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Buletin Ilmiah Informatika Teknologi
Published by AMIK STIEKOM SUMATERA UTARA
ISSN : -     EISSN : 29620945     DOI : -
Core Subject : Science, Engineering,
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Mechanical Engineering
Buletin Ilmiah Informatika Teknologi, merupakan wadah ilmiah yang menampung tulisan yang berasal dari hasil penelitian baik dari dosen maupun mahasiswa. Buletin Ilmiah Informatika Teknologi merupakan jurnal yang menampung berbagai tulisan pada bidang Ilmu Komputer. Artikel ilmiah yang dikirim pada redaksi harus merupakan naskah asli dan tidak pernah dipublikasi ditempat lain. Artikel ilmiah dalam setiap penerbitan merupakan tanggungjawab penulis. Buletin Ilmiah Informatika Teknologi terbit dalam periode 4 (Empat) bulanan Bulan September, Januari, Mei dengan ISSN :2962-0945 (media online) dengan SK Nomor: 005.29620945/K.4/SK.ISSN/2022.11 Topik utama yang diterbitkan pada Jurnal Buletin Ilmiah Informatika Teknologi, yaitu: Decision Support System, Expert System, Kriptografi, AI, Machine Learning, Data Mining, Image Processing, Pengolahan Citra, serta topik lain dalam bidang Informatika (menggunakan Metode dalam penyelesaian masalah).
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 5 Documents
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Search results for , issue "Vol. 3 No. 3: Mei 2025" : 5 Documents clear
Analisis Topik Dominan Dalam Paper Ilmu Komputer Menggunakan TF-IDF Dan K-Means Laksana, Jovansa Putra; Shela, Shela; Irsyad, Hafiz; Rahman, Abdul
Buletin Ilmiah Informatika Teknologi Vol. 3 No. 3: Mei 2025
Publisher : AMIK STIEKOM SUMATERA UTARA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58369/biit.v3i3.122

Abstract

The rapid growth of scientific publications in the field of computer science has created a need to understand the distribution and trends of emerging research topics. This study aims to identify and analyze dominant topics in computer science literature using a text mining approach based on Term Frequency–Inverse Document Frequency (TF-IDF) vectorization and the K-Means clustering algorithm. A total of 1,222 publication titles from Semantic Scholar (2020–2025) were processed through language normalization, text preprocessing, TF-IDF feature extraction, optimal cluster determination, and cluster quality evaluation using Silhouette Score and Davies-Bouldin Index (DBI). The results reveal that topics such as cybersecurity, artificial intelligence, and machine learning are the most prevalent. While some clusters show good internal cohesion, the overall evaluation yielded a Silhouette Score of 0.0585 and a DBI of 4.387, indicating overlapping topics and limited cluster separation. These findings suggest that although the TF-IDF and K-Means approach can highlight general topic trends, it has limitations in capturing semantic context. Future research is encouraged to explore more contextual representation and clustering techniques to improve topic analysis quality.
Penerapan Smart, Edas, Dan Cosine Similarity Dalam Rekomendasi Lowongan Pekerjaan Di Era Digital levid, Jonathan Felix; WIjaya, Daniel; Irsyad, Hafiz; Rahman, Abdul
Buletin Ilmiah Informatika Teknologi Vol. 3 No. 3: Mei 2025
Publisher : AMIK STIEKOM SUMATERA UTARA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58369/biit.v3i3.128

