Article Per Year (5 Year)
p-Index From 2020 - 2025
3.597
P-Index
This Author published in this journals
All Journal Ahkam: Jurnal Ilmu Syariah TEKNIK INFORMATIKA Jurnal Didaktik Matematika Jurnal Ilmiah FIFO Jurnal Pilar Nusa Mandiri Journal of Applied Data Sciences Indo-MathEdu Intellectuals Journal Mimbar Agama Budaya International Journal of Social Science, Education, Communication and Economics Cendikia Pendidikan EDUKASI
Khairani, Dewi
Universitas Islam Negeri Syarif Hidayatullah Jakarta
Religion Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

Published : 20 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Journal of Applied Data Sciences

 1 
CO2 Emission Forecasting in Indonesia Until 2030: Evaluation of ETS Smoothing Prediction Models and Their Implications for Global Climate Change Mitigation Aripiyanto, Saepul; Khairani, Dewi; Hartono, Ambran
Journal of Applied Data Sciences Vol 5, No 1: JANUARY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i1.154

Abstract

The objective of this study is to predict CO2 emissions in Indonesia until 2030 utilizing the ETS smoothing prediction model in line with the pressing demand for viable climate change mitigation approaches. Through an assessment of the model's efficacy, several fundamental evaluation metrics have been identified. The research findings reveal that the Mean Absolute Error (MAE) stands at 146,154.40, presenting an overview of the average absolute disparity between the projected and actual CO2 emission values. The Mean Squared Error (MSE) of 21,838,251,772.37 characterizes the mean of the squared variances between projections and actual values, gauging the variability of predictive errors. The Root Mean Squared Error (RMSE) at 147,777.71, derived from the square root of MSE, reflects the degree of uncertainty in CO2 emission predictions. Simultaneously, the Mean Absolute Percentage Error (MAPE) of 7.24% provides an overview of the average percentage of absolute discrepancies between projections and actual values. Projections suggest that CO2 emissions could potentially reach 1 million tons in 2030. This evaluation furnishes an in-depth comprehension of the precision of the ETS smoothing model in the context of substantial emission escalation. The implications on the challenges of climate change mitigation become increasingly crucial, underscoring the immediacy of preemptive measures and sustainable policies. While the model delineates emission trends, it is imperative to acknowledge that these forecasts are subject to various influences, such as policy and technological shifts. Consequently, this study underscores the necessity for heightened awareness and the formulation of more efficacious policies to address the potential surge in CO2 emissions in the forthcoming years.
Revolutionizing Digit Image Recognition: Pushing the Limits with Simple CNN and Challenging Image Augmentation Techniques on MNIST Hulliyah, Khodijah; Abu Bakar, Normi Sham Awang; Aripiyanto, Saepul; Khairani, Dewi
Journal of Applied Data Sciences Vol 4, No 3: SEPTEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i3.104

Abstract

This study aims to apply Convolutional Neural Networks (CNN) and image augmentation techniques in digit recognition using the MNIST dataset. We built a CNN model and experimented with various image augmentation techniques to improve digit recognition accuracy. The results showed that the use of CNN with image augmentation techniques was effective in improving digit recognition performance. In the data collection stage, we used the MNIST dataset consisting of images of handwritten digits as training and testing data. After building the CNN model, we apply image augmentation techniques such as rotation, shift, and flipping to the training data to enrich the data variety and prevent overfitting. The evaluation results show that the CNN model that has been trained with image augmentation techniques produces significant accuracy, with a maximum accuracy of 99.81%. We also performed an ensemble of several CNN models and found that this approach increased the digit recognition accuracy to 99.79%. This research has the potential for further development. Recommendations for further research include exploring more specific and complex image augmentation techniques, as well as using more challenging datasets. In addition, future research may consider improvements to the CNN architecture used or combining it with other methods such as recurrent neural networks (RNN).
Co-Authors Abu Bakar, Normi Sham Awang Ahmad Fauzan Haryono Ambran Hartono Amenobelia Sitepu Amrullah, Faiq Fawwaz Andi Faisal Bakti & Venny Eka Meidasari Andria Farhan Annisa Daffa Oktriyana Arini Arini Aripiyanto, Saepul Aulia, Atqia Bisri Nursa Fadillah Dede Rosyada Diah Setiawati Ellrica Dewi Herawati Jana Fadillah, Bisri Nursa Fauzan Farhan Antoro Fitri Mintarsih Hendra Bayu Susueno Imam Luthfi Rahmatullah Imam Tri Wibowo Imelda Sari Izwita Dewi I’im Umamil Khoiri Khodijah Hulliyah Kusmana Kusmana Lestania Simatupang Manullang, Thomas Ridoansih Melly Br Bangun Mian, Mian Miftakhul Nuuril Azizah Muhammad Ammaridho Romdhan Siregar Muhammad Azhari Muhammad Iqbal Naibaho, Yuni Naftalia Nashrul Hakiem Nenny Anggraini Nurhayati Nurlatifah Nurlatifah Nurul Faizah Rozi Pebrija, Putri Rena Latifah Rizal Broer Bahaweres Rizka Amalia Putri Rozi, Nurul Faizah Rozy, Nurul Faizah Sahat Saragih SANI SUSANTI Sembiring, Putri Pebrija Sianturi, Mian Sinaga, Jelita Tessalonika Siti Ummi Masruroh Sri Yunita Subaedah, Sitti Subchi, Imam Sulistiya, Noeni Indah Supardi Razak Tabah Rosyadi Tessalonika, Jelita Viva Arifin Winda Tarihoran Wisnu Ari Mukti Yusnadi, Yusnadi Zawawi, Khoirunnisya Zulhazmi, Abraham Zakky Zulkifli Zulkifli Zulkifli Zulkifli