Universitas Dharmas Indonesia

Abstract Barnyard millet is a nutritious, small millet crop grown in rainfed condition, consumed just like rice. Reducing the maturity duration is an important breeding objective and assessing the phenotypic diversity help in identifying those genotypes with desirable traits. Twenty-seven genotypes were subjected to D 2 analysis which revealed the presence of eight different clusters. Cluster I had the maximum number of genotypes. Cluster II had five genotypes, cluster III had three genotypes and in cluster IV, V, VI, VII and VIII with one genotype each. Early maturing germplasm were in cluster III and the check MDU1 was in separate cluster VII. The percentage contribution of the traits towards divergence was maximum by grain yield per plant followed by plant height and net by days to maturity. Stability analysis of these genotypes in three different environments revealed the stable genotypes with high mean were ACM BM-20-019 and MDU1. E2 was the least interactive environment. Using What- Won- Where biplot, the three test environments were divided into two mega environments. In light of this, the genotype ACM BM-20-016 performed well in mega environment 1 (E1), while the genotypes ACM BM-20-019, MDU 1, and ACM BM-20-012 perform similarly in mega environment 2. (E2 and E3).

Marketing is a vital element in maintaining business continuity, especially amidst increasingly fierce competition in the automotive industry. This study aims to analyze the marketing strategy of Honda motorcycles at CV Anugerah Kencana Motor Palembang. The method used is descriptive with a qualitative approach. Data were collected through observation, documentation, and interviews, then analyzed using the marketing mix framework (4P): product, price, place, and promotion. The results show that the company offers various Honda products, such as PCX, Beat, Vario, Scoopy, Genio, and Revo X, with competitive prices and discounts to attract consumers. The business location is considered strategic, although distribution is hampered by limited delivery personnel. Promotion is carried out through personal selling, brochure distribution, and exhibition participation. The company's strategies include market development strategies, product development strategies, defensive strategies, and market penetration strategies. Overall, these strategies help the company increase competitiveness and maintain its business continuity, although further development in distribution and promotional innovation is still needed.

Anemia merupakan kondisi yang sering dialami oleh remaja putri, terutama akibat meningkatnya kebutuhan zat besi selama masa pertumbuhan. Menstruasi bulanan serta kecenderungan membatasi asupan makanan seperti diet vegetarian demi menjaga penampilan, dapat memperburuk risiko terjadinya anemia. Konsumsi sayur daun kelor (Moringa oleifera), yang kaya zat besi, vitamin A, vitamin C dan senyawa bioaktif lainnya, merupakan salah satu cara alami yang berpotensi meningkatkan kadar hemoglobin. Penelitian ini bertujuan untuk mengetahui pengaruh pemberian sayur daun kelor terhadap peningkatan kadar hemoglobin pada remaja putri dengan anemia ringan di Pondok Pesantren Al Barokah tahun 2025. Penelitian ini menggunakan desain pre-eksperimental dengan pendekatan one group pre-test post-test. Sebanyak 15 responden dipilih menggunakan teknik purposive sampling. Perlakuan berupa konsumsi sayur daun kelor diberikan selama 14 hari. Pemeriksaan kadar hemoglobin dilakukan sebelum dan sesudah perlakuan menggunakan alat Easy Touch dan data dianalisis menggunakan uji Wilcoxon Signed Rank Test karena data tidak berdistribusi normal. Hasil penelitian menunjukkan bahwa kadar hemoglobin meningkat dari rata-rata 11,40 g/dL menjadi 12,26 g/dL, dengan nilai signifikansi p = 0,007 (p < 0,05) Sehingga dapat disimpulkan bahwa pemberian sayur daun kelor berpengaruh secara signifikan terhadap peningkatan kadar hemoglobin pada remaja putri dengan anemia ringan.

In social commerce, particularly among small and medium-sized handicraft enterprises (SMEs), personalized recommender systems (RS) are crucial for enhancing store and product discovery. Conventional content-based filtering (CBF) often overemphasizes accuracy, leading to over-specialization and limiting exposure to novel or diverse items, an issue in the handicraft sector where uniqueness is valued. This study proposes a serendipitous recommendation approach using a Genetic Algorithm (GA) with adaptive selection strategies, Roulette Wheel Selection (RWS), Tournament Selection (TnS), and Rank-Based Selection (RBS), to balance relevance and unexpectedness. Handicraft store attributes, such as product types, materials, and services, are encoded in a 19-bit chromosome and evaluated via a hybrid fitness function. Tested on real data from West Sumatra SMEs, the model is assessed using Precision, Recall, Novelty, and Serendipity metrics. Results show that the GA-based adaptive selection approach outperforms baseline CBF in producing more diverse and surprising recommendations, fostering exploratory shopping experiences and supporting the discovery of unique local products in social commerce ecosystems.

