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<![CDATA[Sistem Pakar dalam Mengidentifikasi Minat Vokasi Menggunakan Metode Certainty Factor dan Forward Chaining ]]>
<![CDATA[Kurniawan, Jefdy]]>;
<![CDATA[Defit, Sarjon]]>;
<![CDATA[Yunus, Yuhandri]]>
<![CDATA[ Jurnal Sistim Informasi dan Teknologi 2021, Vol. 3, No. 2 ]]>
Publisher : <![CDATA[Rektorat Universitas Putra Indonesia YPTK Padang ]]>
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DOI: 10.37034/jsisfotek.v3i2.117
<![CDATA[Developing an expert system application in providing an overview of the interests of students to help decision making interests in the vocational field so that they are right on target in choosing a major. In this study, using the Certainty Factor method and the Fordward Chaining method where this expert system can help experts identify vocational interests based on the characteristics of vocational interest in students. The personality types used to determine the type of vocational interest are Tangible, Thinking, Flexible, and Entrepreneur. The results of system calculations with expert decisions are worth 80% of the 4 test data, so a good level of accuracy is obtained. The resulting expert system can help students quickly provide an overview of vocational interest in making department decisions in continuing higher education, can carry out online consultations, document files, and can be used as a consultation portal for students.]]>
<![CDATA[Tingkat Pemahaman Siswa dalam Pembelajaran Daring dan Tatap Muka Langsung dalam Masa Pandemi Covid-19 Terhadap Bimbingan TIK Menggunakan Metode Backpropagation ]]>
<![CDATA[Salmiati, S]]>;
<![CDATA[Yunus, Yuhandri]]>;
<![CDATA[Sumijan, S]]>
<![CDATA[ Jurnal Sistim Informasi dan Teknologi 2021, Vol. 3, No. 3 ]]>
Publisher : <![CDATA[Rektorat Universitas Putra Indonesia YPTK Padang ]]>
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DOI: 10.37034/jsisfotek.v3i3.129
<![CDATA[The Covid-19 pandemic has a major impact on the world of education. Government policies to implement Distance Learning (PJJ) have an impact on learning in schools. Increasing ICT competence is needed to support the smooth running of PJJ. One of them is through ICT guidance activities during the Covid-19 Pandemic. SMP Negeri 1 Lengayang carried out online and face-to-face ICT guidance activities during the Covid-19 Pandemic. However, student learning outcomes in online and face-to-face learning have not shown maximum results. Various obstacles arise that affect student learning outcomes. Teachers have difficulty measuring the level of students' understanding of ICT guidance. Predicting the level of understanding of students is important as a measure of learning success during the Covid-19 Pandemic. This study aims to predict the level of understanding of students in online and face-to-face learning during the Covid-19 period, so that it can also help schools to take the right policies to improve the quality of learning for the future. This study uses the Backpropagation method of Artificial Neural Network (ANN). ANN is a part of artificial intelligence that can be used to predict. The data that is managed is a recap of the value of student cognitive learning outcomes during ICT guidance in online and face-to-face learning during the Covid-19 Pandemic. The results of calculations using the Backpropagation method with the Matlab application produce a percentage value for the level of student understanding, so that the accuracy value in prediction is obtained. With the results of testing the predictive accuracy of the level of understanding online and face-to-face with the 3-10-1 pattern, the best accuracy value is 95%. The prediction results can measure the level of students' understanding of learning during the Covid 19 Pandemic towards ICT guidance.]]>
<![CDATA[Tingkat Korelasi Prestasi Akademik terhadap Siswa SMP Menggunakan Metode Backpropagation ]]>
<![CDATA[Yeni, Nasma]]>;
<![CDATA[Yunus, Yuhandri]]>
<![CDATA[ Jurnal Sistim Informasi dan Teknologi 2021, Vol. 3, No. 3 ]]>
Publisher : <![CDATA[Rektorat Universitas Putra Indonesia YPTK Padang ]]>
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DOI: 10.37034/jsisfotek.v3i3.133
<![CDATA[Student academic achievement plays an important role in determining the quality of a school. Student scores sometimes change each semester, there are increases and decreases. There is an assumption that students who scored well in the previous semester will be good in the next semester and vice versa. This method is expected to make it easier for educators to see the extent of changes in student academic achievement. The data tested were data from 60 grade VII students in 2 semesters. Furthermore, it will be tested using the MatLab application, then the results of the changes that will occur will appear. The results of this study found that the correlation between semester 1 scores and semester 2 TP scores. 2019/2020 is very good with an architectural pattern of 10-10-1 with an accuracy value of 95.3%. So students who excel in semester 1 are likely to excel in the next semester, so that they can help the school see the Correlation Level of Student Academic Achievement at SMPN 3 Lengayang.]]>
