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All Journal International Journal of Electrical and Computer Engineering TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Kursor JUITA : Jurnal Informatika Semesta Teknika Journal of Electrical Technology UMY Indonesian Journal of Information System Jurnal Surya Masyarakat Jurnal Surya Masyarakat Emerging Information Science and Technology Prosiding Seminar Nasional Program Pengabdian Masyarakat Prosiding Seminar Nasional Teknik Sipil UMS Proceeding of Informatics Collaborations and Dessimenation Meeting (Infocoding)
Slamet Riyadi
Universitas Muhammadiyah Yogyakarta
Religion Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Library & Information Science Medicine & Pharmacology Public Health Social Sciences Other

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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.
Evaluating the Hybrid Multi-Protocol Label Switching (MPLS) on the Enhanced Interior Gateway Routing Protocol (EIGRP) Ridho Novradinata; Slamet Riyadi; Ronald Adrian
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.v3i2.16865

Abstract

The development of technology and communication is expanding rapidly. In this case, the internet has become a vital necessity in globalization. Technological innovations are required to create a seamless, fast, and secure communication system. This research aims to evaluate the Enhanced Interior Gateway Routing Protocol (EIGRP) implementation by applying the Multiprotocol Label Switching (MPLS) technology. This study adhered to several stages in the Network Development Life Cycle (NDLC) method. The results of the two technology combinations, EIGRP and MPLS, demonstrated MPLS network simulation testing in several dynamic routing systems: EIGRP and OSPF, identified through the Quality of Service (QoS) value. It revealed that the best performance was EIGRP with a throughput of 2152.5 bps, delay of 335.6 ms, and jitter of 411 ms. Furthermore, MPLS and EIGRP network redundancy was better applied in the mesh topology with a multi or backup link than in the linear topology with a single link.
Development and Testing of a Mathematics Learning Application Arif Ahmad Fadlil; Dwijoko Purbohadi; Slamet Riyadi
Emerging Information Science and Technology Vol 4, No 1 (2023): May
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/eist.v4i1.18886

Abstract

Distance learning, or online learning, is essential for both teachers and students as they work to create a system of education to function during a pandemic. Therefore, this research aims to develop a CAI media-based learning application for teaching fractions in mathematics. The survey results conducted at SDN 1 Sukerejo Boyolali revealed that students encountered the most trouble with fractions. Hence, their comprehension of the mathematics learning application was evaluated. The pre-test and post-test results of 30 students in both the control and experiment classes demonstrated that the application was easier to use than direct or traditional learning methods.
Peningkatan Ketrampilan Guru SD dalam Pembuatan Video Pembelajaran dengan Menggunakan Telepon Cerdas Slamet Riyadi; Erwan Sudiwijaya; Apriliya Kurnianti; Arif Bintoro Johan
Jurnal Surya Masyarakat Vol 6, No 1 (2023): November 2023
Publisher : Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsm.6.1.2023.104-110

Abstract

Learning in digital era demands innovation of learning materials where video is one of the attractive materials. Unfortunately, many teachers don't have skills to make learning videos, including some teachers at the Muhammadiyah Elementary School in Godean. Therefore, this program aims to improve the skills of partners in making learning videos. The main equipment used is a cell phone which is owned by all teachers so there is no need to procure additional equipment. The steps carried out are preparation, implementation and evaluation. Preparations have been made by discussing with partners about the training to be held. The training was completed on April 9, 2022 at the UMY Information Technology Laboratory, attended by 37 teachers from three Muhammadiyah elementary schools in Godean District. Participants received theory and immediately practiced making videos using their respective cell phones. Training evaluation was carried out using pre and post tests as well as observation. This program has succeeded in increasing teachers' knowledge and skills in making learning videos with a significant increase in pre-test and post-test results of 54.7%.
Improving Sentiment Analysis Accuracy Using CRNN on Imbalanced Data: a Case Study of Indonesian National Football Coach Slamet Riyadi; Muhammad Dzaki Mubarok; Cahya Damarjati; Asnor Juraiza Ishak
JUITA: Jurnal Informatika JUITA Vol. 12 No. 2, November 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i2.21847

Abstract

Conducting sentiment research on the perception of the Indonesian people towards Shin Tae Yong's (STY) role as coach of the Indonesian National Football Team (PSSI) is crucial as it can assist PSSI in determining whether to extend STY's contract. Prior studies have demonstrated that Deep Learning achieves a high level of accuracy when applied to sentiment analysis in many domains. Nevertheless, no investigation has been conducted thus far utilizing deep learning techniques to examine emotion towards STY. This study employs modified Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Convolutional Recurrent Neural Networks (CRNN), and CRNN models with and without data oversampling. The research findings indicate that the CRNN model, when combined with data oversampling and a redesigned architecture, achieves the highest level of accuracy (1.00) and consistently performs well. This research provides significant contributions in three areas: firstly, it utilizes Deep Learning techniques for sentiment analysis on STY; secondly, it modifies the CRNN architecture; and thirdly, it applies data oversampling to address the issue of imbalanced data.
Sentiment Analysis of Pro-Israel Product Boycott Action Using IndoBERT Method on Unbalanced Data Auliana Rizky Puspita Dewi; Slamet Riyadi; Cahya Darmajati; Nor Ashidi Mat Isa; Annisa Divayu Andriyani
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 2, July 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i2.25976

