Claim Missing Document
Check
Articles

Found 34 Documents
Search

Klasterisasi Penempatan Siswa yang Optimal untuk Meningkatkan Nilai Rata-Rata Kelas Menggunakan K-Means Yusma Elda; Sarjon Defit; Yuhandri Yunus; Raemon Syaljumairi
Jurnal Informasi dan Teknologi 2021, Vol. 3, No. 3
Publisher : Rektorat Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/jidt.v3i3.130

Abstract

The implementation of learning by teachers can measure the quality of schools and students. Schools with diverse student backgrounds need to take strategic steps in managing learning to get optimal learning outcomes. Good learning designs and techniques can motivate students' interest in learning. The teacher's role is very important in managing learning to create an effective teaching and learning process. Data Mining or also known as Knowledge Discovery in Database (KDD) is the process of extracting knowledge from large data to find new patterns to get new knowledge and information. Data Mining technology is used to explore existing knowledge in the database. One of the methods used in data mining is clustering with the K-Means algorithm. This study aims to conduct student clustering to obtain a balanced class composition in order to improve the quality and student learning outcomes as seen in the increasing in the class average score. The data processed in this study came from the main school data as many as 90 students of the XI class of Computer Network Engineering Skills Competency at SMKN Negeri 2 Padang Panjang in the 2020/2021 school year. The variables used in data processing are student scores, parents' income and the distance from where students live to school. The student clustering calculation using K-Means succeeded in grouping 90 students into 3 clusters where cluster 1 totaled 47 students, cluster 2 totaled 10 students and cluster 3 totaled 33 students. Each member of the cluster will be divided evenly into 3 groups studying to get a balanced class composition. This research can be used as a basis for decision making by schools in clustering student placements to improve learning outcomes. By the increasing in the grade point average, the school average score will also increased.
Clustering Students' Interest Determination in School Selection Using the K-Means Clustering Algorithm Method Suhefi Oktarian; Sarjon Defit; Sumijan
Jurnal Informasi dan Teknologi 2020, Vol. 2, No. 3
Publisher : Rektorat Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/jidt.v2i3.65

Abstract

Education is one of the main focuses of the Indragiri Hilir Regency Government work program. Based on data from the Regional Central Statistics Agency of Indragiri district in 2019, the high level of student interest in attending school is at the elementary and junior high school levels. K-means clustering is a data grouping technique by dividing existing data into one or more clusters. School grouping based on student interest is important because at the high school level students' interest in education has decreased so that information is needed which schools are in great demand, sufficient interest and less interest by students at the junior high school level when after finishing elementary school education. This study aims to assist the Education Office in the decision-making process to determine which school students are most interested in as a reference in development both in terms of quality and quantity. The data used in this study is the Dapodikdasmen data in 2019.Data processing in this study uses the K-means clustering method with a total of 3 clusters, namely cluster 0 (C0) is less attractive, Cluster 1 (C1) is quite attractive, cluster 2 (c2) is very interested in students in choosing a school. The results of the clustering process with 2 iterations state that for cluster 0 there are 6 school data, for cluster 1 there are 3 school data, cluster 2 is 1 school data.
Simulasi Monte Carlo dalam Memprediksi Penerimaan Peserta Pelatihan Dasar CPNS Faisal Roza; Sarjon Defit; Gunadi Widi Nurcahyo
Jurnal Informasi dan Teknologi 2021, Vol. 3, No. 3
Publisher : Rektorat Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/jidt.v3i3.140

Abstract

The implementation of basic training recruit (latsar) of civil servant (CPNS) at Pusat Pengembangan Sumber Daya Manusia (PPSDM) Ministry of Internal Affairs regional Bukittinggi. The leader takes decision in doing the implementation of latsar CPNS recruit in PPSDM scope regional Bukittinggi. Latsar CPNS is one of requirements to be civil servant. Therefore, it is necessary to collect data by doing observation, interview questionings with related party in the implementation of latsar CPNS recruit from 2018 to 2020. It can be predicted for the next recruit. After doing library references by reading some books and journals, the basic training recruit of CPNS sources from PPSDM regional Bukittinggi, and Monte Carlo simulation. By using Monte Carlo simulation in predicting data, it can get closer value of actual value. Based on distribution of sampling data, the method is by choosing random numbers from probability distribution to do simulation. The Monte Carlo result’s examination has got 173 participants for year 2019, 158 participants for year 2020, and 157 participants for year 2021 clearly. Although the rate of the accurate just reaches 81%, but it has been able to be recommended to help PPSDM regional Bukittinggi, Ministry of Internal Affairs in taking decision and planning for basic training recruit of CPNS for the next.
Data Mining dalam Pengelompokan Penyakit Pasien dengan Metode K-Medoids Dwi Utari Iswavigra; Sarjon Defit; Gunadi Widi Nurcahyo
Jurnal Informasi dan Teknologi 2021, Vol. 3, No. 4
Publisher : Rektorat Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/jidt.v3i4.150

