Jurnal Computer Science and Information Technology (CoSciTech)
Jurnal CoSciTech (Computer Science and Information Technology) merupakan jurnal peer-review yang diterbitkan oleh Program Studi Teknik Informatika, Fakultas Ilmu Komputer, Univeritas Muhammadiyah Riau (UMRI) sejak April tahun 2020. Jurnal CoSciTech terdaftar pada PDII LIPI dengan Nomor ISSN 2723-5661 (Online) dan 2723-567X (Cetak). Jurnal CoSciTech berkomitmen menjadi jurnal nasional terbaik untuk publikasi hasil penelitian yang berkualitas dan menjadi rujukan bagi para peneliti. Jurnal CoSciTech menerbitkan paper secara berkala dua kali setahun yaitu pada bulan April dan Oktober. Semua publikasi di jurnal CoSciTech bersifat terbuka yang memungkinkan artikel tersedia secara bebas online tanpa berlangganan.
Articles
358 Documents
<![CDATA[Audit Keamanan Website Menggunakan Acunetix Web Vulnerability (Studi Kasus Di SMK Muhammadiyah 3 Terpadu Pekanbaru) ]]>
<![CDATA[Supriyanto, Boby]]>;
<![CDATA[Sumijan]]>;
<![CDATA[Yuhandri]]>
<![CDATA[ Computer Science and Information Technology Vol 5 No 1 (2024): Jurnal Computer Science and Information Technology (CoSciTech) ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Riau ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.37859/coscitech.v5i1.6705
<![CDATA[Perkembangan teknologi informasi berkembang pesat seiring dengan pertumbuhan penggunanya. Contoh dari perkembangan teknologi adalah penggunaan website untuk mendukung kegiatan pembelajaran. Website merupakan kumpulan halaman web yang dapat diakses secara publik. Website dapat terdiri dari teks, gambar, video, dan media suara lainnya. Namun dengan berkembangnya suatu teknologi, maka perkembangan kerentanan atau serangan terhadap teknologi tersebut juga bertambah. Berdasarkan laporan tahunan monitoring keamanan siber tahun 2021 oleh Badan Siber dan Sandi Negara (BSSN), terdapat lebih dari 1,6 miliar serangan siber yang telah terjadi di Indonesia. Penelitian ini akan menggunakan Acunetix Web Vulnerability Scanner (WVS) untuk mengaudit keamanan website SMK Muhammadiyah 2 Terpadu Pekanbaru (SMK MUTI). Penelitian ini akan mengkaji kelemahan keamanan website SMK MUTI dan membahas bagaimana Acunetix Web Vulnerability dapat membantu dalam meningkatkan tingkat keamanan website tersebut. Metode Vulnerability Assessment (VA) yang digunakan adalah analisis deskriptif, yaitu data yang diperoleh disajikan dalam bentuk tabel, sehingga memungkinkan untuk memperjelas hasil analisis yang dilakukan dalam meng-audit. Berdasarkan data yang diperoleh dari hasil scanning iterasi 1 yang dilakukan, website SMK MUTI berada pada level ancaman 3 tergolong tinggi dengan ditemukan 192 peringatan atau kerentanan, dimana 2 diantaranya berada pada level tinggi dan 11 berada pada level sedang. Berdasarkan audit, dilakukan perbaikan dan pengujian pada penelitian di situs SMK MUTI ini, hasil yang telah dilakukan tingkat ancaman yang dicapai berada pada level 1, dimana pada level tinggi, jumlah kerentanan menjadi 0 dan tingkat dukungan juga menjadi 0, sehingga dapat disimpulkan bahwa situs SMK MUTI saat ini dengan status level 1 dapat bebas dari kerentanan keamanan.]]>
<![CDATA[Implementasi Naïve Bayes dalam M-Series 4 Mobile Legends untuk Prediksi Kemenangan ]]>
<![CDATA[Tamaza, Muhammad Abyanda]]>;
<![CDATA[Defit, Sarjon]]>;
<![CDATA[Sumijan, Sumijan]]>
<![CDATA[ Computer Science and Information Technology Vol 5 No 1 (2024): Jurnal Computer Science and Information Technology (CoSciTech) ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Riau ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.37859/coscitech.v5i1.6707
<![CDATA[Mobile Legends is a game made by a developer from China called Moontoon which implements the Multiplayer Online Battle Arena (MOBA) system which is currently popular. The popularity of this game is proven by the holding of low, middle and high level tournaments. Recently a high level or international tournament called the M-Series World Championship was held in Indonesia. This game is played by two teams consisting of five players with the aim of destroying enemy targets in the form of towers. The problem in this game is winning and losing. One of the factors that determines victory or defeat is the choice of hero. The wrong hero composition during the draft pick stage can make it difficult for your team to play and lead to unexpected results. This research aims to predict the percentage