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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA JURIKOM (Jurnal Riset Komputer) Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) KLIK: Kajian Ilmiah Informatika dan Komputer
Iis Afrianty
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Pengelompokkan Penyakit Pasien Menggunakan Algoritma K-Means Rahayu Anggraini; Elin Haerani; Jasril Jasril; Iis Afrianty
JURIKOM (Jurnal Riset Komputer) Vol 9, No 6 (2022): Desember 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v9i6.5145

Abstract

Health is one of the most important factors besides education and income. Everyone has the same human rights to get good health services. A government agency that functions to serve all people who need medical services in Indonesia, namely the puskesmas. Ujung Batu Health Center which is located in Ujung Batu sub-district, Rokan Hulu Regency as one of the government agencies. The Ujung Batu health center stores patient medical record data, only sorting out the disease. Therefore, the medical record data needs to be processed using clustering or grouping using the K-Means method. This algorithm partitions the data into clusters so that data with the same characteristics are grouped into the same cluster and data with different characteristics are grouped. into another cluster. The data used consisted of 3875 records and 5 attributes, namely Gender, Participant Type, Diagnosis, Return Status, Address. From the test using the K-means algorithm, the clustering results show that cluster 1 has 710 data while cluster 2 has 3165 data. The results of the study show that the use of 2 clusters is the best cluster with a Silhouette Coefficient value showing results with a SC value of 0.646.
Penerapan Algoritma Fuzzy C-Means untuk Melihat Pola Penerima Beasiswa Bank Indonesia Agung Surya Maulana; Alwis Nazir; Lestari Handayani; Iis Afrianty
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 3 No. 6 (2023): Juni 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v3i6.788

Abstract

Scholarship is a program in the form of financial assistance aimed at individuals to continue their education with the aim of helping reduce the financial burden during the study period, especially in difficult situations, so that it can help expedite the learning process. Based on data related to scholarship recipients obtained in 2020, 2021 and 2022, analysis is needed to see the characteristics of Bank Indonesia scholarship recipients because Bank Indonesia does not yet know this, this was said directly by the Bank Indonesia Scholarship supervisor. The method needed for grouping data is data mining with the Fuzzy C-Means algorithm and using a computerized system, namely the RapidMiner application. This study uses the Cumulative Grade Point Average (GPA), Semester, and Study Program variables. The research results obtained were at Riau University for three years, the pattern formed was students of the Faculty of Social and Political Sciences with a large GPA of 3.5. At Sultan Syarif Kasim Riau State University, Riau Muhammadiyah University, and Lancang Kuning University have the same pattern, namely students with a GPA above 3.5. Then at the Dumai College of Technology, namely Informatics Engineering students with a large GPA of 3.5
Prediksi Jumlah Perceraian Menggunakan Metode Extreme Learning Machine (ELM) Mawadda Warohma; Elvia Budianita; Fadhilah Syafria; Iis Afrianty
Journal of Information System Research (JOSH) Vol 4 No 4 (2023): Juli 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v4i4.3581

Abstract

Divorce lawsuits have considerably increased in frequency in Indonesia. According to a Statistics Indonesia estimate, there were 447,743 divorce cases in 2021, up 53.50% from the 291,677 instances that were reported in 2020. According to data from the Pekanbaru Religious Court's Public Relations, there were 1,756 divorce cases conducted in the Pekanbaru region in 2021. Extreme Learning Machine (ELM) is one of the artificial neural network technologies that can forecast. The benefit of this approach is that it has a low error rate and can train data thousands of times faster than typical feedforward algorithms. This study used the Extreme Learning Machine technique to forecast the number of divorces at Bangkinang city's religious court, where 108 divorces are expected to occur between January 2018 and December 2022. The number of neurons in the hidden layer is tested using MSE at random for hidden layer 1, 10, 50, 100, and 200 neurons. The Bangkinang religious court's divorce prediction with the lowest MSE is based on a data comparison of 80%: 20% and produces an up-and-down pattern for the number of divorces predicted for 2023: 164 in January, 66 in February, 72 in March, 74 in April, and 92 in May. If there is an increase in divorce in the upcoming month, the religious court in Kota Bangkinang can use the information that the Extreme Learning Machine can provide to come up with a solution.
Perbandingan Teknik Prediksi Pemakaian Obat Menggunakan Algoritma Simple Linear Regression dan Support Vector Regression Sephia Pratista; Alwis Nazir; Iwan Iskandar; Elvia Budianita; Iis Afrianty
Building of Informatics, Technology and Science (BITS) Vol 5 No 2 (2023): September 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i2.4260

