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All Journal Indonesian Journal of Data and Science Journal of System and Computer Engineering Journal of Information Technology JISTECH: Journal of Information Science and Technology
Josua Josen A. Limbong
Universitas Papua
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics

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Sentiment Classification and Influential Actor Detection on Twitter (Case Study: The Raja Ampat Mining Conflict) Micguel Arter Imbiri; Lorna Yertas Baisa; Josua Josen A. Limbong
Indonesian Journal of Data and Science Vol. 7 No. 1 (2026): Indonesian Journal of Data and Science
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v7i1.376

Abstract

The nickel mining conflict in Raja Ampat has attracted extensive public attention due to the region’s global ecological significance and the potential environmental risks posed by extractive activities. Social media platforms, particularly Twitter, have become important spaces for public discussion and opinion exchange regarding this issue. This study aims to analyze public sentiment and identify influential actors in online discussions of the Raja Ampat mining conflict by integrating sentiment analysis and Social Network Analysis (SNA). This study adopts a cross-sectional design using Indonesian-language tweets collected between 15-27 November 2025. A total of 11,671 tweets were obtained through keyword-based crawling, and after preprocessing and duplicate removal, 8,909 tweets were retained for analysis. Sentiment labeling was performed using a lexicon-based approach, categorizing tweets into positive, neutral, and negative classes. The dataset was divided using an 80:20 train–test split. Sentiment classification was conducted using Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Naive Bayes algorithms. Model performance was evaluated using confusion matrix–based metrics, including accuracy, precision, recall, and F1-score. Social Network Analysis was carried out by constructing a directed interaction network based on mentions, replies, and retweets, with influential actors identified using degree and betweenness centrality measures. The results indicate that neutral sentiment dominates the discourse (51.58%), followed by negative and positive sentiments. SVM and Naive Bayes demonstrate more stable classification performance than KNN, while network analysis shows that influence is concentrated among a limited number of central actors
Evaluasi Usability Aplikasi Absensi Digital Pada Badan Pusat Statistik Kabupaten Manokwari Menggunakan Metode System Usability Scale: Evaluasi Usability Aplikasi Absensi Digital Pada Badan Pusat Statistik Kabupaten Manokwari Menggunakan Metode System Usability Scale Josua Josen A. Limbong; Duta Rael Bintang Pratama
JISTECH: Journal of Information Science and Technology Vol 14 No 1 (2025): Volume 14 Nomor 1 Tahun 2025
Publisher : Universitas Papua

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30862/jistech.v14i1.704

Abstract

The development of information technology has encouraged the digitization of employee attendance systems, including at the Central Bureau of Statistics (BPS) Manokwari which implements a fingerprint-based digital attendance application. Although it aims to improve the efficiency and accuracy of attendance recording, this application still faces various obstacles, such as dependence on internet connection, less than optimal interface display, and compatibility issues with user devices. This study aims to evaluate the usability level of the BPS Manokwari digital attendance application using the System Usability Scale (SUS) method. This method is used to measure aspects of learnability, efficiency, memorability, errors, and satisfaction based on user experience. This study involved 35 respondents who were permanent employees of BPS Manokwari. Data was collected through SUS questionnaire, then analyzed using SPSS software. The evaluation results show that the digital attendance application obtained a score of 68.42 in SUS, which falls into the Marginal High category. Although this application has functioned to meet the needs of employees, there is still significant room for improvement to achieve higher usability quality, especially in the aspects of ease of use and interface appearance. Based on the results of the analysis, this research provides recommendations for improvement to increase efficiency and user comfort in accessing the application
Analisis Sentimen Berbasis Aspek pada Komentar YouTube tentang CoreTax Menggunakan Support Vector Machine dan Random Forest Nur Vadila; Josua Josen A. Limbong; Ratna Juita
Journal of System and Computer Engineering Vol 7 No 3 (2026): JSCE: July 2026
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v7i3.2722

Abstract

The implementation of the CoreTax Administration System (CTAS) by the Directorate General of Taxes has received diverse responses from the public, which are reflected in YouTube comments. This study applies Aspect-Based Sentiment Analysis (ABSA) to identify user opinions regarding CoreTax and compares the performance of Support Vector Machine (SVM) and Random Forest for sentiment classification. Data were collected through web scraping from three Youtube videos, yielding 1.527 valid comments after preprocessing. A rule-based method was used to classify comments into five aspects, namely system, performance, user-friendliness, tax services, and policy. The results indicate that the system aspect was the most frequently discussed (56,12%), while negative sentiment dominated the dataset (59,2%). The highest proportion of negative sentiment was found in the user-friendliness aspect (81,29%), followed by performance (76,44%). In model evaluation, Random Forest achieved better results than SVM, obtaining 0.80 accuracy, 0.84 precision, 0.75 recall, and 0.79 F1-Score. Overall, ABSA provides deeper insights into user perceptions and issues related to CoreTax implementation.
Comparison of K-Means and Hierarchical Clustering Using Silhouette Score on BPBD Data, Papua Barat Najlah Putri Qudsiya; Christian Dwi Suhendra; Josua Josen A. Limbong
J-INTECH ( Journal of Information and Technology) Vol 14 No 02 (2026): Journal of Information and Technology
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v14i02.2315

Abstract

This study compares K-Means Clustering and Hierarchical Clustering to group disaster-prone areas in Papua Barat Province using official data from UPTD Pusdalops BPBD for the period 2020 to November 11, 2025. The dataset covers seven regencies with three aggregated variables: total floods and tidal floods, other disasters, and earthquakes. The research procedure includes data preprocessing, z-score normalization, determining the optimal number of clusters using the Elbow Method and Silhouette Score, and visualizing results with scatter plots and dendrograms. Both algorithms produced two clusters, with Hierarchical Clustering achieving a higher Silhouette Score of 0.4274 compared to 0.339 for K-Means. The scores indicate moderate clustering quality, reflecting the exploratory nature of the study due to the small dataset. Manokwari Regency formed a distinct cluster due to differing disaster characteristics. The novelty lies in employing official BPBD data and Silhouette Score evaluation to compare the two clustering algorithms, supporting data-driven prioritization for disaster mitigation.
Co-Authors Christian Dwi Suhendra Duta Rael Bintang Pratama Lorna Yertas Baisa Micguel Arter Imbiri Najlah Putri Qudsiya Nur Vadila Ratna Juita