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All Journal International Journal of Electrical and Computer Engineering Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Jurnal Buana Informatika Register: Jurnal Ilmiah Teknologi Sistem Informasi Jurnal IPTEK-KOM (Jurnal Ilmu Pengetahuan dan Teknologi Komunikasi) JOIV : International Journal on Informatics Visualization INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Indonesian Journal of Information System J-Dinamika: Jurnal Pengabdian Kepada Masyarakat Community Empowerment Journal of Applied Data Sciences Nakhoda: Jurnal Ilmu Pemerintahan Emerging Information Science and Technology Prosiding Seminar Nasional Program Pengabdian Masyarakat Scientific Journal of Informatics
Cahya Damarjati
Universitas Muhammadiyah Yogyakarta
Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Medicine & Pharmacology Public Health Social Sciences

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Classification of Student Understanding on Covid-19 Booster Vaccine Using Machine Learning Cahya Damarjati; Slamet Riyadi; Ricki Irawan
Emerging Information Science and Technology Vol 3, No 2 (2022): November
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/eist.v3i2.18680

Abstract

The outbreak of COVID-19 has been declared a global pandemic by the World Health Organization (WHO). Developing a vaccine is one of the best ways to reduce the virus's impact. Nevertheless, the development of virus mutations produces new variants that diminish the efficacy of the previous vaccine. Booster doses of the Covid-19 vaccine is still a matter of debate among the public, particularly among students, as evidenced by the low rate of booster vaccinations in the community, which is a result of a lack of knowledge about booster vaccines. The purpose of this study is to assess the level of understanding among Universitas Muhammadiyah Yogyakarta (UMY) students regarding booster vaccinations, with the results subsequently serving as a factor or strategy for future government booster vaccination policy decisions. ANN and SVM algorithms could be used to predict the level of understanding of booster vaccinations among UMY students. However, the maximum level of precision in classifying the level of comprehension is not yet known. To determine which of the two methods, kernel and k-fold, provided the maximum level of accuracy, a comparative study was conducted between them. The research was conducted by disseminating questionnaires containing assessments of booster vaccinations to a total of 2095 respondents. Using randomized sampling type, this study yielded an accuracy of 88.45% for the ANN method and 89.93% for the SVM method in each scenario. In addition, the authors conduct feature efficiency, which aims to reduce the time and cost associated with data computation.
Peningkatan Sumber Daya Manusia Dalam Mengelola Sistem Informasi Desa Berbasis Website Titis Wisnu Wijaya Titis; Cahya Damarjati; Aprilia Kurnianti
J-Dinamika : Jurnal Pengabdian Masyarakat Vol 8 No 2 (2023): Agustus
Publisher : Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/j-dinamika.v8i2.4017

Abstract

Pemerintah desa berhak mendapatkan akses untuk memperoleh dan menyebarluaskan informasi yang mengacu pada Undang-Undang Nomor 6 Tahun 2014. Berdasarkan peraturan ini, pemerintah daerah berkewajiban mengembangkan sistem informasi desa. Dalam hal ini, website merupakan platform yang tepat dan efektif untuk menyebarluaskan informasi desa kepada publik secara transparan. Program pengabdian dilakukan di Desa Darmayasa, Pejawaran, Banjarnegara, Jawa Tengah. Sebelumnya, Pemerintah Desa Darmayasa memiliki website. Masalah muncul ketika admin tidak dapat mengelola konten website dengan baik, selain itu tampilan website juga mengalami tema kuno. Oleh karena itu, para abdi melaksanakan program pengabdian masyarakat dengan tema "Peningkatan SDM dalam Mengelola Sistem Informasi Desa Berbasis Website". Pengumpulan data dalam program pengabdian masyarakat ini menggunakan kuesioner beserta alat ukur skala Likert. Hasil akhir dari evaluasi keseluruhan adalah dengan skor rata-rata 2,8 dalam kategori "Kepuasan". Selain untuk meningkatkan sumber daya manusia, metode yang digunakan adalah bengkel. Hasil dari kegiatan workshop dan pelatihan pengelolaan website desa adalah meningkatkan kemampuan SDM dalam mengelola website dengan target satu berita baru terbit dalam satu minggu selama dua bulan monitoring. Sehingga website yang telah dikembangkan dapat digunakan sesuai dengan tujuan yang diharapkan.
Sentiment Analysis of YouTube Users on Blackpink Kpop Group Using IndoBERT Riyadi, Slamet; Salsabila, Lathifah Khansa; Damarjati, Cahya; Karim, Rohana Abdul
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 8 No 2 (2024): August 2024
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v8i2.22678

