cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Desi Puspita Sari
Contact Email
jimailkom.snnmedia@gmail.com
Phone
+6282180318941
Journal Mail Official
snnmediatechpress@gmail.com
Editorial Address
Jalan Palem VII 18B Beringin Raya, Kec. Kemiling, Kota Bandar Lampung, Provinsi Lampung, Indonesia
Location
Kota bandar lampung,
Lampung
INDONESIA
Jurnal Ilmiah Informatika dan Ilmu Komputer
Published by PT. SNN Media Tech Press
ISSN : 29887461     EISSN : 2988747X     DOI : https://doi.org/10.58602/jima-ilkom.v1i1
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management
Jurnal Ilmiah Informatika dan Ilmu Komputer (JIMA-ILKOM) is a periodical scientific journal that contains research results in the field of informatics and computer science from all aspects of theory, practice and application. Papers can be in the form of technical papers or surveys of recent developments research (state-of-the-art). Topics cover the following areas (but are not limited to): Artificial Intelligence, Decision Support System, Intelligent Systems, Business Intelligence, Machine Learning, Data mining, Network and Computer Security, Optimization, Soft Computing, Software Engineering, Pattern Recognition.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 25 Documents
 1   2   3 
Sistem Pendukung Keputusan Pemberian Kredit Koperasi Simpan Pinjam Menggunakan Metode MARCOS dan Rank Order Centroid Sandi Badiwibowo Atim
Jurnal Ilmiah Informatika dan Ilmu Komputer (JIMA-ILKOM) Vol. 3 No. 1 (2024): Volume 3 Number 1 March 2024
Publisher : PT. SNN MEDIA TECH PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jima-ilkom.v3i1.22

Abstract

Savings and loan cooperatives are an economic organization that aims to provide financial support to their members through savings and lending mechanisms. The problem experienced by XYZ savings and loan cooperatives is that no creditworthiness assessment system exists in the cooperative. The credit granting process is carried out by conducting a survey from survey officers through forms, submission requirements, and member business taxation formats to conduct committee meetings. This study aims to apply a decision model in lending to savings and loan cooperative customers by applying the MARCOS method with the centroid rank order weighting method so that the results of applying this method become a decision recommendation for the cooperative in determining credit. The ranking results above show the recommendation for providing the first credit to customer 5 with a final value of 0.89323 getting rank 1, then customer 6 with a final value of 0.82269 getting rank 2, and customer 2 with a final value of 0.78972 getting rank 3.
Kombinasi Multi-Objective Optimization on the basis of Ratio Analysis (MOORA) dan PIPRECIA dalam Seleksi Penerimaan Barista Sanriomi Sintaro; Setiawansyah Setiawansyah
Jurnal Ilmiah Informatika dan Ilmu Komputer (JIMA-ILKOM) Vol. 3 No. 1 (2024): Volume 3 Number 1 March 2024
Publisher : PT. SNN MEDIA TECH PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jima-ilkom.v3i1.23

Abstract

Barista acceptance selection is a crucial stage in building a quality team in the coffee industry. This process not only looks for individuals with technical skills in processing coffee, but also emphasizes other aspects such as communication skills, customer service, and appearance. The combination of MOORA and PIPRECIA methods can be used to optimize several goals at once by considering the ratio between positive and negative solutions in determining the best alternative based on several criteria. This study aims to select barista acceptance by applying a combination of MOORA and PIPRECIA methods based on the criteria used, namely ability, knowledge, experience, communication, and appearance. So that the results of the combination of MOORA and PIRRECIA methods will be a recommendation for management in selecting barista acceptance. The ranking results for rank 1 with the final value of MOORA optimization of 0.428 were obtained by Yanto, rank 2 with the final value of MOORA optimization of 0.423 was obtained by Ridho, and rank 3 with the final value of MOORA optimization of 0.414 was obtained by Antoni. The benefit of this study will be a recommendation for coffee shop owners in conducting barista selection using a decision support system model. Future research may add other criteria such as barista education and training.
Multi Attribute Decision Making Penentuan Dosen Terbaik Menggunakan Metode Multi-Objective Optimization by Ratio Analysis dan Surrogate Weighting Sitna Hajar Hadad; Muksin Hi Abdullah; Nurnela; Radina Hamza Hairun
Jurnal Ilmiah Informatika dan Ilmu Komputer (JIMA-ILKOM) Vol. 3 No. 1 (2024): Volume 3 Number 1 March 2024
Publisher : PT. SNN MEDIA TECH PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jima-ilkom.v3i1.24

