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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) Jurnal Sistem Komputer dan Informatika (JSON) KLIK: Kajian Ilmiah Informatika dan Komputer
Alwis Nazir
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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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.
Penerapan Algoritma Apriori Pada E-commerce Elektronik Nur Iza; Alwis Nazir; Iwan Iskandar; Elvia Budianita; Pizaini Pizaini
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.v4i3.3403

Abstract

Because there are so many advantages to using e-commerce, it is now expanding quickly. E-commerce, particularly for electronic items, makes it simpler for customers to execute transactions without traveling. Because businesses (business actors) do not yet have a pattern and strategy for the products they sell, the use of e-commerce has not yet reached its full potential. As a result, sales occasionally suffer because the supply of products does not meet consumer needs, forcing consumers to leave without purchasing these products, which has an impact on transactions. sales firm. Businesses (businesspeople) must use data mining to implement data processing. For this reason, researchers use an application strategy that is appropriate in this situation: the a priori algorithm. Finding frequent itemsets that frequently show up in the data set with the strongest pattern is frequently done using the a priori algorithm. This algorithm's output can be used to assist management in making decisions. According to the study's findings, the rule "if you buy AA Batteries (4-pack), you will buy AAA Batteries (4-pack), "if you buy AA Batteries (4-pack), you will buy a USB-C Charging Cable," and "if you buy AA Batteries (4-pack) and AAA Batteries (4-pack), you will buy a USB-C Charging Cable" all have a support and confidence value of 100%.
Clustering Vaksinasi Penyakit Mulut dan Kuku Menggunakan Algoritma Fuzzy C-Means Yusril Hidayat; Alwis Nazir; Reski Mei Candra; Suwanto Sanjaya; Fadhilah Syafria
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.v4i3.3416

Abstract

Foot and Mouth Disease is a disease that attacks cloven hooves, this disease spreads very quickly and the mortality rate of infected animals is up to 100%. FMD is caused by type A picornaviridae virus, namely Apthaee epizootecae, which has a development period of 1-14 days after the animal is infected. The delay in handling it can cause many livestock to die and have an impact on cattle farmers. One of the steps taken to prevent the spread of this disease is to eradicate all livestock. The Riau Provincial Government has taken steps to prevent vaccination of all livestock in Riau Province in the form of preventing this disease from becoming more widespread. From these problems, this research will form a data cluster for the PMK program in Riau Province so that the government can improve supervision of livestock to prevent re-outbreaks of foot and mouth disease in Riau Province. The method used is data mining with the Fuzzy C-means algorithm and the data used comes from the Department of Animal Husbandry and Animal Health in Riau Province. The best cluster results after testing is 2 clusters. The most numerous clusters are in cluster 1 with a total of 48704 cows and cluster 2 with a total of 21232. The validity test using the DBI gets a value of 0.416, so it is still far from good
Implementasi Data Mining Memprediksi Penjualan Crude Palm Oil Berdasarkan Kapasitas Tangki Menggunakan Multiple Linear Regression Ana Komaria Baskara; Alwis Nazir; Muhammad Irsyad; Yusra Yusra; Fitri Insani
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 3 (2023): Maret 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i3.5665

Abstract

Data mining is a process of discovering information from data that can be used to improve business, product development, and other decision-making processes. One application of data mining is in PT. Kerry Sawit Indonesia, which is an agribusiness company in the Wilmar Group that deals with processing crude palm oil (CPO). Sales of CPO are crucial for palm oil plantation companies. To increase efficiency and profitability, palm oil plantation companies can predict CPO sales to optimize sales and CPO inventory. One method that can be used to predict CPO sales is through data mining techniques. In this study, the data mining technique used is multiple linear regression. Multiple linear regression is used to determine the relationship between the tank capacity variable and CPO sales. The data used in this study are CPO production data, CPO sales data, and tank capacity data obtained from palm oil plantation companies over the last five years. The results of the Multiple Linear Regression calculation in this case study show that the coefficient of determination (R-squared) value is 0.9546, indicating that 95.46% of the CPO delivery variability can be explained by the independent variables. Additionally, the MAPE and RMSE tests show that the regression model obtained has good accuracy in predicting CPO deliveries. Therefore, this regression model can be used to predict CPO deliveries in the future, considering the predetermined independent variable values.
Klasifikasi Tingkat Keberhasilan Produksi Ayam Broiler di Riau Menggunakan Algoritma K-Nearest Neighbor Beni Basuki; Alwis Nazir; Siska Kurnia Gusti; Lestari Handayani; Iwan Iskandar
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 3 (2023): Maret 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i3.5800

