleh beberapa faktor seperti resolusi video, kualitas model deteksi, serta kondisi pencahayaan. Meski demikian, sistem ini menunjukkan potensi untuk membantu pengambilan keputusan dalam manajemen dan rekayasa lalu lintas di masa depan, namun masih membutuhkan optimasi lebih lanjut, seperti  peningkatan kualitas video input, pelatihan ulang model dengan dataset lokal, penggunaan model deteksi yang lebih ringan dan akurat serta pengoptimalan algoritma pelacakan. Kata kunci: deep learning deepsort; Nvidia Jetson Nano; traffic flow; YOLOv4
Analisis Sentimen Masyarakat Terhadap Kasus Pembobolan Data Nasabah Bank BSI Pada Twitter Menggunakan Metode Random Forest Dan Naïve Bayes Mualfah, Desti; Prihatin, Ananda; Firdaus, Rahmad; Sunanto
JURNAL FASILKOM Vol. 13 No. 3 (2023): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v13i3.6478

Abstract

Indonesia has recently been enlivened by the data breach case that hit Bank Syariah Indonesia (BSI) in May 2023, this has invited many responses from the public with various kinds of responses, especially on Twitter social media. Some people support BSI bank so they can restore the system they have but many criticize and blaspheme bank BSI for not being able to quickly fix its system which hackers compromised. The purpose of this study is to conduct a sentiment analysis to find out the response of the Indonesian people regarding cases of data breaches by bank BSI customers whether positive, negative or neutral. The methods used in this study are the naive Bayes method and the random forest method. Both of these methods have been widely used in the text data classification process and produce high accuracy. The dataset used is community responses from Twitter social media taken by crawling the data totaling 809 tweets. The results of this study are the accuracy of the Naive Bayes method of 74% and the random forest method of 70%.
Edukasi Antibiotik Untuk Keluarga Sebagai Upaya Preventif Terhadap Risiko Stunting Pada Anak Desa Sungai Kayu Ara Anugerah Putra, Bayu; Dian Utami; Rico Apriandika; Hanum Salsabila; Fakhira Frisya Ramadhani; Alris Gusnanda; Septiana Srinandini; Jihan Aulia; Soni, Soni; Firdaus, Rahmad; Mukhtar, Harun; Br Bangun, Elsi Titasari; Handayani, Fitri; Amran, Hasanatul Fu'adah
Jurnal Pengabdian UntukMu NegeRI Vol. 9 No. 3 (2025): Pengabdian Untuk Mu negeRI
Publisher : LPPM UMRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jpumri.v9i3.10669

Abstract

Stunting masih menjadi salah satu masalah kesehatan masyarakat di Indonesia dengan prevalensi yang cukup tinggi. Salah satu faktor risiko penting terjadinya stunting adalah infeksi berulang pada anak, yang sering kali ditangani dengan pemberian antibiotik. Penggunaan antibiotik yang tepat dapat membantu mencegah stunting dengan cara menekan beban penyakit, namun penggunaan yang tidak rasional berisiko menimbulkan resistensi serta gangguan keseimbangan mikrobiota usus yang berdampak pada penyerapan gizi. Rendahnya literasi penggunaan antibiotik di masyarakat, khususnya di kalangan ibu rumah tangga dan lansia sebagai pengasuh utama dalam keluarga, menjadi tantangan tersendiri. Kegiatan sosialisasi ini bertujuan untuk meningkatkan pemahaman masyarakat mengenai penggunaan antibiotik rasional serta mengingatkannya terhadap pencegahan stunting. Metode yang digunakan adalah ceramah interaktif, diskusi, dan pembagian media edukasi. Hasil kegiatan menunjukkan adanya peningkatan pemahaman peserta mengenai pentingnya penggunaan antibiotik sesuai anjuran tenaga medis. Oleh karena itu, sosialisasi antibiotik kepada ibu dan lansia memiliki peran penting sebagai upaya preventif yang secara tidak langsung dapat mendukung pencegahan stunting pada anak.
Klasifikasi Algoritma Kriptografi pada Pesan Terenkripsi menggunakan Support Vector Machine (SVM) Fatma, Yulia; Gunawan, Rahmad; Fitri, Nurkhairi; Firdaus, Rahmad; Hayami, Regiolina; Soni, Soni
JURNAL FASILKOM Vol. 15 No. 3 (2025): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v15i3.10843

