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All Journal Scientific Journal of Informatics JURNAL REKAYASA TEKNOLOGI INFORMASI Tensor: Pure and Applied Mathematics Journal Journal of Embedded Systems, Security and Intelligent Systems Parameter: Jurnal Matematika, Statistika dan Terapannya
Wattimena, Emanuella M C
Universitas Pattimura
Computer Science & IT Mathematics

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CO and PM10 Prediction Model based on Air Quality Index Considering Meteorological Factors in DKI Jakarta using LSTM Wattimena, Emanuella M C; Annisa, Annisa; Sitanggang, Imas Sukaesih
Scientific Journal of Informatics Vol 9, No 2 (2022): November 2022
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v9i2.33791

Abstract

Purpose: This study aimed to make CO and PM10 prediction models in DKI Jakarta using Long Short-Term Memory (LSTM) with and without meteorological variables, consisting of wind speed, solar radiation, air humidity, and air temperature to see how far these variables affect the model.Methods: The method chosen in this study is LSTM recurrent neural network as one of the best algorithms that perform better in predicting time series. The LSTM models in this study were used to compare the performance between modeling using meteorological factors and without meteorological factors.Result: The results show that the use of meteorological predictors in the CO prediction model has no effect on the model used, but the use of meteorological predictors influences the PM10 prediction model. The prediction model with meteorological predictors produces a smaller RMSE and stronger correlation coefficient than modeling without using meteorological predictors.Novelty: In this paper, a comparison between the prediction model of CO and PM10 has been conducted with two scenarios, modeling with meteorological factors and modeling without meteorological factors. After the comparative analysis was done, it was found that the meteorological variables do not affect the CO index in 5 air quality monitoring stations in DKI Jakarta. It can be said that the level of CO pollutants tends to be influenced by factors other than meteorological factors.  
Perbandingan Model Prediksi Frekuensi Titik Panas di Provinsi Riau dengan menggunakan LSTM Wattimena, Emanuella M C; Tilukay, Meilin Imelda
Tensor: Pure and Applied Mathematics Journal Vol 4 No 2 (2023): Tensor: Pure and Applied Mathematics Journal
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Pattimura University, Ambon, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/tensorvol4iss2pp53-62

Abstract

The high rate of deforestation in Indonesia due to forest and land fires (karhutla) is still a problem that requires the government's attention because it has become a regional and global disaster. The worst forest fire incident in Indonesia occurred in 2019, where the area of ​​the fire was 1,649,258 ha. Riau Province is one of the provinces in Indonesia that often experiences forest fires. Sipongi noted that an average of 52,986 ha of forest and land burned in Riau Province every year from 2016-2020. Thus, this study builds a predictive model for the emergence of hotspots as one of the forest fires that aims to reduce the rate of forest fires. Prediction model built using Long Short-Term Memory Recurrent Neural Network (LSTM-RNN). The modeling is carried out using 2 data scenarios, namely multivariate data and univariate data, where multivariate data uses weather variables as predictors of hotspot frequency, and univariate data is hotspot frequency data. The data used is daily data from 2013-2020. Multivariate scenario dataset that produces RMSE of 23,323 and the correlation between actual and predicted data is 0,675554. The RMSE generated by the multivariate dataset is smaller than the RMSE generated by the model with the univariate dataset scenario, which is 25,750. However, datasets with univariate scenarios produce a larger correlation between actual and predicted values ​​when compared to multivariate dataset scenarios. The addition of weather factors as a predictor of hotspot occurrence can improve model performance, where this model is better at predicting values ​​when compared to univariate dataset scenarios even though the running time is longer. Keywords: forest and land fire, hotspots, Long Short-Term Memory, Recurrent Neural Network, prediction, time series
Penerapan konsep data mining dengan Metode Seasonal ARIMA dalam Peramalan Produksi Padi Radjabaycolle, Jefri Esna Thomas; Waas, Devi Valentino; Pattiradjawane, Victor Eric; Wattimena, Emanuella M. C.; Upuy, Doms; Palembang, Citra Fathia
Jurnal Rekayasa Teknologi Informasi (JURTI) Vol 8, No 1 (2024): Jurnal Rekayasa Teknologi Informasi (JURTI)
Publisher : Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jurti.v8i1.17485

Abstract

Pendekatan data mining menjadi elemen kunci dalam analisis data skala besar untuk mendapatkan informasi yang berguna dalam berbagai bidang, termasuk sektor pertanian. Melalui penerapan teknik data mining, penelitian ini bertujuan untuk mengeksplorasi, mengolah, dan menganalisis data produksi padi di kecamatan Denpasar Selatan dan kecamatan Denpasar Timur, Provinsi Bali, guna mendukung pengambilan keputusan yang lebih akurat. Metode yang digunakan dalam penelitian ini adalah Seasonal ARIMA (SARIMA), yang secara khusus dirancang untuk menangani pola musiman pada data time series. Penelitian ini menitikberatkan pada proses pengolahan data, pemilihan model prediktif yang tepat, dan evaluasi kinerja model. Model SARIMA yang dipilih untuk kedua kecamatan yaitu SARIMA (0,0,1)(1,1,1)6 untuk kecamatan Denpasar Selatan dan SARIMA (0,0,0)(1,1,0)6 untuk kecamatan Denpasar Timur. Hasil penelitian menunjukkan bahwa model SARIMA mampu memberikan prediksi produksi padi yang baik, sehingga dapat menggambarkan tren kenaikan atau penurunan produksi pada periode berikutnya. Temuan ini menunjukkan bahwa penerapan metode SARIMA yang didukung oleh teknik data mining dapat menjadi alat bantu yang efektif untuk analisis data produksi padi.
Improving Accuracy of Software Development Effort Estimation Using Use Case Points and Fuzzy Logic Victor Eric Pattiradjawane; Emanuella M. C. Wattimena; Devi Valentino Waas
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.v6i3.9488

