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Method of TOPSIS in Recipients of Home Improvement Assistance in South Siantar District at Pematangsiantar Tarukim Office Fikri Yatussa'ada; Muhammad Zarlis; Sumarno Sumarno
IJISTECH (International Journal of Information System and Technology) Vol 3, No 1 (2019): November
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v3i1.38

The construction of uninhabitable houses is a government program especially from the Social Service to provide housing development assistance for the poor. However, in its realization, funding assistance from the government is often still lacking and even not on target. Therefore this study aims to build a decision support system that has the ability to analyze in determining the community that is eligible to receive housing repairs. The SPK method used in this study is the TOPSIS method. This method uses the principle that the chosen alternative must have the shortest distance from the positive ideal solution and the farthest from the negative ideal solution from a geometric point of view by using the Euclidean distance to determine the relative proximity of an alternative to the optimal solution. Research data obtained from the Department of public housing and residential areas pematangsiantar city using interviews, observation and literature study methods needed to help solve problems. This study uses 10 alternatives and 7 criteria. After calculating the analysis, families who are entitled to help with house repairs are alternative 7 on behalf of the Piatur Siringgo-Ringgo.

Kombinasi Pembobotan Symmetrical Uncertainty Pada K-Means Clustering Dalam Peningkatan Kinerja Pengelompokan Data Suranta Bill Fatric Ginting; Sawaluddin Sawaluddin; Muhammad Zarlis
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 1 (2022): Januari 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i1.3366

Based on several studies that examine the K-Means Clustering method, it was found that in K-Means Clustering one of the weaknesses lies in the process of determining the center point of the cluster which also has implications for distance calculations in determining the similarity between data to obtain conclusions from the data. a cluster. And this is also caused by the influence of the percentage of the attributes used. If the attributes used are less relevant to their level of influence and also have a low contribution to the data, this can have a significant impact on the results of clustering. So from these problems, in this research, the author proposes to use the method in calculating the weight of data attributes in the clustering process, namely using Symmetrical Uncertainty. To test the proposed method, this research uses a dataset from UCI Machine Learning which consists of Iris with 150 data and Wine Quality with 178 data. The evaluation of the proposed clustering performance is based on the Davies-Bouldin Index (DBI) value. The test results in this study show that the proposed method can produce a significantly smaller Davies-Bouldin Index (DBI) value.

Analisis Komparasi Algoritma Naïve Bayes dan K-Nearest Neighbor Untuk Memprediksi Kelulusan Mahasiswa Tepat Waktu Muhammad Gunawan; Muhammad Zarlis; Roslina Roslina
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 2 (2021): April 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i2.2925

Students are one of the important pillars in the life cycle of a university. In the process of developing, a university can be influenced by how many bachelor degree (S1) graduates from the university are. The number of graduations of a college sometimes has a low ratio when compared to the number of students admitted in the same school year. This low passing rate of students can be caused by several factors, such as the number of student activities that are participated in, economic factors, and several other unexpected factors. This makes a university must have a scheme or a formula that can predict whether the student can graduate on time. Normally, a bachelor (S1) student takes 8 semesters of education. But the existence of several factors that have been mentioned can make the time to take S1 education to be more, or even fail to graduate. This study will try to compare the results of the analysis of the two methods in the classification algorithm to predict student graduation. The algorithm used is the K-Nearest Neighbor and Naïve Bayes Algorithm. This study also aims to identify the best algorithm among the two classification algorithm choices. This research concluded that the Naïve Bayes algorithm has the same level of accuracy as the KNN algorithm in predicting the graduation of students in the Medical Education study program, which is 90%

Penerapan Algoritma Adaboost Untuk Peningkatan Kinerja Klasifikasi Data Mining Pada Imbalance Dataset Diabetes Nia Novianti; Muhammad Zarlis; Poltak Sihombing
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 2 (2022): April 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i2.4017

