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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Teknologi Informasi dan Ilmu Komputer JUSIFO : Jurnal Sistem Informasi Sistemasi: Jurnal Sistem Informasi JURNAL MEDIA INFORMATIKA BUDIDARMA Indonesian Journal of Artificial Intelligence and Data Mining JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jiko (Jurnal Informatika dan komputer) JURTEKSI JOURNAL OF SCIENCE AND SOCIAL RESEARCH Building of Informatics, Technology and Science Jurnal Sains dan Teknologi Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Brilliance: Research of Artificial Intelligence COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat
Panjaitan, Erwin Setiawan
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Physics Social Sciences Other

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A Smart Architecture for Stunting Prediction: Implementing the SOM–Voting Classifier on Healthcare Big Data Kelvin, Kelvin; Winardi, Sunaryo; Sinaga, Frans Mikael; Hardy, Hardy; Panjaitan, Erwin Setiawan; Wong, Ng Poi; Ferawaty, Ferawaty; Lim, Justine; Wijaya, Grace Putri
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 3 (2025): November 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i3.38000

Abstract

Childhood stunting is a persistent public health challenge in Indonesia. This study developed a predictive classification model using healthcare data from hospitals in Medan to enable early identification of at-risk children. A novel framework was proposed that integrated an unsupervised Self-Organizing Map (SOM) for feature engineering with a supervised Voting Classifier ensemble, which combined a Support Vector Classifier (SVC), Random Forest (RF), and Gradient Boosting (GB). The proposed framework achieved an accuracy of 100% on the test set, a substantial improvement over the 91.67% accuracy of the baseline Voting Classifier without SOM. While this result highlighted the model's high predictive potential, it must be interpreted cautiously, acknowledging the need for validation on more diverse datasets to ensure generalizability. The findings demonstrated that this hybrid machine learning approach can serve as a powerful decision-support tool, enabling proactive clinical interventions and aiding public health officials in strategically allocating nutritional resources to support Indonesia's national stunting reduction goals.
PREDIKSI POLUSI UDARA BERDASARKAN TINGKAT CURAH HUJAN MENGGUNAKAN MODEL LSTM, BILSTM DAN PROPHET Nuraina, Nuraina; Panjaitan, Erwin Setiawan; Nurjanah, Sofiana
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 4 (2025): November 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i4.5764

Abstract

Abstract: The city of Jakarta, as the center of Indonesia's economic life and growth, continues to experience an astonishing population surge, reaching 11,248,839 people by 2024. However, this growth is inseparable from negative consequences, such as increased activity and modernization, which significantly affect air quality. Air pollution, as a direct impact of these changes, has exceeded national air quality standards, endangering human, animal, and plant health. Understanding the relationship between air pollution and weather conditions is crucial in determining future control measures. In this study, we used the LSTM, BiLSTM, and Prophet models on air pollution data. The results show that the single BiLSTM model and the BiLSTM-Prophet hybrid model provide the best performance, with accuracy levels reaching 99.32% and 99.31%, respectively. These findings provide a solid basis for forecasting and controlling potential future air pollution levels, as well as identifying key factors contributing to air quality in the capital city. . Keyword: Air Pollution, LSTM, BiLSTM, Prophet, Rainfall Abstrak: Kota Jakarta, sebagai pusat kehidupan dan pertumbuhan ekonomi Indonesia, terus mengalami lonjakan penduduk yang menakjubkan, mencapai 11.248.839 orang pada tahun 2024. Namun, pertumbuhan ini tidak terlepas dari konsekuensi negatif, seperti peningkatan aktivitas dan modernisasi, yang secara signifikan mempengaruhi kualitas udara. Polusi udara, sebagai dampak langsung dari perubahan ini, telah melampaui standar kualitas udara nasional, membahayakan kesehatan manusia, hewan, dan tumbuhan. Memahami hubungan antara polusi udara dan kondisi cuaca sangat penting dalam menentukan langkah-langkah pengendalian di masa depan. Dalam penelitian ini, kami menggunakan model LSTM, BiLSTM, dan Prophet pada data polusi udara. Hasil penelitian menunjukkan bahwa model tunggal BiLSTM dan model hybrid BiLSTM-Prophet memberikan kinerja terbaik, dengan tingkat akurasi masing-masing mencapai 99,32% dan 99,31%. Temuan ini memberikan dasar yang kuat untuk memperkirakan dan mengendalikan potensi tingkat polusi udara di masa depan, serta mengidentifikasi faktor-faktor kunci yang berkontribusi terhadap kualitas udara di ibu kota. Kata kunci: Polusi udara, LSTM, BiLSTM, Prophet, Curah hujan
Co-Authors Akbar, M. Dwi Aulia Andri Andri Angrayni, Cindy Anjar Pinem Chandra, Randy Brilliant Christanto, Gracello Febry Ferawaty, Ferawaty Hardy Hardy Ivan Dika Lesmana Joy Nasten Sinaga Kelvin Kelvin Lim, Justine Ng Poi Wong Nuraina, Nuraina Nurjanah , Sofiana Nurjanah, Sofiana Nurjanah, Sofinana Pasha, Muhammad Fermi Purba, Handryansyah Rivaldi Lubis Robin Robin, Robin Roni Yunis Saragih, Yuni Marlina Setia Budi Sinaga, Frans Mikael Sirait, Pahala Siregar, Kurrota Akyun Susanti, Zenny Suwito, Justin Hadinata Thamrin Thamrin Ulfadinata, Erdio Wijaya, Grace Putri Wijaya, Novresia Winardi, Sunaryo Winata, Kenny Calnelius