Abstract

Disease is a condition in which the mind and body experience a kind of disturbance, discomfort for those who experience it. Day by day, the number of patients at the Kuok Health Center is increasing with various types of different diseases. The increase number of patients requires the Kuok Health Center staff always update the patient's medical record data. The patient's medical record data is the form of a report containing the number of patients and their illnesses. Based on these data, the Puskesmas needs to find out information about the diseases that are most vulnerable and suffered by many patients. This study aims to classify patient disease data to find out the most common diseases suffered by patients at the Kuok Health Center, Kampar Regency. The grouping of patient disease data is carried out with the Data Mining Clustering and followed by the K-Medoids method. Next, cluster testing is carried out using the Silhouette Coefficient. The results of this study indicate that in cluster 1 the most common disease suffered by patients is non-insulin dependent diabetes mellitus (type II) with a total of 435 cases. In cluster 2, the most common disease suffered by patients was Essential Hypertension (Primary) with a total of 2785 cases. For cluster 3, the most common disease suffered by patients was Vulnus Laseratum, Punctum, with a total of 328 cases. From the cluster results obtained, the results of the Silhouette Coeficient test are 0.900033674.
Prediksi dan Klasifikasi Buku Menggunakan Metode Backpropagation R Rahmiyanti; Sarjon Defit; Yuhandri Yunus
Jurnal Informasi dan Teknologi 2021, Vol. 3, No. 3
Publisher : Rektorat Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/jidt.v3i3.116

Abstract

Students of SMP Negeri 2 Lengayang have different interests in determining the books they are interested in, so that the library often has difficulty determining the books that are most entered by students, this is because they have not used the right system in determining the type and number of books, only based on the estimated number. Students and subjects only, as a result school students stock books of the books they want to borrow. Based on the above, a method is needed to predict and classify the amount of book stock in the future. The data used is a recap of monthly book lending, from 2018 to 2020 in the third month, with a total of 1653 transactions and 5 types of books processed, then the data is analyzed using the Backpropogation method. The results obtained are using a 5-3-1 pattern with a learning rate of 0.01, a goal of 0.01, the number of input units for the Weapon layer 5, the number of units in the hidden layer and the number of output layer units that are placed on 1 layer, and to carry out training using two phases namely feedforward and backpropagation phases. It is removed from this research that the backpropagation method can provide a classification prediction of the number of books that must be provided in the following year based on the number of data entered or the number of data entered.
Machine Learning Rekomendasi Produk dalam Penjualan Menggunakan Metode Item-Based Collaborative Filtering Daniel Theodorus; Sarjon Defit; Gunadi Widi Nurcahyo
Jurnal Informasi dan Teknologi 2021, Vol. 3, No. 4
Publisher : Rektorat Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/jidt.v3i4.151

Abstract

The shift towards Industry 4.0 has pushed many companies to adopt a digital system. With the sheer amount of data available today, companies start to face difficulties with providing product recommendation to their customers. As a result, data analysis has become increasingly important in the pursuit of providing the best service (user experience) to customers. The location appointed in this research is PT. Sentral Tukang Indonesia which is engaged in the sale of building materials and carpentry tools such as: paint, plywood, aluminum, ceramics, and hpl. Machine Learning has emerged as a possible solution in the field of data analysis. The recommendation system emerged as a solution in providing product recommendation based on interactions between customers in historical sales data. The purpose of this study is to assist companies in providing product recommendation to increase sales, to make it easier for customers to find the products they need, providing the best service (user experience) to customers. The data used is customer, item, and historical sales at PT. Sentral Tukang Indonesia over a time span of 1 period.data historical sales divide to dataset training 80% and dataset testing 20%. The Item-based Collaborative Filtering method used in this study uses Cosine Similarity algorithm to calculate the level of similarity between products. Score prediction uses Weighted Sum formula while computation of error rate uses the Root Mean Squared Error formula. The result of this study shows top 10 product recommendations per customer. The products displayed are products with the highest score from the individual customer. This research can be used as a reference by companies looking to provide product recommendations needed by their customers.
Prediksi Tingkat Prevalensi Stunting Kabupaten Lima Puluh Kota Menggunakan Metode Monte Carlo Mike Zaimy; Sarjon Defit; Gunadi Widi Nurcahyo
Jurnal Informasi dan Teknologi 2021, Vol. 3, No. 4
Publisher : Rektorat Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/jidt.v3i4.165