level of Mobile Legends wins based on the drafted heroes. Prediction is the process of minimizing errors in systematically estimating the future based on past information. The technique used in this research is the Naïve Bayes algorithm. The Naïve Bayes algorithm is a classification method based on probability. This method consists of four stages, namely data understanding, data preparation, data analysis, and results analysis. This research dataset is provided by Youtube MPL Indonesia. The dataset consists of 880 training data and 90 test data for M-Series 4 Mobile Legends. The results of this research provide a percentage value in the form of prediction of 96.67%, precision of 95.65% and recall of 97.78%. The results of an accuracy rate of 96.67% using the Naïve Bayes algorithm show that predictions using the Naïve Bayes algorithm can be applied to predict win ratios in M-Series 4 Mobile Legends.]]>
<![CDATA[Implementasi Convolutional Neural Netowork Untuk Klasifikasi Citra KTP-El ]]>
<![CDATA[SATRIA, SATRIA]]>;
<![CDATA[Sumijan]]>;
<![CDATA[Billy Hendrik]]>
<![CDATA[ Computer Science and Information Technology Vol 5 No 1 (2024): Jurnal Computer Science and Information Technology (CoSciTech) ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Riau ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.37859/coscitech.v5i1.6708
<![CDATA[The Electronic Identity Card (e-KTP) serves as the official proof of identity for residents, issued by the relevant implementing agency across the entire territory of the Unitary State of the Republic of Indonesia. Mandatory for both Indonesian citizens (WNI) and foreigners (WNA) holding a Permanent Stay Permit (ITAP) and aged 17 or married, the e-KTP is susceptible to potential damage, often arising from factors such as prolonged usage or improper handling. Physical damage to the e-KTP can impede the document's ability to accurately verify identity, potentially impacting public services and government administration. This research aims to assess the condition of e-KTPs, determining whether they are in good or damaged condition. The study employs the Convolutional Neural Network (CNN) method, known for its significant results in image recognition by attempting to emulate the image recognition system in the human visual cortex, facilitating the processing of image information. This method comprises two architectural layers: Feature Learning and Classification. The dataset utilized in this research comprises images of e-KTPs sourced from the Population and Civil Registration Office of Bengkalis Regency, totaling 400 images categorized into two classes: 200 for good condition and 200 for damaged condition. The research findings enable the determination of the e-KTP image's condition, achieving a 90% accuracy rate.]]>
<![CDATA[Prediksi Penjualan Sepeda Motor Yamaha dengan Jaringan Syaraf Tiruan dan Backpropagation (Studi Kasus: CV Sinar Mas) ]]>
<![CDATA[Santriawan, Aji]]>;
<![CDATA[Gunadi Widi Nurcahyo]]>;
<![CDATA[Billy Hendrik]]>
<![CDATA[ Computer Science and Information Technology Vol 5 No 1 (2024): Jurnal Computer Science and Information Technology (CoSciTech) ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Riau ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.37859/coscitech.v5i1.6709
<![CDATA[Perkembangan teknologi yang begitu pesat dengan kebutuhan masyarakat tentang kendaraan pribadi untuk mempermudah segala aktivitas sehari-hari. Pertumbuhan penduduk Indonesia yang meningkat juga mempengaruhi bertambahnya jumlah kendaraan bermotor yang ada di Indonesia. Sepeda Motor Yamaha merupakan salah satu brand sepeda motor yang telah lama berada di Indonesia. Oleh karena itu konsumen menggunakan sepeda motor saat ini sangatlah tinggi. Dengan peningkatan penjualan dan minat masyarakat terhadap sepeda motor untuk tahun berikutnya. Masalah yang terjadi pada CV Sinar Mas adalah tidak ada metode untuk memprediksi bagaimana kecenderungan peningkatan/penurunan jumlah unit tertentu setiap tahun. Sehinggan dengan Jaringan Syaraf Tiruan menggunakan metode Backpropagation dengan Software Matlab dapat menjadi data prediksi penjualan sepeda motor di bulan berikutnya atau yang akan datang. Penelitian ini bertujuan untuk meningkatkan akurasi penjualan sepeda motor Yamaha pada Cv Sinar Mas. Metode yang digunakan dalam penelitian ini adalah Jaringan Saraf Tiruan Backpropagation. Algoritma Backpropagation digunakan untuk memprediksi dengan akurat berdasarkan data historis penjualan sepeda motor Yamaha dari tahun 2019-2022. Dataset yang digunakan terdiri dari 48 data penjualan. Hasil penelitian ini dapat memprediksi penjualan dengan menggunakan pola terbaik yaitu 4-25-1 dengan hasil MSE 0.00010594. Oleh karena itu penelitian ini dapat menjadi acuan untuk mempredisi penjualan sepeda motor Yamaha pada CV Sinar Mas]]>
