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All Journal ILKOM Jurnal Ilmiah JURNAL PENDIDIKAN TAMBUSAI JURTEKSI Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal JOURNAL OF SCIENCE AND SOCIAL RESEARCH Community Development Journal: Jurnal Pengabdian Masyarakat Journal of Applied Data Sciences International Journal of Community Service Journal of Artificial Intelligence and Digital Business Jurnal Penelitian Teknologi Informasi dan Sains The Indonesian Journal of Computer Science
Richi Andrianto
Institut Teknologi Dan Sains Padang Lawas Utara
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Public Health Social Sciences Other

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Journal : JOURNAL OF SCIENCE AND SOCIAL RESEARCH

 1 
EVALUASI MODEL HYBRID NAIVE BAYES-XGBOOST UNTUK KLASIFIKASI SENTIMEN NETIZEN TERHADAP ISU FREE PALESTINE PADA PLATFORM X DI INDONESIA TAHUN 2025 Andrianto, Richi; Lubis, Mustopa Husein; Utami, Urfi; Supriyanto, Asep
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 3 (2025): August 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i3.4103

Abstract

Abstract: The Palestinian conflict has become a global issue that has triggered significant public responses, especially through social media platforms. This study aims to evaluate the performance of a hybrid Naïve Bayes–XGBoost model in classifying netizen sentiment toward the Free Palestine issue on platform X (formerly Twitter) in the year 2025. Data were collected using the X (Twitter) API with keywords such as #freepalestine and #savegaza, then processed through a series of preprocessing stages and sentiment labeling using a lexicon-based approach. The dataset was then split into 80% training data and 20% testing data to compare the performance of the baseline Naïve Bayes model and the hybrid model. The evaluation results show that the baseline Naïve Bayes model achieved an accuracy of 75.7% and an F1-score of 76%, while the hybrid Naïve Bayes–XGBoost model achieved a significantly higher accuracy of 95.5% and an F1-score of 96%. These findings indicate that integrating the two algorithms improves both accuracy and balance in sentiment classification, especially for unstructured and imbalanced data. This study recommends the use of hybrid models for public opinion analysis on social media and suggests further development using deep learning approaches. Keywords: Sentiment Analysis, Free Palestine, Naïve Bayes, XGBoost, Hybrid Model Abstrak: Konflik Palestina menjadi isu global yang memicu respons besar dari masyarakat dunia, terutama melalui media sosial. Penelitian ini bertujuan untuk mengevaluasi performa model hybrid Naïve Bayes–XGBoost dalam mengklasifikasikan sentimen netizen terhadap isu Free Palestine di platform X (Twitter) tahun 2025. Data dikumpulkan menggunakan X (Twitter) API dengan kata kunci #freepalestine dan #savegaza, lalu diproses melalui tahapan preprocessing, dan pelabelan menggunakan pendekatan lexicon-based. Selanjutnya, data dibagi menjadi data latih (80%) dan data uji (20%) untuk membandingkan performa antara model Naïve Bayes dasar dan model hybrid. Hasil evaluasi menunjukkan bahwa model Naïve Bayes dasar menghasilkan akurasi 75,7% dan F1-score 76%, sedangkan model hybrid Naïve Bayes–XGBoost mencapai akurasi 95,5% dan F1-score 96%. Temuan ini menunjukkan bahwa integrasi kedua algoritma mampu meningkatkan akurasi dan keseimbangan klasifikasi sentimen, khususnya pada data yang tidak terstruktur dan imbalanced. Penelitian ini merekomendasikan penggunaan model hybrid untuk analisis opini publik dimedia sosial, serta pengembangan lebih lanjut menggunakan pendekatan deep learning. Kata kunci: Sentimen, Free Palestine, Naïve Bayes, XGBoost, Hybrid Model
INTEGRASI SENSOR IOT DAN OPTIMASI ALGORITMA MACHINE LEARNING UNTUK DETEKSI REAL-TIME TINGKAT STRES MAHASISWA Andrianto, Richi; Lubis, Mustopa Husein; Irawan, Rina; Irawan, Yuda; Utami, Urfi
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 9, No 1 (2026): February 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i1.5178