Abstract

Livestock is a crucial component of the Indonesian agriculture sector. One of the most widely practiced types of livestock farming is broiler chicken farming. The production of broiler chickens continues to increase due to the increasing consumption of broiler chickens. Presently, companies are facing an urgent requirement to support farmers, regardless of their level of experience, whether they are newly entering the sector or have been established for some time. Core companies encounter challenges in modeling the success rate of broiler chicken farmer production because of the vast quantity of data coming from collaborating farmers, which makes it arduous for the company to establish the success rate of broiler chicken production. Establishing the level of production success is very helpful in selecting the appropriate farmers to be guided, thus enabling accurate decision-making. A classification procedure utilizing data mining and K-Nearest Neighbor (KNN) algorithm is necessary to manage the growing volume of data. The study examined 927 livestock production data from Riau, where the data was divided into two sets, with 80% allocated for training and the remaining 20% for testing purposes. The findings of the confusion matrix analysis showed that the optimal result was achieved at k = 3, with an accuracy rate of 86.49%, precision of 75.00%, and recall of 70.21%.
Penerapan Data Mining untuk Menentukan Penyebab Kematian di Indonesia Menggunakan Metode Clustering K-Means Lili Rahmawati; Alwis Nazir; Fadhilah Syafria; Elvia Budianita; Lola Oktavia; Ihda Syurfi
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 3 (2023): Maret 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i3.5912

Abstract

Death in medical science is studied in a scientific discipline called tanatology. death is not only experienced by elderly people, but also can be experienced by young people, teenagers, or even babies. Death can be caused by various factors, namely, due to illness, old age, accidents, and so on. Based on information provided by the World Health Organization (WHO), there are five highest causes of death including ischemic heart disease, Alzheimer's, stroke, respiratory disorders, neonatal conditions. In this study, k-means is used to group causes of death in Indonesia based on the number of deaths that occur to determine the cases of death that have the most impact on the high mortality rate in Indonesia. Knowing what these death cases are will provide early preparation in anticipating the causes of death in Indonesia. The purpose of this study was to classify mortality rates based on the number of causes of death which were included in the low, medium, and high clusters by applying the K-Means method. In this study the authors used the K-Means clustering algorithm to classify death rates in data on causes of death in Indonesia from 2017-2021. The results of this study formed 3 clusters which were evaluated using the Davies Bouldin Index (DBI) in Rapidminer with a value of 0.259. Clustering results from a total of 21 cases obtained high, medium and low clusters. This cluster grouping was obtained according to the number of deaths per case, namely the first cluster (C0) was low with 17 cases, the second cluster (C1) was moderate with 3 cases and the third cluster (C2) was high with 1 case.
Pemodelan Klasifikasi Untuk Menentukan Penyakit Diabetes dengan Faktor Penyebab Menggunakan Decision Tree C4.5 Pada Wanita Nining Nur Habibah; Alwis Nazir; Iwan Iskandar; Fadhilah Syafria; Lola Oktavia; Ihda Syurfi
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 4 (2023): Juni 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i4.6202

Abstract

Diabetes is closely related to the pancreas, where the pancreas produces the natural hormone insulin, but its function is problematic which causes an increase in blood sugar levels in the body. Rising blood pressure can affect organ function in damaging the function of organs in a person's body such as the kidneys, heart and brain. Where makes a person have a history of diabetes. Diabetes that attacks adults can be prevented through exercise and a regular and healthy diet. According to the International Diabetes Federation (IDF) organization, it is estimated that at least 19.5 million Indonesian people between the ages of 20 and 79 will suffer from diabetes in 2021. China is in first place with diabetes with 140.9 million people. India is next in line with the number of people with diabetes of 74.2 million people. Therefore, early diagnosis is very important because it aims to reduce diabetes and diabetes complications in the future. It is necessary to collect data on patients with diabetes who are expected to be able to do prevention. Therefore applying classification techniques with data mining with the C4.5 algorithm. Where the classification can achieve better accuracy. Algorithm C4.5 is generally used in determining the nodes of a decision tree. Based on the test results, the accuracy is 76.67 percent, the precision is 72 percent, and the recall is 41.67 percent.
Klasifikasi Tingkat Keberhasilan Produksi Ayam Broiler di Riau Menggunakan Algoritma Naïve Bayes Syahbudin Hamwar; Alwis Nazir; Siska Kurnia Gusti; Iwan Iskandar; Fitri Insani
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 2 (2023): Desember 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i2.7038