<![CDATA[Penerapan Metode Fuzzy Logic Dalam Sistem Pemantauan Tanaman Berbasis Internet Of Things (Iot) Dengan Arduino ]]>
<![CDATA[sabil, Muhammad]]>;
<![CDATA[Sarjon Defit]]>;
<![CDATA[Gunadi Widi Nurcahyo]]>
<![CDATA[ Computer Science and Information Technology Vol 5 No 1 (2024): Jurnal Computer Science and Information Technology (CoSciTech) ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Riau ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.37859/coscitech.v5i1.6710
<![CDATA[Hydroponic plants in this increasingly modern era, people are increasingly aware that their vegetable needs must be met so that the body's nutritional balance can be met properly. One of the Urban Farming that is suitable in urban areas with narrow dominant land is the hydroponic system. Hydroponics comes from two Greek syllables combined, namely hydro which means water and ponos which means work, so hydroponics means working using air. One of the advantages of this agricultural system is the minimal use of land, where even small areas of land can be utilized. well. Hydroponics is agricultural cultivation without using soil, so hydroponics is an agricultural activity that is carried out using air as a medium to replace soil. Hydroponic systems are increasingly popular among farmers and agricultural service providers because they are able to produce healthier and more productive plants without using soil as a growing medium. This research aims to test the performance of an Internet of Things (IoT) based Hydroponic Monitoring System using Arduino on plants or vegetables with the method used in this research is Fuzzy logic. This method has 3 stages, namely Fuzzification, Defuzzification, Fuzzy Rule. The data set processed in this research was taken from measurements of pH and temperature on hydroponic vegetable plants in the PKK garden of Kemantan Kebalai Village. The dataset consists of 340 data. The results of this research can identify and calculate the percentage of pH and temperature measurements with an accuracy level of 90%. Therefore, this research can be a reference in measuring acid, normal and alkaline levels in hydroponic plants.]]>
<![CDATA[Vision Transformer untuk Identifikasi 15 Variasi Citra Ikan Koi ]]>
<![CDATA[Uthama, Rayhan]]>;
<![CDATA[Yuhandri]]>;
<![CDATA[Billy Hendrik]]>
<![CDATA[ Computer Science and Information Technology Vol 5 No 1 (2024): Jurnal Computer Science and Information Technology (CoSciTech) ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Riau ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.37859/coscitech.v5i1.6711
<![CDATA[This research aims to classify various types of koi fish using Vision Transformer (ViT). There is previous research [1] using Support Vector Machine (SVM) as a classifier to identify 15 types of koi fish with training and testing datasets respectively of 1200 and 300 images. This research was continued by research [2] which implemented a Convolutional Neural Network (CNN) as a classifier to identify 15 types of koi fish with the same amount dataset. As a result, the research achieved a classification accuracy rate of 84%. Although the accuracy obtained from using CNN is quite high, there is still room for improvement in classification accuracy. Overcoming obstacles such as limitations in classification accuracy in previous studies and further exploration of the use of new algorithms and techniques, this study proposes a ViT architecture to improve accuracy in Koi fish classification. ViT is a deep learning algorithm adopted from the Transformer algorithm which works by relying on self-attention mechanism tasks. Because the power of data representation is better than other deep learning algorithms including CNN, researchers have applied this Transformer task in the field of computer vision, one of the results of this application is ViT. This study was designed using class and number datasets retained from two previous studies. Meanwhile, the koi fish image dataset used in this research was collected from the internet and has been validated. The implementation of ViT as a classifier in koi classification in this research resulted in an accuracy level that reached an average of 89% in all classes of test data.]]>
