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All Journal Jurnal Pengabdian UntukMu NegeRI IT JOURNAL RESEARCH AND DEVELOPMENT JURTEKSI Media Informatika Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Computer Science and Information Technology (CoSciTech) Jurnal PkM (Pengabdian kepada Masyarakat) Technologica Journal of Software Engineering and Information System (SEIS) Jurnal Sistem Informasi dan Teknologi Jaringan
Taufiq, Reny Medikawati
Universitas Muhammadiyah Riau
Religion Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Law, Crime, Criminology & Criminal Justice Social Sciences

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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 : Universitas Royal

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
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
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
Deep Learning Untuk Klasifikasi Kematangan Buah Mangrove Berdasarkan Warna Mukhtar, Harun; Alfanico, Febrian; Fu’adah Amran, Hasanatul; Handayani, Fitri; Medikawati Taufiq, Reny
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.v13i3.6292

Abstract

Plants that live between land and sea, such as mangroves, are influenced by the tides and tides. Indonesia has the largest mangrove forest in the world and a variety of biodiversity and structure. People currently detect mangrove maturity by looking directly at the fruit. This study proposes to classify the maturity of mangrove fruit using artificial intelligence techniques, making it easier for farmers to determine the ripeness of the fruit. This proposal uses data from 200 images for mangroves taken directly from Lukit Village, Merbau District, Meranti Islands Regency. This research improves the Convolutional Neural Network (CNN) method to classify mangrove fruit maturity. The results obtained from this research were by classifying ripe and unripe fruit. Based on this research, accuracy reaches a maximum of 96%.
Pengolahan Data Geospasial Menggunakan Pembelajaran Mesin: Sebuah Tinjauan Dwijaya, Muhammad Riza Rio; Taufiq, Reny Medikawati
Media Informatika Vol 24 No 3 (2025)
Publisher : P3M STMIK LIKMI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37595/mediainfo.v24i3.373

Abstract

Machine learning memainkan peran penting dalam analisis data geospasial dengan mengekstraksi pola dari data multidimensi seperti citra satelit dan parameter lingkungan. Studi ini menelaah 20 jurnal untuk membandingkan jenis data spasial, teknik preprocessing, dan algoritma yang digunakan. Hasilnya menekankan pentingnya pemilihan data yang sesuai, pengolahan tepat, serta optimalisasi hyperparameter untuk menghasilkan model yang lebih robust dan akurat dalam berbagai aplikasi spasial
Klasifikasi Citra Penyakit Daun Tomat Menggunakan Metode Convolutional Neural Network (CNN) Dengan Arsitektur VGG-19 Fitri Handayani; Baidarus, Baidarus; Sunanto, Sunanto; Putra, Bayu Anugerah; Anggraini, Chelina; Taufiq, Reny Medikawati
Computer Science and Information Technology Vol 6 No 3 (2025): 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.v6i3.10699

Abstract

Tomatoes, known as Solanum lycopersicum in Latin, are a type of horticultural commodity with high economic value in Indonesia.Tomato production can decrease due to leaf diseases that are hard to identify manually because the symptoms of different diseases often appear similar. The purpose of this study is to apply a deep learning-based tomato leaf disease classification system using the Convolutional Neural Network (CNN) VGG-19 architecture. The dataset was obtained from Kaggle and contains 6,600 images of tomato leaves divided into six disease classes and one healthy leaf class. The research stages include preprocessing (resizing, normalization), data augmentation, dataset division (80% training, 20% testing), model training with transfer learning, and fine-tuning for optimization. The evaluation using the confusion matrix and classification report includes accuracy, precision, recall, and F1-score. Test results show that the VGG-19 model achieved 97% accuracy on the test data, with an average precision, recall, and F1-score of 0.97. These findings show that VGG-19 effectively identifies tomato leaf diseases and could be applied in web- or mobile-based detection systems to help farmers with early diagnosis and proper treatment.
Efektivitas Sosialisasi Narkotika, Psikotropika, dan Zat Adiktif (NAPZA) dalam Meningkatkan Pengetahuan dan Kesadaran Siswa SMP Negeri 05 Sungai Apit Anugerah Putra, Bayu; Soni, Soni; Gunawan, Rahmad; Fatma, Yulia; Firdaus, Rahmad; Taufiq, Reny Medikawati; Handayani, Fitri; Mukhtar, Harun; Mualfah, Desti; Azim, Fauzan; Aprilya, Regiesta Lintang; Ramadhan, Rafi Fakhri; Abadi, Abdi Nauli
Jurnal Pengabdian UntukMu NegeRI Vol. 10 No. 2 (2026): Pengabdian Untuk Mu negeRI
Publisher : LPPM UMRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jpumri.v10i2.11302

