Article Per Year (5 Year)
p-Index From 2021 - 2026
3.158
P-Index
This Author published in this journals
All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Pengabdian UntukMu NegeRI JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Geografi, Edukasi dan Lingkungan JPT : JURNAL PROTEKSI TANAMAN (JOURNAL OF PLANT PROTECTION) JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Computer Science and Information Technology (CoSciTech) International Conference on Health Science, Green Economics, Educational Review and Technology (IHERT) Khidmat: Journal Community Service
Gunawan, Rahmad
Universitas Muhammadiyah Riau
Religion Agriculture, Biological Sciences & Forestry Astronomy Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Health Professions Law, Crime, Criminology & Criminal Justice Social Sciences Other

Published : 21 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 8 Documents
Search
Journal : jurnal computer science and information technology coscitech

 1 
Implementasi Algoritma Brute Force Pada Pencarian Berita Berbasis Web Andriansyah; Soni; Baidarus; Rahmad Gunawan
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.v2i2.3342

Abstract

Pada web berita yang jadi suatu kabar terpercaya dalam mengenali suatu data, namun terdapat sebagian kekurangan pada berita berbasis website khususnya pada pencarian. Perihal tersebut beberapa kendala yang dihadapi yaitu lambat sistem dalam membaca dari tiap- tiap kata kunci yang kita cari pada database yang terdapat dalam sistem tersebut. Penelitian ini bertujuan untuk mengimplementasi Algoritma Brute Force Pada Pencarian Berita Berbasis Web. Algoritma Brute Force bertujuan pencarian seluruh kemunculan string pendek yaitu pattern di string yang lebih panjang yang di inginkan. Hasil dari penelitian ini implementasi algoitma Brute Force pada website berita bisa menuntaskan permasalahan dalam melaksanakan pencarian informasi berita, sebab algoritma ini menciptakan informasi yang dicari.
Peramalan Kedatangan Wisatawan ke Suatu Negara Menggunakan Metode Support Vector Machine (SVM) Harun Mukhtar; Rahmad Gunawan; Amin Hariyanto; Syahril; Wide Mulyana
Jurnal CoSciTech (Computer Science and Information Technology) Vol 3 No 3 (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.v3i3.4211

Abstract

Tourism is one of the most promising ecosystems for economic sectors worldwide. A strong tourism sector directly contributes to the country's national income, fights unemployment, and improves the balance of payments. Tourism development can be seen from the increase in arrivals to a nation; based on data obtained from the UNWTO from 1995-2019, it has increased and decreased. The sudden increase and decrease in tourists will have positive and negative impacts. Forecasting is an activity to predict events that will occur in the future by taking data from the past. So this study will expect tourist arrivals to a country using the Support Vector Machine (SVM) method. SVM has properties about maximizing margins and kernel tricks to map nonlinear data. The results obtained in this study indicate that SVM Confidence is 86.3%, has a MAPE value of 56.00%, and an RMSE worth of 11126.36 from the total data of 53 countries. And forecasting is carried out in 5 countries with the highest tourist visits. The results obtained are excellent: SVM Confidence of 99.13%, a MAPE value of 2.78%, and an RMSE value of 2783.57.
Sistem pakar kerusakan honda beat street 2021 menggunkan metode forward chaining dan certainty factor Yulia Fatma; Rahmad Gunawan; Edi Rian Kartiko; Sunanto
Computer Science and Information Technology Vol 3 No 3 (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.v3i3.4377

Abstract

Kebutuhan masyarakat terhadap kendaraan bermotor sangatlah besar khususnya sepeda motor Honda Beat Street 2021, sebab sepeda motor dianggap sebagai sarana transportasi yang sangat memudahkan pengendara untuk menuju tempat dengan pertimbanganwaktu yang lebih cepat dibandingkan dengan menggunakan kendaraan yang beroda empat. Kurangnya pengetahuan masyarakat tentang kerusakan sepeda motor Honda Beat Street 2021 menimbulkan kerugian bagi pengguna dalam hal waktu dan biaya. Dalam masalah tersebut sepeda motor yang mengalami kerusakan dapat diatasi oleh seorang pakar dengan pengetahuan dan pengalamannya. Untuk itu perlu dibuatkan sebuah sistem pakar yang dapat mendiagnosa kerusakan yang terjadi sepeda motor Honda Beat Street 2021, dimana sistem pakar ini bertujuan untuk mentransfer pengetahuan yang dimiliki seorang pakar ke dalam komputer sehingga pengguna lebih menghemat waktu dan biaya. Sistem pakar kerusakan sepeda motor Honda Beat Street 2021 ini dibangun dengan bahasa pemrograman web PHP dan database MySQL. Proses inferensi sistem pakar ini menggunakan metode forward chaining dan proses perhitungan nilai kepastian menggunakan metode certainty factor. Para pengguna dapat mendiagnosis kerusakan yang terjadi pada sepeda motor Honda Beat Street 2021 mereka dengan mudah dan mengetahui cara penanganan kerusakan dengan memilih gejala yang ada pada sistem. Informasi pengetahuan dasar pada sistem dapat diupdate, ditambah, atau dihapus oleh admin (pakar). Presentase hasil diagnosa dengan menggunakan proses perhitungan Certainty Factor (CF) sangat dipengaruhi pada nilai CF yang diberikan oleh pakar. Uji coba sistem untuk 10 kasus menghasilkan tingkat akurasi sebesar 90%.
K-Nearest Neighbor (KNN) untuk Menganalisis Sentimen terhadap Kebijakan Merdeka Belajar Kampus Merdeka pada Komentar Twitter Febby Apri Wenando; Rahman Septiadi; Rahmad Gunawan; Harun Mukhtar; Syahril
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.v3i2.3841

