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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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Ajax Based Exam Engine with Tagging System to Improve Learning Asroni, Asroni; Abdurrahim, Minhajuddin K.; Damarjati, Cahya
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.113

Abstract

In this paper, we propose exam engine software with tagging system to help students’ study. With this tagging system, they can analyze their subject learning through exam they have done from time to time. When the students see their reports, the students will decide which subjects are low in grade and should be re-studied. We use AJAX technology to enrich the user experience of this exam engine. After we test all feature with unit testing, this exam engine is proven to runs well and do benefits for students.
Optimization of the VGG Deep Learning Model Performance for Covid-19 Detection Using CT-Scan Images Riyadi, Slamet; Damarjati, Cahya; Khotimah, Siti; Ishak, Asnor Juraiza
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 10 No 1 (2024): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v10i1.3598

Abstract

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-Cov-2) causes a pneumonia-like disease known as Coronavirus Disease 2019 (COVID-19). The Reverse Transcription Polymerase Chain Reaction (RT-PCR) test is the current standard for detecting COVID-19. However, CT scans can be applied for radiological inspection to detect infections in their earliest lung stages. Machine learning, specifically deep learning, can potentially speed up the evaluation of CT scan diagnoses of COVID-19. To date, no studies have been discovered that employ SGD, Adamax, or AdaGrad optimization methods with deep learning VGG model variants for COVID-19 detection in CT scan images with datasets comprising 2,038 images. This study aims to assess and compare the performance of various optimization methods for detecting COVID-19 utilizing variations of the VGG-16 and VGG-19 models based on CT scan images. Results from performance optimization comparison tests employing two VGG deep learning models were obtained, demonstrating the influence of optimization methods on model performance. The Adamax optimization method applied to the VGG-16 model performance achieved an average accuracy of 94.11% in COVID-19 detection using CT scan images, while the Adamax optimization method applied to the VGG-19 model performance achieved an average accuracy of 93.77%.
Modified Convolutional Neural Network for Sentiment Classification: A Case Study on The Indonesian Electoral Commission Riyadi, Slamet; Mahardika, Naufal Gita; Damarjati, Cahya; Ramli, Suzaimah
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.v11i2.4929

Abstract

Purpose: This study aims to analyze public sentiment towards the Indonesian Electoral Commission (KPU) performance and evaluate a modified Convolutional Neural Network (CNN) model effectiveness in sentiment analysis. Methods: This research employs several methods to achieve its objectives. First, data collection was conducted using web crawling techniques to gather public opinions on the performance of the Indonesian Electoral Commission for the 2024 elections, with a specific focus on platform X. A total of 5,782 data points were collected and underwent preprocessing before sentiment analysis was performed. This study uses the CNN method due to its exceptional ability to recognize patterns and features in text data through its convolutional layers. CNN is highly effective in sentiment analysis tasks because of its ability to capture local context and spatial features from text data, which is crucial for understanding the nuances of sentiment in comments. The modified CNN model was then trained and evaluated using a labeled dataset, where each comment was classified into positive, negative, or neutral sentiment categories. Modifying the CNN model involved adjusting its architecture and parameters, as well as adding layers such as batch normalization and dropout to optimize its performance. The effectiveness of the modified CNN model was assessed based on metrics such as classification accuracy, precision, recall, and F1 score. Through this methodological approach, the research aims to gain insights into public sentiment towards the KPU performance in the 2024 elections and to evaluate the effectiveness of the modified CNN model in sentiment analysis. Result: The research revealed several significant findings. Firstly, most comments expressed concerns regarding performance aspects of KPU’s, including transparency, fairness, and integrity. Neutral sentiment dominated the discourse, with approximately 23.66% of comments conveying dissatisfaction or skepticism towards KPU's handling of the elections. Additionally, sentiments expressed on social media platform X mirrored those found across other platforms, indicating a consistent perception of KPU performance among users. Furthermore, the evaluation of the modified CNN model demonstrated a substantial improvement in accuracy, achieving an impressive 93% accuracy rate compared to the pre-modification model's accuracy of 77%. These results suggest that the modifications made to the CNN model effectively enhanced its performance in sentiment analysis tasks related to KPU performance during the 2024 elections. These findings contribute to a deeper understanding of public sentiment toward KPU performance and underscore the importance of leveraging advanced technology, such as modified CNN models, for sentiment analysis. Novelty: This study contributes novelty in several ways. Firstly, it provides insights into public sentiment towards the performance of the KPU during the 2024 General Elections, which is crucial for understanding the perception of democracy in Indonesia. Second, the study employs a mixed-methods approach, combining web crawling techniques for data collection and a modified CNN model for sentiment analysis, which offers a comprehensive and advanced methodology for analyzing sentiments on social media platforms. Thirdly, the evaluation of the modified CNN model demonstrates a significant improvement in accuracy, indicating the approach's efficacy in analyzing sentiments related to KPU performance. This study offers valuable contributions to academic research and practical applications in sentiment analysis, particularly in democratic processes and institutional performance evaluation.
Strengthening digital capacity and disaster safety for the Merapi slope tourism jeep community Prawoto, Nano; Nurjanah, Adhianty; Damarjati, Cahya
Community Empowerment Vol 10 No 10 (2025)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/ce.14852