Abstract

Stunting is a condition of failure to thrive in children under five years old (infants under five years old) due to chronic malnutrition so that children are too short for their age. According to available data, the stunting prevalence rate in Lima Puluh Kota Regency in 2020 is quite high, at 8.28%. This has become the attention of the central government by establishing Lima Puluh Kota Regency as one of the Regencies/Cities Locations for the National Integrated Stunting Reduction Intervention Focus. The results of this study aim to assist the District Government of Lima Puluh Kota in planning the convergence of programs/interventions as an effort to accelerate stunting prevention and reduce the percentage of stunting under five in Lima Puluh Kota Regency. This research data uses the stunting prevalence rate from 2018 to 2020 which comes from data on the number of toddlers and the number of stunting toddlers from 22 health centers in Lima Puluh Kota Regency. Furthermore, the data was processed using the Monte Carlo method to predict the stunting prevalence rate in 2021. Based on the tests conducted using the Monte Carlo method, the highest stunting prediction rates were found at the Pakan Rabaa Public Health Center and the Suliki Public Health Center with a stunting prevalence rate of 11.70%. The level of accuracy obtained is 93.73%. The Monte Carlo method is suitable for predicting the prevalence of stunting in Lima Puluh Kota Regency, seen from the high level of accuracy from the results of data processing.
Prediction of Scholarship Recipients Using Hybrid Data Mining Method with Combination of K-Means and C4.5 Algorithms Mardison Mardison; Sarjon Defit; Shaza Alturky
International Journal of Artificial Intelligence Research Vol 5, No 2 (2021)
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v5i2.224

Abstract

Obtaining a scholarship is the desire of every student or student who studies, especially those who come from poor families. The scholarship can lighten the burden on parents who pay for these students and can streamline the lecture process. However, students do not know exactly what they have to do to get the scholarship. Aside from that, students naturally want to know what causes and conditions have the greatest impact on achievement. The objective of this research is how to predict which number of students among them are predicted to get a scholarship at the opening of the scholarship acceptance using the K-Means and C4.5 methods. Apart from that, the aim of this research is to discover how the K-Means algorithm conducts data clustering (clustering) of student data to determine if they will succeed or not, as well as how the C4.5 algorithm makes predictions against students who have been clustered together. The Rapid Miner program version 9.7.002 was used to process the data in this report. The results of this study were that out of 100 students, 32 students were not scholarship recipients and 68 students were scholarship recipients. Another result of this research is that out of 100 students it is predicted that 9 (9%) will receive scholarships and 91 (91%) will not receive scholarships.
Determination of Student Subjects in Higher Education Using Hybrid Data Mining Method with the K-Means Algorithm and FP Growth Larissa Navia Rani; Sarjon Defit; L. J. Muhammad
International Journal of Artificial Intelligence Research Vol 5, No 1 (2021)
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v5i1.223

Abstract

The large number of courses offered in an educational institution raises new problems related to the selection of specialization courses. Students experience difficulties and confusion in determining the course to be taken when compiling the study plan card. The purpose of this study was to cluster student value data. Then the values that have been grouped are seen in the pattern (pattern) of the appearance of the data based on the values they got previously so that students can later use the results of the patterning as a guideline for taking what skill courses in the next semester. The method used in this research is the K-Means and FP-Growth methods. The results of this rule can provide input to students or academic supervisors when compiling student study plan cards. Lecturers and students can analyze the right specialization subject by following the pattern given. This study produces a pattern that shows that the specialization course with the theme of business information systems is more followed by students than the other 2 themes
Hybrid Data Mining with the Combination of K-Means Algorithm and C4.5 to Predict Student Achievement Agung Ramadhanu; Sarjon Defit; Shahab Wahhab Kareem
International Journal of Artificial Intelligence Research Vol 5, No 2 (2021)
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v6i1.225

Abstract

Getting academic achievement is the dream of every student who studies at higher education, especially undergraduate level. Undergraduate students aspire to the highest achievement (champion) at the last achievement of their studies. However, students cannot predict whether these students with the habits that have been done and the current conditions will make them excel or not. Apart from that, of course, students also want to know what factors and conditions influence the achievement the most. The objective to be achieved in this research is how to predict which number of students among them are predicted to excel (champion) at the end of the semester with a combination of the K-Means and C4.5 methods. Besides, the purpose of this study reveals how the K-Means algorithm performs data clustering of student data who will excel or not and how the C4.5 algorithm predicts students who have been grouped. Data processing in this study uses the Rapid Miner software version 9.7.002. The result of this research is that it is easier to group data in numerical form than data in polynomial form. Other results in this study were that out of 100 students, 27 students (27%) were predicted to excel (champions) and 73 (73%) did not achieve (not champions).