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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Retensi
oktaviana, nanda
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Transportation

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Penerapan Metode Crashing (Penambahan Tenaga Kerja Dan Jam Kerja) Pada Pembangunan Struktur Gedung Kantor Fakultas Kehutanan Universitas Tanjungpura: Penerapan Metode Crashing (Penambahan Tenaga Kerja Dan Jam Kerja) Pada Pembangunan Struktur Gedung Kantor Fakultas Kehutanan Universitas Tanjungpura oktaviana, nanda; Arena, Azza; Werda, Werda; Hafiyyan, Qalbi
RETENSI Vol. 3 No. 2 (2023): Retensi
Publisher : Jurusan Teknik Sipil Polnep

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31573/retensi.v3i2.652

Abstract

The office building of the Faculty of Forestry, University of Tanjungpura was built to improve existing facilities and infrastructure. This building is also needed to support academic and non-academic activities of students. So that the building can be used immediately, then the acceleration of time on the construction of the building. This study aims to determine the cost and time of the project due to the acceleration (crashing). The acceleration is carried out by the method of crashing. Calculation of crashing with the alternative addition of the number of workers and working hours (overtime). Where the addition of the number of workers is increased by 15% than the normal number of workers and the addition of working hours (overtime) is done for 3 hours. Calculation of crashing using the alternative addition of the number of workers and the addition of working hours (overtime). Normally, this project requires a cost of Rp 2,410,298,907.92, the duration of the work for 1169 days and a workforce of 525 people. The calculation results with the alternative addition of the number of workers as many as 79 people, requires a cost of Rp 2,419,883,466.82. The time required is 1011 days (158 days more than normal). As for the calculation with the alternative addition of working hours (overtime), the cost of Rp 2,432,295,278.89 and the required duration is 890 days (279 days faster than normal). Based on the calculation results, the addition of Labor and working hours proved to accelerate the implementation time of the project. However, this will also increase the cost of the project.
Comparing XGBoost and LightGBM for Optimizing Health Content Categories Oktaviana, Nanda; Andrianingsih, Andrianingsih
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15545

Abstract

Indonesia’s social media platforms contain large amounts of unverified health information. Research on Indonesian health-text mining still rarely focuses on disease-based classification, leaving a gap compared with studies that only address sentiment or general topic categorization. This study proposes a multi-class classification approach that uses IndoBERT embeddings combined with gradient-boosting classifiers (XGBoost and LightGBM) to categorize tweets into diabetes, hypertension, and heart disease. The dataset comprises 4,075 tweets collected from platform X (Twitter). Preprocessing involves text cleaning, anonymization, normalization, and the extraction of 768-dimensional IndoBERT embeddings. Experiments are conducted in Google Colab (Intel Xeon CPU, 13 GB RAM, optional NVIDIA T4 GPU) using stratified five-fold cross-validation.The best results are obtained by the IndoBERT × LightGBM pipeline, which achieves an accuracy of 0.8526 and a macro-averaged F1-score of 0.8527, outperforming the IndoBERT × XGBoost model (accuracy 0.8325 and macro F1-score 0.8326). Feature-importance analysis shows that contextual terms related to blood sugar, the heart, and blood pressure strongly influence the predictions. Overall, the proposed method provides an effective baseline for monitoring health-related text and supporting disease-oriented analytics in Indonesian-language social media.
Co-Authors Andrianingsih Arena, Azza Hafiyyan, Qalbi Werda, Werda