Abstract

A boycott was an act taken to stop the purchase or use of a particular product or service as a form of public protest against a particular company or group committing a deviation. The Israeli-Palestinian conflict, which had been ongoing since 1948, peaked in October 2023 and had claimed more than 35,000 Palestinian lives. This conflict generated a wide range of public opinions in Indonesia, which were expressed through social media, especially Twitter. Thus, the sentiment analysis of public reactions on Twitter became important to understand the reactions and perspectives of society towards the boycott of Pro-Israel products. This study used the IndoBERT method, which was a variant of the BERT method specifically designed to understand Indonesian. Although many studies had applied the IndoBERT method for sentiment analysis and text classification in Indonesian, none had used the IndoBERT method along with data balancing techniques to analyze Indonesian sentiments regarding the boycott of Pro-Israel products on Twitter. Therefore, this study aimed to develop a sentiment analysis model using the IndoBERT method with more data to examine sentiments related to the boycott of Pro-Israel products on Twitter using imbalanced data, as well as to evaluate the effect of balancing methods using under sampling and oversampling on the model’s accuracy and performance. The methods used included data crawling, data preprocessing, labeling with a Lexicon-Based approach, data balancing, and data splitting. The IndoBERT model was trained with 20 epochs, a batch size of 16, and a learning rate of 2e-5. The results of the study showed that the model with balanced data using the oversampling method achieved an accuracy of 97% and an F1-Score of 97%, which was better compared to the model with imbalanced data and the undersampling method. Thus, data balancing using the oversampling method proved to be effective in improving accuracy in sentiment analysis. This research made a significant contribution to understanding the behavior of Indonesian society towards a product boycott supporting Israel and suggested further exploration in parameter optimization and evaluation with larger and more diverse data, as well as further development of data balancing methods to improve the generalization and capabilities of the model
Peningkatan Ketrampilan Guru SD dalam Pembuatan Video Pembelajaran dengan Menggunakan Telepon Cerdas Slamet Riyadi; Erwan Sudiwijaya; Apriliya Kurnianti; Arif Bintoro Johan
Jurnal Surya Masyarakat Vol 6, No 1 (2023): November 2023
Publisher : Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsm.6.1.2023.104-110

Abstract

Learning in digital era demands innovation of learning materials where video is one of the attractive materials. Unfortunately, many teachers don't have skills to make learning videos, including some teachers at the Muhammadiyah Elementary School in Godean. Therefore, this program aims to improve the skills of partners in making learning videos. The main equipment used is a cell phone which is owned by all teachers so there is no need to procure additional equipment. The steps carried out are preparation, implementation and evaluation. Preparations have been made by discussing with partners about the training to be held. The training was completed on April 9, 2022 at the UMY Information Technology Laboratory, attended by 37 teachers from three Muhammadiyah elementary schools in Godean District. Participants received theory and immediately practiced making videos using their respective cell phones. Training evaluation was carried out using pre and post tests as well as observation. This program has succeeded in increasing teachers' knowledge and skills in making learning videos with a significant increase in pre-test and post-test results of 54.7%.
Comparative analysis of CatBoost and LightGBM algorithms for predicting dental caries risk based on lifestyle factors Chayadi Oktomy Noto Susanto; Muhajirah Ulfah; Bimo Aditya Pangestu; Slamet Riyadi
Jurnal Ilmiah Kursor Vol. 13 No. 3 (2026)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v13i3.504

Abstract

Dental caries is a common oral infection but significantly impacts life. This study aimsto compare the performance of the CatBoost and LightGBM algorithms in predictingdental caries risk based on health and lifestyle data from over 55,000 South KoreanNHIS participants. Imbalanced data was addressed using the SMOTE technique, whilehyperparameter tuning was performed using Optuna to maximize the F1-score. Themodels were evaluated using the confusion matrix, accuracy, logloss, ROC Curve,AUC, Precision, Recall, and F1-Score metrics. The results showed that CatBoost wasslightly superior with an accuracy of 90.4%, an F1-score of 89.7%, and an AUC of 0.95,compared to LightGBM with an accuracy of 90.1%, an F1-score of 89.2%, and an AUCof 0.94. In addition, CatBoost also produced fewer false negatives, making it moresensitive in detecting caries cases. Therefore, CatBoost is recommended as the primarymodel in a caries risk prediction system to support early detection and diseaseprevention more effectively.
Prediction Damage Factor Classification of Airport Pavement using Machine Learning Ibrahim Farah Abdalrahman Abdalla; Sri Atmaja P. Rosyidi; Ahmad Zaki; Slamet Riyadi
Prosiding Seminar Nasional Teknik Sipil UMS 2026: Prosiding Seminar Nasional Teknik Sipil UMS
Publisher : Universitas Muhammadiyah Surakarta

