Extended reality and artificial intelligence (especially LLMs) in recent times have shown an increase in adoption and the number of research projects. This shows not only a growing interest in either topic but also a potential for integration between them. The natural way in which LLM interacts with users transfers well to XR’s immersive characteristics, and XR provides a new environment for LLM where LLM can interact with users via multiple modalities. Other reviews on this topic focus on the application of LLM in XR however, the technical aspect of this integration remains underexplored. This study conducts a systematic literature review of 76 peer-reviewed articles between 2020 and 2025 from three reputable databases and seeks to give an overview of state-of-the-art integration between LLM and XR in architecture, optimization, and evaluation. Findings conclude that LLM integration with XR most commonly uses cloud LLM as their main architecture with speech as the main modality. The most common technique of optimization is quantization with local development, and the most dominant evaluation metrics are latency and user experience. A decision support framework is then constructed to aid researchers in determining the type of architecture and optimization needed for their work. These findings are beneficial to locate technical research gaps in LLM and XR integration and provide XR and LLM researchers a means to formulate and design their future works.

This study develops an automatic system for measuring the arch height of swiftlet nests based on computer vision with the YOLO11n-seg model. This system overcomes the limitations of manual measurement at PT. Waleta Asia Jaya, where the arch height is one of the characteristics used to determine the quality of the product. The dataset contains 300 images captured using ESP32-S3 CAM (resolution 640×480). Images in the dataset is preprocessed using red channel extraction, intensity-based cropping, background correction, and morphology processing. The nest contour is analyzed using polar transformation and peak detection. The evaluation includes four approaches such as No Smoothing Filter, Ramer-Douglas-Peucker (RDP) Filter, Median Filter (MedFilt), and Savitzky-Golay (SavGol) Filter. Statistical analysis shows that the No Smoothing approach provides the best accuracy with a Root Mean Square Error (RMSE) of 2.28 millimeters as well as a time saving of 78.26% compared to manual measurement. The proposed system offers a time-efficient alternative to manual measurement while maintaining high accuracy in arch height estimation.

Tujuan penelitian 1) mengetahui perencanaan digitalisasi kinerja sumber daya guru yang memengaruhi efektivitas perencanaan dan pencapaian tujuan pendidikan, 2) mengetahui digitalisasi pengembangan profesional sumber daya guru, 3) mengetahui evaluasi digitalisasi dalam penilaian kinerja sumber daya guru. Penelitian menggunakan pendekatan kualitatif deskriptif. Penelitian berlokasi di Sekolah Dasar Negeri Prampelan 1 Kecamatan Sayung Kabupaten Dema pada bulan Mei - Juli 2025. Pengumpulan data dengan teknik observasi, wawancara dan dokumentasi. Analisis data meliputi pengumpulan data, kondensasi data, penyajian data dan penarikan kesimpulan. Hasil penelitian menunjukkan (1) perencanaan digitalisasi kinerja sumber daya guru memengaruhi efektivitas perencanaan dan pencapaian tujuan pendidikan meliputi a) analisis kebutuhan sekolah; b) Pembentukan tim; c) Perencanaan anggaran; d) Perencanaan sumberdaya manusia; e). perencanaan anggaran; f). perencanaan RKJM, RKT, RKAS. (2) pelaksanaan digitalisasi diantaranya a) Kegiatan pembelajaran menggunakan media pembelajaran berbasis digital; b) penggunaan media digital seperti aplikasi WhatsApp, jaringan wifi dan proyektor serta laptop. c) pemanfaatan digital untuk tugas siswa dengan mengembangkan materi dan menjawab soal-soal dari guru yang diselesaikan secara mandiri. (3) evaluasi dan pengawasan digitalisasi dalam penilaian kinerja sumber daya guru diantaranya meliputi pengawasan internal dilakukan kepala sekolah dan pengawas, kemudian pengawasan eksternal dilakukan komite sekolah, dilihat dari teknis pengawasan dilakukan dengan dua pendekatan yaitu pengawasan langsung yang bersifat teknis dan pengawasan tidak langsung dalam bentuk laporan. Selain itu pengawasan 1) standar penilaian; 2) asesmen; 3) hasil penilaian berupa rapot projek; 4) tindak lanjut dari kegiatan project.

