Articles
Sistem Pakar Metode Case Based Reasoning untuk Mengidentifikasi Penyakit Psoriasis
M Syahputra;
Sarjon Defit;
S Sumijan
Jurnal Sistim Informasi dan Teknologi 2021, Vol. 3, No. 1
Publisher : Rektorat Universitas Putra Indonesia YPTK Padang
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DOI: 10.37034/jsisfotek.v3i1.39
Proriasis is a type of chronic disease of the human skin.problem of psoriasis At the end of the day, theis becoming more interesting because the main cause of this disease has not been found, which has only been found while the cause of psoriasis is genetics. Because the cause is not known for sure, this disease is difficult to cure. Although this disease is not contagious and life-threatening to sufferers, it can damage internal organs if not handled properly. This study aims to determine the level of accuracy in identifying psoriasis in humans. There are several types of symptoms that refer to psoriasis. Furthermore, the data is processed manually with themethod Case Based Reasoning and continued by using a-based expert system software website. The processing stage is to use theprocess, which retrieve is a process of finding the similarities between new cases and existing cases in the knowledge base. The results of the data processing are continued with the calculation of the level of accuracy. The result of testing this method is that there are 100% of the 12 test data. Based on the accuracy of the identification results of this system, this study is very precise in the level of identifying the level of accuracy of psoriasis in humans. Expert testing system has been able to identify thedisease psoriasis specific. Through thismethod Case Based Reasoning , the level of accuracy that can be obtained is quite accurate and can help skin and genital specialists in improving accuracy in identifyingdiseases Case Based Reasoning in humans.
Akurasi Pemetaan Kelompok Belajar Siswa Menuju Prestasi Menggunakan Metode K-Means
Sri Dewi;
Sarjon Defit;
Y Yuhandri
Jurnal Sistim Informasi dan Teknologi 2021, Vol. 3, No. 1
Publisher : Rektorat Universitas Putra Indonesia YPTK Padang
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DOI: 10.37034/jsisfotek.v3i1.40
The quality of students in school has a lot of diversity, this makes students have different levels of understanding. This can be seen from the variety of student scores obtained on report card scores, this needs to be a concern for the school, especially teachers. One of them is by forming effective study groups so that every student has the opportunity to excel. So this research was carried out with the aim of helping schools, especially teachers, to map student study groups evenly based on student report cards obtained in Semester I to Semester IV. The method used was clustering with the K-Means algorithm on the report card scores of Class IX.C students at SMP Pembangunan Laboratorium UNP. The results in this study obtained 3 clusters of students, namely students with High Achievement, Achievement and Less Achievement. This research can be used as a guide for teaching teachers in making decisions about the formation of student study groups in Class IX.C.
Comparison of Priority Areas and Rehabilitation Risk Areas for Post Disaster by K-Means Method
Arif Budiman;
Sarjon Defit;
Y Yuhandri
Jurnal Sistim Informasi dan Teknologi 2021, Vol. 3, No. 2
Publisher : Rektorat Universitas Putra Indonesia YPTK Padang
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DOI: 10.37034/jsisfotek.v3i2.46
Among the inhibiting factors for rehabilitation in Padang City is the absence of an assessment of priority areas and rehabilitation risk areas.This study aims to classify these factors into three clusters and the method used in this study was K-Means method.Disaster average data from 2017 until 2019 as well as data on rehabilitation efforts are used in this method. The results achieved indicate that the rehabilitation efforts carried out have not been evenly distributed in the areas prioritized for rehabilitation.This result can also be an input for the Regional Disaster Management Agency of Padang City in mapping and rehabilitating post-disaster areas and evaluating previous rehabilitation efforts.
Sistem Pakar dalam Mengidentifikasi Minat Vokasi Menggunakan Metode Certainty Factor dan Forward Chaining
Jefdy Kurniawan;
Sarjon Defit;
Y Yuhandri
Jurnal Sistim Informasi dan Teknologi 2021, Vol. 3, No. 2
Publisher : Rektorat Universitas Putra Indonesia YPTK Padang
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DOI: 10.37034/jsisfotek.v3i2.47
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.
