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Journal Global Technology Computer
Published by Forum Kerjasama Pendidikan TInggi
ISSN : -     EISSN : 28096118     DOI : https://doi.org/10.47065/jogtc.v2i3.3992
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Engineering
Journal Global Technology Computer, ini memiliki bidang kajian: 1. Manajemen Informatika, 2. Sistem Informasi, 3. Game Design, 4. Multimedia System, 5. Sistem Pembelajaran Berbasis Multimedia, 6. GIS, 7. Mobile Programming, 8. Database Design, 9. Network Programming, 10. Distributed System, 11. Data Mining, 12. Sistem Pakar, 13. Kriptografi, dan 14. Sistem Pendukung Keputusan.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 78 Documents
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Pengelompokan Masyarakat Kurang Mampu Dengan Menggunakan Algoritma K-Means Data Mining Siagian, Evan Edward; Lubis, Irfansyah Nuddin; Setya, Monita; Sijabat, Ade Dermawan; Sembiring, David JM; Ginting, Meiliyani Br
Journal Global Technology Computer Vol 4 No 3 (2025): Agustus 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jogtc.v4i3.8215

Abstract

Some villages often experience difficulties in classifying economically disadvantaged communities, resulting in the distribution of social assistance sometimes being misdirected. Various grants are received, such as subsidies provided to the poor. Problems encountered include poorly managed community data, which complicates the analysis process, and the lack of a measurable grouping method, which often misdirects aid. Without an objective, data-driven grouping system, aid distribution errors will continue to recur, resulting in misdirected aid. To address these issues, one solution is the use of data mining techniques. In the past, big data management was often done manually or using conventional methods that required significant time, effort, and expense. Data mining is the process of exploring and analyzing large data sets to discover patterns, relationships, or important information that can support decision-making. The K-Means algorithm is a clustering method in data mining used to group data into groups (clusters) based on similar characteristics. The purpose of this study is to design and implement a system for grouping poor communities based on the K-Means algorithm that can assist village governments in distributing aid precisely to targets, accelerate the data analysis process, and reduce aid distribution errors. This study uses 30 population data with 5 attributes: occupation, income, dependents, home ownership, and assets. The method used in this study is the K-Means Algorithm. From the calculations that have been carried out, it is recommended that there are 3 clusters with the same results, namely cluster 1 with 10 residents, cluster 2 with 10 residents, and cluster 3 with 10 residents as well.
Penerapan Algoritma CLARANS Data Mining untuk Klasterisasi Nilai Mahasiswa Pada Penentuan Bidang Konsentrasi Harmanda, Inke; Sari, Anggun Puspita; Melasari, Melasari; Angkat, Erlita Natasya; Sembiring, David JM; Ramles, Polin
Journal Global Technology Computer Vol 4 No 3 (2025): Agustus 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jogtc.v4i3.8223

Abstract

A major challenge for educational institutions is recognizing their students' academic abilities and guiding them toward the right concentration. Grouping concentration areas for students is not easy. Grouping concentration areas will help students focus more on a concentration they are interested in and align it with their academic grades. The urgency of this research lies in the need to present a more objective, accurate, and data-driven method for grouping student concentration areas. With a recommendation system supported by data mining techniques, the process of determining concentration areas depends not only on students' personal preferences but also considers relevant academic performance patterns. This problem can be solved by utilizing data mining techniques, specifically the clustering method using the CLARANS algorithm. This study aims to analyze student data according to the weighting of certain course grades using the Clarans Algorithm, thus being able to provide decision support for grouping student grades to determine which major a student should be enrolled in. Student grade data with high (Network), medium (Programming), and low (Internet of Things) grades can be grouped into three clusters. The test results showed that 11 students were enrolled in the programming concentration, 5 students in the networking concentration, and 9 students in the Internet of Things concentration.
Implementasi Metode ARAS dan Metode Pembobotan ROC untuk Pendukung Keputusan pada Seleksi Penerimaan Karyawan Baru Arini, Wulan; Sitepu, Yanti Peronika Br; Dewani, Dewani; Fitriani, Nopita; Sembiring, David JM; Ginting, Raheliya Br
Journal Global Technology Computer Vol 4 No 3 (2025): Agustus 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jogtc.v4i3.8225

