Universitas Widya Kartika

INTRODUCTION: Osteoarthritis (OA) constitutes the most prevalent degenerative joint disease worldwide, imposing a profound burden of chronic pain, functional limitation, and disability on the aging population. Conventional physiotherapy serves as the established cornerstone of conservative management, primarily targeting biomechanical optimization and symptom control. Concurrently, Low-Dose Radiation Therapy (LDRT) has experienced a significant clinical resurgence as a non-pharmacological intervention hypothesized to modulate synovial inflammation and halt structural disease progression. However, high-quality clinical evidence remains profoundly contested and polarized. The objective of this comprehensive systematic review is to rigorously evaluate the comparative efficacy of LDRT versus conventional physiotherapy and sham treatments, specifically focusing on the resolution of symptomatic complaints and the alteration of disease progression in OA patients. METHODS: A comprehensive systematic review protocol was executed in strict alignment with PRISMA guidelines. Efficacy data, adverse event rates, and radiological/clinical progression metrics were systematically extracted from peer-reviewed databases and recent oncological and rheumatological trial registries up to the year 2025. The risk of bias was appraised utilizing the Cochrane Risk of Bias 2 (RoB 2) tool. The statistical analysis isolated standardized mean differences (SMD), hazard ratios (HR), relative risks (RR), and confidence intervals (CI) to quantify treatment effects, categorizing findings into symptomatic efficacy, disease progression, and safety profiles. RESULTS: The systematic synthesis integrated diverse trial archetypes, encompassing over 3,000 cumulative patients across multiple cohorts. Conventional physiotherapy demonstrated consistent, short-term efficacy in functional restoration (SMD – 0,166, 95% CI -0,422 to 0.088) and pain reduction (SMD -0.175). Conversely, the most recent 2025 meta-analysis indicates that LDRT yields no statistically significant symptomatic benefit over sham interventions globally (SMD -0.92, P = 0.13). However, modern stratified trials, such as the ASTRO 2025 randomized controlled trial, revealed that a specific 3.0 Gy LDRT regimen yielded significant responder advantages over sham treatments (70.3% vs 41.7%, P=0.014). Most crucially, decade-long longitudinal data indicate that LDRT significantly reduces the hazard of profound clinical disability (Adjusted HR 0.24, 95% CI 0.11 to 0.48) and decreases the incidence of total joint arthroplasty (HR 0.60). Adverse events were measurably higher in LDRT cohorts (RR 1.44). DISCUSSION: The dichotomy in LDRT outcomes points to a highly complex interplay between radiobiological dose thresholds and the profound placebo responses documented in sham-controlled arms. Biologically, LDRT upregulates the Nrf2 antioxidant response, downregulates inducible nitric oxide synthase (iNOS), and polarizes macrophages toward the anti-inflammatory M2 phenotype, effectively arresting the osteoimmunological cascades that drive joint destruction. Physiotherapy remains universally effective for muscular and functional preservation, while LDRT's utility appears tightly restricted to delaying structural progression and end-stage disability in select demographics. CONCLUSION: Conventional physiotherapy maintains its mandate as the primary, biologically safe, and effective first-line intervention for OA. LDRT presents a potent but highly debated alternative that effectively alters long-term disease progression and disability trajectories but carries a measurable increase in localized adverse events and oncogenic risk. LDRT should currently be reserved for refractory clinical phenotypes or implemented strictly within controlled investigational frameworks.

Penelitian ini bertujuan untuk meningkatkan kemampuan berbicara bahasa Mandarin siswa melalui penerapan metode Project Based Learning (PBL) berbantuan media AI-Heyzine Flipbook pada siswa kelas VIII B SMP Santo Borromeus Purbalingga. Penelitian menggunakan desain Penelitian Tindakan Kelas (PTK) yang dilaksanakan dalam dua siklus, masing-masing meliputi tahap perencanaan, pelaksanaan tindakan, observasi, dan refleksi. Media AI-Heyzine Flipbook digunakan sebagai sumber belajar yang memuat teks percakapan, kosakata, audio pelafalan, serta panduan proyek untuk mendukung tahapan PBL. Hasil penelitian menunjukkan bahwa penerapan PBL berjalan efektif dan meningkatkan kemampuan berbicara bahasa mandarin siswa dan partisipasi siswa dalam penyusunan naskah dan pembuatan video percakapan. Kemampuan berbicara siswa mengalami peningkatan signifikan dari pra-siklus hingga siklus II. Rata-rata nilai berbicara meningkat dari 67 (28,5% tuntas) pada pra-siklus menjadi 73,97 (47,6% tuntas) pada siklus I, dan mencapai 82,73(90,4% tuntas) pada siklus II. Perhitungan N-Gain juga mendukung temuan ini. N-Gain siklus I sebesar 0,21 (rendah) dan meningkat menjadi 0,48 (sedang) pada siklus II, yang menunjukkan efektivitas penerapan PBL berbantuan media digital dalam meningkatkan performa berbicara siswa. Dengan demikian, metode Project Based Learning berbantuan AI-Heyzine Flipbook terbukti efektif dalam meningkatkan kemampuan berbicara bahasa Mandarin siswa, khususnya pada aspek pengucapan (发音), tata bahasa (语法), kosakata (词汇), dan kelancaran (流利度).