Trafficking in persons is a transnational crime that violates human dignity and fundamental rights. This study analyzes the application of Law Number 21 of 2007 on the Eradication of Human Trafficking through a case study of Supreme Court Decision Number 1318 PK/Pid.Sus/2023. A normative juridical approach is employed to evaluate the legal grounds and judicial considerations in sentencing the perpetrator. The findings reveal that despite comprehensive legislation, its enforcement remains challenged by limited law enforcement capacity, evidentiary difficulties, and inadequate victim recovery mechanisms. The Supreme Court's ruling reflects legal consistency and rejects the judicial review due to the absence of valid new evidence (novum). This study recommends enhancing the capacity of legal practitioners and adopting a restorative justice approach to strengthen victim protection and improve sentencing effectiveness.

This research aims to analyze the implementation of the restorative justice concept within the law enforcement system by the Indonesian National Police as regulated in Law Number 2 of 2002, the Chief of Police Circular Letter No. SE/8/VII/2018, and Police Regulation No. 8 of 2021. Restorative justice is a criminal case resolution approach that emphasizes restoring the condition of victims, offenders, and the community without undergoing formal judicial processes. However, in practice, inconsistencies often occur, particularly in petty theft cases, where some are resolved through restorative justice while others are processed through formal legal proceedings. This research adopts a normative juridical method supported by an empirical juridical approach and has both prescriptive and descriptive-analytical characteristics. Obstacles identified in the implementation include investigators' lack of understanding of restorative justice, a legalistic work culture among investigators, and limited public acceptance of out-of-court case resolutions. Additionally, Police Regulation No. 8 of 2021 does not specify the threshold of financial loss or consider the offender's motive, leading to varying interpretations. Therefore, regulatory reform, enhanced investigator capacity, and legal education for the public are necessary to ensure a more just and consistent application of restorative justice.

Effective air quality monitoring and mitigation strategies in densely populated cities are affected by the limitations of ground-based sensors, leading to insufficient data. While remote sensing imagery offers a wide-area perspective, detecting small objects, such as industrial facilities and vehicles that contribute to air pollution, remains a significant challenge. To address the difficulty in detecting small objects in remote sensing images for potential air pollution source identification, this research comparatively evaluates two object detection models from the YOLO family: YOLOv8n and YOLOv11n. Furthermore, it investigates the effectiveness of integrating SAHI (Slicing Aided Hyper Inference), a post-processing technique designed to enhance small object detection. The models were trained using the DOTA dataset, a large-scale and high-resolution dataset, containing diverse object categories and numerous small instances relevant to urban and industrial environments. For the comparative evaluation in a real-world scenario, nine high-resolution remote sensing images from three Indonesian cities (Karawang, Semarang, Surabaya), acquired from Google Earth. Initial training on the DOTA dataset showed comparable performance between YOLOv8n (mAP50 of 0.784) and YOLOv11n (mAP50 of 0.801). However, when tested on the Indonesian city imagery, a comparative analysis of detected pollution-relevant objects (planes, small vehicles, large vehicles, and ships) revealed that both base YOLO models struggled with small object detection. From the experiment conducted, the integration of SAHI as a post-processing significantly improves the detection capabilities of both models. Quantitatively, the YOLOv11n with SAHI post-processing achieved mAP50 value on 0.588, a 6.9% increase in performance against its base model, while YOLOv8n with SAHI post-processing receive mAP50 value of 0.566, a 4.6% increase from base model.