Abstract

Employees are one of the most important assets in a company. Their crucial role extends beyond carrying out daily tasks, but also through contributing ideas, innovations, and solutions that can help the company grow. Problems in the recruitment process for new employees often arise due to the large number of applicants with diverse backgrounds, abilities, and experiences. If this problem is not resolved, companies could potentially recruit employees who do not meet the required qualifications. One solution is to implement a Decision Support System (DSS). A DSS is a computer-based system designed to assist decision-makers in solving semi-structured or unstructured problems. In its implementation, a DSS can be integrated with the Additive Ratio Assessment (ARAS) method. To ensure accuracy in the ARAS calculation process, appropriate criteria weighting is required. One such weighting method is Rank Order Centroid (ROC). The purpose of this study is to implement a combination of the ROC and ARAS weighting methods to build a decision support system that can assist companies in selecting new employees who meet predetermined criteria. The combination of the ROC and ARAS methods can be an appropriate solution to overcome the problem of subjectivity, accelerate the selection process, and improve the accuracy of decision-making in hiring new employees. The process obtained a score of 1.000 on A6, indicating that the new employee was selected in the new employee selection process.
Sistem Informasi Terintegrasi Pengelolaan Catatan Kasus Konseling Siswa menggunakan User-Centered Design Prakasa, Anabela Aji; Mardhia, Murein Miksa; Aretama, Lucky Barga; Khusna, Arfiani Nur; Perwira, Luqman Tifa
Journal Global Technology Computer Vol 4 No 3 (2025): Agustus 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jogtc.v4i3.8259

Abstract

Deviant behavior in adolescents is a significant issue in education, particularly in the field of Guidance and Counseling (BK). The paper-based manual recording system makes it difficult for BK teachers to manage data such as case histories, counseling reports, and attendance. This study aims to develop a website-based BK information system using the User-Centered Design (UCD) approach and the Waterfall method. Data was collected through interviews and observations of BK teachers, followed by designing a user-friendly interface, system development, and testing. This system enables BK teachers to manage student data efficiently and allows principals to monitor reports in real time. The results of the System Usability Scale test showed an average score of 80.75 (category B, Good), and the Blackbox test showed appropriate functionality. The system proved effective, efficient, and met user needs in managing BK services in schools thus enabling BK teachers to focus more on quality counseling with data-based decision making.
Penerapan Metode MAUT dalam Penentuan Kelayakan Tenaga Kerja Indonesia Keluar Negeri dengan Pembobotan ROC Ginting, Leonardo; Edelweis, Edelweis; Irpanto, Irpanto; Hulu, Zulima Berkat; Sembiring, David JM; Surbakti, Asprina Br
Journal Global Technology Computer Vol 4 No 3 (2025): Agustus 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jogtc.v4i3.8292

Abstract

Determining the eligibility of Indonesian Migrant Workers (TKI) to travel abroad is a complex process because it involves many criteria that must be considered objectively. This study proposes the application of the Multi-Attribute Utility Theory (MAUT) method in decision-making by weighting criteria using the Rank Order Centroid (ROC) method. The ROC method is used to generate criteria weights based on priority levels, thus providing a fairer proportion in the calculation. Furthermore, the MAUT method is used to normalize the data, calculate utility values, and determine the final score of each alternative. The purpose of this study is to develop a Decision Support System model that can help determine the eligibility of Indonesian Migrant Workers (TKI) to travel abroad more objectively, measurably, and systematically, so that the selection process does not only rely on subjective considerations, but also uses a quantitative approach to improve the accuracy of the decision results. This study uses five assessment criteria with ten alternatives as data samples. The calculation results show that criteria with higher priorities have a significant influence on the final result. From the data processing process, it was obtained that Alternative A7 had the highest preference value of 0.945 and was recommended as the best alternative, followed by A3 with a value of 0.926 and A9 with a value of 0.865, while the alternative with the lowest score was A8 with a value of 0.608. The results of this study prove that the integration of the ROC and MAUT methods can produce an objective, transparent, and systematic decision support system in determining the feasibility of alternatives, as well as assisting decision makers in a more accurate and measurable selection process.
Implementasi Logika Fuzzy dengan Metode Mamdani untuk Menghitung Durasi Penyiraman Air Otomatis Garingging, Keisya Febrika S.; Khomariah, Khomariah; Astanti, Adelia; Ulfa, Adelia; Sembiring, David JM; Ginting, Devita Permatasari Br
Journal Global Technology Computer Vol 4 No 3 (2025): Agustus 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jogtc.v4i3.8335

