Universitas Ahmad Dahlan Journal
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Analisis Kejadian Hujan Es Berbasis Satelit Himawari-9 Dengan Metode Rgb (Studi Kasus Jombang 24 September 2024)
Full text linkHujan merupakan salah satu unsur penting cuaca, terdapat dua jenis awan hujan. Awan konvektif tidak hanya menghasilkan hujan, tetapi berpotensi menimbulkan hujan es. Pada 24 September 2024 telah terjadi hujan es di Kabupaten Jombang yang mengakibatkan sejumlah kerusakan. satelit digunakan untuk mengetahui kondisi perawanan pada saat kejadian hujan es terjadi. Penelitian ini bertujuan untuk mengetahui pembentukan awan konvektif yang menyebabkan hujan es. Data Satelit Himawari-9 digunakan untuk dilakukan pengolahan menggunakan Teknik (Red, Green, Blue) RGB. Metode Day Convective Storm digunakan untuk mengetahui persebaran awan tinggi yang memiliki potensi hujan es. Metode Airmass ditambahkan untuk mengetahui persebaran awan dan massa udara awan tersebut. Hasil menunjukkan bahwa terdapat awan tinggi tebal yang berpotensi hujan es di Kabupaten Jombang. Terdapat massa udara yang hangat dan lembap sehingga awan mudah terbentuk. Adanya konvergensi pada wilayah Kabupaten Jombang menimbulkan updraft sehingga mendukung pertumbuhan awan. Kecepatan angin berkisar 2,4 – 5,6 knot dan kelembapan relatif 70 – 90% yang tergolong tinggi. Nilai CAPE 1050 J/Kg dan CIN 30 J/Kg sehingga menujukkan terdapat aktivitas konveksi kuat
Sistem Informasi Geografis Pemetaan Dampak Tsunami di Kota Pangandaran Berbasis Web
Full text linkPemahaman rendah masyarakat terkait mitigasi bencana alam akan sangat berpotensi mengakibatkan jumlah korban jiwa dengan jumlah yang tinggi. Pemerintah memiliki peran untuk memberikan edukasi terkait bencana alam melalui pemasangan rambu bahaya seperti tanda arah evaluasi, banner peta ancaman bencana, dan pemasangan alarm bencada. Upaya pemerintah terkait sosialisasi dan mitigasi bencana dirasa belum efektif akibat penolakan dari masyarakat yang merasa informasi bencana terlalu menakutkan. Penelitian ini bertujuan menghasilkan sistem informasi geografis untuk pemetaan dampak bencana alam tsunami di wilayah kota Pangandaran. Pelaksanaan penelitian ini akan menggunakan metode Waterfall yang terdiri dari tahap requirement analysis, design, development, testing, dan maintenance. Penelitian ini telah berhasil membangun sistem informasi geografis yang memvisualisasi atribut distribusi spasial potensi acaman bencana alam tsunami di kota Pangandaran melalui penerapan representasi peta. Hasil pengujian fungsional dengan black box test mendapatkan tingkat kesesuaian 100%. Hasil pengujian disimpulkan bahwa sistem informasi dapat digunakan dan diterima secara positif
Interdependence model in cross-disability friendships
Full text linkThe visual limitations experienced by blind individuals necessitate the development of friendships with sighted individuals, who are perceived as available and willing to help. The provision of the aid indicates a dependency of blind individuals on their sighted counterparts. However, the development of friendship broadens the scope of interaction, thereby transforming the dependency pattern into a more dynamic relational spectrum, ranging from independent and dependent to interdependent forms. This study aims to explore models of interdependent manifestations in friendships between individuals who are blind and those who are sighted. A qualitative approach utilizing a descriptive phenomenological method was employed. Data collection involved in-depth interviews with six informants, all of whom were male students from inclusive universities. The interdependent relationship patterns in these friendships are manifested through three key components: joint activities, shared experiences, and social support, each with its own model. The coherence of activities includes accidental, interference, and pure models. Bilateral and unilateral models characterize shared experiences, while social support comprises communal, transactional, and proportional models.