Abstract

The rapid advancement of digital technology has increased the need for intelligent systems to filter job vacancies that match user profiles. This study aims to develop a job recommendation system based on a combination of Cosine Similarity, SMART, and EDAS methods. Job data were obtained from the JobStreet website and processed through text preprocessing stages such as tokenization, stopword removal, and stemming. Job descriptions and job seeker profiles were converted into numerical vectors using the TF-IDF method. Cosine Similarity was used to measure content similarity, SMART to evaluate suitability based on weighted criteria such as education and experience, and EDAS to assess alternatives relative to the average solution. System evaluation was conducted using precision, recall, F1-score, and mean Average Precision (mAP) metrics. Results show that Cosine Similarity alone had the lowest performance (F1-score 41.9%, mAP 42.3%), improved with the addition of SMART (F1-score 51.1%, mAP 50.9%), and achieved the best results with the integration of Cosine Similarity and EDAS (F1-score 66.5%, mAP 65.8%). Therefore, the integration of text similarity and multi-criteria decision-making methods effectively enhances the accuracy and relevance of job vacancy recommendations.
Penerapan Metode Waspas dan Aras Dalam Menentukan Biji Kopi Terbaik Untuk Kopi Lain Hati Yulfanni Br. Tarigan, Ririn; Ramadani
Buletin Ilmiah Informatika Teknologi Vol. 3 No. 3: Mei 2025
Publisher : AMIK STIEKOM SUMATERA UTARA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58369/biit.v3i3.132

Abstract

Coffee beans are the seeds of the coffee plant and are the main source for coffee drinks. In Indonesia, there are two main varieties of coffee beans developed, namely Robusta Coffee (Coffee Robusta) and Arabica Coffee (Coffee Arabica). Coffee is one of Indonesia's main export commodities, occupying the third position in the world after Brazil and Vietnam. This research aims to determine the criteria in determining the selection of quality coffee beans through the Weighted Aggregated Sum Product Assessment (WASPAS) and Additive Ratio Asessment (ARAS) methods. This is done to simplify the process of selecting quality coffee beans. This research uses 5 criteria, namely: Price, Color, Aroma , Taste , and Impurity Content . The results of the study show that the ratings resulting from the application of the two methods have similarities. The best alternative identified after the application of both methods is the C7 alternative. Using the WASPAS method, the C7 alternative obtained a reference value of 0.5255. Meanwhile with the ARAS method, the C7 alternative gets the highest reference value, which is 0.9908.
Sistem Pendukung Keputusan Metode Probabilistic Untuk Pinjaman KUR di Bank BRI Aek Nabara Juniardi, Rizki; Adawiyah, Robiatul
Buletin Ilmiah Informatika Teknologi Vol. 3 No. 3: Mei 2025
Publisher : AMIK STIEKOM SUMATERA UTARA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58369/biit.v3i3.133

Abstract

Credit is a form of financing for most economic activities. Credit is an important activity for cooperatives, because credit is one source of funds for cooperatives. Before starting credit granting activities, a good and thorough analysis of all aspects of credit that can support the credit granting process is required, in order to prevent the emergence of credit risks and the emergence of irregularities, one of which is bad credit. Therefore, it is necessary to have a system that can support decision making in providing credit. With the decision support system created, it is hoped that it can help the cooperative chairman in analyzing credit and reducing the level of bad credit in the cooperative. For the Probabilitic method there are 3 highest alternatives, namely in the first place with the name Pratiwi Amalia getting a value of 0.604, second place with the name Helmi Yanti getting a value of 0.475, third place with the name Cut Arpiah Hasibuan getting a value of 0.439
Penerapan Multi Criteria Decision Untuk Rekomendasi Program Ekstrakulikuler Di Sekolah Menengah Atas Adela Hutauruk, Tascha
Buletin Ilmiah Informatika Teknologi Vol. 3 No. 3: Mei 2025
Publisher : AMIK STIEKOM SUMATERA UTARA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58369/biit.v3i3.137

Abstract

The rapid development of computer technology has had a significant impact on various sectors, including education. The utilization of information technology systems plays a crucial role in supporting data processing and effective, efficient decision-making processes. At SMA Swasta Panti Budaya, the selection of extracurricular activities by students is still conducted manually, which often leads to confusion in choosing activities that match their interests and potential. This can result in a lack of student participation in extracurricular programs and the failure to develop their talents and interests. Therefore, a decision support system is needed to accurately recommend extracurricular activities based on each student's potential. This study aims to design and develop a recommendation system using the Multi-Criteria Decision Making (MCDM) approach, specifically the Elimination and Choice Translation Reality (ELECTRE) method. This method is capable of selecting the best alternative from several options based on specific criteria. With the implementation of this system, the school is expected to provide more accurate and appropriate recommendations for extracurricular activities, thereby supporting students in achieving their full potential and academic success.

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