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All Journal Jurnal Simetris Journal of Telematics and Informatics Proceeding of the Electrical Engineering Computer Science and Informatics TERANG : Jurnal Pengabdian Pada Masyarakat Menerangi Negeri Scientific Journal of Informatics
Ariana Yunita
Universitas Pertamina
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Mechanical Engineering

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Feasibility Study: Online Learning for Supporting Rural Renewable Energy Projects Meredita Susanty; Erwin Setiawan; Ariana Yunita; Herminarto Nugroho
Journal of Telematics and Informatics Vol 7, No 2: JUNE 2019
Publisher : Universitas Islam Sultan Agung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/jti.v7i2.

Abstract

Indonesian government set the target for new and renewable energy (NRE) is expected to be 23% in 2025 and 31% in 2050, where in 2017, NRE was only 12,5%. Building a new and renewable energy project is the easy part. Keeping the generator working over time, however, is a complex business that requires money, training and innovative thinking. One of essential parameters that can help in achieving a successful sustainable project is community involvement. If the community feels a strong sense of ownership, they'll see their generator as a critical asset to everyone and take good care of it collectively. Training and educating the community about how to take care of the generator after the project construction has completed becomes important. Considering Indonesia’s geographical location, e-learning might be a potential tool to reduce training and education cost in new and renewable project. This study aims to identify whether e-learning is an effective and efficient method to deliver training material and educate local people which will improve renewable energy project’s sustainability in Indonesia.
ESTABLISHING REQUIREMENT IN TEACHING NEW AND RENEWABLE ENERGY Meredita Susanty; Ariana Yunita; Erwin Setiawan
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol 10, No 2 (2019): JURNAL SIMETRIS VOLUME 10 NO 2 TAHUN 2019
Publisher : Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (807.988 KB) | DOI: 10.24176/simet.v10i2.3368

Abstract

The distinctive shortage in the availability of skilled labor in new and renewable energy sector becomes the issue that has been prevented this country in achieving the national energy mix in 2015 and 2050. Although both formal and non-formal education initiatives already exist to foster new and renewable energy education, the number of graduates cannot outpace the number of required labor. The number of graduates and their competencies must be considered because local labors are facing intense competition with foreign labors due to ASEAN Economic Community policy. This research aims to find a solution to address the educational issue in new and renewable energy education. This research is using iterative methodology from the software engineering domain to identify issues and problems in new and renewable energy education in Indonesia and suggested alternatives to address it. Combination of techniques (questionnaire, interview, studying documentation, and research a similar product) is used in requirement elicitation stage in order to provide multiple perspectives.
Robust Principal Component Analysis for Feature Extraction of Fire Detection System Herminarto Nugroho; Muhamad Koyimatu; Ade Irawan; Ariana Yunita
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 5: EECSI 2018
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v5.1716

Abstract

Fire detection system with deep learning-based computer vision (DLCV *) algorithm is proposed in this paper. It uses visible light sensor charged-coupled device (CCD) which can be usually found in closed circuit television camera (CCTV). The performance of this DLCV fire detection depends on how many fire image datasets are trained that might lead to the curse of dimensionality. To tackle the curse of dimensionality, Principal Component Analysis (PCA) will be used. PCA is a technique for feature extraction in which the dimensionality of such datasets is reduced significantly. This will results in increasing interpretability but at the same time minimizing information loss.
Peningkatan Literasi Komputer Melalui Pelatihan Micosoft Excel Advanced Untuk Efisiensi Pekerjaan di Instansi Pemerintahan Meredita Susanty; Erwin Setiawan; Wahyu Kunto Wibowo; Herminarto Nugroho; Ade Irawan; Tasmi Tasmi; Muhamad Koyimatu; Aulia Rahma Annisa; Teguh Aryo Nugroho; Ariana Yunita
Terang Vol 4 No 2 (2022): TERANG : Jurnal Pengabdian Pada Masyarakat Menerangi Negeri
Publisher : Sekolah Tinggi Teknik - PLN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33322/terang.v4i2.1528

Abstract

Era digital melahirkan berbagai potensi dan tantangan yang memasuki berbagai bidang seperti politik, ekonomi, sosial budaya, pertahanan dan keamanan serta teknologi informasi. Pemerintahan atau sistem birokrasi di Indonesia juga tidak luput dari potensi dan tantangan perkembangan era digital ini. Salah satu tantangan besar yang harus dihadapi oleh sistem birokrasi Indonesia adalah tuntutan lahirnya inovasi yang berorientasi pada teknologi digital, sehingga inovasi ini diharapkan dapat memudahkan Aparatur Sipil Negara (ASN) dalam melaksanakan tugas dan fungsinya. Kemudahan segala pekerjaan dengan berbasis aplikasi dan teknologi ini selanjutnya diharapkan mampu memberikan pelayanan yang lebih optimal kepada masyarakat. Menanggapi tantangan ini, civitas Universitas Pertamina melalui program Pengabdian Kepada Masyarakat (PKM) berbagi pengetahuan, ilmu dan keahlian dalam penggunakan Microsoft Excel untuk mendukung efektivitas pengerjaan pekerjaan harian pada ASN Kantor Pelayanan Kekayaan Negara dan Lelang (KPKNL) Bekasi. Kegiatan ini diharapkan meningkatkan kemampuan Sumber Daya Manusia di kalangan KPKNL Bekasi, meningkatkan efektifitas dan efisiensi pelaksanaan tugas dan fungsi sehari-hari, serta menjadi katalis dalam munculnya inovasi yang berorientasi pada teknologi digital.
Identifying Relevant Messages from Citizens in a Social Media Platform for Natural Disasters in Indonesia Using Histogram Gradient Boosting and Self-Training Classifier Ariana Yunita; Zhafran Ramadhan; Angelia Regina Dwi Kartika; Ade Irawan
Scientific Journal of Informatics Vol. 11 No. 2: May 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i2.2287

Abstract

Purpose: This research aims to develop a classification model using histogram-based gradient boosting to identify relevant contextual tweets about disasters. This model can then be used for subsequent data cleaning stages. Methods: This study uses a semi-supervised approach to develop a classification model using histogram-based gradient boosting. The model is trained to identify and remove irrelevant tweets that are related to disasters and gathered from Twitter. Optimization techniques, such as the AdaBoost classifier, calibrated classifier, and self-training classifier, are used to enhance the model's performance. The goal is to accurately recognize and categorize relevant tweets for additional data analysis and decision-making. Result: The classification model that has been developed has achieved a high F1-score of 93.07%, which indicates its effectiveness in filtering disaster-related tweets that are relevant. This highlights the potential of the model to enable more precise aid distribution and faster decision-making in disaster response efforts. The successful implementation of the model also demonstrates its usefulness in utilizing social media data to enhance disaster management practices. Novelty: This research contributes to the analysis of social media through machine learning algorithms. By utilizing social media, specifically Twitter, as a valuable resource for disaster response efforts, this study tackles challenges related to data collection and analysis in disaster management. The classification of relevant tweets into different types of natural disasters offers opportunities to enhance stakeholder decision-making processes in disaster scenarios.
Co-Authors Ade Irawan Angelia Regina Dwi Kartika Aulia Rahma Annisa Erwin Setiawan Erwin Setiawan Herminarto Nugroho Meredita Susanty Muhamad Koyimatu Tasmi Tasmi Teguh Aryo Nugroho Wahyu Kunto Wibowo Zhafran Ramadhan