Abstract

Public Health Centers (Puskesmas) had a crucial role in furnishing society essential healthcare services and medication management. To preempt errors in stock management, a predictive approach is employed. This prediction methodology involves comparing Data Mining techniques utilizing the Simple Linear Regression algorithm and Machine Learning methodologies harnessing the Support Vector Regression algorithm. This research uses Paracetamol 500 mg and Cetirizine drug data from January 2020 to June 2023. The selection of these algorithms is motivated by the continuous nature of the data variables and their temporal span, spanning 42 months (period). The core aim of this study is to evaluate the magnitude of predictive errors using the Mean Absolute Percentage Error (MAPE) methodology. Implementing these methods was effectuated through the programming language Python with an 80%:20% partitioning of training and testing data. Drawing from experimental endeavors conducted concerning Paracetamol 500 mg, the utilization of the Simple Linear Regression algorithm, yields a MAPE score of 20.85%, categorized as 'Moderate,' whereas the application of the Support Vector Regression algorithm generates a MAPE of 18.39%, classified as 'Good.' Otherwise, experimentation on Cetirizine employing the Simple Linear Regression algorithm, employing an identical division of training and testing data, results in a MAPE of 18.39%, also classified as 'Good.' Meanwhile, resorting to the Support Vector Regression algorithm leads to a MAPE of 17.14%, falling under the 'Good' category. Based on the MAPE obtained, the Support Vector Regression algorithm has better prediction results than the Simple Linear Regression algorithm
Prediksi Jumlah Perceraian Menggunakan Metode Multilayer Perceptron Ikhsanul Hamdi; Elvia Budianita; Fadhilah Syafria; Iis Afrianty
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6291

Abstract

Divorce is a situation when a married couple decides to end their relationship and separate legally. The increasing number of cases in divorce cases filed at the Bangkinang Religious Court every month has led to a gradual increase and decrease. This study uses the Multilayer Perceptron (MLP) method and evaluates using Mean Squared Error (MSE) to determine prediction accuracy. The data used is divorce data from the Bangkinang Religious Court from January 2014 to December 2022 collected and processed from the Religious Court office. A total of 102 data in the form of time series data. In this study using MLP which consists of three layers, namely the input layer, hidden layer, and output layer. And using architectural testing consisting of 6-7-1, 6-9-1, and 6-12-1 with learning rate parameters: 0.01, 0.03, 0.09 with a comparison of training and test data 70:30, 80:20, 90 :10. Based on the test results using MSE, the best architecture was obtained, namely by comparing data 90:10 with 6-9-1 architecture, learning rate: 0.03, Epoch: 300, Alpha fixed value: 0.1, MSE results were successfully obtained: 0.01144 and the pattern of the number of splits from January until May 2023 has decreased, thus, this MLP can provide predictive results that help in predicting the number of divorces.
Perbandingan Prediksi Obat Berdasarkan Pemakaian Menggunakan Algoritma Single Moving Average dan Support Vector Regression Said Nurfan Hidayad Tillah; Alwis Nazir; Iwan Iskandar; Elvia Budianita; Iis Afrianty
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6859

Abstract

To ensure the availability and quality of drugs, Public Health Centers (PHC) must pay attention to the planning and procurement process. The problem that often arises is the increase in drug stock due to the stable use of drugs each month, resulting in excess and expired drugs that are not used. In addition, it is necessary to avoid inappropriate drug demand, which affects stock availability. Drug usage prediction is done with several methods such as the Single Moving Average (SMA) algorithm in the data mining method and the Support Vector Regression (SVR) algorithm in the machine learning method. This algorithm was chosen because the drug data of Diazepam 5 mg and Mefenamic Acid 500 mg is sustainable from January 2020 to June 2023 (42 months). Implementation using the Phyton programming language. Testing using the Mean Absolute Percentage Error (MAPE) method, this study aims to measure the accuracy of predictions in each algorithm. In research with Diazepam 5 mg and Mefenamic Acid 500 mg drugs, with a division of 80% in training data and 20% in test data. With a calculation of 3 periods, the SMA algorithm produces MAPE values of 4.10% and 4.29%, in the "very good" range. The SVR algorithm, which uses an RBF kernel with a complexity parameter of 1.0 and an epsilon parameter of 0.1, produces MAPE results of 7.35% and 9.52%, in the "Very Good" range. Thus, the SMA algorithm predicts better than the SVR algorithm.
Pengelompokan Produk Berdasarkan Data Persediaan Barang Menggunakan Metode Elbow dan K-Medoid Nurafni Syahfitri; Elvia Budianita; Alwis Nazir; Iis Afrianty
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 3 (2023): Desember 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i3.1525