<![CDATA[Prediksi Pencapaian Target Peserta Keluarga Berencana Pasca Persalinan Menggunakan Algoritma Backpropagation ]]>
<![CDATA[Putri, Stefani]]>;
<![CDATA[Yuhandri, Y]]>;
<![CDATA[Nurcahyo, Gunadi Widi]]>
<![CDATA[ Jurnal Sistim Informasi dan Teknologi 2021, Vol. 3, No. 3 ]]>
Publisher : <![CDATA[Rektorat Universitas Putra Indonesia YPTK Padang ]]>
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DOI: 10.37034/jsisfotek.v3i3.144
<![CDATA[Population growth in Indonesia continues to increase, so the government makes a program to control the rate of growth of the population, namely the Family Planning Program (KB). The implementation of family planning also has another objective, namely to reduce the risk of maternal death after childbirth. To measure the level of increasing target achievement of postpartum family planning participants. So that it can be a reference for the DPPKBP3A in carrying out the postpartum family planning program. Data from the Population Control, Family Planning, Women Empowerment and Child Protection (DPPKBP3A) District Lima Puluh Kota data processed in this study is data on the achievement of postpartum family planning participants from 2018 to 2020. Data processing uses the Backpropagation algorithm through several stages, namely the stage initialization, activation stage, weight training (weight change) and iteration stage. One of the results obtained from the calculation is the comparison of the target with the output gradient error in Suliki District in 2018, namely the target of 0.11311 and the result of the error gradient output is -0.1171. The prediction results obtained from this process become a reference for the Population Control, Family Planning and Women Empowerment and Child Protection Agency (DPPKBP3A) of District Lima Puluh Kota to implement the implementation of postpartum family planning programs to the community the following year.]]>
<![CDATA[Klasterisasi Tingkat Masa Studi Tepat Waktu Mahasiswa Menggunakan Algoritma K-Medoids ]]>
<![CDATA[Firzada, Fahmi]]>;
<![CDATA[Yunus, Yuhandri]]>
<![CDATA[ Jurnal Sistim Informasi dan Teknologi 2021, Vol. 3, No. 3 ]]>
Publisher : <![CDATA[Rektorat Universitas Putra Indonesia YPTK Padang ]]>
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DOI: 10.37034/jsisfotek.v3i3.146
<![CDATA[The period of study on time is one of the parameters of a student's success in completing college to obtain a bachelor's degree. A student is said to have completed his studies on time if he is able to complete his studies less than or equal to the predetermined time. Academic Provides facilities to find out the estimated time of student graduation. By providing information on which students are included in the cluster, they can complete their studies on time and which students do not complete their studies on time. In this study, the data processed were data from students who had graduated in the previous year. Then the data is processed using rapidminer software. This study applies the K-Medoids algorithm in clustering. The result of testing this method is to determine the student clusters who can complete the study period on time and the student clusters who cannot complete the study period on time. This research is expected to contribute to the campus in evaluating the tendency of students to complete their studies on time or not. The results of the evaluation of performance can produce information for study programs, lecturers and students in making policies.]]>
<![CDATA[Klasifikasi Kualitas Mutu Daun Gambir Ladang Rakyat Menggunakan Metode Convolutional Neural Network ]]>
<![CDATA[Winanda, Teddy]]>;
<![CDATA[Yunus, Yuhandri]]>;
<![CDATA[Hendrick, H]]>
<![CDATA[ Jurnal Sistim Informasi dan Teknologi 2021, Vol. 3, No. 3 ]]>
Publisher : <![CDATA[Rektorat Universitas Putra Indonesia YPTK Padang ]]>
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DOI: 10.37034/jsisfotek.v3i3.156
