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All Journal ELINVO (Electronics, Informatics, and Vocational Education) Journal of Robotics and Control (JRC) Journal of Engineering and Applied Technology Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal INFOTEL Control Systems and Optimization Letters
Indra Hidayatulloh
(Scopus ID: 56288307000) Department Of Electronics And Informatics Engineering Education, Faculty Of Engineering, Universitas Negeri Yogyakarta, Yogyakarta
Aerospace Engineering Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Mechanical Engineering

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Designing of english challenge mobile game application as the media of english language learning Vito Pratama Putra Setyadharma; Emi Iryanti; Indra Hidayatulloh; Novanda Alim Setya Nugraha
Journal of Engineering and Applied Technology Vol 1, No 1 (2020): (March)
Publisher : Faculty of Engineering, Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jeatech.v1i1.34756

Abstract

English became the first foreign language that came to Indonesia in 1967. The human needs of English can already be seen through lots of official scientific works in English and its intense competitions globally. However, still, many people finds it difficult to learn English, for example, the difficulty in understanding tenses, differences between writing, reading and pronouncing. By relying on the latest features on smartphone, which on the mean time, smartphone is one of many gadgets that is the most practical to use in anywhere and anytime, plus, with gaming contents as the best multimedia entertainer application, learning English will be more interactive and easier to use. In this research, Construct 2 is used as The Developer App and Game Development Life Cycle (GDLC) method of Rido Ramadan and Yani Widyani’s version is used as The Development Method. The game system tested by using black box and by giving out Questionnaire for User Interface Satisfaction to 24 respondents, to test the system’s usability. It resulted 80,83% for the whole of the system,  meaning that the whole system is quiet interesting in the respondents’s view. Then, the screen display gained 80,41%, this means that the screen display is very interesting and did not confuse the respondents. Technology and information game gained 75,41%, this means that the information contained in the game is quiet good, however, further development is needed. The introduction of the game’s system gained 71,63%, means that some of the system’s ability did not run effectively and needed further repairment and development. The last, usability and user interface gained 76,25%, this means that the game is quiet interactive to the respondents.  
Hybrid method integrating SQL-IF and Naïve Bayes for SQL injection attack avoidance Faisal Yudo Hernawan; Indra Hidayatulloh; Ipam Fuaddina Adam
Journal of Engineering and Applied Technology Vol 1, No 2 (2020): (August)
Publisher : Faculty of Engineering, Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jeatech.v1i2.35497

Abstract

Web applications are the objects most targeted by attackers. The technique most often used to attack web applications is SQL injection. This attack is categorized as dangerous because it can be used to illegally retrieve, modify, delete data, and even take over databases and web applications. To prevent SQL injection attacks from being executed by the database, a system that can identify attack patterns and can learn to detect new patterns from various attack patterns that have occurred is required. This study aims to build a system that acts as a proxy to prevent SQL injection attacks using the Hybrid Method which is a combination of SQL Injection Free Secure (SQL-IF) and Naïve Bayes methods. Tests were carried out to determine the level of accuracy, the effect of constants (K) on SQL-IF, and the number of datasets on Naïve Bayes on the accuracy and efficiency (average load time) of web pages. The test results showed that the Hybrid Method can improve the accuracy of SQL injection attack prevention. Smaller K values and larger dataset will produce better accuracy. The Hybrid Method produces a longer average web page load time than using only the SQL-IF or Naïve Bayes methods.
Gamification on Chatbot-Based Learning Media: a Review and Challenges Indra Hidayatulloh; Sigit Pambudi; Herman Dwi Surjono; Totok Sukardiyono
Elinvo (Electronics, Informatics, and Vocational Education) Vol 6, No 1 (2021): Mei 2021
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (602.284 KB) | DOI: 10.21831/elinvo.v6i1.43705

Abstract

The mobile learning sector has exploded, implying that the e-Learning trend is shifting to mobile platforms. As a result, chatbots have become increasingly popular alternatives for online learning and examinations on mobile platforms. However, it did not provide enough motivation for the student. On the other hand, gamification in a typical e-Learning platform is a widely used technique for increasing students' learning motivation. Therefore, combining gamification with chatbot-based learning and examinations possibly offer benefits, including increase student learning motivation. This study explored the possibilities and future challenges of the development of gamification within chatbot-based learning media. We discussed four aspects: architecture's reliability, security and privacy issue, user’s acceptance and motivation, and gamification feature challenges.
Integrasi Sentiment Analysis SentiWordNet pada Metode MOORA untuk Rekomendasi Pemilihan Smartphone Indra Hidayatulloh; Muhammad Zidny Naf’an
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 7 No 1: Februari 2018
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1280.07 KB)

Abstract

Besides specification and price, smartphone reviews can affect on consumer interest buying. This study aims to use the value of smartphone review sentiment as one of the attributes/criterias in addition to specifications and prices on the calculation of Decision Support System using MOORA method to generate smartphone recommendations. Sentiment value is obtained from sentiment analysis using SentiWordNet. There are two approaches of MOORA method used in this research, Ratio System and Reference Point Approach. Testing has been done by comparing the results of smartphone recommendations between approaches on the MOORA method, with or without sentiment analysis, on smartphone rankings based on the number of smartphone fans on the GSM Arena site. The test results show that the method of MOORA with Ratio System approach without sentiment analysis has the best accuracy among other approaches.
Desain dan Implementasi Platform Manajemen Historis Harga Saham dengan Kurasi Data dan Analisis Teknikal Indra Hidayatulloh
JURNAL INFOTEL Vol 9 No 1 (2017): February 2017
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v9i1.152