Prediksi Hasil Belajar Siswa Secara Daring pada Masa Pandemi COVID-19 Menggunakan Metode C4.5
Yetti Fitriani;
Sarjon Defit;
Gunadi Widi Nurcahyo
Jurnal Sistim Informasi dan Teknologi 2021, Vol. 3, No. 3
Publisher : Rektorat Universitas Putra Indonesia YPTK Padang
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DOI: 10.37034/jsisfotek.v3i3.54
Student learning in schools has changed since the Covid-19 pandemic. Student learning in normal conditions is carried out face-to-face and turns into online or online learning. The research was conducted to predict student learning outcomes during the COVID-19 pandemic so that the results of this study can be used as a reference in policymaking in schools. The C4.5 method was used in the study to classify the data for class XII of the Multimedia Department at SMKN 2 Padang Panjang and the classification results could predict student learning outcomes during the pandemic. Processed student value data were taken from 1 (one) subject as the research data sample. Analysis of the value of student learning outcomes using the C4.5 Method to obtain new knowledge from student learning outcomes data carried out during the COVID-19 pandemic. The data analyzed consisted of attributes of attendance, assignments, daily tests, and test scores which influenced the decision criteria for student learning outcomes in online learning. The learning outcome decision criteria consist of "Satisfactory" and "Not Satisfactory" which refer to the Minimum Completion Criteria. Tests conducted on the training data of learning outcomes show that the value of the Daily Test is the most influential attribute in decision making. Implementation of the results using the RapidMiner Studio 9.2.0 software and produces an accuracy of 83.33% of the test data testing with the rules of data analysis training results. The results of the C4.5 classification testing method in this study can be used to predict student learning outcomes. The test results with an accuracy of 83.33% can be recommended to help schools in making policies
Akurasi Klasifikasi Pengguna terhadap Hotspot WiFi dengan Menggunakan Metode K-Nearest Neighbour
Raemon Syaljumairi;
Sarjon Defit;
S Sumijan;
Yusma Elda
Jurnal Sistim Informasi dan Teknologi 2021, Vol. 3, No. 3
Publisher : Rektorat Universitas Putra Indonesia YPTK Padang
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DOI: 10.37034/jsisfotek.v3i3.55
The Current wireless technology is used to find out where the user is in the room. Utilization of WiFi strength signal from the Access Point (AP) can provide information on the user position in a room. Alternative determination of the user's position in the room using WiFi Receive Signal Strength (RSS). This research was conducted by comparing the distance between users to 2 or more APs using the euclidean distance technique. The Euclidean distance technique is used as a distance calculator where there are two points in a 3-dimensional plane or space by measuring the length of the segment connecting two points. This technique is best for representing the distance between the users and the AP. The collection of RSS data uses the Fingerprinting technique. The RSS data was collected from 20 APs detected using the wifi analyzer application, from the results of the scanning, 709 RSS data were obtained. The RSS value is used as training data. K-Nearest Neighbor (K-NN) uses the Neighborhood Classification as the predictive value of the new test data so that K-NN can classify the closest distance from the new test data to the value of the existing training data. Based on the test results obtained an accuracy rate of 95% with K is 3. Based on the results of research that has been done that using the K-NN method obtained excellent results, with the highest accuracy rate of 95% with a minimum error value of 5%.
Sistem Pendukung Keputusan bagi Penerima Bantuan Komite Sekolah Menggunakan Metode Topsis
Suci Mardayatmi;
Sarjon Defit;
Gunadi Widi Nurcahyo
Jurnal Sistim Informasi dan Teknologi 2021, Vol. 3, No. 3
Publisher : Rektorat Universitas Putra Indonesia YPTK Padang
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DOI: 10.37034/jsisfotek.v3i3.56
Vocational High School Number 3 Mukomuko was the school that has given assistance for the learners. It was by exempting learners from paying committee charge monthly, it called Bantuan Komite Sekolah (BKS). In order to give motivation for the learners who was unfortunate to keep staying at the school, so it can make the learners to keep going on teaching and learning process (KBM). This research used Topsis method by collecting data for the prospective scholarship learners as many as 20 learners by categorizes were parents’ revenue, the total numbers of duties, the distance of residence, the average score of report and the condition of living environment. The result of try out from 20 learners who was obtain BKS by using Topsis Method showed that there were 18 learners who were significant to obtain scholarship by validity score was 90%. It was be a sample, before Topsis Method was used and the data was reliable after using Topsis Method. The development of supporting decision application system used Topsis Method that was getting in more accurated qualification. Futhermore, this system can help the school in constructing decisions to get the result be more advantageous in determining for the next BKS recipients.