Abstract

On December 11, 2019, the Minister of Education and Culture of the Republic of Indonesia Nadiem Anwar Makarim issued a policy of "Merdeka Belajar". Netizens on Twitter have debated this Merdeka Belajar and became a trending topic. This study tries to analyze the sentiment of tweets about opinions on this policy by classifying whether it is a positive opinion or a negative opinion. The classification method applied is the K-Nearest Neighbor algorithm. In this study, four main processes were carried out, namely text-preprocessing, word-weighting (TF-IDF), classification and validation using k-fold cross validation. Tests were carried out with a dataset of 700 data, training was carried out using 630 training data and 70 testing data. In testing, the highest accuracy of the K-Nearest Neighbor algorithm was obtained at the k-8 value, namely 84.28%. Furthermore, validation is carried out using k-fold cross validation with a value of fold = 10 to get an accuracy of 84.42%.
Pendekatan Transfer Learning untuk Klasifikasi Penyakit Mata Menggunakan Citra dengan CNN InceptionV3 Gunawan, Rahmad; Fathurrahman, Raihan; Widyaningrum, Amelia Ismania Sita; Issandra, Febri; Abdurachman, Muhammad Andhika; Putra, Yogi Ernanda; Naufal
Computer Science and Information Technology Vol 6 No 1 (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.v6i1.8509

Abstract

Eye diseases are a leading cause of vision impairment and blindness worldwide. Therefore, detection of eye diseases is crucial in the prevention of blindness. This study develops an eye disease classification model based on Convolutional Neural Network (CNN) using Transfer Learning with InceptionV3. The dataset consists of 1559 images, divided into 1249 training images and 310 validation images, covering 8 eye disease classes. The model was trained using 40 epochs with the Adam optimizer. Evaluation results show a validation accuracy of 81.29%. While the model performed well, some classes, such as hordeolum, showed lower accuracy, indicating areas that need further improvement. This study confirms that Transfer Learning with InceptionV3 is an effective approach for eye disease classification.
Klasifikasi Penyakit Daun Kentang dengan Transfer Learning Menggunakan CNN optimalisasi Arsitektur MobileNetV2 Gunawan, Rahmad; Fauzan Salim; Wahyudhy, Adhe Indra; Wibowo, Angga Yudha; Yordan, Gibril; Filamori, Refly Fauzan
Computer Science and Information Technology Vol 6 No 2 (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.v6i2.8599

Abstract

Potatoes are a major food crop with high economic value, but they are susceptible to various Diseases impacting potato leaves can significantly influence their quality and productivity. This research focuses on identifying diseases in potato leaves through the Convolutional Neural Network (CNN) approach, leveraging transfer learning with the MobileNetV2 architecture. The dataset utilized comprises 4,072 images of potato leaves. categorized into three groups: non-infected leaves (healthy ), Early Blight-infected leaves, and Late Blight-infected leaves. The dataset is processed through data augmentation and normalization to enhance data quality. The resulting model demonstrates excellent performance, achieving an accuracy of 95.31%, a precision of 95.81%, a recall of 95.31%, and an F1-Score of 95.38%. These findings indicate the approach demonstrates its ability to identify the condition of potato leaves with a low classification error rate, especially in the healthy category. However, there are challenges in classifying between Early Blight and Late Blight that require further analysis and method improvement. This study contributes to the development of efficient and accurate plant disease detection systems.
Perbandingan model SARIMA dan Prophet dalam memprediksi jumlah kunjungan wisatawan mancanegara ke Indonesia berdasarkan data deret waktu bulanan Alfaridzi, M Ilmi; Gunawan, Rahmad; Alfian, Haris; Mirano, Muhammad Fitter; Nazifah, Hayatun; Wahyuni, Sri; Illahi, Kevanda Sondani
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.v6i3.9963

