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All Journal Indonesian Journal of Artificial Intelligence and Data Mining Indonesian Journal of Data and Science Jurnal Informatika Teknologi dan Sains (Jinteks) JISTECH: Journal of Information Science and Technology
Lorna Yertas Baisa
Universitas Papua
Computer Science & IT Control & Systems Engineering 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
Aplikasi Sistem Informasi Bursa Kerja Khusus (BKK) Bagi Alumni Universitas Papua Dengan Metode Rapid Application Development (RAD) Menggunakan Framework Codeigniter: Application Of The Special Job Exchange Information System (BKK) For University Of Papua Alumni With Rapid Application Development (RAD) Method Using The Codeigniter Framework Herlina Barrang; Christian Dwi Suhendra; Lorna Yertas Baisa
JISTECH: Journal of Information Science and Technology Vol 13 No 1 (2024): Volume 13 Nomor 1 Tahun 2024
Publisher : Universitas Papua

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30862/jistech.v13i1.385

Abstract

This research was conducted to create an Information System that can make it easier for alumni to find jobs according to their competence. The goal is that alumni can access job vacancy information that matches their interests and expertise through the platform provided by this application. Job search can be adjusted according to certain criteria, and the system provides automatic notifications for suitable vacancies. In addition, Special Job Exchange (BKK) managers can track alumni activities, add job vacancies, and track alumni placements using this application. The speed, scalability, and performance that the CodeIgniter framework offers are advantages. Using the Rapid Application Development (RAD) development method, the system can be developed with client orientation. The test results show that this application can help alumni find jobs. This will benefit both parties.
ANALISIS SENTIMEN MAHASISWA KABUPATEN TELUK BINTUNI TERKAIT BANTUAN SOSIAL PENDIDIKAN MENGGUNAKAN ALGORITMA NAIVE BAYES Niken Rahma Mahmud; Lorna Yertas Baisa; Marlinda Sanglise
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 8 No 2 (2026): EDISI 28
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v8i2.7479

Abstract

Program bantuan sosial pendidikan Pemerintah Daerah Kabupaten Teluk Bintuni merupakan upaya strategis dalam memperluas akses pendidikan tinggi bagi mahasiswa asal daerah. Penelitian ini membangun model klasifikasi sentimen berbasis algoritma Multinomial Naive Bayes untuk menganalisis persepsi mahasiswa penerima bantuan. Sebanyak 442 data komentar diperoleh melalui kuesioner Google Form, diproses melalui tahapan preprocessing (case folding, stopword removal, tokenisasi, dan stemming), serta direpresentasikan menggunakan Term Frequency-Inverse Document Frequency (TF-IDF) dengan pendekatan bigram. Pembagian data dilakukan dengan rasio 80:20 dan ketidakseimbangan kelas diatasi menggunakan Random Oversampling. Optimasi parameter alpha dilakukan melalui Grid Search dengan 5-Fold Cross Validation menghasilkan nilai alpha terbaik 0,1. Model mencapai akurasi 85,39%, precision rata-rata tertimbang 84%, recall 85%, dan F1-Score 83%. Distribusi sentimen menunjukkan 79,0% positif, 14,7% netral, dan 6,3% negatif, mengindikasikan program secara umum dinilai bermanfaat oleh mahasiswa.
Comparative Study of Machine Learning Methods for Sentiment Analysis of TikTok Comments Related to Cyberbullying Celestina Florecita Mariwy; Lorna Yertas Baisa; Andreas Leonardo Sumendap
Indonesian Journal of Artificial Intelligence and Data Mining Vol. 9 No. 1 (2026): March 2026
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

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Abstract

The rapid growth of internet use in Indonesia has contributed to the rise of cyberbullying on TikTok, increasing the importance of automated sentiment analysis for digital safety. This study compares the performance of Support Vector Machine, K-Nearest Neighbors, and Naive Bayes in classifying sentiments in TikTok comments related to cyberbullying. The dataset was collected via web scraping and processed through several preprocessing stages, yielding 7,900 unique comments. Sentiment labeling used a lexicon-based approach, and the data were split into training and testing sets with an 80:20 ratio. Results show that 34.18% of comments were negative, indicating a notable level of harmful content. Among the three models, Support Vector Machine performed best with an accuracy of 91.5%, followed by Naive Bayes at 82.8% and K-Nearest Neighbors at 80.8%. These findings suggest Support Vector Machine is the most effective method for sentiment classification in this context and offer a useful reference for developing more accurate content moderation systems on social media.
Analysis of Students’ Perceptions of the Free Nutritious Food Program (MBG) Based on K-Means Clustering Nur Rahmi; Lorna Yertas Baisa; Andreas Leonardo Sumendap
Indonesian Journal of Artificial Intelligence and Data Mining Vol. 9 No. 1 (2026): March 2026
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

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Abstract

The Free Nutritious Food Program is a strategic policy to support students’ nutritional resilience and readiness to learn. This study examined students’ perceptions of the program and identified respondent profiles using the K-Means clustering algorithm. Data from 501 students were collected through a Likert-scale questionnaire and analyzed to determine distinct perception patterns. The results revealed five clusters with strong validity, indicated by a silhouette value of 0.917. Overall, 74.6% of respondents expressed positive perceptions, suggesting that the program has been well received and supports school nutrition. However, some groups reported concerns regarding menu variety and cleanliness at distribution points. These findings underscore the need for routine quality monitoring, standardized implementation procedures, and greater attention to service consistency. Future studies should also include objective indicators such as body mass index and school attendance to provide a more comprehensive evaluation of program impact
Public Sentiment Analysis of the Affan Kurniawan Social Issue: A Comparison of Naïve Bayes and SVM Algorithms Marsella Iriana Mamusung; Lorna Yertas Baisa; Andreas Leonardo Sumendap
Indonesian Journal of Artificial Intelligence and Data Mining Vol. 9 No. 1 (2026): March 2026
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

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Abstract

Social media X is a dynamic public space where opinions on social issues, including the Affan Kurniawan case, spread rapidly. This study aims to analyze sentiment distribution, compare the performance of Multinomial Naïve Bayes and Linear Support Vector Machine (LinearSVC), and evaluate classification consistency under a unified evaluation framework. Indonesian-language posts were collected using keyword-based crawling and cleaned from 10,624 to 7,431 valid records (28 August–2 September 2025). The data were preprocessed through normalization, tokenization, stopword removal, and stemming, and labeled into negative, neutral, and positive sentiments using a lexicon-based approach. The results show a dominance of negative sentiment (50.26%), followed by neutral (30.96%) and positive (18.77%). Using Bag-of-Words features and an 80:20 train–test split, LinearSVC outperformed Naïve Bayes with higher accuracy (0.826 vs 0.745) and macro F1-score (0.759 vs 0.579). This study highlights the effectiveness of SVM as a stronger baseline model for Indonesian sentiment classification on social media data.
Co-Authors Andreas Leonardo Sumendap Celestina Florecita Mariwy Christian Dwi Suhendra Herlina Barrang Josua Josen A. Limbong Marlinda Sanglise Marsella Iriana Mamusung Micguel Arter Imbiri Niken Rahma Mahmud Nur Rahmi