Abstract

Background: The Korean Pop (K-Pop) phenomenon has become an important part of popular culture worldwide, with Blackpink being one of the most influential groups. Analyzing sentiment toward Blackpink is urgent, given its growing popularity and wide influence among fans worldwide. In the present technological era, social media platforms such as YouTube have evolved into a space where artists and their fans may interact with each other. As a consequence, social media has become a powerful tool for assessing the emotional tone and sentiment conveyed by individuals. Objective: This research aims to explore the trend of public sentiment towards Blackpink and evaluate how well the IndoBERT model analyzes the sentiment of Indonesian texts. Methods: The objective of this study is to examine the pattern of public sentiment towards Blackpink and assess the proficiency of the IndoBERT model in analyzing the sentiment of Indonesian writings. Results: The findings demonstrated that the IndoBERT model had an exceptional level of precision, achieving a 98% accuracy rate. In addition, it obtained a f1, recall, and accuracy score of 95%. The remarkable results demonstrate the efficacy of the IndosBERT technique in evaluating the emotion of Indonesian-language literature towards Blackpink. Conclusion: This study enhances the knowledge of how fans and audiences react to K-pop material and establishes a foundation for future research and advancement. The impressive precision of the IndoBERT model showcases its capacity for sentiment analysis in Indonesian literature, making it a useful tool for future research endeavors.
Partial Adaptive Multi-Level Block Truncation Coding (Ambtc) Of Spinal X-Ray Image For Efficient Compression Damarjati, Cahya
Emerging Information Science and Technology Vol 5, No 1 (2024): May
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/eist.v5i1.22409

Abstract

This study aims to explore various adaptations of the AMBTC compression model applied to lumbar spine radiographic images, focusing on minimizing image size while preserving essential information. The approach involves adjusting several technical aspects of the AMBTC model, including the number of blocks, block size, and compression rate. The quality of the compressed images is assessed using image quality metrics such as PSNR (Peak Signal-to-Noise Ratio) and MSE (Mean Squared Error). The findings indicate that a modified AMBTC compression model can significantly enhance the quality of lumbar spine radiographic images, evidenced by increased PSNR values, while substantially reducing the file size without compromising crucial image details
Laravel Framework-Based Information System of the Department of Information Technology of Universitas Muhammadiyah Yogyakarta Musyary, Musyary; Kurniati, Aprilia; Damarjati, Cahya
Emerging Information Science and Technology Vol 4, No 2 (2023): November
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/eist.v4i2.20736

Abstract

It is crucial to have a reliable and adequate information system to implement information technology. It explains why technology must continue advancing in this area, specifically regarding the Information Technology Department’s information management at Universitas Muhammadiyah Yogyakarta (UMY). Information has been disseminated through the WhatsApp and Telegram applications, leading to improper conveying of the made and supplied information due to excessive stacking. Hence, a web-based information system was developed in PHP with the help of the Laravel framework to overcome the issue. Moreover, a database was set up using MySQL to circumvent the issue. The newly constructed information system could enhance information management, leading to more accurate and efficient information generation for various uses.
Discrete Curvelet Transform Feature Extraction for Mangosteen Fruit Surface Damage Detection Utama, Nafi Ananda; Triyani, Wahyu Indah; Riyadi, Slamet; Damarjati, Cahya
Emerging Information Science and Technology Vol 5, No 1 (2024): May
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/eist.v5i1.22602