Abstract

Forecasting international tourist arrivals is a critical aspect of tourism planning and policy-making. This study compares two time series forecasting methods, Seasonal Autoregressive Integrated Moving Average (SARIMA) and Prophet in modeling and predicting the monthly number of international tourists visiting Indonesia, based on data from January 2018 to May 2025. The methodology includes data preprocessing, stationarity testing using the Augmented Dickey-Fuller test, and selecting optimal SARIMA parameters based on the lowest AIC. Model performance was evaluated using MAE and RMSE on the testing data from January to May 2025. The results indicate that SARIMA outperforms Prophet, with a lower average MAE of 1336.41 and RMSE of 1616.67, compared to Prophet’s MAE of 5591.33 and RMSE of 5739.71. Based on this evaluation, SARIMA was selected as the best model and used to project international tourist visits for the period June to December 2025. These findings highlight SARIMA’s superior ability to capture seasonal patterns in tourism data, making it a reliable tool for short-term tourism forecasting in Indonesia.
Klasifikasi serangan DDoS dengan metode random forest dan teknik class weight pada dataset CICDDoS2019 Mualfah, Desti; Ardiansyah, Rudi; Gunawan, Rahmad
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.v6i3.10731

Abstract

The rapid advancement of information technology has significantly influenced various aspects of life, including an increasing reliance on network-based services. However, this dependence has also led to the emergence of more complex cybersecurity threats, one of the most prominent being Distributed Denial of Service (DDoS) attacks. These attacks can disrupt service availability by overwhelming target systems with excessive traffic. A major challenge in detecting DDoS attacks lies in the wide variety of attack patterns and the class imbalance that commonly occurs in network traffic datasets. To address these issues, a machine learning–based approach capable of handling complex attack behaviors while compensating for imbalanced data distribution is required. One potential solution is the use of the Random Forest algorithm with class-weight techniques, applied to the CICDDoS2019 dataset. The research procedure includes data collection and exploration, preprocessing steps such as handling missing and infinite values, encoding categorical attributes, and feature normalization. The dataset is then divided into training and testing subsets before being processed by the Random Forest model. Model evaluation is conducted using a confusion matrix along with accuracy, precision, recall, and F1-score metrics. Experimental results show that incorporating class weight significantly improves model performance, achieving an accuracy of 99.98%, precision of 99.98%, recall of 99.97%, and an F1-score of 99.97%. These findings demonstrate that the proposed approach is highly effective for accurately detecting and classifying DDoS attacks.
Co-Authors . Reflinaldon Abdurachman, Muhammad Andhika Ade Pratama Alfaridzi, M Ilmi Alfian, Haris Alistraja Dison Silalahi Amin Hariyanto Aminuyati Andi Nur Insani ANDRIANSYAH Apri Yanto Arfa, Laura Zevira Avicenna, Achyar Zein Baidarus Brantas Suharyo G Damayanti, Risma Danang Mulyadipa Suratno Desti Mualfah Edi Ismanto Edi Rian Kartiko Evans Fuad Fadilla, Niken Rahma Fathurrahman, Raihan Fatma, Yulia Fauzan Salim Febby Apri Wenando Filamori, Refly Fauzan Fitri, Nurkhairi Hadhrami Ab Ghani Harmawan, Muhamad Rizki Harun Mukhtar Hasnah Faizah AR Hayami, Regiolina Hotma RS I Wayan Medio Illahi, Kevanda Sondani Ima Damayanti Imer HPS Issandra, Febri Januar Al Amien Jasmin, Muhammad Iqbal Maysa Putri, Yulia Mirano, Muhammad Fitter Mualfah, Desti Muhammad Dimas Alfahri Nadira, Besti Zahratul Naufal Nazifah, Hayatun Nofrial . Noh Aisyah Mohd Ali Nugroho, Altaric Nurul Izrin Md Saleh Ovami, Debbi Chyntia Pradipa, Raditya Pratiwi, Husnatul Fadillah Putra, Yogi Ernanda Rahmad Firdaus Rahman Septiadi Rahmania, Marsha Nailah Rais, Muhammad_Akmal Ramadhan, Syahrudin Ramadhoni Razkia, Binta Riani, Della Ayunda Rizqy Fadhlina Putri Rudi Ardiansyah Soni Sri Wahyuni Sugiyadi, Riski Syahril Tania, Manzilah Ditiara Ulva Elviani Vania, Azra Gusti Wahyudhy, Adhe Indra Wesley, Royman Wibowo, Angga Yudha Wide Mulyana Widyaningrum, Amelia Ismania Sita Yaherwandi Yanti, Elis Yordan, Gibril Yulia Fatma Yuliskania, Aisyara Yusgiantoro, Purnomo Zaskiv S, Marshal Khairana Zilham, Adib