<![CDATA[Indonesia is one of the countries which have the best Gambier quality in the world. Those are a few areas in Indonesia which have best gambier quality such as Aceh, Riau, North Sumatera, Bengkulu, South Sumatera and West Sumatra. Kabupaten 50 Kota is one of the regencies in west Sumatra that supplies gambier in Indonesia. The gambier leaf selection is mostly done by manual inspection or conventional method. The leaf color, thickness and structure are the important parameters in selecting gambier leaf quality. Farmers usually classify the quality of gambier leaves into good and bad. Computer Vision can help farmers to classify gambier leaves automatically. To realize this proposed method, gambier leaves are collected to create a dataset for training and testing processes. The gambier image leaves is captured by using DLSR camera at Kabupaten 50 Koto manually. 60 images were collected in this research which separated into 30 images with good and 30 images with bad quality. Furthermore, the gambier leaves image is processed by using digital image processing and coded by using python programming language. Both TensorFlow and Keras were implemented as frameworks in this research. To get a faster processing time, Ubuntu 18.04 Linux is selected as an operating system. Convolutional Neural Network (CNN) is the basis of image classification and object detection. In this research, the miniVGGNet architecture was used to perform the model creation. A quantity of dataset images was increased by applying data augmentation methods. The result of image augmentation for good quality gambier produced 3000 images. The same method was applied to poor quality images, the same results were obtained as many as 3000 images, with a total of 6000 images. The classification of gambier leaves produced by the Convolutional Neural Network method using miniVGGNet architecture obtained an accuracy rate of 0.979 or 98%. This method can be used to classify the quality of Gambier leaves very well.]]>
<![CDATA[Identifikasi Penderita COVID-19 Berdasarkan Chest X-Ray Menggunakan Algoritma Jaringan Syaraf Tiruan Backpropagation ]]>
<![CDATA[Putra, Heru Rahmat Wibawa]]>;
<![CDATA[Yuhandri, Y]]>
<![CDATA[ Jurnal Sistim Informasi dan Teknologi 2021, Vol. 3, No. 4 (Accepted) ]]>
Publisher : <![CDATA[Rektorat Universitas Putra Indonesia YPTK Padang ]]>
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DOI: 10.37034/jsisfotek.v3i4.169
<![CDATA[Corona Virus Disease 2019 (COVID-19) is an infectious respiratory disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV2). This disease first appeared in Wuhan, China and spread throughout the world. COVID-19 has had a major impact on public health around the world. On March 9, 2020, the World Health Organization (WHO) declared COVID-19 a pandemic. Early identification of people with COVID-19 can help limit the wider spread. One of the factors behind the rapid spread of the disease is the long clinical trial time. Rapid clinical testing is a challenge facing the spread of COVID-19. Most countries, including Indonesia, face the problem of lack of detection equipment and experts in diagnosing this disease. Chest X-Ray is one of the medical imaging techniques and also an alternative to identify the symptoms of pneumonia caused by COVID-19. This study aims to identify pneumonia caused by COVID-19 and other diseases based on Chest X-Ray. 107 Chest X-Ray images used as material for this study were obtained from the General Hospital of Ibnu Sina Padang Indonesia, which consisted of 27 images of pneumonia caused by COVID-19, 51 images with other diseases and 29 images of normal lungs. Then pre-processing is carried out as an initial stage and then feature extraction is carried out. Furthermore, the learning and identification process is carried out using the Backpropagation Artificial Neural Network (ANN) algorithm. In this study, 92 images were used as training data, and 15 images were used as test data. The results of calculations carried out using a network with a pattern of 16-100-100-100-2 obtained an accuracy value of 73%. The results of the identification prediction can be used as consideration in establishing a diagnosis of COVID-19 sufferers, but cannot be used as an absolute reference.]]>
<![CDATA[Sistem Pakar Menggunakan Metode Certainty Factor dalam Menganalisis Penyakit Karies Gigi pada Manusia ]]>
<![CDATA[Andrean, Fajri Ilhami]]>;
<![CDATA[Yuhandri, Y]]>
<![CDATA[ Jurnal Sistim Informasi dan Teknologi 2021, Vol. 3, No. 4 (Accepted) ]]>
Publisher : <![CDATA[Rektorat Universitas Putra Indonesia YPTK Padang ]]>
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DOI: 10.37034/jsisfotek.v3i4.171
<![CDATA[Karies adalah penyakit gigi yang kerap ditemukan, yaitu suatu penyakit pada jaringan keras gigi berupa hilangnya ion – ion mineral secara terus menerus pada permukaan enamel gigi yang sebagian besar disebabkan oleh metabolisme bakteri. Tingkat kesadaran yang rendah dalam merawat gigi menyebabkan dampak buruk pada kesehatan gigi dan terhadap kesehatan tubuh. Pada saat sekarang ini umumnya masyarakat belum memiliki pengetahuan dalam menganalisis tentang penyakit karies gigi yang nantinya dapat mengakibatkan kerusakan yang parah terhadap gigi seperti matinya pulpa gigi. Penelitian ini bertujuan untuk menganalisis penyakit karies dengan menggunakan metode Certainty Factor. Dalam penelitian ini diolah data sebanyak 50 data yang diperoleh dari hasil wawancara dengan pakar pada Klinik Rahmatan Lil Alamin Padang Indonesia. Ditemukan beberapa faktor