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Sentence embedding to improve rumour detection performance model Anggrainingsih, Rini; Wihidayat, Endar Suprih; Widoyono, Bambang
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp115-121

Recently, most individuals have preferred accessing the most recent news via social media platforms like Twitter as their primary source of information. Moreover, Twitter enables users to post and distribute tweets quickly and unsupervised. As a result, Twitter has become a popular platform for disseminating false information, such as rumours. These rumours were then propagated as accurate and influenced public opinion and decision-making. The issue will arise when a decision or policy with substantial consequences is made based on rumours. To avoid the negative impacts of rumours, several researchers have attempted to detect them automatically as early as feasible. Previous studies employed supervised learning methods to identify Twitter rumours and relied on feature extraction algorithms to extract tweet content and context elements. However, manually extracting features is time-consuming and labour-intensive. To encode each tweet's sentence as a vector based on its contextual meaning, we proposed utilising Bidirectional Encoder Representation of Transformer (BERT) as a sentence embedding. We then used these vectors to train some classifier models to detect rumours. Finally, we compared the performance of BERT-based models to feature engineering-based models. We discovered that the suggested BERT-based model improved all parameters by around 10% compared to the feature engineering-based classification model.

Analisis Tingkat Kematangan Open Government Data Menggunakan OD-MM di Pemerintah Provinsi Aceh Sudarwono, Dianto Adwoko; Prastowo, Rahardito Dio; Ruldeviyani, Yova; Widoyono, Bambang
Jurnal Informatika Ekonomi Bisnis Vol. 6, No. 3 (September 2024)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v6i3.988

Pemerintah Indonesia telah memulai inisiatif open data sejak tahun 2008 dengan menerbitkan Undang-undang tentang Keterbukaan Informasi Publik. Gerakan Open Government Indonesia (OGI) yang meluncurkan Rencana Aksi Nasional (RAN) Open Government yang pertama pada tahun 2012. Implementasi Portal Open Data di Pemerintah Aceh dimulai tahun 2018 dengan tujuan optimalisasi penggunaan data dan informasi publik dalam pembangunan Aceh yang lebih baik. Namun berdasarkan data yang dianalisis bahwa terdapat beberapa kendala dalam pelaksanaan Portal Open Data seperti kekurangan SDM yang terampil, ketidakmampuan untuk mengumpulkan dan mengintegrasikan data yang relevan, kelemahan dalam keamanan data, sehingga belum dapat dipastikan apakah proses OGD telah berjalan dengan optimal atau belum. Oleh sebab itu penting dilakukan pengukuran tingkat kematangan Open Government Data (OGD) pada Pemerintah Aceh. Pengukuran tingkat kematangan menggunakan Open Data Maturity Model (OD-MM), dengan memberikan kuesioner kepada 12 pengelola Portal Open Data Aceh. Dari hasil pengukuran diperoleh hasil bahwa tingkat kematangan OGD Aceh berada pada level 3 dari skor maksimal 4. Sebanyak 22 rekomendasi perbaikan disampaikan untuk mengembangkan tingkat kematangan OGD Aceh ke level yang lebih tinggi. Selain itu juga dilakukan simulasi fitur roadmap generator pada OD-MM yang dapat digunakan sebagai alat self-assessment kedepannya.

Analisis Tingkat Kematangan Open Government Data Menggunakan OD-MM di Pemerintah Provinsi Aceh Sudarwono, Dianto Adwoko; Prastowo, Rahardito Dio; Ruldeviyani, Yova; Widoyono, Bambang
Jurnal Informatika Ekonomi Bisnis Vol. 6, No. 3 (September 2024)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v6i3.988

Pemerintah Indonesia telah memulai inisiatif open data sejak tahun 2008 dengan menerbitkan Undang-undang tentang Keterbukaan Informasi Publik. Gerakan Open Government Indonesia (OGI) yang meluncurkan Rencana Aksi Nasional (RAN) Open Government yang pertama pada tahun 2012. Implementasi Portal Open Data di Pemerintah Aceh dimulai tahun 2018 dengan tujuan optimalisasi penggunaan data dan informasi publik dalam pembangunan Aceh yang lebih baik. Namun berdasarkan data yang dianalisis bahwa terdapat beberapa kendala dalam pelaksanaan Portal Open Data seperti kekurangan SDM yang terampil, ketidakmampuan untuk mengumpulkan dan mengintegrasikan data yang relevan, kelemahan dalam keamanan data, sehingga belum dapat dipastikan apakah proses OGD telah berjalan dengan optimal atau belum. Oleh sebab itu penting dilakukan pengukuran tingkat kematangan Open Government Data (OGD) pada Pemerintah Aceh. Pengukuran tingkat kematangan menggunakan Open Data Maturity Model (OD-MM), dengan memberikan kuesioner kepada 12 pengelola Portal Open Data Aceh. Dari hasil pengukuran diperoleh hasil bahwa tingkat kematangan OGD Aceh berada pada level 3 dari skor maksimal 4. Sebanyak 22 rekomendasi perbaikan disampaikan untuk mengembangkan tingkat kematangan OGD Aceh ke level yang lebih tinggi. Selain itu juga dilakukan simulasi fitur roadmap generator pada OD-MM yang dapat digunakan sebagai alat self-assessment kedepannya.