Abstract

The Merapi slopes area in Sleman is a disaster-prone region that also serves as a potential destination for jeep tourism. The main challenges faced are a lack of digital promotion capacity and disaster preparedness. This community service aimed to enhance the economic independence and safety of the Merapi Jeep Community by strengthening digital promotion, implementing a Disaster Response Information System (SIMANTAB), and developing Safe Tourism SOPs. The implementation methods included socialization, training, technology application, and mentoring. Interventions encompassed digital marketing training, SIMANTAB integration, SOP development, and fulfillment of safety standards (SNI helmets). The program results showed a significant increase in partner understanding (averaging over 80%) in disaster mitigation and digital marketing aspects, as well as 88% success in developing Safe Tourism SOPs. This program impacted professionalism, tourist trust, and income potential. Program sustainability is ensured through the formation of a local master trainer team, establishing an effective collaborative model for sustainable tourism in disaster-prone areas.
Tree-based Filtering in Pulse-Line Intersection Method Outputs for An Outlier-tolerant Data Processing Damarjati, Cahya; Trinanda Putra, Karisma; Wijayanto, Heri; Chen, Hsing-Chung; Nugraha, Toha Ardi
JOIV : International Journal on Informatics Visualization Vol 6, No 1 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.1.861

Abstract

Pulse palpation is one of the non-invasive patient observations that identify patient conditions based on the shape of the human pulse. The observations have been practiced by Traditional Chinese Medicine (TCM) practitioners since thousands of years ago. The practitioners measure the patient’s arterial pulses in three points of both patient wrists called chun, guan, and chy, then diagnose based on their knowledge and experience. Pulse-Line Intersection (PLI) method extract features of each pulse from the observed pulse wave sequence. PLI is performed by summing the number of intersections between the artificial line and the pulse wave. The method is proven in differentiating between hesitant with moderate pulse waves. As the method implemented in Clinical Decision Support System (CDSS) related to pulse palpation, some outlier data might emerge and affect the measurement result. Thus, outlier filtering is needed to prevent unnecessary prediction processes by machine learning (ML) models inside CDSS. This study proposed an outlier filtering model using a decision tree algorithm. This concept is designed by analyzing pulse features values and the chance of odd values combination. Then inappropriate values are excepted using several rules. Every pulse feature list that did not pass the filtering rule is categorized as outliers and were not included for further process. The proposed model works more efficiently than ML models dealing with outliers since this procedure is unsupervised learning with a small number of parameters. Overall, the proposed filtering method can be used in pulse measurement applications by eliminating outlier data that might decrease the performance of ML models.
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