<![CDATA[Implementasi Data Mining untuk Pemetaan Persebaran Infeksi Human Imunodeficiency Virus di Provinsi Riau ]]>
<![CDATA[Fadillah, Riszki]]>;
<![CDATA[Sarjon Defit]]>;
<![CDATA[Sumijan]]>
<![CDATA[ Computer Science and Information Technology Vol 5 No 1 (2024): Jurnal Computer Science and Information Technology (CoSciTech) ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Riau ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.37859/coscitech.v5i1.6712
<![CDATA[Based on data released by the Riau Provincial Health Service until October 2022, there were 8034 people living with HIV/AIDS (PLWHA), of which 3,711 were in the AIDS stage. Human Immunodeficiency Virus is a virus that attacks the body's immune system, while Acquired ImmunoDeficiency Syndrome (AIDS) is a collection of diseases caused by the HIV virus due to damage to the immune system in humans, resulting in the body being susceptible to potential diseases. This research aims to map the spread of HIV/AIDS in Riau Province to prevent and control the spread of the HIV/AIDS virus by the relevant agencies. The method used in this research is Fuzzy C-Means to carry out clustering in districts/cities which will then be visualized using a map or with a Geography Informatics System (GIS). The Fuzzy C-Means method is a data grouping technique that uses the existence of each data point in A cluster as determined by the degree of membership. The output from Fuzzy C-Means is a series of cluster centers and several degrees of membership for each data point. The data used in this research is HIV/AIDS data in Riau Province from 1997 to 2023. Based on the results of the tests that have been carried out, the results obtained are 3 clusters, namely the safe zone has 5 districts/cities, the alert zone has 5 districts/cities, and There are 2 districts/cities in the dangerous zone. There needs to be treatment through the Health Service, the AIDS Control Commission, and related Non-Governmental Organizations (NGOs) to prevent and control HIV/AIDS in Riau Province for areas that have a high potential for the spread of HIV/AIDS. The tests that have been carried out obtain a minimum error value of 0.008251 in the 8th iteration with the performance of Fuzzy C-Means being 13.271 in the distance between clusters.]]>
<![CDATA[Penerapan Convolutional Neural Network pada Klasifikasi Citra Pola Kain Tenun Melayu ]]>
<![CDATA[Mukhlis Santoso]]>;
<![CDATA[Sarjon Defit]]>;
<![CDATA[Yuhandri]]>
<![CDATA[ Computer Science and Information Technology Vol 5 No 1 (2024): Jurnal Computer Science and Information Technology (CoSciTech) ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Riau ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.37859/coscitech.v5i1.6713
<![CDATA[The use of electronic computerized media is growing along with advances in hardware and software as an analytical tool with various algorithms and methods for classifying and measuring objects in various contexts. This progress aims to overcome the weaknesses that exist in conventional methods used in the identification process. The identification process can be applied to various objects, one of which is an image object. An image is a visual representation of an object formed through a combination of RGB (red, green, blue) colors. RGB color components or features have a range of values from 0 to 255 in an image. Weaving is a type of fabric that is specially made with distinctive motifs. Malay weaving motifs have a lot of diversity, this diversity makes it difficult to distinguish the motifs of these fabrics.This study aims to recognize and distinguish the pattern of Malay woven fabric. The method used in this research is Convolutional Neural Network (CNN). The CNN method has several stages, namely Convolution Layer, Pooling Layer, Rectifed Linear Unit (ReLU) Function, Fully-Connected Layer, Transfer Learning, Optimizer and Accuracy. The dataset used in this research is sourced from Tenun Putri Mas Bengkalis. The dataset used consists of 1000 images of weaving motifs which are divided into 80% training data and 20% testing data, from the existing dataset divided into three categories of weaving motifs namely pucuk rebung, elbow clouds and elbow keluang. The results in this study are considered good because they produce accuracy with a result of 95% with an epoch value of 15. From the results of good enough accuracy, it is hoped that it can help the community in recognizing Malay weaving motifs.]]>