Faith-based musical events in Manado represent a unique intersection of religion, culture, and social connectivity in a region deeply rooted in Christian traditions. These gatherings serve not only as spiritual experiences but also as platforms for intergenerational bonding and digital expression. Generation Z (born 1997–2012) plays a central role—not just as participants but also as digital storytellers, using social media to share and shape narratives of faith. Grounded in electronic word-of-mouth (eWOM) theory and generational media habit frameworks, this study addresses a gap in prior research that has largely overlooked faith-based events and their digital impact. The study investigates whether digital usage (such as booking, payment, and information sources) influences sharing behavior (H1), and whether event satisfaction (content, performers, facilities) also affects such behavior (H2). It further examines the impact of sharing behavior on loyalty (H3), and its mediating role between digital usage and loyalty (H4). This research contributes to a deeper understanding of how Gen Z’s online engagement with faith-based events shapes loyalty and intergenerational communication. The findings provide valuable insights for faith-based event organizers aiming to optimize attendee participation, digital outreach, and visitor retention through socially-driven strategies.

Latar Belakang: Pelatihan ini dilatarbelakangi oleh permasalahan guru dalam menyusun alat evaluasi mengingat keterbatasan waktu dan kesibukan dalam menjalankan perannya. Situasi ini perlu diselesaikan dengan meningkatkan kemampuan guru dalam memanfaatkan AI yang mendukung proses pembelajaran yang dilakukan.Tujuan: Pelatihan ini bertujuan untuk memanfaatkan teknologi secara efektif untuk meningkatkan kualitas pembelajaran.Metode: SMA Antartika Sidoarjo menjadi lokasi pelatihan ini yang melibatkan 61 guru yang dilaksanakan dalam durasi waktu satu bulan. Metode yang digunakan dalam pelatihan ini yaitu service learning terdiri tahapan identifikasi kebutuhan, integrasu pembelajaran, pemberian pelayanan, kolaborasi dengan pihak terkait, refleksi, dan evaluasi. Ada empat platform digital yang dilatihkan dalam pelatihan ini yaitu Gemini AI, Quizizz, Kahoot, dan Quizlet.Hasil: Pelatihan ini terlaksana secara lancar sehingga memberikan pemahaman mendalam pada guru tentang pemanfaatan AI dalam menyusun alat evaluasi. Hasilnya memberikan keterampilan praktis para guru di SMA Antartika Sidoarjo dalam memanfaatkan teknologi AI melalui platform digital sesuai kebutuhan pembelajaran. Evaluasi dan refleksi dilakukan oleh tim dan diperoleh informasi bahwa kegiatan ini menambah wawasan baru tetapi juga menginspirasi guru meningkatkan efektifitas pembelajaran di kelas masing-masing sesuai mata pelajarannya dan memberikan kontribusi pada penerapan teknologi AI yang inovatif dalam pembelajaran.

This study aims to find out the difference in the level of model accuracy among the Modified Altman Prediction (Z-Score), Springate (S-Score), and Zmijewski prediction models in predicting financial distress as a model of predicting operational Management and investment performance benchmarks in Transportation sub-sector firms in the Indonesian Stock Exchange (IDX) for the 4-period time. This study is a quantitative descriptive approach. The sampling technique is purposive sampling. This study utilizes sample data from the IDX, specifically www.idx.co.id, as well as the official websites of each firm. The results demonstrate that the Modification Altman Z-Score model can predict financial distress or potential bankruptcy by correctly assigning as many as 26 out of 48 samples, achieving an accuracy rate of 54.17%. The Springate S-Score model can predict financial distress or potential bankruptcy by assigning as many as 24 samples from 48 samples with an accuracy rate of 50%. The Zmijewski model was able to predict financial distress or potential bankruptcy with the highest accuracy level among the models used in this study, achieving an accuracy rate of 70.83% on 34 out of 48 samples. The conclusion from the three model bankruptcies is that the Zmijewski model is the most suitable for firms to use if they want to attract potential investors. It is used to predict financial distress and operational performance, as well as to inform firms' investment decisions. The findings of this study suggest that additional financial distress prediction models, such as Ohlson, Grover, and others, can be utilized to compare and contrast the yields of financial distress analysis.

