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All Journal Jurnal Teknovasi : Jurnal Teknik dan Inovasi Mesin Otomotif, Komputer, Industri dan Elektronika METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Jurnal Sistem Informasi Triguna Dharma (JURSI TGD)
M. Safii
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Computer Science & IT Economics, Econometrics & Finance Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology

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MODEL JARINGAN SYARAF TIRUAN MEMPREDIKSI PRODUKSI PADI INDONESIA BERDASARKAN PROVINSI Ahmad Revi; Iin Parlina; M. Safii
Jurnal Teknovasi : Jurnal Teknik dan Inovasi Vol 5, No 2 (2018): Teknovasi Oktober 2018
Publisher : LPPM Politeknik LP3I Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55445/teknovasi.v5i2.236

Abstract

Prediction is a process for estimating how many needs will be in the future. This study aims to predict the amount of rice production by province. The role of the agricultural sector in the national economy is very important and strategic. The rice plant (Oryza sativa L.) is an important food crop which is a staple food for more than half of the world's population because it contains nutrients that the body needs. Domestic production made the government still carry out the food import policy even though a number of regions claimed to have surplus rice production. This causes a lot of the country's foreign exchange to be used because of the operational costs of rice imports. By using Artificial Neural Networks and backpropagation algorithms, an architectural model will be sought to predict the amount of rice production by province in order to determine the steps to meet domestic rice demand based on the amount of rice consumption of the community. This study uses 6 input variables, namely data from 2010 to 2016 with 1 target, the data of 2017. Using 5 architectural models to test the data to be used for prediction, namely the 6-4-1 model, 6-8-1 , 6-16-1, 6-2-3-1 and 6-3-2-1. Obtained the results of the best architectural model is 6-8-1 architectural model with truth accuracy of 100%, the number of epochs 145 and MSE is 0.010250963.
Model Prediksi Penjadwalan Produksi Energi Terbarukan dengan Algoritma XGBoost dan Analisis Interpretatif Menggunakan SHAP M. Safii; Husain; Ika Okta Kirana; Sasha Aiko Leana; Yuli Indahwati Gultom
Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Vol. 4 No. 4 (2025): EDISI JULI 2025
Publisher : STMIK Triguna Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53513/jursi.v4i4.11443

Abstract

Penjadwalan produksi energi terbarukan adalah kegiatan untuk menyeimbangkan antara pasokan dan permintaan energi dalam siklus sistem energi berkelanjutan. Berbagai jenis energi terbarukan seperti hidro, angin, matahari, dan lainnya akan melalui pemodelan prediktif dari jadwal produksi menggunakan algoritma Extreme Gradient Boosting (XGBoost) yang dikombinasikan dengan pendekatan interpretabilitas model menggunakan SHapley Additive exPlanations (SHAP). Penelitian ini menggunakan data sekunder dengan parameter Tahun, Negara, Energi Surya, Energi Angin, Energi Hidro, Energi Terbarukan Lainnya, dan Total Energi Terbarukan. Pemodelan menunjukkan bahwa energi angin dan energi matahari memiliki prediksi produksi yang meningkat ketika nilai fitur tinggi dan energi angin memiliki efek negatif ketika nilai fitur rendah. Penelitian ini memiliki kontribusi yang signifikan terhadap faktor yang mempengaruhi penjadwalan dan juga berpeluang untuk penerapan sistem cerdas dalam pengambilan keputusan sektor energi. Hasil penelitian ini dapat menjadi dasar untuk merumuskan strategi manajemen energi berkelanjutan yang memiliki potensi untuk mengintegrasikan kecerdasan buatan dan transparansi model dalam kebijakan energi terbarukan.
Optimasi Random Forest Klasifikasi Pola Konsumsi Energi Rumah Tangga pada Smart City Alfina, Ommi; M. Safii
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 9 No. 2 (2025): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol9No2.pp300-306

Abstract

Population growth in urban areas drives a significant increase in household energy consumption. This condition poses a major challenge for the implementation of the smart city concept, particularly in achieving energy efficiency and sustainability. This study aims to classify household energy consumption patterns based on household power consumption data to support intelligent decision-making in urban energy management. The research method includes data preprocessing, data cleaning, and aggregation of daily energy consumption by utilizing key attributes such as Global Active Power, Voltage, Global Intensity, and three sub-metering variables. Consumption pattern categories are formed using the tertile method into three classes: Low, Medium, and High. Several machine learning algorithms are applied to build the classification model, including Logistic Regression, K-Nearest Neighbors (KNN), Random Forest, and Gradient Boosting. The test results show that the Random Forest model with hyperparameter adjustments produces the best performance with an accuracy value of 0.98 and an F1-macro value of 0.98, surpassing other models. These findings indicate that the ensemble learning approach is able to capture the complexity of household energy consumption patterns more effectively than conventional linear models. The contribution of this research lies in the development of a machine learning-based predictive model to support adaptive energy consumption monitoring and control systems in smart city implementations.
Klasifikasi Pola Konsumsi Energi Rumah Tangga Menggunakan Algoritma Machine Learning untuk Mendukung Implementasi Smart City Alfina, Ommi; M. Safii
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 9 No. 2 (2025): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Population growth in urban areas drives a significant increase in household energy consumption. This condition poses a major challenge for the implementation of the smart city concept, particularly in achieving energy efficiency and sustainability. This study aims to classify household energy consumption patterns based on household power consumption data to support intelligent decision-making in urban energy management. The research method includes data preprocessing, data cleaning, and aggregation of daily energy consumption by utilizing key attributes such as Global Active Power, Voltage, Global Intensity, and three sub-metering variables. Consumption pattern categories are formed using the tertile method into three classes: Low, Medium, and High. Several machine learning algorithms are applied to build the classification model, including Logistic Regression, K-Nearest Neighbors (KNN), Random Forest, and Gradient Boosting. The test results show that the Random Forest model with hyperparameter adjustments produces the best performance with an accuracy value of 0.98 and an F1-macro value of 0.98, surpassing other models. These findings indicate that the ensemble learning approach is able to capture the complexity of household energy consumption patterns more effectively than conventional linear models. The contribution of this research lies in the development of a machine learning-based predictive model to support adaptive energy consumption monitoring and control systems in smart city implementations.
Co-Authors Ahmad Revi Alfina, Ommi Husain Iin Parlina Ika Okta Kirana Sasha Aiko Leana Yuli Indahwati Gultom