yang menyebabkan penyakit karies gigi pada manusia. Data tersebut diperoleh dari catatan medis pasien yang telah melakukan pemeriksaan di klinik. Data tersebut digunakan untuk menganalisis jenis penyakit karies berdasarkan bimbingan dari pakar tersebut. Tahapan pengolahan yang dilakukan adalah pemecahan rule, menentukan nilai bobot setiap gejala dan menghitung nilai Certainty Factor. Hasil yang didapatkan setelah dilakukan pengujian terhadap metode ini adalah terdapat 94% yang mengidap penyakit karies dengan jenis yang paling sering diderita pasien karies superfisialis. Hasil pengujian dapat menganalisis penyakit karies secara spesifik, dengan demikian sistem pakar yang digunakan telah dapat direkomendasikan untuk membantu dokter gigi menganalisis penyakit karies gigi pada manusia.]]>
<![CDATA[Optimalisasi dalam Penetrasi Testing Keamanan Website Menggunakan Teknik SQL Injection dan XSS ]]>
<![CDATA[Zikir Risky, Muhammad Arif]]>;
<![CDATA[Yuhandri, Y]]>
<![CDATA[ Jurnal Sistim Informasi dan Teknologi 2021, Vol. 3, No. 4 (Accepted) ]]>
Publisher : <![CDATA[Rektorat Universitas Putra Indonesia YPTK Padang ]]>
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DOI: 10.37034/jsisfotek.v3i4.172
<![CDATA[SQLI (SQL Injection) and XSS are hacking techniques that are often used by hackers. This technique can find out the contents of the database by inserting a script on the website. This technique can be a threat if a website does not have security that can ward off such attacks. Hackers will look for loopholes using this technique in a login menu, searching, upload menu, input menu and URLs that have parameters ending in numbers, but not all websites that can be attacked use this technique if you don't limit the use of characters. This research was conducted to find out the gaps in a website that can be attacked with SQLI and XSS techniques and help optimize website security to avoid these attacks. Penetration testing will be carried out on a CV car rental website. Merdeka Auto Rental which is located in Padang City. This penetration testing uses SQLI and XSS techniques to find security holes in a website. The result of this test is that on the car rental website there are 12 gaps that are vulnerable to SQLI and XSS attacks, based on the results of these tests, a PHP script function is made that can remove all dangerous special characters. The script function is inserted in the PHP input, process and output files. The use of this script function does not apply to attacks other than SQLI and XSS so that if hackers use attack techniques other than that, this website is vulnerable to these attacks. After the script is inserted in the source code of the website, it can be concluded that the 12 known loopholes in the previous test without using the script function have changed status to not vuln or not vulnerable to SQLI and XSS attacks.]]>
<![CDATA[Sistem Pakar dalam Menganalisis Gangguan Jiwa Menggunakan Metode Certainty Factor ]]>
<![CDATA[Putra, Rafi Septiawan]]>;
<![CDATA[Yuhandri, Y]]>
<![CDATA[ Jurnal Sistim Informasi dan Teknologi 2021, Vol. 3, No. 4 (Accepted) ]]>
Publisher : <![CDATA[Rektorat Universitas Putra Indonesia YPTK Padang ]]>
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DOI: 10.37034/jsisfotek.v3i4.177
<![CDATA[People with Mental Disorders (ODGJ) as a trigger for people who suffer from disorders of thought, feeling and behavior cause changes in attitudes and behavior that hinder normal human functioning. Mental disorders as a syndrome characterized by a change in a person's behavior that will be associated with symptoms such as difficulties or disorders, as well as psychological functions and behavior that are not confident in dealing with people but can also be with that person. An expert system is an intelligent computer technology that is based on solving problems using inferential knowledge and procedures. As a problem solver, expert systems will also find it easier to make decisions or policies like humans do. This study aims to produce an expert system that is used to analyze mental disorders who can make similar decisions, as well as psychiatric specialists. The data processed in this study is scientific data on mental disorders ranging from types of mental illness, early symptoms of disease and patient diagnosis data by mental health specialists, then the data is processed using the Certainty Factor method and displayed in the form of a web-based application using the PHP programming language. and MySQL databases. The results obtained from testing the expert system using the Certainty Factor method show that there is a match between the results of an expert diagnosis of depression with a certainty level of 73%. An expert system for analyzing mental disorders using the Certainty Factor method can make it easier for sufferers to understand the type of mental disorder they are experiencing.]]>
