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SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN KARYAWAN TERBAIK MENGGUNAKAN SIMPLE ADDITIVE WEIGHTING Ila Yati Beti
ILKOM Jurnal Ilmiah Vol 11, No 3 (2019)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v11i3.480.252-259

Human Resources is an important asset in the company to be able to achieve company goals, the selection of the best employees is a way that companies do to motivate employee performance. Company leaders usually have difficulty in evaluating employee performance with various assessment indicators available. This results in decisions that are not objective, to be able to process the best employee selection data that is more accurate and more objective results. Then the Decision Support System is needed in the selection of the best employees. In this study there were 25 Alternative Employees who had met the best employee selection requirements that were processed using the Simple Additive Weighting method and based on 5 assessment criteria, namely the criteria of loyalty, responsibility, behavior / ethics, cooperation, and attendance. From the results of the calculation of the SAW method obtained a top 10 ranking and also obtained that employee work loyalty is very influential on the calculation results with a weight of 30% of the overall weight.

Peran Teknologi Informasi Pada Masa Pandemi Terhadap Kegiatan Belajar Di Sekolah Jusuf Wahyudi; Khairil Khairil; Ila Yati Beti; Yupianti Yupianti; Devina Ninosari
Jurnal Dehasen Untuk Negeri Vol 1 No 2 (2022): Juli
Publisher : Universitas Dehasen Bengkulu

Information and communication technology is progressing faster and faster, and human activities can complete tasks more quickly and efficiently. Information technology makes it easier for people to communicate, interact and act in various ways. The technology that can use to meet face-to-face needs in large quantities is Zoom Cloud Meeting, Google Meet, and Cisco Webex, and there are several other applications for similar purposes but are less desirable. During a pandemic such as Covid 19 and its various derivatives, since it was known until now, it is a scary thing for the community because the impact arising from contracting this virus can result in death. So from 2020 until early 2022, school learning activities were closed due to the PSBB policy from the government. It will be very detrimental to society, especially for school-age children who still need guidance from teachers in understanding lessons. So we need another policy in its implementation, namely the implementation of online or online learning. The utilization of information technology such as Zoom, Google Meet or Cisco Webex as a face-to-face tool between teachers and students through internet media is essential. This situation also does not escape the State 3 Seluma Senior High School, which also carries out online teaching and learning activities.

Implementation Of Data Mining Using Algorithms A Priori in Determining The Pattern Of Product Purchases Perfume Sold Case Study On (Rafflesia Aromatic Professional Perfume) Kadek Dwi Andi Sasrawan; Dewi Suranti; Ila Yati Beti
Jurnal Media Computer Science Vol 1 No 2 (2022): Juli
Publisher : Fakultas Ilmu Komputer Universitas Dehasen Bengkulu

At the rafflesia aromatic professional perfume store, we have not used a special application in determining the purchase pattern of perfume products sold and still use manual data processing such as writing in books, and do not utilize existing sales data and sales data only as an archive. So tejadi accumulation of data that is not in the know the benefits. Basically, the data set has useful information to make a decision about the pattern of perfume sales that are sold. One method that can solve the above problems is the Apriori algorithm method. Because the apriori algorithm is a suitable algorithm used to determine the search for Frequent itemsets using the Assciation rule technique with transaction data that is used for 1 month (January 1, 2022 – January 30, 2022).With the amount of 25 data from 15 transactions, support items can be displayed minimum support Value = 10% as many as 10 perfumes consisting of Celebrity, Citra Edition , Alasca , Topaza , Rock Star ,Be Delicitions , La Verne , B Agua Marine, Jasmin Note , and garuda. For a combination of 2 itemset is to use support 15% which consists of, Celebrity-Citra Edition, Celebrity-Alasca, Topaza-Rock Star, Rock Star-be Delicitionse, La Verne-B aqua Marine, B aqua Marine- Jasmine Note, then look for the rules of association that can meet the minimum requirement for confidence is to calculate the confidence associative rules based on a ❸ ( ❸ ) B with a minimum value of support taken is 20%, then that meets that there is a perfume most purchased by consumers is Citra edication, celebrity, alasca ,celebrity topaza, rock star the minimum value of support taken is 20%, then that there is a perfume most purchased by consumers is Citra edication, celebrity, alasca ,celebrity topaza, rock star.