Design and Implementation of a Mobile Tourist Recommendation System for Sleman Using the Haversine Formula
Full text linkTourists often face challenges in accessing relevant information and navigating destinations due to the fragmented nature of existing mobile applications. This study addresses these issues by developing a mobile-based tourist recommendation system tailored to Sleman Regency, Yogyakarta. The system integrates features such as destination recommendations using the Haversine formula for proximity calculations, ticket purchasing, real-time navigation, a curated list of local culinary options, and a bug-reporting mechanism. Developed using the Agile methodology, the system underwent iterative enhancements based on user feedback. User preferences, including travel categories (e.g., nature, culture, family, temple) and culinary interests, are dynamically analyzed to provide personalized recommendations. The culinary feature highlights traditional dishes and popular dining spots, promoting local gastronomy. Black-box testing verified the system’s functionality, achieving a 100% success rate, while feedback mechanisms were incorporated to enable continuous improvement. The system ensures data privacy and security through robust encryption and regulatory compliance. It is scalable and adaptable, with potential applications beyond Sleman Regency. Furthermore, the system promotes sustainable tourism by encouraging eco-friendly destinations, respecting local cultural values, and supporting local culinary businesses. A comparative analysis highlights its advantage over conventional multi-application solutions by consolidating key features into a unified platform. This integrated solution streamlines tourist planning, navigation, and culinary exploration, enhancing convenience, user engagement, and satisfaction while contributing to the growth of Sleman’s tourism sector
Impact of Cosine Similarity Function on SVM Algorithm for Public Opinion Mining About National Sports Week 2024 on X
Full text linkPublic opinion on PON 2024 (National Sports Week in Indonesia) became a trending topic on X (formerly Twitter), reflecting both positive and negative sentiments. Understanding these sentiments is important for evaluating the event and preparing for the upcoming. However, baseline SVM algorithms using standard kernel functions are not optimized for text similarity and limit performance in sentiment analysis. This research proposes cosine similarity as a substitution for the kernel function on SVM, enhancing the sentiment analyzer's performance on public opinions about PON 2024. The approach leverages cosine similarity's strength in handling text-based data. The key contribution of this research is the integration of cosine similarity into the SVM algorithm as a replacement for kernel functions, improving performance in sentiment analysis. Additionally, this study offers a comprehensive comparison with baseline SVM and provides actionable insights for upcoming PON. The study collected 1,011 tweets related to PON 2024 using web scraping and the Twitter API, followed by labeling sentiments as positive, neutral, or negative. Several preprocessing techniques also were applied to prepare the data. Two models were developed: baseline SVM and another using SVM integrated with cosine similarity, both evaluated through accuracy, precision, recall, and F1-score. The baseline SVM achieved 85.1% accuracy, 85% precision, 83% recall, and 83.3% F1-score, struggling particularly with negative sentiment. Opposite, by integrating cosine similarity on SVM, the performance improved to 88.73% accuracy, 88.3% precision, 89.3% recall, and 88.3% F1-score—a boost of 3.3-6.3%. Additionally, the public opinion revealed that positive sentiments mostly focused on athlete achievements and medal awards, while negative sentiments highlighted issues like referee performance and specific sports (e.g., football). This approach can serve as a valuable tool for event organizers to identify public concerns and maintain positive aspects for the upcoming PON 2028
Factors influencing student engagement in online ideological and political education: a qualitative study of vocational college students in China
Full text linkThe COVID-19 pandemic accelerated the global shift to online education, exposing both opportunities and challenges in ideological and political (I&P) courses at Chinese higher vocational colleges, where student engagement remains pivotal yet underexplored. This qualitative study examines how students perceive and experience engagement in online I&P courses, framed by Activity Theory, Social Interaction, and Critical Pedagogy. Semi-structured interviews were conducted with 30 students (15 males, 15 females; freshman to junior cohorts) from Anhui Vocational and Technical College, using Tencent Meetings for 30-minute sessions. Thematic analysis identified two core themes: (1) Infrastructure—students emphasized the necessity of clear rules, stable platforms (e.g., MOOCs, ClassIn), and teacher responsiveness to foster accountability; (2) Engagement Dynamics—peer collaboration, real-world case studies, and critical discussions enhanced motivation, while poor internet connectivity, abstract content, and self-regulation struggles impeded participation. Notably, students highlighted the transformative potential of interactive tools (e.g., real-time Q&A, role-playing simulations) in bridging theory and practice. Limitations include the single-institution sample, potential response bias, and lack of longitudinal data. Nevertheless, findings offer actionable insights: educators should design modular content aligned with vocational contexts, integrate adaptive technologies to mitigate connectivity issues, and implement structured peer-review systems to sustain motivation. Institutional support for digital literacy training and hybrid learning models is also critical. Future research should expand to diverse regions, incorporate mixed methods, and track long-term outcomes to strengthen pedagogical strategies in online I&P education
PROSES BERPIKIR SISWA LEVEL VISUALISASI DALAM MENYELESAIKAN MASALAH DIMENSI TIGA BERDASARKAN LANGKAH-LANGKAH POLYA