Teknologi, khususnya di bidang teknik elektronika berkembang pesat dan sering memiliki dampak yang merugikan terhadap lingkungan. Oleh karena itu, literasi teknologi di bidang teknik elektronika serta energi baru dan terbarukan sangat penting diberikan kepada anak-anak sedini mungkin. Kegiatan pengabdian kepada masyarakat ini difokuskan pada bidang elektronika dasar dan energi baru/terbarukan. Sasaran kegiatan ini adalah siswa-siswi SD kelas 3 - 6 di SD Kanisius Gendongan, Salatiga. Tujuan kegiatan ini adalah agar siswa-siswi yang berpartisipasi memperoleh pengetahuan dasar mengenai teknik elektronika dan sumber energi baru. Kegiatan pengabdian dilaksanakan dalam bentuk pemberian materi di kelas yang disertai dengan kegiatan praktek. Evaluasi terhadap penyerapan materi dilakukan dengan cara pengamatan dan penilaian sumatif berupa kuis. Hasil evaluasi kegiatan menunjukkan bahwa para peserta dapat menyerap materi yang diberikan dengan sangat baik yang dibuktikan dengan hasil penilaian kuis yang mencapai nilai rata-rata 87,1 dari 100 untuk kelas elektronika dasar dan 88 dari 100 untuk kelas energi baru dan terbarukan. Selain itu, pengamatan yang dilakukan selama kegiatan juga menunjukkan antusiasme para peserta yang sangat tinggi.

The increasing number of cyber attacks will result in various damages to the functioning of technological infrastructure. A prediction model for the number of cyber attacks based on the type of attack, handling actions and severity using time-series data has never been done. A deep learning-based LSTM prediction model is proposed to predict the number of cyberattacks in a time series on 3 evaluated data sets MSLE, MSE, MAE, RMSE, and MAPE, and displays the predicted relationships between prediction variables. Cyber attack dataset obtained from kaggle.com. The best prediction model is epoch 20, batch size 16, and neuron 32 with the lowest evaluation value on MSLE of 0.094, MSE of 9.067, MAE of 2.440, RMSE of 3.010, and MAPE of 10.507 (very good model because the value is less than 15) compared other variations. There is a negative correlation for INTRUSION-MALWARE, BLOCKED-IGNORED, IGNORED-LOGGED, and LOW-MEDIUM. The predicted results for the next 12 months will increase starting from the second month at the same time. The resulting predictions can be used as a basis for policy and strategy decisions by stakeholders in dealing with fluctuations in cyber attacks that occur.

The ability of storytelling greatly affects a person’s success. These abilities are usually taught by parents at home or teachers at school. With the development of Artificial Intelligent technology, it is now possible to automatically generate stories using Large Language Model (LLM) which can understand and create language like humans. In this paper, the authors propose a self-learning system in storytelling by utilizing and combining four models from Hugging Face Hub. The proposed system is a web chat-based application so that users can communicate with LLM where LLM has received an image input from the users. The four models are as follows. Falcon 7B Instruct model as LLM that gets caption information from BLIP Image Captioning Large model. Its responses in the form of text can be read by the users and can be heard through audio synthesized by the MMS TTS Eng model. The user can also see the detected objects in the image which is detected by DETR ResNet 50 model. Our experiments show that the proposed system is sufficient to produce a good story and fit the context of the image, with an average user score of 89.76.

A digital platform called the Alpukat Population Application is used to handle statistics and information regarding DKI Jakarta's population. Using the Naive Bayes Classifier (NBC) approach, sentiment analysis for applications using satellite placement. The Nave Bayes Classifier technique is utilized for sentiment analysis because of its benefits in modeling and categorizing complicated data. The user reviews and comments gathered from the Google Play Store were the source of the data utilized in this research. Feature extraction using methods like TF-IDF, sentiment labeling on data, and the development of Nave Bayes Classifier models for sentiment classification were all part of the research project. It is anticipated that the study's findings would help us better understand how users interact with the Alpukat population app. This sentiment analysis may assist app administrators and developers in identifying the positives and negatives of applications and planning updates and advancements based on user feedback. It is anticipated that the sentiment classification model created using the Naive Bayes Classifier approach would be able to classify user evaluations into positive, negative, or neutral sentiment categories with a high degree of accuracy. The creation of improved alpukat positioning apps and decision-making may both benefit from this emotive analysis.