According to the World Health Organization (WHO), it has been recorded that up to now more than 150 million people have diabetes, whether they are elderly people, adults, teenagers, men or women. Early knowledge of diabetes can be seen based on data from patients who already have diabetes. The patient's disease data has previously been stored and arranged in a data warehouse or what is commonly referred to as a dataset. Therefore, it is necessary to process the data contained in the dataset. But the use of data mining techniques themselves must be assisted by using the techniques contained in the data mining, namely classification techniques. K-Nearest Neighbor (K-NN) is one of the methods used in the classification technique. In the results of the classification of the level of confidence obtained in the process, it is seen based on the amount of accuracy. However, there are important issues that need special attention. In the dataset used for the classification process, the data collected contains unbalanced class results (balance). The unbalanced data classification process becomes an important problem, this is because it can cause a decrease in performance. Adaboost is a technique in data mining that can be used to increase the level of accuracy in classification methods. The results showed that the adaboost algorithm can help improve classification performance. This can be seen from the increasing level of accuracy obtained from the process carried out before and after using the adaboost algorithm. The results obtained from the research show that the adaboost algorithm can be used properly to help the performance of the K-Nearest Neighbor algorithm for the classification process on diabetes datasets. It can be seen from 5 tests with values of K = 7, 13, 19, 25 and 31 there is an increase in the accuracy results obtained after using the adaboost algorithm.

Analisis Decision Support System Perbandingan Metode Smarter dan Saw Dalam Menentukan Pemilihan Staff Pegawai Terbaik Laboratorium Komputer Nandri Marsan Sitinjak; Muhammad Zarlis; Roslina Roslina
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 2 (2021): April 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i2.2926

The best employees will make an agency to increase in its operations and can develop rapidly. However, relations on the part of an agency are still not optimal in selecting the best staff. Therefore, human resource management is needed in an agency with the selection of the best employee staff to spur staff morale in improving operations, dedication and performance in an agency, so that it becomes better, namely by creating a Decision Support System in selecting the best staff employees by using the Smarter and Saw method comparison method. The Smarter and Saw method is a decision-making method using multiplication to determine the value of a criterion, where the value for each criterion must be determined in advance with the weight of each criterion concerned. By utilizing the advantages and disadvantages of each method, the comparison of this method is able to give a value of 79.5 for the Smarter method, 93 for using SAW. Decision support systems using this comparison method can assist in the selection of the best employees and provide an alternative choice of ranking results

Analisis Perbandingan Metode Certainty Factor dan Teorema Bayes untuk Mendiagnosa Penyakit Autis Pada Anak Ramadhanu Ginting; Muhammad Zarlis; Rika Rosnelly
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 2 (2021): April 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i2.2930

This study discusses the design of a system that specifically addresses the problem of autism in children. Autism is a behavior that occurs to a person, especially a child, which makes social interaction difficult. The expert system is a system that represents the ability of an expert into a machine, where in this study, it discusses how to analyze 2 methods in an expert system, namely the Certainty Factor and the Bayes Theorem for diagnosing autism in children which aims to determine which method is the most appropriate and good to use. or implemented into an application. which will be useful later in determining the category of children with autism. Based on the research conducted by the author, a simple description of the comparative analysis of the Certainty Factor and Bayes Theorem methods is obtained where the Certainty Factor method has a calculation accuracy above 90%.

Penerapan Metode K-Means Clustering Untuk Daging Ayam Buras Viya Miralda; Muhammad Zarlis; Eka Irawan
Building of Informatics, Technology and Science (BITS) Vol 2 No 2 (2020): Desember 2020
Publisher : Forum Kerjasama Pendidikan Tinggi

Free-range chicken is a local Indonesian poultry whose population is widely found in every region of Indonesia. However, the level of consumption of free-range chicken meat in North Sumatra Province is still relatively low. This research data contains data on the production of free-range chicken in North Sumatra Province. One data mining technique is clustering. Grouping itself is a method by grouping data. The data of this study contains data on the production of free-range chicken in North Sumatra Province which began in 2010, 2011, 2012, 2013, 2014, 2017. (7 years), and data obtained from the Directorate General of Animal Husbandry and Animal Health as well as on animal access to BPS Sumatra site North (Central Statistics Agency). The purpose of this study is to group Regencies / Cities in North Sumatra Province into 2 parts. These are the Regency / City group with high free-range chicken meat and Regency / City with low-free chicken meat. This can be a contribution of the provincial government of North Sumatra, Regency / City which is of more concern than free-range chicken based on the cluster that has been done. The results of this research are taken from 33 data of free-range chicken meat in regencies / cities in North Sumatra province, 3 high-level districts, namely: Mandailing Natal, Langkat, Serdang Bedagai and 30 other regencies / cities including