Abstract

Mangosteen (Garcinia mangostana L) is one of the commodities of Indonesian fruit and is used as an export primadona that became the basis of Indonesia to increase the currency of the country. The quality of the fruit can be seen from the surface, whether there is damage or not. The sorting that the farmers have been doing all this time is still using the conventional way, that is, with the sense of sight. This conventional method seems to be less effective because it takes a lot of energy, takes a long time, and there are different perceptions between farmers. To solve this problem, a method of surface quality extraction of mango fruit will be developed based on image processing. The initial stage of image processing is with the image size equation then the image is converted to grayscale mode, then a discrete curvelet transformation is performed. The next stage is the extraction of mean, energy, entropy, standard deviation, variance, sum, correlation, contrast, and homogeneity. The result of the subsequent feature extraction is used to enter a value at the classification stage. From some of these extractions it will be known which extraction has the highest accuracy value. The method of classification used is Linear Discriminant Analysis (LDA) with the method of K-Fold Cross Validation which in this study is divided into 4-fold cross validation. After testing on 120 images, the highest value of accuracy is with extraction of standard characteristics deviation of 91.7% and variance of 88.4%.
Diskursus Publik Terhadap Transportasi Berkelanjutan: Studi Kasus Elektrifikasi Moda Transportasi di DKI Jakarta Nugroho, Aris; Prasetyo, Satria; Damarjati, Cahya; Rochmah, Faizatur
Nakhoda: Jurnal Ilmu Pemerintahan Vol 23 No 1 (2024)
Publisher : Laboratorium Jurusan Ilmu Pemerintahan FISIP Universitas Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35967/njip.v23i1.659

Abstract

The policy of electrification of transportation modes in DKI Jakarta has created debate among the public, especially on social media. The policy of creating an environmentally friendly electric transportation ecosystem is not accompanied by supporting aspects of sustainable transportation. This research explores this debate by linking it to aspects of sustainable transportation, such as the environment, society, and economy, to understand and assess the development of transportation electrification. The qualitative method used in this research is a Q-Das approach using NVivo 12 Plus as software that helps collect and analyze data found on social media. The study found that the public needs to view transportation electrification policies as fully sustainable. Firstly, in the social dimension, the public criticized the policy of transitioning transportation to electricity without being accompanied by an energy transition as a "false solution". This is based on the energy source for electric transportation originating from coal-fired power plants, which are also the main contributors to pollutants. Second, in the economic dimension, the public views that the massive electric transportation ecosystem still needs to be supported by supporting aspects such as the availability of SPKLU. The estimated time for recharging batteries, which is relatively long compared to refueling conventional transportation, is considered to hamper and influence public economic activities. In the environmental dimension, the public criticizes that if a massive transportation electrification policy is carried out, it will require excessive electricity use. This is seen as inefficient and creates new problems that previously stemmed from excessive fuel use.
Classification of Political Party Conflicts and Their Mediation Using Modified Recurrent Convolutional Neural Network Riyadi, Slamet; Suradi, Muhamad Arief Previasakti; Damarjati, Cahya; Chen, Hsing-Chung; Al-Hamdi, Ridho; Masyhur, Ahmad Musthafa
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.513

Abstract

The rapid proliferation of political information on the internet has exacerbated conflicts within political parties, including elite disputes, dualism, candidate controversies, and management issues, which can undermine political stability and public trust. To address these challenges, this study introduces the Modified Recurrent Convolutional Neural Network (M-RCNN), an enhanced RCNN model designed to improve classification accuracy and mitigate overfitting by incorporating additional layers and dropout mechanisms. The primary objective of this research is to provide an efficient and accurate framework for classifying political conflicts and mediation strategies, overcoming the limitations of traditional methods, particularly in handling imbalanced datasets and intricate data patterns. Using a dataset of 1,106 Indonesian news articles categorized into four conflict types—elite disputes, management, presidential, and legislative candidate conflicts—and four mediation strategies—leadership decisions, deliberation, legal channels, and none—the data underwent extensive preprocessing, tokenization, and an 80:20 training-testing split. The M-RCNN achieved a conflict classification accuracy of 98.0%, a precision of 99.0%, and a loss of 0.03, significantly outperforming baseline models, including CNN (85.0% accuracy), RNN with LSTM (88.0%), and standard RCNN (85.0%). For mediation strategy classification, the model demonstrated exceptional performance with an accuracy of 99.0%, a precision of 99.0%, and a loss of 0.01, highlighting its robustness and scalability. This study’s novelty lies in its ability to process imbalanced and complex datasets with unparalleled precision and efficiency, providing a practical framework for automated political conflict analysis and mediation. The findings underline the potential of the M-RCNN model to revolutionize political science applications by delivering reliable, fast, and accurate tools for analyzing and resolving political conflicts, thereby contributing to the advancement of artificial intelligence in promoting political stability and fostering public trust.
Performance Analysis of Quantum Key Distribution B92 Protocol Using Qiskit Prasetyo, Eko; Damarjati, Cahya; Mahmudi, Muhammad Nazih; Cahyadi, Eko Fajar
Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Vol 12 No 3 (2024): Vol. 12, No. 3, December 2024
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JIM.2024.v12.i03.p05

