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Emerging Information Science and Technology
Published by Universitas Muhammadiyah Yogyakarta
ISSN : 27226042     EISSN : 27226050     DOI : https://doi.org/10.18196/eist
Core Subject : Science,
Computer Science & IT
Emerging Information Science and Technology is a double-blind peer-reviewed journal which publishes high quality and state-of-the-art research articles in the area of information science and technology. The articles in this journal cover from theoretical, technical, empirical, and practical research. It is also an interdisciplinary journal that interested in both works from the boundaries of subdisciplines in Information Science and Technology and from the boundaries between Information Science and Technology with other disciplines. EIST is an Open Access Journal to advance sharing science and technology. People have rights to read, download, copy, distribute, print and use with proper acknowledgment and citation. There is no publication fees for authors.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 141 Documents
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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.
The Development of Serious Game to Teach the Concept of Dunya and Akhirah to Young Muslims Setyawan, Haris; Isnanda, Reza Giga; Diano, Elvan; Subarkah, Muhammad Fadlun
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.112

Abstract

Nowadays, young Muslims are so busy with their needs in Dunya (this World) that they overlook the importance of preparing for life in Akhirah (the Hereafter). Afterall, one of the teaching in Islam is to prioritize the Akhirah than the Dunya. Because of the lack of awareness, many young Muslims neglect their religious duties such as Sholat or reciting al-Qur’an. To help introduce, teach, and remind them the concept of balancing life in Dunya and Akhirah, a solution that can pique their interest is a necessity. Therefore, this paper explain the development process of serious game to teach the concept of Dunya and Akhirah. The game implements the main mission and hidden mission that resemble Dunya and Akhirah responsibility respectively. The game’s effectivity was then evaluated with pre-test and post-test method. The result suggested that the game can increase the player’s knowledge in Dua and awareness in Dunya and Akhirah. This shows the potential of the game as learning media.
Additive Links on Multiple Access (ALOHA) Method to LoRaWAN Satellite-based communication Adi, Puput Dani Prasetyo; Stekelorom, Kevin; Vasista, Tatapudi Gopikrishna
Emerging Information Science and Technology Vol. 6 No. 2 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/eist.v6i2.26406

Abstract

LoRaWAN communication systems continue to improve with various advantages that continue to improve, for example, terrestrial LoRaWAN which increases satellite communication, and improves performance in terms of wider range, up to> 100 km. However several problems arise, including multiple end-node connection conditions. Not only point-to-point but already multi-point which causes several obstacles including data collisions, thus requiring several methods such as ADR (Adaptive Data Rate) or ALOHA (Additive Links on Multiple Access). The role of ALOHA is to be able to build an inter-node communication system that can prevent data collisions. In this case, ALOHA is used to improve the performance of IoT-LoRaWAN on satellite infrastructure. Some of the simulation components of this research lie in the data transmission protocol mechanism and collision management strategy, as well as spectrum efficiency in essential satellite-based LoRaWAN networks. The core of the research is how to reduce signal interference and optimize power consumption. Hopefully, ALOHA can be used as an effective method to build LoRaWAN Satellite-based IoT in the future. The future applications are for tracking, environmental monitoring, and disaster warning systems.
Influence of Social Media on Student Academic Achievement Based on K-Means to Support Indonesia Emas 2045 Himawan, Arif; Linawati, Linawati; Asnawi, Choerun; Subekti, Dayat; Setiawan, Chanief Budi
Emerging Information Science and Technology Vol. 6 No. 2 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/eist.v6i2.27024

Abstract

Social media is a digital platform used as a means of communication through text, images,videos, and information exchange. Among students, social media is often used beforelectures, during breaks, and after teaching and learning activities. This study aims toanalyze social media usage patterns and their impact on student academic achievement,particularly at the Faculty of Engineering and Information Technology (FTTI) of JenderalAchmad Yani University Yogyakarta (Unjaya). The method applied is the K-Meansalgorithm to identify social media usage patterns and their correlation with academicachievement. The research stages included data collection, data pre-processing,application of the K-Means algorithm, evaluation, and conclusion of results. Based on asample of 107 students using the Elbow method, three data clusters were obtained, namelyCluster 0 (3 members), Cluster 1 (47 members), and Cluster 2 (57 members). Testing usingthe Silhouette Score produced a value of 0.196, while the Davies-Bouldin Index showed avalue of 1.490. The results indicate that social media use has a positive impact on learningachievement, as reflected in the Grade Point Average (GPA) above 3.6 among FTTI Unjayastudents
A Data-Driven Framework for Analyzing Popularity of Indian Film Adaptations Using K-Means and Random Forest Al Ghifari, Nasy'an Taufiq; Deni Arif Wibowo
Emerging Information Science and Technology Vol. 6 No. 2 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/eist.v6i2.28415

