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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Indonesian Journal of Artificial Intelligence and Data Mining JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Indonesian Journal of Data and Science Journal of Applied Computer Science and Technology (JACOST) 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 Education 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.
Evaluasi Pengaruh Kualitas Website Kampung Kopo terhadap Kepuasan Pengguna Menggunakan Model WebQual 4.0 Anan Ansi Ahoren; Christian Dwi Suhendra; Lorna Yertas Baisa
Journal of Applied Computer Science and Technology Vol. 6 No. 2 (2025): Desember 2025
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/tftzct83

Abstract

This study evaluates the quality of the Kampung Kopo website using the WebQual 4.0 model, which comprises three dimensions: usability, information quality, and interaction quality. A quantitative survey was conducted with 109 purposively selected respondents, and data were analyzed using multiple linear regression. Descriptive results showed that usability (4.03) and information quality (4.03) received high scores, while user satisfaction was moderate (3.41). Regression analysis revealed that all three dimensions significantly influenced satisfaction: interaction quality (β = 0.401; p < 0.001), information quality (β = 0.345; p < 0.001), and usability (β = 0.306; p < 0.001). The model was significant (F = 308.281; p < 0.001) and explained 89.8% of satisfaction variance (R² = 0.898). These findings confirm that while interaction quality has the strongest effect, all three dimensions play essential roles in determining user satisfaction. Village websites should adopt a comprehensive approach to improve usability, information quality, and interaction quality simultaneously to strengthen digital transformation at the village level.
Comparative Sentiment Analysis of GrabFood Reviews Using BiLSTM and BiGRU Jakasurya Siswoyo; Andreas Leonardo Sumendap; Lorna Yertas Baisa
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.16165

Abstract

The exponential growth of user-generated reviews on digital platforms has made manual sentiment interpretation of Online Food Delivery (OFD) services increasingly impractical. GrabFood, operating within the Grab ecosystem, has accumulated over 16.1 million reviews on the Google Play Store, necessitating an automated and scalable approach to sentiment monitoring. Conventional labeling approaches, including star-rating proxies and lexicon-based annotation, are inadequate for capturing contextual nuance, negation, and informal linguistic patterns prevalent in Indonesian-language OFD reviews. Furthermore, limited research has systematically compared BiLSTM and BiGRU architectures within a transformer-assisted labeling framework for Indonesian OFD sentiment analysis. This study aims to implement RoBERTa-based automatic sentiment labeling and to comparatively evaluate BiLSTM and BiGRU models for three-class sentiment classification of GrabFood reviews. A corpus of 265,500 raw reviews was collected via web scraping, filtered to 17,709 reviews through rigorous preprocessing, and annotated using the w11wo/indonesian-roberta-base-sentiment-classifier. Random Oversampling was applied to address class imbalance. BiLSTM and BiGRU models were trained and benchmarked against Support Vector Machine (SVM) and Naïve Bayes baselines. BiLSTM achieved 86% accuracy while BiGRU attained 85%, both substantially outperforming SVM (82%) and Naïve Bayes (77%). However, BiGRU demonstrated superior convergence speed and more stable per-class performance, particularly on the neutral category (F1: 51% vs. 50%). Transformer-assisted automatic labeling combined with bidirectional recurrent architectures constitutes an effective and scalable pipeline for Indonesian OFD sentiment classification, with neutral sentiment remaining the primary classification challenge.
FAKTOR-FAKTOR YANG MEMPENGARUHI ADOPSI SISTEM INFORMASI MANAJEMEN KEPEGAWAIAN (SIMPEG) PADA KEMENTERIAN AGAMA PROVINSI PAPUA BARAT Foebryani Elsa Sarungallo; Lion Ferdinand Marini; Lorna Yertas Baisa
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 4 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i4.6767

Abstract

Saat ini, penggunaan teknologi informasi di era globalisasi semakin disadari oleh berbagai organisasi modern dan instansi pemerintah. Oleh karena itu, sangat penting bagi instansi pemerintah untuk memanfaatkan teknologi sistem informasi yang ada. Penggunaan teknologi sistem informasi di instansi pemerintah telah meningkat. Penelitian ini bertujuan untuk mengidentifikasi faktor-faktor yang mempengaruhi adopsi sistem informasi di Kementerian Agama Provinsi Papua Barat, dengan menerapkan model UTAUT 2. Model ini terdiri dari sembilan variabel, yaitu: performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivations, price value, habit, behavioral intention, dan use behavior. Penelitian ini tergolong eksplanatori, yang bertujuan menjelaskan hubungan kausal antara variabel-variabel melalui pengujian hipotesis. Data dikumpulkan dengan menyebarkan kuesioner melalui Google Form. Metode analisis data yang digunakan adalah Structural Equation Modelling (SEM) PLS. Hasil penelitian menunjukkan bahwa performance expectancy, facilitating conditions, price value, dan habit tidak berpengaruh signifikan terhadap behavioral intention, sedangkan effort expectancy, social influence, dan hedonic motivation memiliki pengaruh positif dan signifikan terhadap behavioral intention. Behavioral intention, pada gilirannya, memberikan pengaruh positif dan signifikan terhadap use behavior. Penelitian ini menekankan faktor-faktor yang berkontribusi pada adopsi sistem informasi untuk pengelolaan kepegawaian yang lebih efisien dan mudah digunakan.
Co-Authors Anan Ansi Ahoren Andreas Leonardo Sumendap Celestina Florecita Mariwy Christian Dwi Suhendra Foebryani Elsa Sarungallo Herlina Barrang Jakasurya Siswoyo Josua Josen A. Limbong Lion Ferdinand Marini Marlinda Sanglise Marsella Iriana Mamusung Micguel Arter Imbiri Niken Rahma Mahmud Nur Rahmi