Breast cancer remains one of the leading causes of death among women worldwide, primarily due to late detection and diagnosis. Early and accurate prediction is essential to improve survival rates. Machine learning (ML) techniques have proven effective in supporting early diagnosis. This study aims to evaluate and compare the performance of three different approaches: traditional ML, ensemble ML, and deep learning (DL) for early prediction of breast cancer using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset. The methodology includes data collection, preprocessing, and the design of predictive models. Traditional ML algorithms used include Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naïve Bayes (NB), and Decision Tree (DT). Ensemble ML techniques comprise Random Forest (RF), XGBoost, and AdaBoost, while DL models include Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). The models were evaluated using precision, recall, F1-score, and accuracy. The results indicate that XGBoost achieved the highest accuracy (0.99), with strong recall (0.98) and F1-score (0.986), outperforming all other ensemble and traditional ML methods. CNN achieved 0.99 in all evaluation metrics, slightly outperforming RNN, which attained 0.98 accuracy and 0.985 F1-score. These findings confirm that ensemble ML techniques outperform traditional models, while CNN leads among DL models. Furthermore, the proposed models demonstrated superior prediction performance compared to existing studies, particularly in minimizing false negatives, which is critical for healthcare applications.

Purpose: This research aims to address the methodological challenges posed by imbalanced data in classification tasks, where minority classes are severely underrepresented, often leading to biased model performance. It evaluates the effectiveness of hybrid sampling techniques specifically, the Synthetic Minority Oversampling Technique combined with Neighborhood Cleaning Rule (SMOTE-NCL) and with Edited Nearest Neighbors (SMOTE-ENN) in improving the predictive performance of ensemble classifiers, namely Double Random Forest (DRF) and Extremely Randomized Trees (ET), with a focus on enhancing minority class detection.
Methods: A total of eighteen simulated scenarios were developed by varying class imbalance ratios, sample sizes, and feature correlation levels. In addition, empirical data from the 2023 National Socioeconomic Survey (SUSENAS) in Riau Province were employed. The data were partitioned using stratified random sampling (80% training, 20% testing). Models were trained with and without hybrid sampling and optimized through grid search. Their performance was evaluated over 100 iterations using balanced accuracy, sensitivity, and G-mean. Feature importance was interpreted using Shapley Additive Explanations (SHAP).
Results: DRF combined with SMOTE-NCL consistently outperformed all other models, achieving 87.56% balanced accuracy, 82.17% sensitivity, and 86.75% G-mean in the most extreme simulation scenario. On the empirical dataset, the model achieved 76.37% balanced accuracy and 75.49% G-mean.
Novelty: This study introduces a novel integration of hybrid sampling techniques and ensemble learning within an interpretable machine learning framework, providing a robust solution for poverty classification in imbalanced datasets.

This research have a purpose to prove the influence of social support on self confidence of students at SMAN 100 Jakarta. This research was motivated by the large number of students who feel inferior or lack self-confidence so that it can interfere with and hinder the improvement of their achievements, skills, or interests both academically and non-academically. Quantitative with a sampling technique, namely simple random sampling, is the foundation of this study. Students of SMA Negeri 100 Jakarta class 10 became the population in the study conducted. With an R Square value of 0.512, it proves that there is an influence of social support on self-confidence which is presented at 51.2%, while 48.8% is the contribution of variables other than social support such as self-concept which the researcher did not include in this study. The findings are reinforced by the t-test analysis value with an acquisition of 15.928> 1.978 which can be concluded that there is an influence of social support on the self-confidence of class X students at SMA Negeri 100 Jakarta.

Pasar produk perawatan kulit di Indonesia yang sangat kompetitif menuntut inovasi berkelanjutan, terutama pada aspek kemasan yang menjadi titik interaksi pertama dengan konsumen. Kemasan produk yang ada saat ini seringkali memiliki keseragaman visual dan belum sepenuhnya memenuhi harapan fungsional konsumen. Penelitian ini bertujuan untuk mengembangkan desain kemasan sunscreen XYZ yang lebih representatif terhadap preferensi konsumen dengan menggunakan metode Quality Function Deployment (QFD). Pendekatan ini secara sistematis menerjemahkan kebutuhan konsumen, atau Voice of Customer (VoC), yang diperoleh dari analisis Kansei Engineering sebelumnya, ke dalam spesifikasi teknis yang terukur melalui matriks House of Quality (HoQ). Hasil analisis menunjukkan bahwa atribut fungsional seperti 'Praktis', 'Informatif', dan 'Pump' merupakan prioritas utama bagi konsumen. Melalui HoQ, atribut-atribut ini berhasil diterjemahkan menjadi tiga respons teknis dengan prioritas tertinggi: penggunaan botol pipih dengan sudut membulat, penggunaan material HDPE, dan posisi gantungan berada di atas. Berdasarkan spesifikasi tersebut, dikembangkan tiga alternatif desain, di mana Alternatif 3 dengan gaya visual illustrated modern terpilih sebagai konsep terbaik dengan skor kinerja tertinggi. Penelitian ini membuktikan bahwa QFD adalah metode yang efektif untuk memastikan proses pengembangan desain berpusat pada konsumen, menghasilkan konsep kemasan yang unggul secara fungsional dan estetis.

This study aims to explore the influence of digital media on language variation among college students. Using qualitative methods and a literature review design, this study analyzes how digital media, such as social media, online communication platforms, and instant messaging applications, influence language use among college students. The results show that digital media triggers the emergence of language variation that includes digital slang, changes in grammar and spelling, and the transfer of foreign culture and vocabulary. Although digital media provides a space for students to express themselves creatively, there are concerns about the negative impact on formal language proficiency. Therefore, this study emphasizes the importance of collaborative efforts from educational institutions and students to maintain a balance between linguistic creativity and the preservation of formal language.