Peningkatan Kualitas Administrasi Pendidikan melalui Implementasi Sistem Edu Berbasis ERP di SMP IT Insan Mulia Surakarta, Jawa Tengah Widoyono, Bambang; Saptono, Ristu; Rohmadi, Arif; Syaifuddin, Akhmad; Hendra, Brilyan; Anggoro, Rizal Dwi; Ibrahim, Muhammad Syafiq
Jurnal Abdi Masyarakat Indonesia Vol 5 No 6 (2025): JAMSI - November 2025
Publisher : CV Firmos

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54082/jamsi.2130

SMP Islam Insan Mulia Surakarta-Jawa Tengah, mengalami kendala administrasi akibat sistem manualnya, terutama dalam penerimaan siswa baru (PPDB), pencatatan pembayaran, dan pengelolaan bank soal. Kendala-kendala ini menyebabkan keterlambatan, kesalahan, dan inefisiensi, sehingga membatasi kualitas layanan. Untuk mengatasi hal ini, dalam program pengabdian masyarakat kami mengimplementasikan sistem EDU berbasis ERP sebagai solusi terintegrasi. Sistem ini menggunakan model waterfall untuk analisis, perancangan, implementasi, pelatihan, dan pengujian yang diterapkan selama tiga bulan. Tiga modul diimplementasikan: PPDB, pembayaran, dan bank soal, yang diuji coba kepada 23 peserta. Evaluasi menunjukkan hasil positif dengan efisiensi (4,08), efektivitas (4,08), dampak (4,38), kepuasan (4,28), dan kemudahan penggunaan (4,17) pada rentang skala 1-5. Program pengabdian ini tidak hanya menyelesaikan kendala administratif di SMP Islam Insan Mulia Surakarta, tetapi juga menghadirkan model implementasi sistem informasi berbasis ERP yang dapat direplikasi di sekolah lain. Digitalisasi administrasi melalui modul PPDB, pembayaran, dan bank soal terbukti meningkatkan efisiensi, transparansi, dan profesionalisme tata kelola pendidikan secara umum.

Aspect-Based Sentiment Analysis of Access by KAI Application Reviews Using IndoBERT for Multi-Label Classification Tasks Nur Alfiana, Hilda; Doewes, Afrizal; Widoyono, Bambang
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.5402

Ratings and reviews on mobile applications provide valuable insights into user experience and satisfaction with app features and services. However, ratings are subjective and often inconsistent with the content of the reviews. Therefore, a more in-depth analysis of the review content is necessary to identify evaluation points accurately. This study aims to evaluate the performance of IndoBERT in Aspect-Based Sentiment Analysis (ABSA) on Access by KAI application reviews. Data were collected by scraping user reviews from the Google Play Store, then annotated using a hybrid labeling approach. The resulting dataset was used to fine-tune the IndoBERT model across three ABSA tasks: aspect classification, sentiment classification for each aspect, and joint aspect-sentiment classification. We also benchmarked the model against baseline models to demonstrate its effectiveness. The results show that IndoBERT achieved the best performance across all tasks, specifically aspect classification (accuracy 0.928, F1-score 0.785), sentiment classification (accuracy 0.928, F1-score 0.752), and joint aspect-sentiment classification (accuracy 0.962, F1-score 0.549). Overall, IndoBERT successfully outperformed SVM and XGBoost with TF-IDF, BiLSTM with pre-trained IndoBERT embeddings, mBERT, and XLM-R. This study contributes a new dataset that provides resources for further research and development in Indonesian Natural Language Processing (NLP). These findings also highlight the advantages of a monolingual model trained specifically on Indonesian-language data.

Evaluating Single and Hybrid Feature Selection for Rainfall Prediction Using XGBoost Widoyono, Bambang; Nadhif, Muhammad Fahmy; Eryadi, Ridha Adjie
Indonesian Journal of Artificial Intelligence and Data Mining Vol 9, No 1 (2026): March 2026
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v9i1.39110

Rainfall prediction is challenging due to the complex and nonlinear nature of meteorological data. Previous studies using XGBoost with feature selection have demonstrated superior performance compared to other models, but evaluations have focused solely on error metrics (RSME, SME, MAE). Recent research suggests that predictive models should be evaluated for generalization, stability, interpretability, and computational efficiency to ensure their reliability. To close this gap, this study uses 8,750 hourly records obtained from Open-Meteo with 81 engineered features to evaluate XGBoost under three scenarios: without feature selection, single feature selection (MI, Boruta, SHAP, mRMR, ReliefF), and hybrid feature selection. Our findings demonstrate that accuracy is not always increased by feature selection. It does, however, increase interpretability, decrease overfitting, and improve computational efficiency. SHAP provides the most reliable performance among single methods, achieving lower RMSE (0.72632) and improved stability. Hybrid feature selection produces the most balanced performance gap = 0.01325, and stable variance = 0.03315 while reducing feature complexity to 35 variables. This study theoretically shows the value of multidimensional evaluation that goes beyond error metrics. In practical terms, this study suggests a feature selection method for rainfall prediction systems that are effective, reliable, and simple to understand.

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