Method of TOPSIS in Recipients of Home Improvement Assistance in South Siantar District at Pematangsiantar Tarukim Office Fikri Yatussa'ada; Muhammad Zarlis; Sumarno Sumarno
IJISTECH (International Journal of Information System and Technology) Vol 3, No 1 (2019): November
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

The construction of uninhabitable houses is a government program especially from the Social Service to provide housing development assistance for the poor. However, in its realization, funding assistance from the government is often still lacking and even not on target. Therefore this study aims to build a decision support system that has the ability to analyze in determining the community that is eligible to receive housing repairs. The SPK method used in this study is the TOPSIS method. This method uses the principle that the chosen alternative must have the shortest distance from the positive ideal solution and the farthest from the negative ideal solution from a geometric point of view by using the Euclidean distance to determine the relative proximity of an alternative to the optimal solution. Research data obtained from the Department of public housing and residential areas pematangsiantar city using interviews, observation and literature study methods needed to help solve problems. This study uses 10 alternatives and 7 criteria. After calculating the analysis, families who are entitled to help with house repairs are alternative 7 on behalf of the Piatur Siringgo-Ringgo.

Analisis Klasifikasi Sentimen Mahasiswa Terhadap Strategi Pembelajaran Online Pada Media Sosial Twitter Menerapkan Metode Naïve Bayes Astri Syah Putri; Muhammad Zarlis; Suherman Suherman
KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Vol 4, No 1 (2020): The Liberty of Thinking and Innovation
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/komik.v4i1.2568

Selama masa pandemi proses pembelajaran ditetapkan oleh pemerintah agar dilaksanakan dengan model daring atau pembelajaran online, hal tersebut membuat seluruh mahasiswa harus menjalani proses yang berbeda dengan pembelajaran sebelumnya ketika bertatap muka langsung di ruang kelas. Mahasiswa memiliki pandangan yang berbeda terhadap strategi pembelajaran online yang sudah dijalani dan membuat sentimen pribadi di media sosial twitter dengan memberikan penilaian yang menurut pengalaman pribadi selama mengikuti proses pembelajaran online sudah berlangsung di kampus tempat mereka menuntut ilmu. Berdasarkan sentimen mahasiswa terhadap strategi pembelajaran online yang disampaikan melalui media sosial twitter pada penelitian ini dilakukan analisis klasifikasi sentimen menggunakan metode naïve bayes. Hasil analisis klasifikasi sentimen mahasiswa yang dilakukan menggunakan metode naïve bayes memperoleh hasil pengkategorian negatif atau positifnya strategi pembelajaran online yang telah diterapkan pada mahasiswa yang menjalani proses pendidikan di perguruan tinggi yang dimana nilai klasifikasi yaitu precision=80%, recall = 80% dan accuracy = 80%.

Analisis Kinerja SMARTER Pada Sistem Pendukung Keputusan Pemilihan Tukang Las Terbaik Untuk Menerima Penghargaan Nasib Marbun; Muhammad Zarlis; Rahmad Widya Sembiring
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 3 (2022): Juli 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i3.4095

One of the policies that can be carried out by welding workshop entrepreneurs to maintain the best welders is by giving awards at the beginning of each month to the chosen alternative from the results of decision making by the leadership. The selection of the best welder who is entitled to receive the award can be done with several assessment criteria, including communication skills, knowledge of welding, welding techniques, and teamwork. In order for the decision making to produce good results, of course, a tool is needed in the form of a decision support system. In this study, the SMARTER method was applied in completing the selection of the best welder to receive the award. The results of the analysis of SMARTER's performance in solving the problem of selecting the best welder based on the relative standard deviation, it is known that the accuracy rate is 24%.

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