Abstract

The abuse of narcotics, psychotropic drugs, and addictive substances (NAPZA) is a major problem that threatens the future of adolescents, especially junior high school students. Many students do not fully understand the dangers of NAPZA, making them vulnerable to the influence of their surroundings. Therefore, this study was conducted to determine the extent to which NAPZA awareness activities have succeeded in increasing the knowledge and awareness of students at SMP Negeri 05 Sungai Apit regarding the dangers of drug abuse. This study used a qualitative descriptive method with a field study approach. Data were obtained through material presentations and questions given to students after the socialization was conducted. The results showed an increase in students' knowledge about the types of NAPZA, their negative effects, and ways to prevent their abuse. Students were also better able to recognize the factors that could trigger abuse and showed a refusal to try NAPZA. The conclusion of this study states that NAPZA socialization is effective in increasing knowledge and forming a preventive attitude among students at SMP Negeri 05 Sungai Apit. Therefore, activities such as this need to be carried out regularly and continuously to create a healthy, safe, and NAPZA-free school environment.
Ablasi Kelompok Fitur Multi-View pada Random Forest untuk Deteksi Intrusi IIoT Amien, Januar Al; Anugrah Putra, Bayu; Azim, Fauzan; Medikawati Taufiq, Reny; Syahril, Syahril
JURNAL FASILKOM Vol. 16 No. 2 (2026): 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.v16i2.12438

Abstract

Internet of Things (IIoT) systems generate heterogeneous multisource telemetry data, including network traffic, host resource usage, and security logs. Intrusion detection studies commonly combine all available feature sources based on the assumption that incorporating more sources (multi-view) will always improve detection performance. This study examines this assumption using the X-IIoTID dataset through two experiments. First, feature selection based on Random Forest Gini importance was evaluated using five feature sizes (K = 10, 20, 30, 45, and 61), with cross-algorithm robustness assessed using Decision Tree, Logistic Regression, and K-Nearest Neighbors. Second, a systematic ablation study was conducted on seven combinations of three feature groups: Network (N), Host (H), and Log (L), with Timestamp excluded from the Network group to ensure consistent feature treatment. Using 299,999 samples, comprising 239,999 training and 60,000 test samples across 19 attack classes and a normal class, the results show that multiclass performance increased with the number of features, achieving an F1-macro of 0.876 at K = 10 and 0.912 at K = 61. The ablation study showed that the Full MultiView (N+H+L) achieved the best performance (F1-macro = 0.912), followed by N+H (0.905) and N+L (0.879). The Log group alone yielded low performance (0.098) but provided additional value when combined with Network features. These findings demonstrate that the effectiveness of multi-view intrusion detection depends on feature-source combinations rather than merely the number of sources, highlighting the importance of feature-group ablation in designing IIoT intrusion detection systems.
MACHINE LEARNING UNTUK PREDIKSI SUHU: SEBUAH TINJAUAN Adityo, Tri Novian; Mukhtar, Harun; Firdaus, Rahmad; Taufiq, Reny Medikawati; Gunawan, Rahmad
Jurnal Rekayasa Perangkat Lunak dan Sistem Informasi Vol. 6 No. 2 (2026)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/seis.v6i2.12177