Implementasi Metode K-Means Clustering Untuk Pengelompokan Data Penjualan Pada Minimarket Remaja Kampus Bengkulu Roki Aprinsa; Siswanto Siswanto; Ila Yati Beti
INCODING: Journal of Informatics and Computer Science Engineering Vol 2, No 2 (2022): INCODING OKTOBER
Publisher : Mahesa Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34007/incoding.v2i2.302

Campus Youth Minimarket is one type of business in the field of selling daily necessities. For decision making in determining the amount of product inventory that can be adjusted to market demand, the Campus Youth Minimarket has not used the system and is still calculated manually. Therefore, this research was conducted with the aim of implementing the K-means Clustering method in grouping sales data at the Bengkulu Campus Youth minimarket. So that it can easily determine and classify high, medium and low product sales. The implementation of the system uses the PHP programming language and MySQL database and the method used in this research is the waterfall method. After the K-means process was carried out at the Campus Youth Minimarket with 15 data data tests, 3 clusters of goods were obtained, namely cluster 1 as a high sales cluster with 7 items, cluster 2 with moderate sales of 4 items and 4 items in a low sales cluster. Based on the results of processing 278 data on sales of goods in December 2021 at the Campus Youth Minimarket using the K-Means Clustering Method, the results of the grouping of product sales levels at the Bengkulu Campus Youth Minimarket were 3 clusters. Namely cluster 1 group with a high level of product sales with a total of 54 product data, cluster 2 with a moderate level of product sales with 165 types of products and cluster 3 with a low level of product sales with 51 total products. Based on the data cluster, it can be used as a reference by the Campus Youth Minimarket for the following month's product inventory. Which product clusters that have a high level of sales have a high or stable number of orders as before. Then product clusters with low sales levels, then the amount of product inventory for the next is reduced so that there is no accumulation of products in the warehouse and experiencing expiration.

Pengenalan Artificial Intelligence Kepada Siswa Di Lingkungan Sekolah Menengah Atas Lena Elfianty; Deri Lianda; Ila Yati Beti; Kanita Febianda; Panca Nugraha
Jurnal PADAMU NEGERI (Pengabdian pada Masyarakat Bidang Eksakta) Vol 3, No 2 (2022)
Publisher : Perkumpulan Dosen Muda (PDM) Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37638/padamunegeri.v3i2.586

Kegiatan pengabdian pada masyarakat berupa sosialisasi tentang Pengenalan Artificial Intelligence Kepada Siswa Di Lingkungan Sekolah Menengah Atas ini bertujuan untuk membantu para siswa SMA Negeri 3 Bengkulu Utara mendapatkan informasi tentang kemajuan teknologi yang berhubungan dengan Artificial Intelligence. Proses sosialisasi ini dilakukan dengan memberikan informasi tentang kecerdasan buatan yang dikhususkan untuk memecahkan masalah kognitif yang umumnya terkait dengan kecerdasan manusia, seperti pembelajaran, pemecahan masalah, dan pengenalan pola.Hasil dari kegiatan pengabdian pada masyarakat ini adalah agar para siswa siswi bisa lebih memahami dalam kemajuan teknologi kecerdasan buatan di Indonesia dengan memanfaatkan Sumber Daya Manusia (SDM) yang ada karena SDM dalam negeri tak kalah berkualitas dengan SDM luar negeri, juga diharapkan dengan adanya Artificial Intelligence ini dapat mengembangkan SDM di negara Indonesia.

A Decision Support System For The Selection Of The Best Employees At CV. Adiguna By Applying The Preferences Selection Index Method Repal Kesatria Putra; Yupianti Yupianti; Ila Yati Beti; Deri Lianda
Jurnal Media Computer Science Vol 2 No 1 (2023): Januari
Publisher : Fakultas Ilmu Komputer Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v2i1.3440

Technological Developments at this time, competition in the business world is getting tougher, to support this, companies must also improve their quality. On CV. Adiguna to increase the resources of his employees in improving the quality of his company is of course supported and influenced by the performance of employees who are competent in their field. Where in CV. Adiguna of Bengkulu City in giving awards to the best employees every year so far it is still done manually, this can certainly increase the enthusiasm of employees at work and always run a business by fulfilling several criteria set by CV Adiguna of Bengkulu City. Therefore to provide an objective assessment of employee performance, a Decision Support System is needed to select the best employees and provide rewards to employees, then to support the above, it is necessary to implement several Decision Support Systems at CV Adiguna of Bengkulu City based on the criteria that have been determined by the management of CV Adiguna of Bengkulu City. Decision support system for selecting the best employees at CV Adiguna of Bengkulu City by applying the Preferences Selection Index method is a desktop-based application that has implemented the Preferences Selection Index (PSI) method. This application can be used to assist in the process of selecting the best employees on CV Adiguna of Bengkulu City uses 5 predetermined criteria, then the assessment of these 5 criteria is processed using the Preferences Selection Index (PSI) method to produce a ranking that will be used to determine the best employee. Based on the results of the tests that have been carried out, it can be concluded that the decision support system for selecting the best employees at CV Adiguna of Bengkulu City by applying the Preferences Selection Index method is able to run well and can overcome data input errors besides that calculations are done manually with those carried out by the application producing the same output.

