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Journal of Big Data Analytic and Artificial Intelligence
Published by Sekolah Tinggi Ilmu Manajemen Informatika dan Komputer Tarakanita Rahmawati
ISSN : 25979604     EISSN : 27223256     DOI : https://doi.org/10.71302
Core Subject : Science,
Computer Science & IT
JBIDAI adalah jurnal nasional berbahasa Indonesia versi online yang dikelola oleh Prodi Sistem Informasi STMIK PPKIA Tarakanita Rahmawati. Jurnal ini memuat hasil-hasil penelitian dengan cakupan fokus penelitian meliputi : Artificial Intelligence, Big Data, Data Mining, Information Retrieval, Knowledge Doscovering in Database dan bidang-bidang lainnya yang termasuk ke dalam rumpun ilmu tersebut.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 53 Documents
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Desain Aplikasi Rekomendasi Lowongan Kerja dan Tenaga Kerja Menggunakan Metode Cosine Similarity Wanda Yulia Ulang Dari; Dikky Praseptian M; Risma Sakila
Journal of Big Data Analytic and Artificial Intelligence Vol 9 No 1 (2026): JBIDAI Juni 2026
Publisher : STMIK PPKIA Tarakanita Rahmawati

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71302/jbidai.v9i1.95

Abstract

The availability of a workforce that aligns with company requirements significantly affects organizational productivity and success. The dissemination of job vacancy information, which is still carried out manually through bulletin boards, newspapers, and job fairs, has limitations in terms of reach and efficiency. On the other hand, job seekers often face difficulties in finding employment opportunities that match their qualifications and expertise. This study aims to design a job and workforce recommendation application based on text similarity using the TF-IDF and Cosine Similarity methods. The research process begins with text preprocessing, including case folding, tokenizing, filtering, and stemming applied to job vacancy and workforce profile data. Furthermore, word weighting is performed using the TF-IDF method, followed by similarity measurement using Cosine Similarity. The testing results on three workforce profiles indicate that the system is capable of ranking candidates based on their similarity level to job vacancies, with the highest similarity score reaching 1.03%, followed by 0.67% and 0.20%. Although the similarity values are relatively low due to the mismatch between workforce backgrounds and job requirements, the system is able to provide objective recommendations based on data relevance.
Analisis Preprocessing Pada Similarity Judul Skripsi Menggunakan TF-IDF Dan Cosine Similarity Eviana Tjatur Putri; Mohamad Ardi
Journal of Big Data Analytic and Artificial Intelligence Vol 9 No 1 (2026): JBIDAI Juni 2026
Publisher : STMIK PPKIA Tarakanita Rahmawati

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71302/jbidai.v9i1.98

Abstract

The similarity of undergraduate thesis titles may lead to the repetition of research topics and reduce the diversity of student research. This study aims to analyze the effect of preprocessing stages on thesis title similarity using the TF-IDF and Cosine Similarity methods. The dataset consists of 480 information technology thesis titles used as reference data for similarity computation. System evaluation was conducted using 50 testing titles outside the reference dataset, comprising 25 thesis titles from previous academic periods and 25 newly proposed titles submitted in the 2025 even semester. The preprocessing stages evaluated include raw text, cleaning, stopword removal, and stemming. The results indicate that each preprocessing stage produces variations in similarity values. For the previous thesis title group, the average similarity values were 36.89%, 37.07%, 36.61%, and 38.28%, respectively, while the corresponding values for the newly proposed title group were 39.18%, 39.44%, 39.07%, and 39.62%. Among the evaluated preprocessing stages, stemming produced the highest average similarity values for both testing groups. However, the improvement was relatively small, indicating that the effect of preprocessing on similarity values was limited for the dataset used in this study. In addition, this research developed a web-based system that can support a faster and more objective evaluation of undergraduate thesis title submissions.
Model Pengambilan Keputusan untuk Evaluasi Karyawan Terbaik Menggunakan Integrasi Metode ROC dan MOORA Fitria Fitria; Rosmini Rosmini; Indra Tri Saputra
Journal of Big Data Analytic and Artificial Intelligence Vol 9 No 1 (2026): JBIDAI Juni 2026
Publisher : STMIK PPKIA Tarakanita Rahmawati

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71302/jbidai.v9i1.101

Abstract

Employee evaluation is an important aspect in human resource management because it is related to awarding rewards, career development, and increasing company productivity. However, the manual assessment process is often influenced by subjectivity, resulting in less than optimal results. This study aims to apply a combination of the Rank Order Centroid (ROC) and Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) methods as a decision support system in determining the best employees. The ROC method is used to obtain the weight of each criterion based on its order of importance, while the MOORA method is used to normalize, weight, and calculate preference values ​​to obtain an alternative ranking order. This study uses four assessment criteria, namely performance, discipline, attendance, and absenteeism, involving ten employees as alternatives. The results of the weighting using the ROC method produce a weight of 0.5208 for the performance criterion, 0.2708 for discipline, 0.1458 for attendance, and 0.0625 for absenteeism. Furthermore, the ranking results using the MOORA method showed that alternative A7 received the highest preference score of 0.327, followed by A3 with 0.308 and A9 with 0.305. The research findings indicate that the integration of the ROC and MOORA methods can produce a more objective, measurable, and consistent evaluation process, thus serving as a basis for supporting decision-making in identifying the best employees in a company.

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