Abstract

Savings and Loan Cooperatives (KSP) play a crucial role in providing access to financing for the public, particularly in underbanked areas. However, lending through KSPs often faces challenges related to the accuracy of creditworthiness assessments, which largely rely on subjective assessments and manual procedures, resulting in the risk of non-performing loans. This study aims to develop a creditworthiness prediction model using the Decision Tree algorithm to improve the accuracy and efficiency of the credit decision-making process. The Decision Tree algorithm was chosen for its ability to classify customers based on historical data in a manner that is easy to understand and interpret. In this study, customer data, including attributes such as Borrower Credit History, Financial Status, Income Amount, Employment Status, and Loan Amount, was used to construct a decision tree. The results showed that the Decision Tree model achieved an accuracy of 86.67%, indicating its effectiveness in predicting creditworthiness and its reliability in supporting credit granting decisions in savings and loan cooperatives. This research contributes to reducing the risk of non-performing loans and improving the efficiency of decision-making in savings and loan cooperatives through the application of data mining techniques based on historical customer data analysis.
Penerapan Algoritma Decision Tree Data Mining untuk Prediksi Pola Pemberian Kredit pada Koperasi Simpan Pinjam Ginting, Winda Widia Br; Sitepu, Harun Rivaldo; Nainggolan, Laksono; Purba, Andrean Saputra; Surbakti, Asprina Br; Utomo, Dito Putro
Journal Global Technology Computer Vol 4 No 3 (2025): Agustus 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jogtc.v4i3.8336

Abstract

Savings and Loan Cooperatives (KSP) play a crucial role in providing access to financing for the public, particularly in underbanked areas. However, lending through KSPs often faces challenges related to the accuracy of creditworthiness assessments, which largely rely on subjective assessments and manual procedures, resulting in the risk of non-performing loans. This study aims to develop a creditworthiness prediction model using the Decision Tree algorithm to improve the accuracy and efficiency of the credit decision-making process. The Decision Tree algorithm was chosen for its ability to classify customers based on historical data in a manner that is easy to understand and interpret. In this study, customer data, including attributes such as Borrower Credit History, Financial Status, Income Amount, Employment Status, and Loan Amount, was used to construct a decision tree. The results showed that the Decision Tree model achieved an accuracy of 86.67%, indicating its effectiveness in predicting creditworthiness and its reliability in supporting credit granting decisions in savings and loan cooperatives. This research contributes to reducing the risk of non-performing loans and improving the efficiency of decision-making in savings and loan cooperatives through the application of data mining techniques based on historical customer data analysis.
Penerapan Data Mining untuk Mengukur Prestasi Kinerja Dosen dengan Menggunakan Algoritma C4.5 Sihotang, Ester Ulina; Ginting, Gogor Abiezer; Dahlia, Icha; Amanda, Rizka; Sembiring, David JM; Peranginangin, Sinek Mehuli Br
Journal Global Technology Computer Vol 4 No 3 (2025): Agustus 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jogtc.v4i3.8355

Abstract

Lecturer performance is an important factor in improving the quality of higher education, because lecturers not only act as educators, but also as researchers and community service. However, lecturer performance assessment often faces obstacles, such as the lack of uniform evaluation standards, a tendency for subjectivity in assessments, and limited evaluation instruments capable of assessing performance comprehensively. To overcome these problems, a data-driven approach is needed that can provide objective and measurable analysis results. One method that can be used is data mining with the C4.5 algorithm, which is a decision tree-based classification algorithm. This study aims to apply the C4.5 algorithm to measure lecturer performance achievements based on historical data that includes various indicators of the tridharma of higher education. The research stages include problem identification, literature review, data collection, selection of analysis techniques, implementation of the C4.5 algorithm with the help of RapidMiner software, and analysis of test results. The resulting classification model is visualized in the form of a decision tree so that it is easy to understand and can be used as a basis for evaluation. The test results show that the C4.5 algorithm is able to produce a classification model with an accuracy level of 86.67%. This demonstrates that C4.5 is effective in processing lecturer performance data and producing more objective and transparent evaluations, while also reducing the potential for subjectivity in assessments. This research provides a strategic contribution to supporting managerial decision-making in higher education, particularly in formulating policies for improving the quality of education and sustainable professional development of lecturers.

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