Pengenalan Artificial Intelligence Kepada Siswa Di Lingkungan Sekolah Menengah Atas Lena Elfianty; Deri Lianda; Ila Yati Beti; Kanita Febianda; Panca Nugraha
Jurnal INDONESIA RAYA (Pengabdian pada Masyarakat Bidang Sosial, Humaniora, Kesehatan, Ekonomi dan Umum) Vol 3, No 2 (2022)
Publisher : Perkumpulan Dosen Muda Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37638/indonesiaraya.3.2.33-38

Kegiatan pengabdian pada masyarakat berupa sosialisasi tentang Pengenalan Artificial Intelligence Kepada Siswa Di Lingkungan Sekolah Menengah Atas ini bertujuan untuk membantu para siswa SMA Negeri 3 Bengkulu Utara mendapatkan informasi tentang kemajuan teknologi yang berhubungan dengan Artificial Intelligence. Proses sosialisasi ini dilakukan dengan memberikan informasi tentang kecerdasan buatan yang dikhususkan untuk memecahkan masalah kognitif yang umumnya terkait dengan kecerdasan manusia, seperti pembelajaran, pemecahan masalah, dan pengenalan pola.Hasil dari kegiatan pengabdian pada masyarakat ini adalah agar para siswa siswi bisa lebih memahami dalam kemajuan teknologi kecerdasan buatan di Indonesia dengan memanfaatkan Sumber Daya Manusia (SDM) yang ada karena SDM dalam negeri tak kalah berkualitas dengan SDM luar negeri, juga diharapkan dengan adanya Artificial Intelligence ini dapat mengembangkan SDM di negara Indonesia.

SISTEM PAKAR DIAGNOSA PENYAKIT GANGGUAN TIDUR DENGAN METODE FORWARD CHAINING BERBASIS WEB (STUDI KASUS : UPTD PUSKESMAS TELAGA DEWA KOTA BENGKULU) Ahmad Revaldo; Yupianti Yupianti; Ila Yati Beti
Jurnal Media Infotama Vol 19 No 1 (2023): April
Publisher : UNIVED Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmi.v19i1.3314

Abstract—The necessities of life force people to work to fulfill their daily lives. In general, humans work during the day and rest at night. A lot of time spent working requires humans to rest to recover their physical condition. Sleep is an important phase in daily activities that is useful for balancing human life. Everyone's sleep needs are different. Many people are long-sleepers who need 9 to 10 hours of sleep a night while others are short-sleepers who only need less than 6 hours of sleep each night. Long sleep is not always associated with sleep disturbances. Besides that most people are not medically trained, therefore the authors intend to design an "expert system for diagnosing sleep disorders using the web-based forward chaining method" which can be accessed via http://puskesmastelagadewa.com/. This application is expected to be used by the community in early diagnosis as a prevention of more severe disease. This system is designed using the PHP programming language and MySQL database, the resulting expert system is able to help patients diagnose sleep disorders while providing solutions to the disease. From the test results, it is obtained that 100% functionality runs according to system requirements. In the system testing carried out at the Telaga Dewa Health Center UPTD, Bengkulu City, symptoms and diseases were obtained from 7 existing sample data.

Penerapan Data Mining Dalam Pengelompokkan Buku Yang Dipinjam Menggunakan Algoritma K-Means Herlina Latipa Sari; Ila Yati Beti
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.v3i6.826

The purpose of this study is to apply data mining in grouping books that are borrowed based on the number of available books, so that by implementing data grouping with the k-means algorithm it will determine the books that are most in demand as seen from the number of available books, books read, and books borrowed from the Dehasen Bengkulu University Library. Data mining is a process or activity carried out to collect large data and then extract the data so that it becomes information that can be used for something useful. From the results of applying data mining to grouping book titles borrowed at the Dehasen Bengkulu University library using the k-means algorithm which has been applied the book titles have been grouped into 2 clusters according to the specified criteria, namely the number of books available, the number of books read and the number of books read. borrowed with the calculation results shown from cluster I (high) from the k-means data grouping there are 10 the number of codes and book titles with the number of books available being 295 with the average number of books read being 9 books and the average number of books read 11 books were borrowed and in cluster II (low) of the k-means data grouping there were 10 numbers of codes and book titles with 115 available books with an average number of books read 5 books and an average number of books borrowed as many as 4 books.

Application of the Myers-Birggs Type Indicator Method in the Member Personality Test Application (Case Study: Bengkulu Regional Police Mobile Brigade Corps) Oktian Dwi Pradana Putra; Khairil Khairil; Ilayati Beti
Jurnal Komputer Indonesia Vol. 1 No. 2 (2022): Juli-Desember
Publisher : Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar

The Bengkulu Police Mobile Brigade Corps conducts a personality test every year for members which is used as a basis for determining the personality of members. However, so far the implementation is still done conventionally and not computer-based, so it takes quite a long time to get the personality test results for these members. The application of the Myers-Birggs Type Indicator Method to the member personality test application at the Bengkulu Regional Police Mobile Brigade Corps can make it easier for members to carry out personality tests and find out the results of each member's personality test more quickly and accurately. Based on the black box testing that has been done, the results show that the functionality of the member personality test application at the Bengkulu Regional Police Mobile Brigade Corps is running as expected and the application is able to display the personality test results for each member based on personality type from the Myers-Birggs Type Indicator Method

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