To assess the effectiveness of software security measures in government whistleblower systems, we will utilize the ISO/IEC 25010 standard and the Analytic Hierarchy Process (AHP) methodology. Through the integration of various frameworks, our objective is to build a complete evaluation model that effectively identifies and enhances any vulnerabilities in these crucial systems. The strategy we employ combines the qualitative and quantitative evaluation capabilities of ISO/IEC 25010 and AHP, respectively, to offer a comprehensive perspective on software security performance. The results indicate substantial improvements in the security and reliability of whistleblower software, underscoring the effectiveness of our suggested evaluation technique in identifying crucial areas for refinement. Moreover, the utilization of AHP permitted the ranking of security qualities, guaranteeing focused and efficient improvements. Ultimately, the study emphasizes the significance of thorough security assessments for government whistleblower systems and verifies the effectiveness of utilizing ISO/IEC 25010 and AHP as a methodical approach to improve software security. This research enhances the ongoing endeavor to protect confidential data, fostering a more secure and reliable atmosphere for individuals who expose wrongdoing.

In response to the escalating cybersecurity threats, this research focuses on leveraging text mining techniques to analyze network security data effectively. The study utilizes user-generated reports detailing attacks on server networks. Employing clustering algorithms, these reports are grouped based on threat levels. Additionally, a classification algorithm discerns whether network activities pose security risks. The research achieves a noteworthy 93% accuracy in text classification, showcasing the efficacy of these techniques. The novelty lies in classifying security threat report logs according to their threat levels. Prioritizing high-risk threats, this approach aids network management in strategic focus. By enabling swift identification and categorization of network security threats, this research equips organizations to take prompt, targeted actions, enhancing overall network security.

Internet access at this time is a daily necessity that cannot be denied. It is certain that most institutions and business entities require internet access in carrying out their activities, including educational institutions. With the development of mobile computer technology in which more users use mobile devices to access the internet, wireless-based network infrastructure is a demand that cannot be postponed any longer. By using a wireless connection to connect to the network, authentication becomes something that must be considered, the use of access to the network by unwanted parties can harm other parties. Changing passwords regularly is important to avoid misuse of access to the network by other parties. This paper presents a problem where when an educational institution implements the Bring Your Own Device (BYOD) program, students and teachers cannot change passwords using the personal device used, this is because the personal device is not registered with the domain controller at the institution. The solution proposed in this article is to move the NPS RADIUS server function on the local site to LDAP in the cloud using a combination of WLC which handles Wi-Fi clients and Fortinet which handles authentication to the cloud. The implementation results show that the WLC-Forti framework functions well.

Data is the most important thing, the use of data can be useful to get an evaluation from the user of a system or application that is built based on mobile. Not only, the assessment or acceptance results of mobile applications during the trial stage are considered important, assessments and comments from direct users are also important things that can be input for mobile application developers. Data mining, or known in English as data mining, is the answer to the process of retrieving data on any media. In this research, data mining is carried out on the media mobile application download service provider Google Playstore, which provides data in the form of comments and ratings. After scraping the data and obtaining the latest data parameters determined by the latest 2000 comments, the data is pre-processed by removing the emot icon character and eliminating unneeded variables so that the data obtained can be processed to the next stage, namely classification based on ratings and sentiment comments. The algorithms used or compared in this research are Support Vector machine, logistic regression and naïve bayes which are known to be reliable in data mining processing. In this research, the accuracy results are 88% for SVM, 90.5% for Logistic Regression and 91% for naïve bayes.

