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All Journal Building of Informatics, Technology and Science Jurnal Inovasi dan Teknologi Pembelajaran (JINOTEP) Kajian dan Riset Dalam Teknologi Pembelajaran U-NET Jurnal Teknik Informatika Bulletin of Information Technology (BIT) Journal of Computer Science and Information Systems (JCoInS) Journal of Student Development Information System (JoSDIS) J-COSCIS : Journal of Computer Science Community Service Jurnal Teknologi dan Manajemen Industri Terapan Jurnal Bisantara Informatika
Dalimunthe, Abdul Hakim
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering Industrial & Manufacturing Engineering Library & Information Science Social Sciences

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Journal : Building of Informatics, Technology and Science

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Implementation Naïve Bayes Classification for Sentiment Analysis on Internet Movie Database Samsir, Samsir; Kusmanto, Kusmanto; Dalimunthe, Abdul Hakim; Aditiya, Rahmad; Watrianthos, Ronal
Building of Informatics, Technology and Science (BITS) Vol 4 No 1 (2022): June 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (376.705 KB) | DOI: 10.47065/bits.v4i1.1468

Abstract

A film review is a subjective opinion of someone who has different feelings about each film. As a result, film enthusiasts will struggle to assess whether the film meets their requirements. Based on these issues, sentiment analysis is the best way to fix them. Sentiment analysis, also known as opinion mining, is the study of assigning views or emotional labels to texts in order to determine if the text contains positive or negative thoughts. The Nave Bayes method was chosen because it can classify data based on the computation of each class's probability against objects in a given data sample. The best model was created utilizing data without lemmatization, 500 vector sizes, and Nave Bayes classification, with an accuracy of 78.96 percent and a f1-score of 78.81 percent. Changes in vector size affect the system's capacity to foresee positive and negative sentiments. The difference in accuracy and recall values shows that when vector size 300 is utilized, the precision and recall outcomes are lower than when vector size 500 is used.
Applying Data Mining Techniques to Investigate the Impact of Smoking Prevalence on Life Expectancy in Indonesia: Insights from Random Forest Models Dalimunthe, Abdul Hakim; Samsir, Samsir; Subagio, Selamat; Siagian, Taufiqqurrahman Nur; Watrianthos, Ronal
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5201

Abstract

This study investigates the relationship between smoking prevalence and life expectancy in Indonesian provinces using data mining techniques, specifically focusing on the application of random forests. The primary objective is to quantify the potential impact of reducing smoking prevalence on population health outcomes. Data were sourced from the Indonesian Central Bureau of Statistics, which included life expectancy and smoking prevalence data from 2021 to 2023. The methodology involved aggregating life expectancy data from the district to the province level, followed by the application of a random forest model to predict life expectancy based on smoking prevalence and other socioeconomic indicators. Key findings indicate a weak to moderate negative correlation between smoking prevalence and life expectancy, with higher smoking rates associated with lower life expectancies. Predictive modeling suggests that a reduction in smoking prevalence could lead to significant improvements in life expectancy. For example, a 5% reduction in smoking rates could increase the average life expectancy by approximately 0.3 years, while a 15% reduction could result in an increase of about 0.9 years by 2025. These results underscore the detrimental impact of smoking on population health and highlight the importance of effective tobacco control measures. The predictive models developed in this study provide valuable information for policymakers, enabling targeted public health strategies and resource allocation. This research contributes to the field by demonstrating the utility of data mining techniques in public health and offering a comprehensive analysis of the relationship between smoking and life expectancy in Indonesia. The findings advocate for the urgent implementation of smoking cessation programs to enhance life expectancy and improve public health outcomes
Co-Authors Basyarul Ulya Has, Syahnila Natasya Hilman Taufik, Riski Nurjannah Kusmanto Kusmanto Maisyaroh, Nadilatul Marpaung, Radinda Tamara Ayu Misman Nasution, Juni Anggriyani Nasution, Nur Hayati Rahmad Aditiya Rahmad Aditya Rambe, Ade Nopriansyah Rambe, Nur Belila Rambe, Nurhabibah Ritonga, Sahbuki Ronal Watrianthos Samsir Samsir Samsir, Samsir Sangkot Idris Ritonga Sari, Sindi Uba Siagian, Taufiqqurrahman Siagian, Taufiqqurrahman Nur Siregar, Eka Maya Putri Subagio, Selamat Subagio, Selamet Wahyu Azhar Ritonga