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All Journal bit-Tech Jurnal Informatika dan Rekayasa Perangkat Lunak KREATIF: Jurnal Pengabdian Masyarakat Nusantara
Fathur Riski
Institut Teknologi Nasional Malang
Computer Science & IT Economics, Econometrics & Finance Education Engineering Health Professions Law, Crime, Criminology & Criminal Justice

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Figma Masuk Sekolah: Inovasi Pembelajaran UI Design untuk Generasi Digital Karina Auliasari; Mira Orisa; Mariza Kertaningtyas; Zulkifli Abdillah; Muhammad Rafi Faddilani; Fathur Riski; Jamil Nashrulloh; Bagaskara Adhi P
KREATIF: Jurnal Pengabdian Masyarakat Nusantara Vol. 5 No. 2 (2025): Jurnal Pengabdian Masyarakat Nusantara
Publisher : AMIK Veteran Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/kreatif.v5i2.6053

Abstract

UI design training using the FIGMA platform implemented at SMKN 2 Singosari is a community service activity carried out by a team of lecturers and students of Informatics Engineering S1 and Industrial Engineering S1 at ITN Malang. Students of the RPL expertise program class XI SMKN 2 Singosari have gained an understanding and expertise in UI design from the results of the training activities. The mastery and understanding of the training material by students is shown from the questionnaire score, which averages 5 on a scale of 5 for understanding the material and a value of 4.2 for teaching satisfaction during the training. As many as 80% of students also expressed their satisfaction with the quality of the teaching team in delivering the material and their patience in overseeing the UI design practice material. The results shown show that the FIGMA community service program entering schools has succeeded in supporting innovative learning that supports the skills of the current digital generation in the vocational high school education environment.
Job Vacancy Recommendation System Based on Text Description Analysis Using Word Embedding and Cosine Similarity Fathur Riski; Karina Auliasari; Mira Orisa
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3690

Abstract

The rapid expansion of digital recruitment platforms has intensified information overload, making it increasingly difficult for job seekers to identify vacancies aligned with their skills and professional interests. In response to this challenge, this study develops a semantic-based job recommendation system that leverages word embedding and cosine similarity to enhance retrieval relevance within the Indonesian labor market context. The primary contribution lies in the empirical examination of embedding-driven semantic ranking applied to Indonesian job descriptions, with a focus on ranking coherence and contextual alignment rather than binary classification accuracy. The proposed framework transforms both user-entered skill keywords and job vacancy descriptions into dense vector representations within a shared embedding space. Semantic similarity is then computed using cosine similarity, enabling the system to rank job postings according to their contextual proximity to the user query. The recommendation output is presented in a Top-N format, prioritizing vacancies with the highest semantic correspondence. Experiments conducted on a dataset of 523 job postings demonstrate that the system consistently produces semantically coherent ranking patterns, where vacancies emphasizing relevant competencies are positioned at higher ranks. Qualitative evaluation further indicates stable ranking behavior across repeated queries, suggesting robustness in similarity-based ordering. These findings support the feasibility of embedding-based semantic retrieval as a practical and interpretable solution for content-driven job recommendation in dynamic digital recruitment environments.
Pemilihan Dosen Pembimbing Berdasarkan Judul Skripsi Mahasiswa menggunakan Metode Cosine Similarity Ratri Andinisari; Fathur Riski; Karina Auliasari
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 2 (2025): September
Publisher : Universitas Wahid Hasyim

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

Abstract

The process of selecting a thesis advisor that aligns the thesis topic with the advisor's area of expertise will greatly assist in the thesis preparation process for students. However, in reality, the process of determining the thesis advisor is often not based on the advisor's area of expertise. The process of determining the thesis advisor is often done manually and inefficiently, which can lead to a mismatch between the advisor's area of expertise and the student's thesis topic. From this problem, this research developed a recommendation model for selecting thesis supervisors using the Cosine Similarity method, utilizing thesis title data and the list of lecturers' areas of expertise. The process begins with preprocessing the text data of thesis titles, then calculating word weights using TF-IDF and measuring similarity to finally match the students' thesis titles with the lecturers' areas of expertise, which are calculated using the Cosine Similarity algorithm. From the results of testing the process of matching research topics from student thesis titles with the expertise and research of lecturers, it was produced accurately, as indicated by a precision value of 81% and a recall of 90%. This result also shows that the relevance level of the recommendations is very high. The F1 score obtained, which is 85%, indicates that the modeling has a good balance between precision and sensitivity (recall).
Co-Authors Bagaskara Adhi P Jamil Nashrulloh Karina Auliasari Karina Auliasari Mariza Kertaningtyas Mira Orisa Muhammad Rafi Faddilani Ratri Andinisari Zulkifli Abdillah