Identifikasi dalam Penentuan Prioritas Usulan Kenaikan Jabatan Fungsional Pegawai Menggunakan Metode TOPSIS
Z Zulvitri;
s Defit;
S Sumijan
Jurnal Sistim Informasi dan Teknologi 2021, Vol. 3, No. 3
Publisher : Rektorat Universitas Putra Indonesia YPTK Padang
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DOI: 10.37034/jsisfotek.v3i3.61
Padang State Polytechnic (PNP) is one of the state universities located in the city of Padang, which has 39 Learning Laboratory Institution Functional Officials, who were later told by PLP. PLP is a Civil Servant (PNS) who is given the task, responsibility, authority and right to carry out activities in the field of learning laboratory management. The problem that occurs is that the PLP does not know the exact time of application for promotion and functional positions of each. Some of the difficulties occur in managing the sub-division of personnel in finding archives. This article is always increasing and accumulating each period of acceptance. So this research aims to process this staffing data to make it easier and to accelerate the promotion process. The method used is the Decision Support System (DSS) in identifying priorities for proposals for functional promotion. The DSS method used is Technique For Order Preference By Similarity to Ideal Solution (TOPSIS). The results of this study have the reliability in considering the shortest distance to the positive ideal solution and also the longest distance to the negative ideal solution. The alternatives and criteria used in this study consisted of 5 alternatives and 3 criteria. The value of ideal positive and negative solutions has a maximum value of K1 which is 0.66, K2 is 0.022, K3 is 0.05 and a minimum value of K1 is 0.1, K2 is 0.017, K3 is 0.022. The highest score in ranking is 2 people with a score of 1 and the lowest is 1 person with a score of 0.0008. So this research is very helpful in identifying promotion priorities appropriately.
Sistem Pakar dalam Menganalisis Defisiensi Nutrisi Tanaman Hidroponik Menggunakan Metode Certainty Factor
Yerri Kurnia Febrina;
Sarjon Defit;
Gunadi Widi Nurcahyo
Jurnal Sistim Informasi dan Teknologi 2021, Vol. 3, No. 4
Publisher : Rektorat Universitas Putra Indonesia YPTK Padang
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DOI: 10.37034/jsisfotek.v3i4.66
Currently the Expert system has become a field of research for computer scientists as well as agricultural scientists for applications in various information development. The Expert System can be designed to simulate one or more of the ways an agricultural expert uses his knowledge and experience in making the diagnosis and passing on the necessary recommendations regarding nutritional deficiencies. Nutrient deficiency is a lack of food for survival in plants. The nutrient content of plant parts, especially the leaves, is very relevant to be used to identify nutritional deficiencies. Provide the results of a diagnosis of nutritional deficiency to farmers to be a benchmark for improving plant nutrients and providing good nutrition for hydroponic plants. The data used are nutritional deficiency data and symptoms as well as nutritional solutions obtained from farmer data at the Payakumbuh City Agriculture Office. The method used in this expert system is the Certainty Factor (CF) method. This method provides a diagnosis in the form of certainty or uncertainty of conditions in the rules used to conclude. The results of testing this method showed as many as 12 nutritional deficiencies were detected with 41 symptoms experienced. So that it can measure the level of nutritional deficiency that occurs. Expert System in Analyzing Hydroponic Plant Nutrient Deficiency Using Certainty Factor Method can show that predictions are almost 94% accurate.
Algoritma K-Means Clustering dalam Mengklasifikasi Data Daerah Rawan Tindak Kriminalitas (Polres Kepulauan Mentawai)
Yoni Aswan;
Sarjon Defit;
Gunadi Widi Nurcahyo
Jurnal Sistim Informasi dan Teknologi 2021, Vol. 3, No. 4
Publisher : Rektorat Universitas Putra Indonesia YPTK Padang
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DOI: 10.37034/jsisfotek.v3i4.73
Crime is all kinds of actions and actions that are economically and psychologically harmful that violate the laws in force in the State of Indonesia as well as social and religious norms. Ordinary criminal acts affect the security of the community and threaten their inner and outer peace. The research location is the Mentawai Islands Police, which is an agency that can provide security and protection for the community, especially those in the Mentawai Islands Regency. The problem is that it is difficult for the Mentawai Islands Police to classify areas that are prone to crime in the most vulnerable, moderately vulnerable and not vulnerable categories. Especially considering the condition of the Mentawai, there are four large islands consisting of 10 sub-districts, where crime is increasing every year, especially those in the Mentawai Islands Regency area such as motor vehicle theft. Based on the background of the problem above, the researcher is interested in taking research in creating a system to predict the crime rate in the Mentawai Islands Regency in order to anticipate the surge in crime that will come. The method used is the K-Means Clustering Algorithm as a non-hierarchical data clustering method to partition existing data into one or more clusters or groups. This method partitions data into clusters so that data with the same characteristics are grouped into the same cluster and data with different characteristics are grouped into other clusters. Clustering is one of the data mining techniques used to get groups of objects that have common characteristics in large enough data. The data used is data on cases of criminal theft of motor vehicles for the last 5 years from 2016 to 2020. The results of the test show that South Sipora District is an area prone to the crime of motor vehicle theft.