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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Jurnal Informatika Jurnal Manajemen dan Keuangan Proceeding International Conference on Information Technology and Business International conference on Information Technology and Business (ICITB) jurnal Teknologi Informasi Magister Jurnal Manajemen Magister Darmajaya Jurnal SIMADA (Sistem Informasi dan Manajemen Basis Data) Sinkron : Jurnal dan Penelitian Teknik Informatika International Journal of Artificial Intelligence Research JIM UPB (Jurnal Ilmiah Manajemen Universitas Putera Batam) JMM (Jurnal Masyarakat Mandiri) Prosiding Seminar Nasional Darmajaya Jurnal Informasi dan Komputer Jurnal Teknologi Komputer dan Sistem Informasi Building of Informatics, Technology and Science JSR : Jaringan Sistem Informasi Robotik JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Inovasi Penelitian Journal of Applied Data Sciences Jurnal Publika Pengabdian Masyarakat Jurnal INFOTEL Neraca Manajemen, Akuntansi, dan Ekonomi JTKSI (Jurnal Teknologi Komputer dan Sistem Informasi)
Aziz, RZ. Abdul
IIB Darmajaya Bandar Lampung
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Journal : Building of Informatics, Technology and Science

 1 
Perbandingan Prediksi Penyakit Stunting Balita Menggunakan Algoritma Support Vektor Machine dan Random Forest Wiratama, Yunada; Aziz, RZ Abdul
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5543

Abstract

Stunting in toddlers is a serious health problem, especially in developing countries, where toddlers experience stunted growth due to chronic malnutrition. This condition not only affects the child's height but also their cognitive development and overall health. Identifying risk factors and classifying stunting can help in addressing and preventing this issue. In this study, we applied two machine learning methods to compare which one performs better in classification, namely Random Forest and Support Vector Machine (SVM), to classify stunting in toddlers. The data used is public data consisting of 97,873 entries. After undergoing preprocessing steps such as data cleaning, normalization, and splitting, the data was divided into training and testing sets. The Random Forest and SVM models were then trained using the training set and evaluated using metrics such as accuracy, precision, and recall. The analysis results showed that both methods perform well in classifying stunting in toddlers, with Random Forest achieving an accuracy of 0.9997 and SVM achieving an accuracy of 0.9951. These findings are expected to aid in the development of more effective intervention strategies to address stunting in toddlers. With this approach, it is hoped to make a significant contribution to reducing the prevalence of stunting in developing countries and improving the quality of life for children in the future. Additionally, this research opens opportunities for further exploration of other machine learning techniques for other health issues.
Implementasi Model LSTM, CNN+LSTM Hybrid, dan Transformer untuk Prediksi Cuaca Harian Berbasis Data Multivariat Wulandari, Heptyana Sri; Aziz, RZ Abdul
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.7655

Abstract

Global climate change and the increasing frequency of extreme weather events demand more accurate and adaptive weather prediction systems. This study aims to implement and compare three deep learning models, Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN)+LSTM Hybrid, and Transformer for predicting next-day weather events using daily multivariate meteorological data. The dataset was obtained from the Climatology Station Class IV Lampung and includes air temperature, rainfall, humidity, solar radiation, air pressure, wind direction, and wind speed, collected in CSV format from February 2000 to March 2025. The analysis results indicate that the CNN+LSTM Hybrid model achieved the best performance, with an RMSE of 1.158, MAE of 0.521, R² Score of 0.323, accuracy of 75%, and Macro F1 score of 0.75. The LSTM model demonstrated moderate performance, while the Transformer model yielded the lowest results among the three. These findings suggest that combining CNN's spatial feature extraction with LSTM's sequential processing enhances the prediction quality of short-term weather forecasts based on multivariate data. This study is expected to contribute to the development of AI-based weather forecasting systems in Indonesia, particularly for hydrometeorological disaster mitigation.
Co-Authors -, Sukatmi Achmad Nuzul Mariyus Agustian Prakarsyah anggalia wibasuri Anggawidia Wibaselppa Ari Kurniawan Saputra Asnusa, Soraya Delli Maria Delli Maria Delli Maria Dewi, Deshinta Arrova Eka Suci Puspita Wulandari Esti Dwi Putri Firmansyah Firmansyah Hardiansyah, Deni Hasibuan, M Said Herwanto, Riko Herwanto, Riko Ikhsanudin, Anas Jeprianto Jeprianto Joko Triloka Khodijah Khodijah Kurniawan, Tri Basuki Kurniawati . M Said Hasibuan Maizary, Ary Mawarni, Rima Muhamad Galih Ramaputra Muhammad Galih Ramaputra Mutiara, Suci Neni Purwati Nurkholish Setya, Yoga Nurpambudi, Ramadhan Onno W. Purbo Pakarti Riswanto R Rizal Isnanto Ramadhan Nurpambudi Retno Dwi Handayani Retno Dwi Handayani Rini Gustini Rini Nur Listiani Rionaldi Ali Rivardi, Ganda Syahertian Rustam Rustam Satria, Fiqih Selamat Soni Harsono Wijaya Sri Karnila Sri Karnila Sri Lestari SRI RAHAYU Sriyanto . Sriyanto Sriyanto Sriyanto Sriyanto Suci Mutiara Suhendro Suhendro Tri Nurul Fajarotun Triloka, Joko Wibaselppa, Anggawidia Wibaselppa, Anggawidia Wiratama, Yunada Wulandari, Heptyana Sri Y. Suhendro Yan Aditiya Pratama Yeremia Firhan Subagja Yoga Pratomo Yulmaini Yulmaini Yuni Puspita Sari