Blockchain technology will soon change the way reliable transactions work. By combining distributed cryptographic methods, immutable ledgers, Transactions, and financial audit trails that will be recorded in consensus validation, communication can now use blockchain technology for business advantage. A blockchain-based business version is currently under consideration because they promise to guarantee trust, identity management, reputation management, processing time, reduced fees, and fraud in commercial transactions. However, the current problem is that there needs to be more research into how ready businesses are to use blockchain and at what cost. This study examines the mediating and moderating effects of perceived costs on the relationship between an organization’s readiness to adopt blockchain and its intention to accept it as something new. This study uses empirical and quantitative analysis methods in reviewing the document-based multiscale questionnaire that has been developed. In this study, we collected data from blockchain experts and used the Partial Least Square Structural Equation Model (PLS-SEM) analysis tool to test the assumptions of our framework. This study also uses Key Performance Map Analysis (IPMA) to explore the potential management impact of this issue, which is a novelty in previous studies. The results show that rather than mediating the relationship between Acceptance of Technology Readiness (TeRa) and intention to adopt blockchain, the perceived costs change. According to IPMA’s analysis, the most critical factor behind the desire to adopt blockchain is the perceived cost, with the highest support structure rating. This research is expected to assist decision-makers and stakeholders in developing and improving the latest performance and features needed for the successful implementation of this blockchain technology.

The development of electronics, computers, and information technology is very rapid. Still, in several developing and underdeveloped countries, certain groups, including farmers, have not felt significantly the benefits of these technological advances. One of the farmers’ problems is if there is a drought where the rice plants need water, but there is no rain, and the water supply from the river is insufficient, so the rice production process decreases, or the harvest even fails. Previous research stated that in India, most of the population, 70% of whom live in rural areas and are very dependent on agriculture, and research in Indonesia in 2019 showed that in 2017 44% of the population lived in rural areas with most of their profession being farmers. This article proposes to create a groundwater pump system for irrigating crops, especially rice. Groundwater pumps in rice fields far from fuel and electricity supply facilities are a problem. Therefore, using solar energy (PV) and wind energy (WT) is an effective and efficient alternative. A groundwater pump system can be placed in rice fields using batteries, battery energy obtained from photovoltaic (PV) solar panels, or wind energy using a wind turbine (WT). This article contributes to an experiment in making a water pump system by utilizing green energy from PV and WT to provide a plant irrigation system in rice fields. The energy source system can be monitored, and the water pump system can be controlled wirelessly using IoT technology. The experiments in this research use devices that represent the energy and water pump systems at a minimum. The experimental results show that the proposed system works well and can be implemented in a real rice field irrigation system.

Even though COVID-19 still exists, people are more reluctant to wear masks in public places, in fact only 73% of Indonesian still do. Hence, automatic mask surveillance in public places is still needed. In this paper, we compare two algorithms named YOLO-X and MobileNetV2 to detect face masks. YOLO-X was able to outperform other YOLO algorithms in object detection. While, according to researchers, MobileNetV2 achieved 9S% in face mask detection. To fairly evaluate both algorithms we need to conduct research under controlled variables including using the same datasets and devices. We used public datasets which consists of 1493 mask images and 6451 non mask images for training and testing. The results show that YOLO-X outperforms MobileNetV2 as it achieves 95.0%, 98.7%, 93.7%, and 96.1% for accuracy, average precision, recall, and F1-score respectively. YOLO-X also performs better in detecting faces with occlusion such as glasses, hands, and postures than MobileNetV2. However, YOLO-X detects faces and face masks 31.9% slower than MobileNetV2.