Abstract

Quantum Key Distribution is a cornerstone of secure communication, utilizing quantum mechanics to achieve unparalleled security. This study evaluates the performance of the B92 protocol, a simplified scheme using non-orthogonal states, through simulation with Python3 and the Qiskit library. The research focuses on the variability of key lengths and the ability of the protocol to detect eavesdropping attempts. Results show an average key length of 14.3 bits per 100 transmitted qubits, with variability ranging from 3 to 29 bits. Detection accuracy improves significantly with sample size, achieving 95% accuracy with a sample size of 5 and 100% with a sample size of 10. These findings highlight the trade-off between key length and detection reliability, emphasizing the importance of optimization. While simulations confirm the protocol’s robustness, further studies under real-world conditions are essential. This work advances the understanding of quantum cryptographic systems and lays the foundation for secure quantum communication.
Classification of Mangosteen Surface Quality Using Principal Component Analysis Riyadi, Slamet; Ayu Ratiwi, Amelia Mutiara; Damarjati, Cahya; Hariadi, Tony K.; Prabasari, Indira; Utama, Nafi Ananda
Emerging Information Science and Technology Vol. 1 No. 1: February 2020
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/eist.115

Abstract

Mangosteen (Garcinia mangostana L) is one of the primary contributor for Indonesia export. For export commodity, the fruit should comply the quality requirement including its surface. Presently, the surface is evaluated by human visual to classify between defect and non- defect surface. This conventional method is less accurate and takes time, especially in high volume harvest. In order to overcome this problem, this research proposed images processing based classification method using principal component analysis (PCA). The method involved pre-processing task, PCA decomposition, and statistical features extraction and classification task using linear discriminant analysis. The method has been tested on 120 images by applying 4-fold cross validation method and achieve classification accuracy of 96.67%, 90.00%, 90.00% and 100.00% for fold-1, fold-2, fold-3 and fold-4, respectively. In conclusion, the proposed method succeeded to classify between defect and non-defect mangosteen surface with 94.16% accuracy.
Co-Authors Abdul Eriawan Nahar Abdurrahim, Minhajuddin K. Abyiyansyah Meidy Laksono Adhianty Nurjanah Amelia Mutiara Ayu Ratiwi Andhika Putra Setianto Aprilia Kurnianti Aprilia Kurniati, Aprilia Arifia Kasastra R ARIS NUGROHO Asri Tri Wulandari Asroni Asroni Asroni Asroni Asroni Asroni Asroni, Asroni Ayu Ratiwi, Amelia Mutiara Azizah, Laila Marifatul Chen, Hsing-Chung Dimas Bagas Ajipratama Eko Fajar Cahyadi Eko Prasetyo Fadhan Anwarodin Fauri Hakim Gilang Hendra Dita Edifianto Heri Wijayanto Indira Prabasari Indira Prabasari, Indira Ishak, Asnor Juraiza Kamarul Hawari Ghazali Karim, Rohana Abdul Laila Ma’rifatul Azizah Lukito Edi Nugroho Mahardika, Naufal Gita Mahmudi, Muhammad Nazih Masyhur, Ahmad Musthafa Maududi Nur Imani Tarigan Minhajuddin K. Abdurrahim Muhammad Alfadha Termahadi Mukhtar Hanafi Musyary, Musyary Nafi Ananda Utama Nano Prawoto Nasrul Saefullah Nisrina Akbar Rizky Putri Prasetyo, Satria Ramli, Suzaimah Ricki Irawan Ridho Al-Hamdi Rochmah, Faizatur Salsabila, Lathifah Khansa Sartika Puspita Sifa Dinia Siti Khotimah Slamet Riyadi Slamet Riyadi Slamet Riyadi Sofiani, Erma Sofran Bahrurozi Sri Suning Kusumawardani Suparman Suradi, Muhamad Arief Previasakti Suwadi Suwadi Titis Wisnu Wijaya Titis Toha Ardi Nugraha Tony K Hariadi Trinanda Putra, Karisma Triyani, Wahyu Indah Utama, Nafi Ananda Yunita Lestari