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All Journal SITEKIN: Jurnal Sains, Teknologi dan Industri Prosiding Semnastek Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Computer Science and Information Technology (CoSciTech) Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Jurnal Komtekinfo
Sarjon Defit
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Industrial & Manufacturing Engineering Other

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Journal : Jurnal Computer Science and Information Technology (CoSciTech)

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Penerapan Metode Fuzzy Logic Dalam Sistem Pemantauan Tanaman Berbasis Internet Of Things (Iot) Dengan Arduino sabil, Muhammad; Sarjon Defit; Gunadi Widi Nurcahyo
Computer Science and Information Technology Vol 5 No 1 (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.v5i1.6710

Abstract

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.
Implementasi Data Mining untuk Pemetaan Persebaran Infeksi Human Imunodeficiency Virus di Provinsi Riau Fadillah, Riszki; Sarjon Defit; Sumijan
Computer Science and Information Technology Vol 5 No 1 (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.v5i1.6712

Abstract

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.
Penerapan Convolutional Neural Network pada Klasifikasi Citra Pola Kain Tenun Melayu Mukhlis Santoso; Sarjon Defit; Yuhandri
Computer Science and Information Technology Vol 5 No 1 (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.v5i1.6713

Abstract

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.
Penerapan jst perceptron untuk mengenali huruf hijaiyah sebagai media pembelajaran anak usia dini Dwiprihatmo, Mohammad Reza; Sarjon Defit; Sumijan
Computer Science and Information Technology Vol 5 No 1 (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.v5i1.6718

Abstract

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.
Co-Authors Abulwafa Muhammad Ade, Ade Puspita Sari Afriosa Syawitri Agustin, Riris Angga Putra Juledi Boy Sandy Dwi Nugraha.H Chairun Nas Cyntia Trimulia Dwiprihatmo, Mohammad Reza Fadillah, Riszki Febi Nur Salisah Firdaus Firdaus Gunadi Widi Nurcahyo Julius Santony Leony Lidya Lubis, Siti Sahara Muhammad, Abulwafa Mukhlis Santoso Pipin Refina Afindania Randy Permana Ruri Hartika Zain Sabil, Muhammad Sumijan Yudha Aditya Fiandra Yuhandri Yuhandri Yuhandri Yuhandri Yuhandri Yuhandri Yuhandri, Yuhandri Yul Antonisfia Zulharbi Zulharbi