Pada Masa Pandemi Covid 19 yang belum benar – benar berakhir ditambah keadaan ekonomi yang semakin sulit karena dampak perang Rusia dan Ukraina serta perubahan iklim yang memberikan dampak terhadap kehidupan masyarakat, karena hal tersebut baik secara langsung maupun tidak sering kita lihat baik melalui media cetak maupun elektronik yang menayangkan terjadinya tindak kejahatan termasuk meningkatnya kekerasan dalam rumah tangga. Himpunan Serikat Perempuan Indonesia (HAPSARI) mengungkapkan kasus kekerasan terhadap perempuan di Sumatera Utara meningkat selama pandemi Corona. Peningkatan tercatat sepanjang Januari hingga Agustus 2020. Kordinator Divisi Advokasi HAPSARI Sri Rahayu menjelaskan, data sepanjang Januari hingga Agustus 2020 terdapat 35 kasus kekerasan yang dilaporkan dari Wilayah Kabupaten Deli Serdang dan Serdang Bedagai. "Dari data yang ada, 26 kasus dari jumlah tersebut merupakan kekerasan dalam rumah tangga dan 6 kasus lainnya adalah kasus kekerasan seksual. Bentuk kekerasan yang dialami para korban mulai dari kekerasan fisik, psikis, ekonomi hingga penelantaran. Berdasarkan pertimbangan fakta di lapangan saat ini, maka penting bagi kami untuk dapat member masukan dengan cara penyuluhan kepada Ibu – Ibu dalam hal ini Ibu – ibu yang tergabung dalam Wanita Pujakesuma untuk dibekali pengetahuan tentang Kekerasan Dalam Rumah Tangga dengan mengajarkan Sosialisasi tentang Sosialisasi Undang – Undang No 23 Tahun 2004 tentang Penghapusan Kekerasan dalam Rumah Tangga untuk Mewujudkan Rumah tangga yang bahagia tanpa Kekerasan Metode yang digunakan dalam kegiatan ini adalah ceramah, diskusi, dan tanya jawab tentang permasalahan yang berkaitan dengan Undang – Undang No 23 Tahun 2004 tentang Penghapusan Kekerasan dalam Rumah Tangga . Program Kemitraan Masyarakat ini rencananya akan dilakukan secara berkesinambungan untuk tahap ini akan dilakukan dalam masa 1 Tahun

Saat era pandemic Covid-19 mulai menyerang, beberapa negara dalam pemerintahannya termasuk negara indonesia, sudah membuat rencana untuk mencegah virus, yang mencakup kebijakan menutup sekolah dan mengharuskan siswa untuk menyelesaikan kursus mereka di rumah. Wabah virus Corona telah menimbulkan banyak kekhawatiran, ketakutan, bahkan banyak menimbulkan banyak korban jiwa. Masalah utamanya, dikarenakan tidak semua guru dan sekolah dapat dengan cepat dan kompeten dalam melakukan pembelajaran online. Salah satu pendekatannya adalah dengan terlibat dalam proyek pengabdian masyarakat yang mencakup pelatihan tentang bagaimana cara membuat situs web yang berfungsi sebagai alat e-learning untuk pengajaran di kelas maupun di dalam rumah. Hal ini dikarenakan para guru dan sekolah mulai menyadari bahwa di era teknologi informasi, pembelajaran secara online merupakan salah satu sarana yang penting dalam menyampaikan materi pelajaran kepada siswa sekolah. Adapun tujuan kegiatan pengabdian ini adalah untuk meningkatkan kemampuan guru dan sekolah dalam penggunaan media pembelajaran, khususnya media Web-blog. Pelaksanaan kegiatan meliputi tahap persiapan, pertemuan melalui Google Meet, dan tahap pemantauan melalui WhatsApp, pendekatan yang digunakan dalam kegiatan ini adalah metode pelatihan, praktik, dan bimbingan. Hasil dari kegiatan pelatihan ini adalah para guru kini memiliki pengetahuan, kecakapan, dan keterampilan yang lebih dalam mengembangkan website sebagai platform e-learning di sekolah, sebagai sarana meningkatkan kualitas pengajaran di indonesia.

To find out people's opinions about student exchange, this research was made to find out by taking a data set from Twitter with a machine learning calculation within the form of Tweepy and after that handling it with TextBlob. TextBlob is one of the libraries in python 2 and python 3 which is used to process textual data. After getting the results, it is expected to use the data to help the community know what student exchange is and its benefits. Social media (Facebook, Instagram, Twitter, etc.) these days can be utilized for analysing the objects, e.g. political sees, items, administrations, etc. Social media is widely used at this time and we choose twitter because twitter is a social media application that is popular in the world and indeed a lot of research using twitter. Twitter had become the third-highest social organization in January 2009 from its previous position of twenty-two. Currently, more than 500 million selected clients in 2012, generating more than 340 million tweets. The conclusion is that there are still many people who do not know what student exchange is as much as 22% and 54% consider student exchange to be negative because they do not know the benefits of student exchange. Positive knowledge about student exchange is only 24%, this will support further research of creating a knowledge management system on student exchange. So hopefully that many people are interested in joining the student exchange program.