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All Journal Journal of Software Engineering and Information System (SEIS) TECHNOVATAR
Sukarsa
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Industrial & Manufacturing Engineering Mathematics

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IMPLEMENTASI ALGORITMA GREEDY MATCHING PADA SISTEM REKOMENDASI JASA TUKANG BANGUNAN Rosidin; Sudur, Misbahus; Dana, Raditya Danar; Lestyorini, Ratna Dewi; Sukarsa
Jurnal Rekayasa Perangkat Lunak dan Sistem Informasi Vol. 6 No. 2 (2026)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/seis.v6i2.10848

Abstract

The small-scale construction industry in Warnasari District, Brebes Regency, still faces inefficiencies in finding daily construction workers because the recruitment process is commonly carried out through gathering points and word-of-mouth information. This condition creates income uncertainty for workers and makes it difficult for employers to assess workers’ skills, location, and reputation. This study aims to develop a web-based recommendation system for construction worker services by implementing the Greedy Matching algorithm. The main contribution of this research is the application of weighted matching based on skill, location distance, and performance rating criteria to generate fast and measurable worker recommendations. The system was developed using the Rapid Application Development (RAD) method, while the recommendation process was carried out by calculating the heuristic value of each candidate and selecting the candidate with the highest score as a local optimal solution. The evaluation was conducted through Black Box testing to assess the validity of system functions and a Greedy Matching calculation simulation to evaluate the candidate ranking output. The test results showed that all system features functioned validly with a success rate of 100%. The calculation simulation results showed that the candidate with a score of 97.0 was placed in the first position compared to another candidate with a score of 74.0. These results indicate that the system is able to generate measurable construction worker service recommendations based on the applied weighted criteria.
ANALISIS SENTIMEN TERHADAP PEMBENTUKAN UNIVERSITAS REPUBLIK INDONESIA MENGGUNAKAN NAIVE BAYES PADA KOMENTAR YOUTUBE Arlovin, Teguh; Sukarsa; Sulaeman, Dicky Andika
TECHNOVATAR Jurnal Teknologi, Industri, dan Informasi Vol 4 No 3 (2026): Juli
Publisher : Awatara Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61434/technovatar.v4i2.379

Abstract

The establishment of Universitas Republik Indonesia (URI) by the Indonesian government in July 2026 triggered diverse public reactions on social media, particularly YouTube, given its organizational structure has no precedent in the national higher education system  . This study analyzes public sentiment toward the policy based on YouTube comments and evaluates the performance of the Naive Bayes algorithm in classifying that sentiment  . Comments from fifteen relevant YouTube videos were collected, labeled using the InSet Lexicon, represented using TF-IDF, and classified using the Complement Naive Bayes algorithm  . After preprocessing, 1,552 clean comments were obtained, comprising 58.2 percent negative, 27.6 percent positive, and 14.2 percent neutral sentiment  . The classification model achieved an overall accuracy of 64.3 percent, performing best in recognizing negative sentiment, while most misclassifications occurred between the positive and negative classes due to lexical similarity  . The dominance of negative sentiment indicates public concern over budget transparency and institutional overlap, while positive sentiment reflects hopes for expanded free education access  . This study provides an initial map of public perception toward a newly launched education policy and confirms the relevance of lexicon-based and Naive Bayes approaches for rapid public opinion mapping by policymakers
Co-Authors Dicky Andika Sulaeman Raditya Danar Dana Ratna Dewi Lestyorini Rosidin Sudur, Misbahus Teguh Arlovin