Abstract

Data protection has become a highly critical aspect, particularly in addressing ransomware threats that illegally encrypt data. This study is important to evaluate the capability of machine learning techniques in identifying encryption algorithms used in encrypted data, especially in ransomware attacks. This work represents an initial step that can assist cybersecurity practitioners in more rapidly understanding attack patterns, determining appropriate response strategies, and enhancing proactive mitigation and response efforts to protect data against increasingly complex cyber threats. The machine learning algorithm employed in this study is the Support Vector Machine (SVM). The dataset consists of ciphertext generated using the AES, DES, and Vigenère Cipher cryptographic algorithms. The feature extraction process utilizes ten statistical features to capture the distinctive patterns of each type of ciphertext. The SVM model is developed using a data split of 90% for training and 10% for testing. Performance evaluation is conducted using a confusion matrix with accuracy, precision, recall, and F1-score metrics. The result demonstrate an average accuracy 0f 92,33%, with the vigenere cipher being perfectly classified (100% accuracy). Howefer, slight misclassifications occured beetween AES and DES duet o their similiar entropy chraracteristic. Experimental results demonstrate that the SVM model is capable of identifying encryption algorithms with high accuracy and balanced classification performance across the three algorithm classes. These findings highlight the potential of machine learning approaches for detecting encryption algorithms in cyber-attacks, thereby making a meaningful contribution to the improvement of proactive data security mitigation and response strategies.
Convolutional Neural Network dengan Arsitektur InceptionV3 untuk Klasifikasi Citra Makanan Berdasarkan Asal Daerah Jawa dan Sumatera Khasanah, Diva Nayla; Firdaus, Rahmad
Computer Science and Information Technology Vol 7 No 1 (2026): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v7i1.11331

Abstract

This study aims to improve the accuracy of classifying traditional food images based on the regions of Java and Sumatra using the Convolutional Neural Network (CNN) algorithm with the InceptionV3 architecture. Traditional foods from these two regions are often difficult to distinguish due to visual similarities. The dataset consists of 472 food images processed through segmentation, augmentation, and rescaling. The InceptionV3 model was selected for its ability to capture complex visual patterns with high efficiency. The training process employed the Adam optimizer, a learning rate of 0.001, and a 50% dropout regularization technique to prevent overfitting. The model was evaluated using accuracy, precision, recall, and F1-score metrics. The results show that the model achieved an accuracy of 90.42%.precision of 91.07%, recall of 92.72%, and F1-score of 90%, significantly improving compared to previous research, which only achieved an accuracy of 64% using CNN without a specific architecture. This study is expected to contribute to the preservation of local culinary culture and support the promotion of tourism and technology-based culinary industries in Indonesia.
Co-Authors Aditya Ahmad Fauzi Alris Gusnanda Amran, Hasanatul Fu'adah Andinata Anton, Anton Kurnia Hadisaputro Bayu Anugerah Putra Br Bangun, Elsi Titasari Dian Utami Dicky Pratama Fadila, Wahyu Fakhira Frisya Ramadhani Fatma, Yulia Fithriawan Nugroho Fitri Handayani Fitri, Nurkhairi Fu'adah Amran, Hasanatul Gunawan, Rahmad Hanum Salsabila Harun Mukhtar Hayami, Regiolina Hermanto Hermanto Jihan Aulia Karan Khasanah, Diva Nayla Marna, Marna Maryani, Cik Mualfah, Desti Muarif, Putri Fadhilla Muhammad Suhairi, Prihatin, Ananda Putra, Gope Mandala Rafdi, Faris Abi Rajaguguk, Rifaldi Febriansyah Rico Apriandika Rizqy, Riza Rindriani Sartika, Cici Septiana Srinandini Soni Suhardi Suhardi Sunanto Sunanto Sunanto Suwanto, Fredy Syahril Syahril Taufiq, Reny Medikawati Wahyudi, Ashari Yeremia, Deryl Andeya Zidan, Abiyyu Al Thoriq Zusyah Porja Daryanto