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All Journal Information System for Educators and Professionals : Journal of Information System Informatics for Educators and Professional : Journal of Informatics
Darusman Darusman
Universitas Nusa Mandiri Jakarta
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Other

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Perbandingan Kinerja Machine Learning dan Deep Learning untuk Analisis Sentimen Fufufafa Darusman Darusman; Windu Gata
INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System Vol 10 No 1 (2025): INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS (Juni 2025)
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Bina Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51211/isbi.v10i1.3333

Abstract

This study examines the performance comparison of various machine learning and deep learning models in sentiment analysis of the fufufafa phenomenon on Twitter. The models tested include Naive Bayes, Random Forest, Support Vector Machine, Logistic Regression, Decision Tree, and Long Short-Term Memory (LSTM). The study aims to evaluate the effectiveness of these models in classifying sentiment into positive, negative, and neutral categories. Experimental results show that the Decision Tree model achieved the highest accuracy of 96%, followed by LSTM with 95%. Other models, such as Random Forest, Support Vector Machine, and Logistic Regression, achieved an accuracy of 94%, while Naive Bayes recorded the lowest accuracy at 81%, primarily due to its limitations in handling more complex sentiments. The LSTM model proved to be superior in capturing temporal contexts and word relationships, delivering more accurate predictions despite requiring greater computational resources. The findings of this study affirm that deep learning models, particularly LSTM, are more effective in analyzing sentiment in dynamic and complex data, such as that found on Twitter, compared to machine learning models. These results provide valuable insights for developing more efficient and accurate sentiment analysis methodologies in the future.
Analisis Smart Pole Berbasis IoT Untuk Mitigasi Kecelakaan Lalu Lintas Di Jalan Raya Achmad Pahrul Rodji; Darusman Darusman; Anung Wicaksono; Dwi Kuswarno
INFORMATICS FOR EDUCATORS AND PROFESSIONAL : Journal of Informatics Vol. 10 No. 2 (2025): INFORMATICS FOR EDUCATORS AND PROFESSIONAL : JOURNAL OF INFORMATICS (Desember
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Bina Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51211/itbi.v10i2.3561

Abstract

The highway transportation industry faces significant challenges in managing road safety, especially related to monitoring dynamic environmental and traffic conditions. Systems relying on manual inspection or limited surveillance often fail to respond quickly to dangerous situations. This research aims to address these challenges by developing and analyzing the implementation of an IoT-based Smart Pole system for mitigating road accidents. The system integrates various sensor devices such as CCTV, air quality sensors, weather sensors, and warning speakers, all connected through a LAN network and supported by solar panels. The research findings show that the Smart Pole system can monitor air quality (PM2.5, PM10), weather conditions, and other traffic-related factors in real-time, enabling traffic managers to provide early warnings and rapid responses to potential hazards. With the application of Power over Ethernet (PoE) and solar panels, the system can operate efficiently without relying on external energy sources. The use of Face Analytics allows for the detection of risky behavior from drivers, while audio and visual warnings enhance driver awareness. The implementation of IoT-based Smart Pole technology can improve road safety by providing real-time data that is useful for traffic managers and road users, and it significantly contributes to the development of safer and more efficient smart city concepts.
Co-Authors Achmad Pahrul Rodji Anung Wicaksono Dwi Kuswarno Windu Gata