Abstract

Livestock is becoming one of the important animal protein source providers, along with the fisheries sector, to meet the protein needs of the community at large. One type of livestock business that is popular is the maintenance of broiler chickens because of the potential for meat yield. Today, many breeders run a partnership pattern with large companies where breeders play the role of the main supplier and the company as the core. This step helps maintain the stability of production and income of farmers. The success of farmers in broiler chicken production can be measured by looking at the performance index (IP), if the performance is not good then coaching from the core company is needed. The large amount of data obtained from farmers makes it difficult for core companies to model the success rate of farmer production, this can make it difficult for core companies to choose farmers who need coaching. The application of data mining methods using the Naïve Bayes algorithm classification model has the potential to provide solutions to this problem. The purpose of this study was to predict how much success rate of broiler chicken production in Riau region by utilizing the Naïve Bayes Classifier algorithm. This study utilizes a production data set involving 952 broiler chicken farmers in Riau, with 3 scenarios dividing the data ratio of 90:10, 80:20, and 70:30. The results of the analysis showed that through the evaluation of the confusion matrix, it was best found in a data ratio of 90:10 with accuracy results reaching 89,58%, precision reaching 89,89%, and recall reaching 90,16%.
Clustering Data Persediaan Barang Menggunakan Metode Elbow dan DBSCAN Trisia Intan Berliana; Elvia Budianita; Alwis Nazir; Fitri Insani
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 2 (2023): Desember 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i2.7089

Abstract

In the world of business and inventory management, efficient inventory management is very important. If a company does not have inventory, it is impossible to fulfill consumer desires. Managing inventory requires careful inventory management and good data analysis. Challenges in inventory involve unpredictable fluctuations in demand, making it difficult to determine optimal inventory levels. Product diversification with various characteristics is also an obstacle, hindering grouping and formulating inventory management strategies. The lack of clear product segmentation adds to the inhibiting factor, making it difficult to identify groups of similar goods. Inefficient stockpiling can be detrimental to the business as a whole, so implementing clustering is necessary to optimize inventory strategies based on product characteristics. By analyzing product groups, companies can develop more efficient and effective inventory management strategies. This research uses a clustering method using the elbow method and DBSCAN (Density-Based Spatial Clustering of Applications with Noise). The elbow method is used to determine the most optimal EPS and Minpts values. The aim of this research is to group goods inventory data using the attributes Initial quantity (initial stock), quantity sold (stock sold), and quantity available (available product stock). So that grouped data can make it easier for companies to optimize the inventory of the most sold goods. and fans. Based on the elbow and DBSCAN test results, 144 clusters and 0 noise data were obtained, with cluster 2 being the product with the largest number of sales and inventory. The DBSCAN method which was tested without using elbows obtained cluster 3 results and 959 noise data.
Co-Authors Adrian Maulana Agung Surya Maulana Ana Komaria Baskara Beni Basuki Elvia Budianita Fadhilah Syafria Fitri Insani Gusti, Siska Kurnia Ihda Syurfi Iis Afrianty Iwan Iskandar Lestari Handayani Lili Rahmawati Lola Oktavia Muhammad Irsyad Muhammad Rifaldo Al Magribi Nining Nur Habibah Nur Iza Nurafni Syahfitri Pizaini Pizaini Qurrata A'yuni Reski Mai Candra Reski Mei Candra Said Nurfan Hidayad Tillah Sephia Pratista Suwanto Sanjaya Syahbudin Hamwar Trisia Intan Berliana Yusra Yusra Yusril Hidayat