Preventive, corrective, and maintenance activities are crucial tasks in the NE & OM Division of PT Telkom Indonesia Regional V. Despite the availability of a reporting system through Telegram bot and PACMAN-NEO web, the recording and reporting process is still considered less than optimal and is often complained about at the regional level. The main problems include data inaccuracy, lack of innovation and credibility, and visualization that has not met user expectations. This research aims to overcome the problems of slow, inaccurate, and less real-time reporting, as well as improve the features of navigation, verification, and monitoring of technician activities. The development method used is the System Development Life Cycle (SDLC) Waterfall model. The application was developed using Dart and Flutter as front-end frameworks, PHP and Laravel for API management, and PostgreSQL as the database. The black box test results show that all features function according to user needs. This application has been tested by 3 regional managers, 4 witel managers, 1 regional admin, and 10 technicians. User evaluation showed results of 75% for usability, 63% for ease of use, and 78% for user satisfaction. These results indicate that the developed application is feasible to use as a replacement for the previous Telegram-based PACMAN-NEO system.

Social media platforms provide celebrities with strategic opportunities to construct and project identities, yet studies employing semiotic frameworks remain limited. This research investigates how Indonesian musician and public figure Ahmad Dhani constructs self-branding through a single Instagram post, analyzed using Roland Barthes’ semiotic theory. Employing a qualitative case study, the study examines how visual and textual elements interact to create layered meanings across denotation, connotation, and myth. The findings reveal that Dhani’s post—featuring orchestral imagery and the textual message “Sorry if I am different”—functions not as an apology but as an affirmation of uniqueness. At the denotative level, the tuxedo, conductor’s stance, and blurred audience emphasize authority, refinement, and public recognition. Connotatively, the warm color palette and self-referential caption highlight creativity and authenticity. At the mythic level, the post transforms perceived arrogance into a narrative of leadership, positioning Dhani as a visionary figure whose difference signifies intellectual depth and cultural capital. This analysis underscores how Instagram can serve as a site for myth-making, where visual and textual signs operate as strategic tools for personal branding and reputation management. While the study offers insights into the cultural work of celebrity self-representation, its scope is limited to a single post. Future research should extend this analysis to multiple figures and longitudinal datasets to capture the evolving dynamics of digital identity construction.

The oil spill detection and assessment study conducted in the Banten Province of Indonesia involves the application of Sentinel-1 satellite data and machine learning tools in the year 2024. Synthetic Aperture Radar (SAR) data were used with VV polarization to observe the surface characteristics, using an oil spill threshold of - 25 dB to differentiate clean water from the oil spill based on low backscatter intensity. After desiring image processing and binary masking applications on the data that improve visibility of the oil spill-affected zones, vectorization was conducted for integration into geographic information systems (GIS). A temporal analysis indicated high variability across the spill sizes with an extreme peak on May 16 (79.686 km2) and July 3 (41.593 km2), which are likely dictated by the weather and oceanographic conditions plus the ship traffic of that time. Wind pattern analysis via ERA5 reanalysis data presented more insight into spill dispersion dynamics. Three machine learning classifiers were applied toward oil spill detection, namely Artificial Neural Networks (ANN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). Performance metrics indicate the ANN outperformed in discriminative ability (AUC = 0.92), while RF was highly accurate (99.01%) and precise (99.02%). This clearly demonstrates the viability of using an integrated approach of remote sensing, advanced image processing, and supervised learning for environmental monitoring and provides important information for minimizing ecological impacts and optimizing disaster response plans for maritime areas. Such an integrated scheme calls for advanced technology to combat ecological threats in maritime areas and provides crucial evidence toward ongoing interventions to protect and manage marine ecosystems and the associated local communities.

Penelitian ini bertujuan untuk menganalisis pengaruh Customer Engagement (CE) dan Electronic Word of Mouth (eWOM) terhadap Brand Awareness (BA) pada merek fashion lokal Indonesia dalam konteks ekonomi digital. Dengan menggunakan metode kuantitatif dan pendekatan Partial Least Squares konstrukl Equation Modeling (PLS-SEM), data dikumpulkan dari konsumen merek fashion lokal dan dianalisis untuk menguji dua hipotesis utama. Hasil penelitian menunjukkan bahwa baik CE maupun eWOM berpengaruh positif dan signifikan terhadap BA, masing-masing dengan nilai p < 0.05 dan T-statistik yang melebihi ambang batas. Temuan ini mengindikasikan bahwa keterlibatan pelanggan dan promosi dari mulut ke mulut secara digital merupakan strategi efektif dalam membangun kesadaran merek. Penelitian ini memberikan implikasi praktis bagi pelaku usaha fashion lokal agar lebih fokus pada interaksi digital dan strategi berbasis komunitas guna meningkatkan daya saing di pasar domestik yang semakin kompetitif.