Abstract

This study proposes a machine learning-based approach to predict the success and failure of Indian film adaptations in the box office market. Leveraging a dataset of more than 5,000 movies from the Kaggle platform, the study integrates the K-Means Clustering algorithm to group movies based on numerical fea-tures (vote_average, vote_count, and popularity), as well as the Random Forest Classifier to predict popularity. The analysis was balanced on two main categories: popular and unpopular films. The cluster-ing results showed that only a small percentage of film adaptations met the popular criteria, while most were in the unpopular category. The classification model achieves an accuracy of 82% and an F1-score of 0.79, with high performance in detecting films at risk of failure in the market. The study's main contribu-tion lies in the critical exploration of the two sides of film performance, which provides strategic insights for the film industry in designing more targeted production and distribution and avoiding investment mis-takes in less potential adaptation projects.
Integration of Social, Organizational, and Technological Factors to Improve the Effectiveness of Environmental Policies in Waste Management in Bima City Sri Sumanti, Endang; Prasetya, Didik Dwi; Patmanthara, Syaad
Emerging Information Science and Technology Vol. 6 No. 2 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/eist.v6i2.29171

Abstract

Bima City faces serious challenges in waste management, characterized by low service covera- ge (53.16%), limited processing facilities, and low public awareness and participation. This study aims to comprehensively evaluate the waste management system in Bima City and formulate sustainable strate- gies by integrating social, organizational, and technological factors. The research approach is quantitative with Structural Equation Modeling (SEM) analysis of 200 respondents from the community, sanitation workers, and environmental managers. The conceptual model was developed by adapting the Human–Or- ganization–Technology Fit (HOT-Fit) framework and Sustainability Metrics dimensions that include po- licy, participation, community behavior, and infrastructure technology. The results showed that organizational factors and public policy significantly influenced the effectiveness of waste management (β = 0.36; p < 0.001). Community participation was the dominant factor with a di- rect influence on management effectiveness (β = 0.45; p < 0.001), while community behavior acted as a mediator between technology and system effectiveness (β = 0.32; p < 0.001). The Goodness of Fit value showed a statistically appropriate model (CFI = 0.957; TLI = 0.951; RMSEA = 0.039). This study empha- sized the importance of synergy between policy support, social participation, and technological infrastruc- ture in building a sustainable waste management system.
Convolutional Neural Network-Based Model for Indonesian Offensive Text Classification Mayndeta, Daniel; Setyawan, Ryan Ari; Haryanto, Eri
Emerging Information Science and Technology Vol. 6 No. 2 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/eist.v6i2.29704

Abstract

This study presents a Convolutional Neural Network (CNN)-based model for classifying offensive and non-offensive Indonesian text using a dataset of 10,054 tweets collected from Twitter/X. The dataset was manually annotated into two classes and processed through a series of text-cleaning, tokenization, and padding steps before being used to train the model. Several training durations were tested to evaluate the effect of epoch variation on model performance. The results show that the model trained for 70 epochs achieved the best overall performance, with a testing accuracy of 86.73%, precision of 0.8793, recall of 0.8834, F1-score of 0.8814, and a ROC-AUC value of 92.08%. The confusion matrix analysis indicates strong classification capability for both classes, with the model performing slightly better in identifying offensive text due to distinctive lexical patterns. These findings demonstrate that the CNN architecture, supported by trainable word embeddings, is effective for Indonesian offensive-text classification. Future improvements may include integrating pretrained language models or expanding the dataset to enhance contextual understanding and robustness.
YOLO26-Based ASL Sign Language Gesture Detection on Android Isnaeni, Nenen; Wisesa, Bradika Almandin; Febrianto, Dany Candra
Emerging Information Science and Technology Vol. 7 No. 1 (2026): May
Publisher : Universitas Muhammadiyah Yogyakarta

