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All Journal 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
Taufiq, Reny Medikawati
Universitas Muhammadiyah Riau
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Social Sciences

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Journal : Jurnal Computer Science and Information Technology (CoSciTech)

 1 
Peramalan Kedatangan Wisatawan Mancanegara Ke Indonesia Menurut Kebangsaan Perbulannya Menggunakan Metode Multilayer Perceptron Harun Mukhtar; Muhammad Rifaldo; Reny Medikawati Taufiq; Yoze Rizki
Jurnal CoSciTech (Computer Science and Information Technology) Vol 2 No 2 (2021): 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.v2i2.3324

Abstract

The beauty of tourist attractions in Indonesia has a certain attraction for foreign tourists to serve as a place for vacation. However, the number of visitors needs to be predicted to anticipate an increase or decrease in the number of visitors, so that the state can determine policies regarding changes in the number of visitors in the future. Forecasting is used to predict previous data patterns so that further data patterns can be known. Multilayer Perceptron (MLP) is a neural network development that can be used for modeling time series data. Several researchers have conducted research using the Multilayer Perceptron method in making predictions. Forecasting systems or forecasting are very helpful in the current era, forecasting aims to predict future conditions. Prediction results, obtained 82% accuracy for tourist predictions in Period 7, namely September 2020, 97% for Prediction Period 8, namely December 2020 so that the Number of Tourists for Period 9 is 7,106 people.
Simulasi Deteksi Golongan Kendaraan pada Gerbang Tol Menggunakan YOLOv4 Reny Medikawati Taufiq; Sunanto; Yoze Rizki; M. Rizki Amanda Pratama
Computer Science and Information Technology Vol 3 No 2 (2022): 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.v3i2.3928

Abstract

Jalan tol merupakan infrastruktur vital yang membuat pelayanan distribusi barang dan jasa menjadi lebih produktif dan efisien. Namun pada kenyataannya, di kota besar tingginya kemacetan juga terjadi di jalan tol. Salah satu titik rawan kemacetan adalah di gerbang tol. Kemacetan ini tidak hanya terjadi pada jam sibuk, tetapi juga terjadi sepanjang hari. Kemacetan disebabkan waktu tunggu pada proses pembayaran. Kemacetan yang terjadi di Gerbang Tol Otomatis (GTO) Multi Kendaraan salah satunya disebabkan oleh adanya proses penentuan golongan kendaraan secara manual oleh petugas pada control room. Kendaraan yang menggunakan jalan tol digolongkan kedalam 5 golongan berdasarkan jumlah gandar. Petugas melihat satu persatu kendaraan yang melintas dan menentukan golongan kendaraan tersebut, biaya tol yang harus dibayar oleh pengguna jalan tol adalah bedasarkan golongan kendaraan yang digunakan. Kemacetan pada jalan tol menimbulkan dampak negatif seperti seperti pemborosan bahan bakar dan waktu, dan juga dampak lingkungan yang dapat menyebabkan kerugian secara ekonomi. Oleh sebab itu, pada penelitian ini akan dilakukan simulasi deteksi golongan kendaraan pada gerbang tol dengan menggunakan teknologi computer vision dan deep learning, dengan algoritma Yolov4. Dengan adanya pendeteksian golongan kendaraan secara otomatis maka diharapkan waktu tunggu pada gerbang tol dapat memenuhi Standar Pelayanan Minimal (SPM) Jalan Tol yaitu maksimal 5 detik. Dataset berupa 650 gambar golongan kendaraan, setelah di augmentasi menjadi 1547 gambar. Proses training dilakukan menggunakan Google Colabs. Video rekaman lalu lintas kendaraan yang sedang berjalan akan menjadi inputan pada pengujian implementasi aplikasi Python. Dari pengujian yang telah dilakukan dapat dilihat bahwa model sudah dapat mendeteksi golongan kendaraan dengan baik.
Komparasi Algoritma Menggunakan Teknik Smote Dalam Melakukan Klasifikasi Penyakit Stroke Otak Fitri Handayani; Reny Medikawati Taufiq
Computer Science and Information Technology Vol 5 No 2 (2024): 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.v5i2.7439

Abstract

Stroke is a deadly disease. This can occur due to disturbances in brain function that occur suddenly, progressively and quickly. However, it is difficult to know the early symptoms of stroke. The application of data mining knowledge can be used to diagnose disease. This research was conducted to implement data mining in classifying brain stroke. The dataset used was obtained from Kaggle, totaling 4891 data. However, the dataset does not have a balanced amount of data for each class. To balance the data, the SMOTE technique is used which aims to increase accuracy. The application of the classification algorithms used, namely the Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) algorithms aims to determine the best algorithm performance. This research resulted in a comparison of the four algorithms which showed that the LR, RF and SVM algorithms produced the highest accuracy, precision, recall and f1-score values, namely 95% accuracy, 95% precision, 100% recall and 97% f1-score. The KNN algorithm produces lower accuracy, precision, recall and f1-score values, namely 90% accuracy, 95% precision, 85% recall and 90% f1-score.
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.
Co-Authors Aldi, M Tri Alfanico, Febrian Aminuyati Anggraini, Chelina Baidarus Bayu Anugerah Putra Chan, Ridzky Dinia Putri Dwijaya, Muhammad Riza Rio Eka Putra Fajrizal Fajrizal, Fajrizal Fatma, Yulia Fitri Handayani Fitri Handayani Fitriani, Aisyah Fu'adah Amran, Hasanatul Fu’adah Amran, Hasanatul Harun Mukhtar Hayami, Regiolina M. Rizki Amanda Pratama Muarif, Putri Fadhilla Muhammad Rifaldo Munanda, Rizka Prastiwi, Adila Pramudiah Rafdi, Faris Abi Rahmad Firdaus Rahmad Firdaus Rahmat Febri Rahmawilda, Rahmawilda Rizqy, Riza Rindriani Sunanto Sunanto Suryanti, Anggi Aprilia Susi Handayani Syahril Syahril Taslim Taslim Toresa, Dafwen Wahyudi, Ashari Yoze Rizki