Abstract

Global climate change has increased the demand for accurate temperature prediction to support decision-making in sectors such as agriculture, disaster mitigation, and energy management. Machine Learning (ML) and Deep Learning (DL) approaches have been widely applied to model the non-linear and dynamic characteristics of temperature data. This study presents a Systematic Literature Review (SLR) following the PRISMA protocol. From 125 identified articles, 45 studies published between 2021 and 2025 were selected for detailed analysis. The results indicate that Long Short-Term Memory (LSTM) is the most frequently used algorithm, both as a standalone model and within hybrid architectures. Most studies employ multivariate datasets sourced from BMKG, ERA5 Reanalysis, satellite imagery, and the Internet of Things (IoT). Data preprocessing techniques, particularly norssmalization and time-series construction, play a crucial role in improving model stability. However, challenges remain, including hyperparameter sensitivity, complex weather data characteristics, and geographical variability. Future research opportunities include adaptive model development, multi-source data integration, and comprehensive comparative studies among algorithms.
Segmentasi Mahasiswa Berdasarkan Nilai Akademik dan Kehadiran Menggunakan Algoritma Clustering: Sebuah Tinjauan Yoga Surya Agustion; Reny Medikawati Taufiq
Jurnal Sistem Informasi dan Teknologi Jaringan Vol 7 No 2 (2026): September
Publisher : CV. ADMITECH SOLUTIONS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63703/sisfotekjar.v7i2.168

Abstract

Penelitian ini menyajikan tinjauan literatur sistematis mengenai penerapan algoritma clustering untuk segmentasi mahasiswa berdasarkan nilai akademik dan kehadiran. Tinjauan ini mengkaji 21 artikel ilmiah yang dipublikasikan pada tahun 2022 hingga 2026 dari jurnal nasional terakreditasi SINTA dan sumber internasional. Analisis dilakukan untuk: (RQ1) mengidentifikasi algoritma clustering yang paling dominan digunakan dalam segmentasi mahasiswa; (RQ2) mengevaluasi perbedaan kinerja antara model tunggal dan model hybrid berdasarkan metrik evaluasi internal seperti Silhouette Score dan Davies-Bouldin Index; serta (RQ3) mengkaji faktor-faktor yang memengaruhi kualitas segmentasi pada data pendidikan. Hasil kajian menunjukkan bahwa K-Means tetap menjadi algoritma paling dominan karena kesederhanaannya dan skalabilitasnya, diikuti DBSCAN yang efektif untuk deteksi outlier, dan K-Medoids yang lebih robust terhadap noise. Pendekatan hybrid secara konsisten menghasilkan nilai metrik evaluasi yang lebih unggul dibandingkan model tunggal. Strategi pra-pemrosesan, penentuan jumlah klaster optimal menggunakan Elbow Method dan Silhouette Analysis, serta rekayasa fitur diidentifikasi sebagai tiga faktor paling kritis dalam meningkatkan kualitas segmentasi mahasiswa untuk pengambilan keputusan akademik berbasis data.
Co-Authors Abadi, Abdi Nauli Adityo, Tri Novian Al Amien, Januar Aldi, M Tri Alfanico, Febrian Aminuyati Anggraini, Chelina Anugrah Putra, Bayu Aprilya, Regiesta Lintang Baidarus Bayu Anugerah Putra Chan, Ridzky Dinia Putri Dwijaya, Muhammad Riza Rio Eka Putra Fajrizal Fajrizal, Fajrizal Fatma, Yulia Fatma, Yulia Fauzan Azim Fitri Handayani Fitri Handayani Fitriani, Aisyah Fu'adah Amran, Hasanatul Fu’adah Amran, Hasanatul Gunawan, Rahmad Harun Mukhtar Hayami, Regiolina M. Rizki Amanda Pratama Mualfah, Desti Muarif, Putri Fadhilla Muhammad Rifaldo Munanda, Rizka Prastiwi, Adila Pramudiah Rafdi, Faris Abi Rahmad Firdaus Rahmad Firdaus Rahmat Febri Rahmawilda, Rahmawilda Ramadhan, Rafi Fakhri Rizqy, Riza Rindriani Soni Soni, Soni Sunanto Sunanto Suryanti, Anggi Aprilia Susi Handayani Syahril Syahril Taslim Taslim Toresa, Dafwen Wahyudi, Ashari Yoga Surya Agustion Yoze Rizki