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

Abstract

This study proposes a real-time Android-based American Sign Language (ASL) gesture detection system using YOLO26. The model recognizes 26 static gesture classes consisting of 24 alphabet gestures (a-i and k-y) and two common expressions, namely “I love you” and “thank you”, from images captured by a smartphone camera. A custom dataset of 7,800 images was prepared and divided into 5,460 training images, 1,560 validation images, and 780 testing images. Preprocessing included resizing, normalization, horizontal flipping, random rotation, brightness adjustment, contrast variation, and zoom augmentation to improve robustness under different acquisition conditions. Three YOLO26 variants, namely YOLO26s, YOLO26n, and YOLO26m, were trained and evaluated using precision, recall, F1-score, mAP@50, mAP@50-95, latency, frames per second, and lighting robustness. Experimental results show that YOLO26n provided the most balanced deployment performance with 96.4% precision, 95.8% recall, 96.1% F1-score, 97.8% mAP@50, and 84.2% mAP@50-95. Real-time testing on an Infinix X6726 device produced an average latency of 118 ms per frame or 8.47 FPS. Robustness testing under high, medium, and low lighting produced detection success rates of 98.3%, 96.7%, and 93.3%, respectively. The findings indicate that YOLO26 is feasible as an exploratory architecture for Android-based ASL gesture detection, although broader cross-device and cross-dataset validation is still required before claiming general real-world superiority.
Explaining OpenStack Failure-Injection Log Anomalies with Retrieved Normal Prototypes Xin, Qi
Emerging Information Science and Technology Vol. 6 No. 2 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/eist.v6i2.31232

Abstract

OpenStack benchmark logs are widely used in log anomaly detection, but the official release also supports a more specific task: failure-injection-aware explanation. It provides normal logs and abnormal cases with injected failures, which makes it possible to evaluate anomaly ranking and case-based explanation on the same corpus. This paper reports a complete empirical evaluation of the official OpenStack files. We parsed 207,820 raw lines, constructed 2,070 file-scoped virtual-machine cases, and evaluated template, timing, and retrieval-based methods against the four officially labeled injected anomalies. The labeled failures are not characterized by anomaly-exclusive templates. Instead, they preserve the normal template vocabulary and are dominated by a large creating_image→vm_started delay, together with increased /servers/detail polling. A template-only Isolation Forest fails in this setting, achieving ROC-AUC 0.418 and F1@4 0.000, whereas timing-aware baselines reach ROC-AUC values between 0.978 and 0.997. We then introduce FIA-RAG, a deliberately simple and auditable explanation pipeline. FIA-RAG scores each case with three standardized benchmark-aligned signals case duration, detail-polling count, and create-to-start delay and retrieves nearby normal cases as prototypes for counterfactual repair. On the official abnormal file, it ranks all four labeled anomalies in the top four alerts and identifies the create-to-start delay as the dominant explanation signal. These results show that the official OpenStack failure cases are timing-and-context anomalies rather than lexical anomalies. More broadly, the study argues that failure-injection benchmarks should be characterized before model complexity claims are made. The three features used here are OpenStack-specific, but the protocol, case construction, prototype retrieval, and counterfactual validation, can be adapted to other cloud-log benchmarks after system-appropriate milestones and context counters are defined.
A Comparison of the Performance of CNN, LSTM, and RNN Models for Stock Price Prediction Kusuma, Candra Juni Cahyo; Khairunnisa, Khairunnisa
Emerging Information Science and Technology Vol. 6 No. 2 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/eist.v6i2.31747

Abstract

Stock price prediction is a highly complex problem in the world of finance because it is influenced by various nonlinear and dynamic factors. This study compares the performance of three deep learning models Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Recurrent Neural Network (RNN) in predicting the closing price of PT Bank Rakyat Indonesia (Persero) Tbk. (BBRI) stock, with the ticker symbol BBRI.JK. The dataset used consists of historical BBRI stock price data from January 2, 2014, to December 30, 2024, covering 2,719 active trading days. The data was divided into 80% training data (2,115 sequences) and 20% test data (544 sequences) with a lookback window length of 60 days. Model evaluation was performed using three primary metrics: Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The experimental results show that the LSTM model provides the best prediction accuracy with a MAPE of 3.8722%, an RMSE of 194.25 IDR, and an MAE of 162.22 IDR, followed by the RNN model (MAPE 9.8298%, RMSE 493.57 IDR, MAE 417.36 IDR), and the CNN, which had the lowest performance (MAPE 25.3342%, RMSE 1,060.64 IDR, MAE 1,030.64 IDR). The LSTM’s coefficient of determination (R²) of 0.9531 confirms its ability to capture long-term patterns in stock time series data. This study recommends the LSTM model as the preferred choice for implementing a stock price prediction system for the banking sector in Indonesia.

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