Abstract

The best lecturers are people who not only have high academic expertise in their fields, but also have the ability to inspire and guide students well. The main problem in determining the best lecturer is subjectivity in the evaluation process. Evaluation of lecturer performance often tends to be based on parameters that are qualitative in nature and can be influenced by personal preferences, or subjective perceptions. This study aims to solve problems in the selection of the best lecturers using the MADM approach with the method used, namely MOORA in the selection of the best lecturers and the surrogate weighting method to determine the weight of criteria to be used in the selection of the best lecturers. The ranking results show no difference in ranking from the rank sum, rank order centroid, and reciprocal rank weighting methods. The results for rank 1 were obtained on behalf of ID Lecturers, rank 3 was obtained on behalf of SH Lecturers, and rank 3 was obtained on behalf of KU Lecturers, using the rank sum, rank order centroid, and rank reciprocal weighting methods. The comparison results that the rank sum, rank order centroid, and reciprocal rank weighting methods are very suitable in recommending the best lecturers by applying the MOORA method. The combination of rank sum weighting method and MOORA, the combination of rank order centroid and MOORA weighting method, and the combination of reciprocal rank weighting method and MOORA there is no difference in the final ranking of the best lecturers.
Sistem Pendukung Keputusan Pemilihan E-Commerce Menggunakan Pembobotan Entropy dan COPRAS Puspa Citra; Heri Bambang Santoso; I Wayan Sriyasa
Jurnal Ilmiah Informatika dan Ilmu Komputer (JIMA-ILKOM) Vol. 3 No. 1 (2024): Volume 3 Number 1 March 2024
Publisher : PT. SNN MEDIA TECH PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jima-ilkom.v3i1.25

Abstract

The choice of e-commerce platform is crucial for businesses that want to expand their reach and increase sales online. By choosing the right e-commerce platform, businesses can optimize their online operations, increase customer satisfaction, and achieve sustainable growth in an increasingly competitive digital marketplace. This study aims to select e-commerce using a combination of entropy weighting methods and COPRAS in producing alternative assessments and ratings of existing e-commerce, so that it becomes a recommendation for the public in choosing an e-commerce as a transaction platform. The ranking results in the final e-commerce score are rank 1 obtained for Shoope e-commerce with a value of 100%, rank 2 obtained for Tokopedia e-commerce with a value of 95.93%, rank 3 obtained for Lazada e-commerce with a value of 80.93%, and rank 4 obtained for Blibli e-commerce with a value of 58.07%. The recommendation results for selecting an E-Commerce platform using a combination of Entropy and COPRAS weighting methods provide the highest recommendation to the Shoope E-Commerce platform with the highest value of 100%.
Penerapan Data Mining Menggunakan Algoritma K-Means Clustering Dalam Evaluasi Hasil Pembelajaran Siswa Nirwana Hendrastuty
Jurnal Ilmiah Informatika dan Ilmu Komputer (JIMA-ILKOM) Vol. 3 No. 1 (2024): Volume 3 Number 1 March 2024
Publisher : PT. SNN MEDIA TECH PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jima-ilkom.v3i1.26

Abstract

Evaluation of student learning outcomes is a critical process in education that aims to measure the achievement of learning objectives. Through various methods such as tests, projects, and observations, teachers can assess students' understanding, skills, and progress in the subject matter. The purpose of applying data mining using the K-Means Clustering algorithm in evaluating student learning outcomes is to identify patterns that may be hidden in learning outcome data, divide students into groups based on their level of achievement or learning characteristics, and provide valuable insights to teachers and education stakeholders. The results of clustering student learning assessment data can uncover patterns that are beneficial to educators and school administrators. Analysis of these clusters can reveal information about achievement trends, trends in success or difficulty in specific subjects, as well as allow identification of students who need additional help. Grouping of cluster results based on student assessment data with k-means obtained 2 groups of students, namely Diligent students with group C0 and group students Very Diligent with group C1. The C0 group of Diligent students consists of 63 students and the C1 group consists of 91 Very Diligent students. The silhouette score test results for cluster 2 are as high as 0.9168 and show that grouping data into these groups is better, the use of silhouette score as an evaluation metric provides useful guidance in determining the optimal number of clusters in clustering analysis and data interpretation.

Page 3 of 3 | Total Record : 25

 1   2   3 

Filter by Year

2022 2024


Reset
Filter By Issues
All Issue Vol. 3 No. 1 (2024): Volume 3 Number 1 March 2024 Vol. 2 No. 2 (2023): Volume 2 Number 2 September 2023 Vol. 2 No. 1 (2023): Volume 2 Number 1 March 2023 Vol. 1 No. 2 (2022): Volume 1 Number 2 September 2022 Vol. 1 No. 1 (2022): Volume 1 Number 1 March 2022