Dinamika pendidikan di Indonesia berjalan begitu cepat. Perubahan kurikulum yang didasarkan pada kebijakan politik membuat banyak pihak sulit untuk beradaptasi. Salah satunya yakni pola belajar di rumah dengan kurikulum Merdeka. Orang tua terbukti mengalami kesulitan melakukan pembagian waktu bekerja dan pendampingan anak. Pemahaman kurikulum yang sering berganti membuat orang tua bingung tentang definisi ideal seorang anak yang telah mereka sekolahkan. tim Pengabdian kepada Masyarakat FKIP UKSW merancangkan suatu bentuk pembekalan dengan metode On the Job Training bertajuk Parents Empowerment terkait kurikulum, literasi, sains dan pengelolaan pembelajaran di rumah. Peserta kegiatan yakni 17 orang tua SD Kristen 1 Salatiga. Penguatan ini berjalan baik dan memberikan manfaat penambahan wawasan, tips praktek berkomunikasi dan pengelolaan belajar anak di rumah.

This study uses a qualitative method with a literature review approach to examine the effectiveness of the two-phase method in solving linear programming problems. The two-phase method is one of the techniques in linear programming used when the objective function does not have a feasible initial solution directly. Through the exploration and analysis of various scientific literature sources from 2019 onwards, this study finds that the two-phase method can systematically provide optimal solutions, especially in complex problems with multiple constraints. Previous research has shown that this method is effective in accelerating the identification of basic feasible solutions and minimizing unnecessary iterations compared to the standard simplex method. Additionally, the two-phase method offers better numerical stability and high reliability in both industrial and educational applications. However, its effectiveness largely depends on the understanding of the algorithm and the ability to design an appropriate mathematical model. The results of this study are expected to contribute theoretically to the development of efficient solution techniques for linear optimization problems.

Hipertensi merupakan kondisi ketika tekanan darah sistolik mencapai atau lebih dari 140 mmHg dan/atau tekanan darah diastolik mencapai atau lebih dari 90 mmHg saat diukur di fasilitas pelayanan kesehatan. Pada tahun 2021, tercatat sebanyak 69.247 namun, kasus hipertensi di Kota Salatiga hanya 20.310 kasus yang mendapat pelayanan kesehatan. Pengabdian kepada masyarakat ini bertujuan untuk memberikan promosi kesehatan kepada masyarakat terkait hipertensi dan cara mencegah hipertensi dengan jus Mesem. Jus Mesem dibuat dari 4 bahan yaitu mentimun, seledri, lemon dan madu. Melalui kuesioner yang dibagikan kepada peserta promosi kesehatan, ditemukan bahwa 39 dari 40 (97.5%) responden menyetujui bahwa kegiatan promosi kesehatan hipertensi dan demonstrasi pembuatan jus Mesem ini meningkatkan pengetahuan mereka terkait hipertensi dan meningkatkan minat mereka untuk mencegah hipertensi dengan membuat jus Mesem di rumah. Dengan adanya promosi kesehatan ini, masyarakat akan mampu untuk mencegah hipertensi dengan cara yang mudah dan dapat dimulai dari keluarga masing-masing.

Effective interpersonal communication by teachers supports learning through empathetic, argumentative, and ethical verbal and nonverbal approaches, building students’ motivation, trust, and engagement in an inclusive learning environment. This study aims to understand the interpersonal communication of Islamic Religious Education teachers in establishing positive relationships to support students' academic success. This research uses a qualitative descriptive approach with a case study design to explore interpersonal communication between Islamic Education teachers and students through participatory observation, in-depth interviews, and documentation. Data were analyzed using the Miles, Huberman, and Saldana method, which includes data reduction, presentation, and conclusion drawing. Data validity was maintained through source and method triangulation, member checking, and peer discussions. The results indicate that teachers’ interpersonal communication strategies involve a harmonious blend of verbal and nonverbal communication. Teachers deliver material through lectures, advice, and praise, while reinforcing messages with facial expressions and gestures. Values such as empathy, openness, support, positive attitudes, and equality form the foundation for building strong emotional connections with students. Supporting factors such as extended learning time and school programs also strengthen communication. Despite challenges like large class sizes and negative student perceptions, adaptive and empathetic strategies successfully create a positive and inclusive learning atmosphere. Conclusion: The interpersonal communication strategies of teachers enhance learning through verbal and nonverbal approaches that foster closeness, participation, and a positive atmosphere.

This study investigates the influence of Provincial Minimum Wage (PMW), Labor Force Participation Rate (LFPR), and Population Growth Rate (PGR) on Employment Opportunities (EO) across 20 most populous provinces in Indonesia from 2017 to 2024. Using a quantitative approach and panel data sourced from the Central Statistics Agency, the analysis applies the Fixed Effect Model selected through Chow and Hausman tests. The Outcomes indicate that PMW has a significant negative effect on EO, implying that wage increases without corresponding productivity growth can reduce job absorption. Conversely, LFPR has a significant positive effect, reflecting that a higher labor force engagement boosts employment. PGR has a negative but statistically insignificant effect. The F-test confirms that all three variables jointly influence EO. These results highlight the need to harmonize wage regulations, human capital development (HDI), and population planning to support inclusive and sustainable employment in regions experiencing high demographic pressures.