Abstract

Abstract: High levels of stress among university students are a critical issue that can affect mental health, well-being, and academic performance. This study aims to develop a real-time student stress detection system using physiological data integrated with IoT technology and machine learning algorithms. The data used includes body temperature, blood oxygen saturation (SpO₂), heart rate, and blood pressure, acquired via embedded sensors and automatically transmitted to the cloud. The classification model was built using a combination of Random Forest and XGBoost, with enhanced accuracy through SMOTE-based data balancing and hyperparameter optimization using Optuna. The system was tested on a dataset of 3,420 records, classified into four stress levels: anxious, calm, tense, and relaxed. Evaluation results showed that the Random Forest model achieved the highest accuracy of 91%, followed by RF + XGBoost and RF + XGBoost + Optuna with accuracies of 90% each. The final model was deployed in a user interface using Streamlit, allowing real-time stress classification from IoT sensor input and manual input testing. The system proved to be effective and responsive in detecting stress objectively and can support digital-based mental health monitoring and counseling services for students. Keywords: Stress detection, IoT, Machine Learning, Random Forest, XGBoost Abstrak: Tingkat stres yang tinggi di kalangan mahasiswa merupakan permasalahan serius yang dapat memengaruhi kesehatan mental, kesejahteraan, dan performa akademik. Penelitian ini bertujuan untuk mengembangkan sistem deteksi tingkat stres mahasiswa secara real-time menggunakan data fisiologis berbasis teknologi IoT dan algoritma machine learning. Data yang digunakan meliputi suhu tubuh, kadar oksigen dalam darah (SpO₂), detak jantung, dan tekanan darah yang diperoleh melalui sensor terintegrasi dan dikirim ke cloud secara otomatis. Model klasifikasi yang dikembangkan memanfaatkan kombinasi algoritma Random Forest dan XGBoost, dengan peningkatan akurasi melalui teknik balancing data menggunakan SMOTE dan optimasi hyperparameter otomatis menggunakan Optuna. Sistem diuji menggunakan dataset berjumlah 3.420 data dengan distribusi empat kelas stres: cemas, tenang, tegang, dan rileks. Hasil evaluasi menunjukkan bahwa model Random Forest menghasilkan akurasi tertinggi sebesar 91%, disusul oleh RF + XGBoost dan RF + XGBoost + Optuna dengan akurasi masing-masing sebesar 90%. Model akhir kemudian diintegrasikan ke dalam antarmuka pengguna berbasis Streamlit, yang memungkinkan klasifikasi stres secara real-time dari data sensor IoT dan juga melalui input manual. Sistem ini terbukti efektif dan responsif dalam mendeteksi stres secara objektif dan dapat digunakan untuk mendukung layanan konseling atau pemantauan kesehatan mental mahasiswa secara digital. Kata kunci: Deteksi stres, IoT, Machine Learning, Random Forest, XGBoost
Co-Authors Abdul Azis Akhyar Nasution Alfian Hasonangan Hasibuan Amanda, Ade Trya Amanda, Putri Ida Anita Febriani Anita Sri Rejeki Hutagaol, Anita Sri Rejeki Annisa Hannum Harahap Ari Rahmad Rahmad Siregar Babang Andika Citra Khoiriah Dandi Rusadi Dermilan Dermilan Dewi Elok Dewi Liana Dini Mawarni F. Irawan Fajri Rahmansyah Farhan Fikriyan Febri Listiana Harahap Feri Irawan Fransiska Harahap Hanif Ahmad Hasibuan Hannum, Sopia Hanum, Fauziah Harahap, Aminah Harahap, Arman Harahap, Aziddin Harahap, Yuskana Harianja, Noviyarnita Hartati, Juni Hasibuan, Hotmaini Hasibuan, Maya Gusrina Ibra Dianaran Siregar Intan Maimunah Irawan, Rina Khoirinnysa Harahap Lubis, Mustopa Husein Muhammad Gusti Fhaturrahman Daulay Muhammad Husein Muhammad Sampurna Mulia Garang Munawir Sajali Harahap Munthe, Ibnu Rasyid Nasution, Rina Irawan Nidar, Putri Novali, Mhd Noviyanti, Risma Nurhadi Panusunan Panusunan Patimah Harahap Pebriana Panggabean Pohan, Eviana Purnomo, Nopi Putri Hanafia Putri, Perra Budiarti Rahayu Putri, Perra Budiarti Rahyu Qodri Mahendar Siregar Rahayu Putri, Perra Budiarti Rahmat Maulana Rambe, Bhakti Helvi Ranizah Mungkur Refni Wahyuni Rina Irawan Sahroki Fikri Harahap Salimah, Sukrina Saputra, Haris Tri Sariona Sariona Siregar, Agus Trinanda Siregar, Novita Siregar, Putri Irawan Sundari, Ayu Supriyanto, Asep Syahputra Harahap, Hasmi Tanjung, Akhir Abadi Tiara Siregar Utami, Urfi Yuda Irawan