Full text linkTujuan dari penelitian ini adalah mendeskripsikan proses berpikir siswa level visualisasi dalam menyelesaikan masalah dimensi tiga berdasarkan langkah-langkah polya. Jenis penelitian ini adalah penelitian deskriptif dengan pendekatan kualitatif. Penelitian ini dilakukan di SMK AR-RAUDLAH. Subjek penelitian ini terdiri dari 2 siswa level visualisasi. Penelitian ini menggunakan metode tes dan wawancara. Instrumen yang digunakan dalam penelitian ini antara lain tes VHGT, tes penyelesaian masalah dan pedoman wawancara. Hasil tes VHGT pada penelitian ini, hanya ditemukan level pra visualisasi, visualisasi dan analisis dengan presentase berturut-turut adalah 58,33%, 29,17% dan 4,17%. Selanjutnya, siswa level visualisasi diberi tes penyelesaian masalah untuk mengetahui proses berpikir siswa dalam menyelesaikan masalah dimensi tiga. Hasil penelitian ini diperoleh bahwa proses berpikir siswa level visualisasi dalam menyelesaikan masalah dimensi tiga menjadi empat langkah yaitu memahami masalah, menyusun rencana, melaksanakan rencana, dan memeriksa kembali. Pada saat memahami permasalahan dimensi tiga, peristiwa equilibrium terjadi ketika siswa mampu mengidentifikasi syarat-syarat dalam permasalahan serta mampu membuat sketsa gambar dari permasalahan. Pada saat menyusun rencana, peristiwa equilibrium terjadi ketika siswa mampu menentukan rencana yang akan digunakan dengan menggunakan semua informasi yang telah didapatkan untuk menyelesaikan permasalahan melalui gambar. Pada saat melaksanakan rencana, peristiwa equilibrium terjadi ketika siswa mampu memastikan dan menyelesaikan permasalahan sesuai dengan rencana. Pada saat memeriksa Kembali, peristiwa equilibrium terjadi ketika siswa mampu menjelaskan kembali hasil penyelesaian dari permasalahan
Exploring a Teacher’s Knowledge in Constructing Mathematical Lateral Thinking Problems to Teach Creativity
Full text linkThis study examined the process of constructing mathematical problems to teach creativity using the concept of lateral thinking. It employed a case study design and involved a senior junior high school mathematics teacher who has won mathematics learning innovation competitions. The teacher has understood what is meant by mathematical creativity and lateral thinking, and has proven his mathematical ability and lateral thinking ability. He was asked to create his own math problems, teach creativity, and assess students' math learning outcomes using the problems. The findings of this study highlight three key relationships: (1) the connection between mathematical content knowledge and problem-structuring ability, (2) the influence of training experience on methods for teaching creativity, and (3) the interrelationship among content knowledge, training experience, and assessment practices. These highlight the importance of integrating content knowledge and pedagogical approaches in mathematics learning to enhance creative problem-solving skills
Tailoring Data Storage Configuration for Efficient Fraud Detection Model Training
Full text linkThe rapid growth of e-commerce in Indonesia, with a record 88.1% growth rate, has been accompanied by a surge in online fraud, leading to an estimated loss of 4.62 trillion rupiahs. Current fraud prevention methods, such as the widely used 3D-Secure system, though effective, result in a high rate of transaction abandonment (approximately 16%), which is undesirable for merchants. To address this, we propose an AI-based fraud detection system that leverages machine learning models to identify potentially fraudulent transactions. By employing a combination of classification algorithms, including logistic regression and neural networks, security protocols are activated only for high-risk transactions, optimizing transaction processing efficiency and improving detection accuracy. Our study focuses on fine-tuning key parameters of the AI-Fraud Detector model, specifically some parameters such as ∆ttrain, ∆tlag and f rac hr pass, to enhance detection performance over time. Simulation performances using ROCAUC, false positive rate (fpr), and true positive rate (tpr) metrics show that a configuration with a training period (∆ttrain) of 180 days, a lag period (∆tlag ) of 90 days, and a high-risk pass fraction (f rac hr pass) of 10% yields a balance between detection efficiency (∼ 50%) and a reduced false positive rate. It means that the model is able to identify approximately 50% of the actual high-risk events while minimizing the number of times it incorrectly identifies a low-risk event as high-risk. However, further research is required to refine these results, explore parameter optimization strategies, and enhance the model’s adaptability to evolving fraud patterns. Future work will focus on optimizing thresholds, improving model robustness over time, and ensuring effective detection of new fraud schemes. This research improves model performance by optimizing key parameters and enhancing detection accuracy while minimizing false positive
KNN-Based Music Recommender System with Feedforward Neural Network
Full text linkMusic, as a form of entertainment, is now an essential element in the lives of many individuals. Access to music-related information has become widespread through various websites and applications, leading to a significant increase in music data. Technological advancements have driven the development of music recommendation system research, which utilizes multiple methods, algorithms, and classification techniques to present recommendations that match user preferences. This research contributes to integrating the K-Nearest Neighbors (KNN) method for initial classification and the more advanced Feedforward Neural Network (FNN) model. In addition, this research also recommends songs with similar audio features. The main focus of this research is to design and evaluate a song recommendation system by combining such methods while comparing various hyperparameter results to find the most suitable model. The best model found will be incorporated into Content-Based Filtering (CBF) to provide song recommendations based on genre. This research uses the GTZAN dataset of 1,000 audio data from ten music genres. The K-NN model test assesses how well the model maintains consistency and achieves optimal performance. This study conducted three tests to find the best-performing model by integrating the model and hyperparameters. The results showed that the third FNN model showed the best performance after being optimized using the SGD optimizer. Furthermore, this model was combined with the CBF method using cosine similarity calculation. The system effectively recommended songs based on the blues genre, with five relevant nearest neighbors and an average score reaching 98%