<![CDATA[Penerapan metode forward chaining dan certainty factor untuk mengetahui gangguan mental pada remaja ]]>
<![CDATA[Hafizhah Mardivta]]>
<![CDATA[ Computer Science and Information Technology Vol 5 No 1 (2024): Jurnal Computer Science and Information Technology (CoSciTech) ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Riau ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.37859/coscitech.v5i1.6716
<![CDATA[Mental disorders are a condition where people have mental, social, growth and development problems or disorders that hinder their life processes and interactions with other people. Mental health can be influenced by several factors, for example friendships, family, lifestyle, and many other factors. Many people do not want to undergo mental examinations from psychologists due to several factors, namely people feel embarrassed and afraid to talk about their problems, lack of knowledge of the symptoms and types of mental disorders, and fear of the surrounding environment. One way to help overcome this problem is to use an expert system. This expert system was built to determine mental disorders in adolescents using the Forward Chaining and Certainty Factor methods. The Forward Chaining method will be collaborated with the Certainty Factor method to calculate the level of accuracy of the type of mental disorder experienced. The use of these two methods aims to provide better results in identifying mental disorders in adolescents. The data taken in this research is data on mental disorders at the UPI YPTK Psychology Institute. The data used consists of 50 symptom data and 7 disease data. The results of this research are an Expert System application using the PHP programming language which is used to determine mental disorders in adolescents. From the tests that have been carried out, results were obtained with an accuracy level of 0.9998%. Expert system applications can be used for early action in preventing mental disorders in adolescents.]]>
<![CDATA[Penerapan jst perceptron untuk mengenali huruf hijaiyah sebagai media pembelajaran anak usia dini ]]>
<![CDATA[Dwiprihatmo, Mohammad Reza]]>;
<![CDATA[Sarjon Defit]]>;
<![CDATA[Sumijan]]>
<![CDATA[ Computer Science and Information Technology Vol 5 No 1 (2024): Jurnal Computer Science and Information Technology (CoSciTech) ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Riau ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.37859/coscitech.v5i1.6718
<![CDATA[Computer vision is the transformation of data obtained or taken from a webcam into another form to determine the decisions to be taken. All forms of transformation are carried out to achieve certain goals. One of the techniques that supports the application of computer vision to a system is digital image processing, because the aim of digital image processing techniques is to transform images into digital format so that they can be processed by a computer. Computer vision and digital image processing can be implemented into a hijaiyah letter pattern recognition system on cards that have been prepared and placed on a white board which is supported by the perceptron algorithm artificial neural network method which is used as a learning technique for the system to be able to learn and recognize hijaiyah letter patterns. This research aims to enable computers to read hijaiyah letters using a camera. The methods used in this research are image processing and the perceptron algorithm. The data set processed in this research comes from 783 hijaiyah letters consisting of 29 hijaiyah letters and 30 samples per each hijaiyah letter. How it works is that each hijaiyah letter is captured using a webcam and produces a continuous image which is transformed into a digital image and processed using several techniques including grayscale images, binary images and cropping images. The results of this research are that the system is able to identify and classify hijaiyah letters with a testing rate of 99,746%. Therefore, this research can be a reference in the modern teaching and learning process and is expected to help children's interest in learning hijaiyah letters.]]>