Abstract

Current research topics related to the stock is dominated by stock price prediction. To predict the stock price, it needs historical stock prices data. Many providers are providing the data, but not all of them free. Some providers that provide historical stock prices data for free are Yahoo Finance, Google Finance, Stooq, and the National Stock Exchange (NSE) of India. However, often there are differences in data between providers both in terms of availability, content, data formats, and so on. Thus, investors need to take data from some providers to be compared in order to obtain optimal analytical results. Therefore, this study builds data management platform to curate the historical stock prices data from the four data sources as well equipped with technical analysis to facilitate analysis of investment. In this study it was found that the data curation of historical stock prices for the four data sources can be performed with RDBMS technology as the database. However, there is a weakness that is less flexible when required adding additional data sources of unstructured data or have a different column. But, with the data from the four data sources that have been integrated, technical analysis can provide a broader picture the trend of stock price movements by comparing the results of the analysis for each data source.
Enhance Deep Reinforcement Learning with Denoising Autoencoder for Self-Driving Mobile Robot Pratama, Gilang Nugraha Putu; Hidayatulloh, Indra; Surjono, Herman Dwi; Sukardiyono, Totok
Journal of Robotics and Control (JRC) Vol 5, No 3 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i3.21713

Abstract

Over the past years, self-driving mobile robots have captured the interest of researchers, prompting exploration into their multifaceted implementation. They have the potential to revolutionize transportation by mitigating human error and reducing traffic accidents. The process of deploying self-driving mobile robots can be divided into several steps, such as algorithm design, simulation, and real-world application. This research paper presents a simulation using DonkeyCar on the Mini Monaco track, employing a Soft Actor-Critic (SAC) alongside a denoising autoencoder. At this point, it is limited to the simulation, serving as a proof of concept for further research with hardware implementation. The simulation verifies that relying solely on SAC for the convergence of policy is not sufficient; it yields a mean episode length of only 28.82 steps and a mean episode reward of 0.7815. The simulation ended after 3557 steps due to the inability of SAC alone to converge, without completing a single lap. Later, by integrating the denoising autoencoder, convergence of policy can be achieved. It enables DonkeyCar to adeptly track the lane of the circuit. The denoising autoencoder plays an important role in accelerating the convergence of transfer learning. Notably, the mean reward per episode reached 2380.4387, with an average episode length of 771.71 and a total of 114357 steps taken. DonkeyCar manages to complete several laps. These results affirm the effectiveness of SAC with a denoising autoencoder in enhancing the performance of self-driving mobile robots.
Benchmark Analysis of Sampling Methods for RRT Path Planning Pratama, Gilang Nugraha Putu; Dhewa, Oktaf Agni; Priambodo, Ardy Seto; Baktiar, Faris Yusuf; Prasetyo, Rizky Hidayat; Jati, Mentari Putri; Hidayatulloh, Indra
Control Systems and Optimization Letters Vol 2, No 2 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v2i2.132

Abstract

Path planning is a crucial aspect of mobile robot navigation, ensuring that robots can safely travel from their initial position to the goal. In real-world applications, path planning is essential for autonomous vehicles, drones, warehouse robots, and rescue robots to navigate complex environments efficiently and safely. One effective method for path planning is the Rapidly-exploring Random Tree (RRT) algorithm, which is particularly practical in maze-like environments. The performance of RRT depends on the sampling methods used to explore the maze. Sampling methods are important because they determine how the algorithm explores the search space, affecting the efficiency and success of finding an optimal path. Poor sampling can lead to suboptimal or infeasible paths. In this study, we investigate different sampling strategies for RRT, specifically focusing on uniform sampling, Gaussian sampling, and the Motion Planning Network (MPNet) sampling. MPNet leverages a neural network trained on past environments, allowing it to predict promising regions of the search space quickly, unlike traditional methods like RRT that rely on random exploration without prior knowledge. This makes MPNet much faster and more efficient, especially in complex or high-dimensional spaces. Through a benchmarking analysis, we compare these methods in terms of their effectiveness in generating feasible paths. The results indicate that while all three methods are effective, MPNet sampling outperforms uniform and Gaussian sampling, particularly in terms of path length. The mean path length generated, based on a sample size of 30, is 13.115 meters for MPNet, which is shorter compared to uniform and Gaussian sampling, which are 18.27 meters and 18.088 meters, respectively. These findings highlight the potential to enhance path planning algorithms using learning-based sampling methods.
Co-Authors Ardy Seto Priambodo Emi Iryanti Faisal Yudo Hernawan Faris Yusuf Baktiar Gilang Nugraha Putu Pratama Herman Dwi Surjono Ipam Fuaddina Adam Mentari Putri Jati Muhammad Zidny Naf'an Naf’an, Muhammad Zidny Novanda Alim Setya Nugraha Oktaf Agni Dhewa Rizky Hidayat Prasetyo Sigit Pambudi Totok Sukardiyono Vito Pratama Putra Setyadharma