Abstract

Inventory has a very important role in the company, because it indirectly influences the company's income. If a company does not have inventory, it will experience the risk of not being able to fulfill consumer desires. One data mining technique that can help in processing data to obtain useful information is clustering. The aim of this research is to group inventory of goods, by attributes, initial quantity, quantity sold, and quantity available. Management of inventory data using data mining techniques with the elbow and K-Medoid methods. Then the data that has been grouped can make it easier for stores to determine inventory carefully in terms of procuring stock of goods or products. The results of this research are the use of the elbow method in determining the optimal number of clusters using Python at point 7 (cluster). The clustering results using the k-medoid method with elbow show 7 clusters using the RapidMiner tool. Cluster 0 has 145 products, cluster 1 has 135 products, cluster 2 has 200 products, cluster 3 has 76 products, cluster 4 has 101 products, cluster 5 has 208 products, and cluster 6 has 135 products. Where cluster grouping is based on initial quantity, sold quantity and available quantity with the same or similar value. Clustering results using the k-medoid method without elbows, the clustering process uses 3 clusters with the RapitMiner tool. Cluster 0 has 169 products, cluster 1 has 410 products, and cluster 2 has 421 products. Cluster 0 grouping is based on quantity sold and available quantity, the value is the same, cluster grouping 1 is based on greater quantity sold, and cluster grouping 2 is based on greater quantity available. From the two analysis results it can be seen that the analysis using the k-medoid method with elbows is quite good because in determining the optimal number of clusters using the elbow method and the clustering results in grouping inventory are more effective.
Klasifikasi Sentimen Masyarakat di Twitter Terhadap Ganjar Pranowo dengan Metode Support Vector Machine Syaiful Azhar; Yusra; Muhammad Fikry; Surya Agustian; Iis Afrianty
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 3 (2023): Desember 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i3.1537

Abstract

The classification of public sentiment towards Ganjar Pranowo on Twitter can provide insights into his popularity, support, or criticism. This research aims to classify public sentiment towards Ganjar Pranowo on Twitter using the Support Vector Machine (SVM) method. The research data consists of 4000 tweets collected from Twitter. After undergoing preprocessing, these tweets are classified using SVM into positive or negative classes. The classification method is optimized to produce the most optimal model by testing the influence of feature selection stages and SVM parameter tuning. The data is divided into 80% training (TRAIN_SET) and 20% testing (TEST_SET). The optimal model is validated using 10% of the randomly selected TRAIN_SET for validation data. Sixteen experiments are conducted to explore the optimal model, with the highest validation results (top rank 4 models) tested on the TEST_SET, yielding F1-scores of 84.13%, 84.13%, 84.13%, and 84.13% for experiment IDs 1, 7, 14, and 16, respectively. In this research, SVM proves to be sufficiently effective in classifying sentiment-related tweets about Ganjar Pranowo on Twitter
Penerapan Algoritma K-Means Clustering untuk Mengetahui Pola Penerima Beasiswa Bank Indonesia (BI) Qurrata A'yuni; Alwis Nazir; Lestari Handayani; Iis Afrianty
Journal of Computer System and Informatics (JoSYC) Vol 4 No 3 (2023): May 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v4i3.3343

Abstract

Bank Indonesia Scholarships are a type of scholarship sourced from Bank Indonesia for students from selected State Universities, Private Universities, and Polytechnics. From the data on scholarship recipients who have passed the selection from 2020, 2021, 2022 universities in Riau, it is necessary to look for the behavior patterns of scholarship recipien because Bank Indonesia does not yet have a pattern. To find the pattem from scholarship recipients using the method of data mining with K-Means Clustering algorithm. The parameters used are 4, namely study program, semester, GPA, and level. The results of the study using RapidMiner showed that cluster 0 was dominated by students from the Commerce Shipping Management study program, who were in semester 5 and D3 level. Cluster 1 is dominated by students from the Accounting and Management study program, in semester 7, with GPA greater than or equal to 3.51, and S1 level. Cluster 2 is dominated by students from the Nursing study program, in semester 5, with GPA greater than or equal to 3.51, and D3 level. Cluster 3 is dominated by students from the International Relations study program, in semester 7, with GPA greater than or equal to 3.51, and S1 level. Cluster 4 is dominated by students from the Informatics Engineering study program, in semester 5, with GPA greater than or equal to 3.51, and S1 level. It show that the recipients of Bank Indonesia scholarships are dominated by students with high GPA scores or equal to 3.51. In addition, it is also dominated by students who are at the S1 level. Tests were carried out using DBI with k=5 resulting in a validity value of 0.121.
Co-Authors Agung Surya Maulana Alwis Nazir Elin Haerani Elvia Budianita Fadhilah Syafria Ikhsanul Hamdi Iwan Iskandar Jasril Jasril Lestari Handayani Mawadda Warohma Muhammad Fikry Nurafni Syahfitri Qurrata A'yuni Rahayu Anggraini Said Nurfan Hidayad Tillah Sephia Pratista Surya Agustian Syaiful Azhar Yusra