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All Journal Journal of Embedded Systems, Security and Intelligent Systems PENGAMATAN: Jurnal Pengabdian Masyarakat untuk Ilmu MIPA dan Terapannya
Radjabaycolle, Jefri E. T.
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Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Economics, Econometrics & Finance Mathematics

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Improving Air Quality Forecasts with LSTM and SHAP Explainability: A Case Study in Jakarta Radjabaycolle, Jefri E. T.; Wattimena, Emanuella M C; Pattiradjawane, Victor Eric
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 3 (2025): September 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i3.9512

Abstract

Accurate air-quality forecasting is essential for public-health advisories in large tropical megacities such as Jakarta. This study develops an explainable deep-learning pipeline to predict Indonesia’s Air Pollution Standard Index (ISPU) at the DKI-5 station using daily data from 2017–2021. After handling missing values and integrating meteorological variables, all features were min–max normalized and framed with a lag window of five days. A stacked LSTM (128 and 64 units, dropout 0.2, Adam optimizer, MSE loss) was trained with an 80/20 train–test split. Model performance was assessed using MAE, RMSE, and R2R^2R2. To open the “black box,” SHAP was applied to quantify each feature’s contribution to the predictions. Results show stable convergence of training and validation losses and good generalization. The best configuration achieved MAE ≈ 7.96, RMSE ≈ 10.26, and R2≈ 0.52 on the test set. SHAP analysis indicates that PM10_lag1 is the most influential predictor, followed by wind speed (ff_avg_lag1), relative humidity (RH_avg_lag1), and average temperature (Tavg_lag1), confirming the joint role of recent pollutant levels and meteorology in driving ISPU variability. Compared with a previous LSTM configuration on the same site, the proposed model lowers RMSE by ≈25%, evidencing a more accurate and reliable ISPU forecast while providing transparent feature attributions. The proposed LSTM–SHAP framework offers an interpretable decision-support tool for air-quality management in Jakarta.
PELATIHAN PEMANFAATAN APLIKASI ONLINE UNTUK BAHAN EVALUASI BELAJAR SISWA PADA MTS NEGERI BATU MERAH AMBON Palembang, C. F.; Radjabaycolle, Jefri E. T.
PENGAMATAN: Jurnal Pengabdian Masyarakat untuk Ilmu MIPA dan Terapannya Vol 1 No 1 (2023): PENGAMATAN: Jurnal Pengabdian Masyarakat untuk Ilmu MIPA dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/pengamatanv1i1p18-22

Abstract

In 2020 Indonesia was hit by the Covid-19 pandemic which also occurred in various countries so that it had an impact on the world of education. Teaching and learning activities that were previously accustomed to face-to-face learning have changed to online or online learning. Every level of education must be able to adapt to the online learning model, so good cooperation between teachers, parents and students is needed. For the education level of Junior High School or State Madrasah Tsanawiyah (MTsN) Ambon, parents must spend more time than usual accompanying student learning so that they can assist teachers in controlling children. Conversely, teachers are required to be able to provide material as interesting as possible so that students are not bored in the teaching and learning process and the learning evaluation process can run well. The UNPATTI Computer Science Study Program took the initiative to conduct training on the use of online applications for student learning evaluation materials in the city of Ambon addressing the dynamics of online learning for MTsN level students. This activity uses a website-based application that can be used to create learning media such as quizzes, tests, and evaluation of student scores
Co-Authors Palembang, C. F. Pattiradjawane, Victor Eric Wattimena, Emanuella M C