Abstract

Effort estimation in software development is essential for effective project planning and resource management. The Use Case Points (UCP) method is one of the most recognized estimation techniques; however, its accuracy is often constrained by the subjectivity involved in determining the Environmental Complexity Factor (ECF). This study introduces an enhanced estimation model that integrates Fuzzy Logic into the UCP framework to reduce subjectivity and improve precision. Six software project datasets were analyzed—one institutional project and five publicly available datasets—using Python-based simulations. The proposed Fuzzy-UCP model redefines ECF through fuzzy membership functions and rule-based inference, transforming linguistic assessments into quantitative outputs. Evaluation metrics, including Mean Magnitude of Relative Error (MMRE) and Estimation of Mean Magnitude of Error (EMMER), were employed to assess prediction accuracy. The results demonstrate that the Fuzzy-UCP model improves estimation accuracy by 4% to 12% compared to the standard UCP method, with lower standard deviation values. These findings confirm that incorporating fuzzy reasoning enhances reliability in handling uncertainty during effort estimation. Consequently, the Fuzzy-UCP approach provides a practical, adaptive, and computationally efficient alternative for software engineering practitioners seeking consistent and data-driven estimation results.
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.
Optimization of LSTM Model for Rainfall Prediction in Ambon City: Comparison of Mean Imputation and Interpolation in Time Series Data Prediction Wattimena, Emanuella M. C.; Taihuttu, Pranaya D. M.; Waas, Devi V.; Palembang, Citra F; Pattiradjawane, Victor E.
Tensor: Pure and Applied Mathematics Journal Vol 6 No 1 (2025): Tensor: Pure and Applied Mathematics Journal
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Pattimura University, Ambon, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/tensorvol6iss1pp49-56

Abstract

Rainfall prediction is an essential aspect of meteorology, agriculture, and disaster management, particularly in regions like Ambon, where rainfall patterns significantly impact daily life. However, one of the major challenges in developing an accurate predictive model is handling missing values in the dataset. This study aims to optimize the Long Short-Term Memory (LSTM) model for rainfall prediction in Ambon by comparing two missing value handling techniques: mean imputation and interpolation. The dataset used in this study consists of daily rainfall data from 2021 to 2024, with approximately 26.89% missing values. Two experimental scenarios were conducted: the first using mean imputation to fill in missing values with the average rainfall, and the second using linear interpolation. Both scenarios utilized the same LSTM architecture to evaluate their impact on model performance. The evaluation metrics used in this study include Root Mean Square Error (RMSE) and R-squared (R²). The results show that the interpolation-based model achieved a lower RMSE and a slightly higher R² value than the mean imputation-based model, indicating better predictive performance. However, both models struggled to capture extreme values, necessitating further improvements. To address this limitation, a more complex LSTM architecture was implemented in the subsequent experiments, incorporating additional layers and optimized hyperparameters. The findings suggest that choosing an appropriate missing value handling method significantly influences the predictive accuracy of LSTM models for rainfall forecasting. This research contributes to the development of more reliable weather prediction models, which can aid in agricultural planning, flood risk assessment, and climate change adaptation in Ambon.
Some NECESSARY AND SUFFICIENT CONDITIONS OF COMULTIPLICATION MODULE Wattimena, Emanuella M C; W.M. Patty, Henry; Patty, Dyana; L. Rahakbauw, Dorteus
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 1 No 2 (2022): Parameter: Jurnal Matematika, Statistika dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/parameterv1i2pp97-102

Abstract

In ring theory, if and be ideals of , then the multiplication of and , which is defined by is also ideal of . Motivated by the multiplication of two ideals, then can be defined a multiplication module, a special module which every submodule of can be expressed as the multiplication of an ideal of ring and the module itself, and can simply be written as . Furthermore, if the module become a comultiplication module. By the definition, it concludes that every comultiplication module is a multiplication module but the converse is not necessarily applicable. Keywords: annihilator, ideal, module, comultiplication module, multiplication module, ring, submodule.
Co-Authors Annisa Annisa Devi Valentino Waas Imas Sukaesih Sitanggang L. Rahakbauw, Dorteus Meilin Imelda Tilukay Palembang, Citra F Palembang, Citra Fathia Pattiradjawane, Victor E. Pattiradjawane, Victor Eric Patty, Dyana Radjabaycolle, Jefri E. T. Radjabaycolle, Jefri Esna Thomas Taihuttu, Pranaya D. M. UPUY, DOMS Victor Eric Pattiradjawane W.M. Patty, Henry Waas, Devi V. Waas, Devi Valentino