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

Abstract

Airport pavements experience continuous stress from operating large aircraft, compromising their structural integrity. Maintaining pavement resilience is crucial for ensuring flight safety. The Damage Factor (DF) is a crucial metric for evaluating the cumulative effect of aircraft loads on surface conditions. Accurate damage assessment and prediction are crucial for strategic maintenance planning and operational stability. This research examines the capacity of advanced mathematical methodologies, particularly machine learning, to identify and predict damage determinants. The researchers created sophisticated models that utilize extensive datasets, encompassing historical pavement performance records, aircraft movements, meteorological data, and material characteristics. The study aimed to enhance prediction capabilities using advanced algorithms, including random forest, logistic regression approaches linear SVM, tree and ensemble models. The findings indicated that the Majority Voting (MV) (random forest, decision tree and logistic regression) model attained the highest test accuracy of 81.82%, and ROC AUC value 0.8461, validating its robust capacity to accurately discern data patterns. Conversely, the naïve Bayes model exhibited markedly inferior performance, with a test accuracy of only 52.27%, underscoring its limited applicability to the dataset compared to alternative methodologies. The results reveal that the exceptional performance of the (MV) classification effectively identify underlying patterns in airport pavement deterioration data. This allows for extrapolation to a substantial training set under novel and unforeseen conditions.
Analisis Pengaruh Perkembangan Harga Emas terhadap Inflasi di Indonesia Menggunakan Regresi Linear Husna Kamila Syahida; Ahmad Musthafa Masyhur; Slamet Riyadi
Proceeding of Informatics Collaborations and Dessimenation Meeting Vol. 1 No. 1 (2025)
Publisher : Universitas Muhammadiyah Surakarta

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

Abstract

Emas merupakan salah satu aset investasi yang diminati banyak orang. Nilainya yang cukup stabil seringkali digunakan sebagai aset lindung nilai (safe haven) untuk melawan inflasi. Oleh karena itu, penelitian ini bertujuan untuk menganalisis hubungan antara harga emas dan tingkat inflasi. Data yang digunakan dalam penelitian ini diambil dari data sekunder, baik dari website resmi harga emas maupun Bank Indonesia. Proses analisis dilakukan menggunakan pendekatan regresi linier sederhana untuk menghitung hubungan antara satu variable dependen dan independen. Hasil penelitian menunjukkan bahwa variasi data pada harga emas (Sxx) cenderung fluaktif dan jauh dari rata-rata dengan nilainya yang sangat besar, sedangkan variasi data pada tingkat inflasi (Syy) cenderung stabil dan tidak jauh dari rata-ratanya karena nilainya yang sangat kecil. Untuk keterkaitan antara harga emas dan tingkat inflasi (Sxy) menunjukkan bahwa keduanya memiliki keterkaitan yang berlawanan arah dengan hasil angka negatif, yakni -6.219. Selain itu, terdapat hubungan negatif yang kuat, dengan koefisien korelasi ????=-0,81, yang menunjukkan bahwa peningkatan harga emas berkontribusi terhadap penurunan tingkat inflasi. Hal ini didukung dengan adanya perhitungan koefisien determinasi, yang menunjukkan bahwa 65% variasi dalam penurunan tingkat inflasi dapat dijelaskan oleh harga emas. Adapun hasil persamaan regresi pada penelitian ini adalah .
Co-Authors Abdul Eriawan Nahar Abyiyansyah Meidy Laksono Agus Jamal Ahmad Musthafa Masyhur Ahmad Zaki Amelia Mutiara Ayu Ratiwi Anna NN Chamim Annisa Divayu Andriyani Aprilia Kurnianti Arif Ahmad Fadlil Arif Bintoro Johan Asnor Juraiza Ishak Asroni Asroni Auliana Rizky Puspita Dewi Badrahini Masajeng Respati Bimo Aditya Pangestu Cahya Damarjati Cahya Darmajati Cut Maya Putri Audilla Dwijoko Purbohadi Dyah Mutiarin Fadhan Anwarodin Fauri Hakim Giant Prakoso Amukti Wibowo Haris Setyawan Husna Kamila Syahida Ibrahim Farah Abdalrahman Abdalla Indira Prabasari Kamarul Hawari Ghazali Kurnianti, Apriliya Laila Ma’rifatul Azizah Lusiana Ning Saputri Milou Derks Muhajirah Ulfah Muhammad Aldi Joko Satria Perdana Muhammad Dhafa Muhammad Dzaki Mubarok Muhammad Habil Muhammad Rizki Frebian Nadiyah Maharty Ali Nafi A Utama Nafi Ananda Utama Nasrul Saefullah Nisrina Akbar Rizky Putri Nor Ashidi Mat Isa Nurfahmi Nurfahmi Rammadhany Rammadhany Ricki Irawan Ridho Novradinata Rohmana Zulfa Bakhtiar Ronald Adrian Ronald Adrian Sakir Ridho Wijaya Sartika Puspita Sofiani, Erma Sri Atmaja P. Rosyidi Sudiwijaya, Erwan Suparman Susanto, Chayadi Oktomy Noto Suwadi Suwadi Tony K Hariadi Yunita Lestari Zulfan Fadholi