Pope Francis realizes that there are still many women in various parts of the world who face suffering, injustice and inequality. The challenges faced by these women include violence, discrimination and marginalization in various aspects of life. However, women have a very important role in cultivating and maintaining human values. This research aims to increase awareness of all parties, especially women, to be more actively involved in fostering and developing human values. One of the main approaches to supporting this role is through education, both formal and informal, which is seen as the key to advancing holistic human development. The method used in this research is literature study. In the previous article, the role of women and politics was written which was used as a slogan in political parties. This research explores three main focuses: Pope Francis' views on women, the contribution of women in cultivating human values, and the application of these human values in life. The results of this research confirm that the role of women is very significant in strengthening human values, both through formal and informal education. These two educational pathways can help society better appreciate human dignity as God's noble creation. So there is a need for collaboration from several parties to realize the role of women in fostering human values.AbstrakPaus Fransiskus menyadari bahwa masih banyak perempuan di berbagai belahan dunia yang menghadapi penderitaan, ketidakadilan, dan ketidaksetaraan. Tantangan yang dihadapi perempuan ini meliputi kekerasan, diskriminasi, serta marginalisasi dalam berbagai aspek kehidupan. Namun demikian, perempuan memiliki peran yang sangat penting dalam menumbuhkan dan menjaga nilai-nilai kemanusiaan. Penelitian ini bertujuan untuk meningkatkan kesadaran semua pihak, terutama perempuan, agar lebih aktif terlibat dalam pembinaan dan pengembangan nilai-nilai kemanusiaan. Salah satu pendekatan utama untuk mendukung peran tersebut adalah melalui pendidikan, baik formal maupun informal, yang dipandang sebagai kunci dalam memajukan pembangunan manusia secara holistik. Metode yang digunakan dalam penelitian ini adalah studi pustaka. Dalam tulisan sebelumnya dituliskan peran perempuan dan politik yang dijadikan slogan dalam partai politik. Penelitian ini mengeksplorasi tiga fokus utama: pandangan Paus Fransiskus tentang perempuan, kontribusi perempuan dalam menumbuhkan nilai-nilai kemanusiaan, serta penerapan nilai-nilai kemanusiaan tersebut dalam kehidupan. Hasil penelitian ini menegaskan bahwa peran perempuan sangat signifikan dalam memperkuat nilai-nilai kemanusiaan, baik melalui pendidikan formal maupun informal. Kedua jalur pendidikan ini dapat membantu masyarakat lebih menghargai martabat manusia sebagai ciptaan Tuhan yang mulia. Maka perlu adanya kolaborasi dari beberapa pihak untuk mewujudkan peran perempuan dalam menumbuhkan nilai-nilai kemanusiaan.

Background: Childhood stunting and undernutrition remain critical global health challenges. While long-term interventions are common, the efficacy of short-term, community-based intensive nutritional interventions (Pos Gizi) requires further empirical evaluation.Objective: This study aimed to analyze the clinical effectiveness of a 12-day intensive nutritional package on weight and height gain among underweight children under five in Garut District, West Java, using a quasi-experimental one-group pretest-posttest design.Methods: The study included 69 children (aged 10–55 months) meeting the inclusion criteria, who received supplementary feeding and intensive health education over 12 days. Anthropometric data were collected at baseline and endline using standardized instruments. Data were analyzed using the Paired t-test and Cohen’s d effect size calculation.Results: Significant improvements were observed in both weight (p < 0.001) and height (p < 0.001) post-intervention. The mean weight gain was 0.20 kg (95% CI: 0.16–0.25), and the mean height gain was 0.65 cm (95% CI: 0.42–0.88). Effect size analysis indicated a large clinical impact on weight (d = 1.01) and a medium-to-large impact on height (d = 0.68).Conclusion: Short-term intensive nutritional interventions are effective in stimulating rapid weight rehabilitation. While linear growth showed statistical improvement, this likely reflects acute physical recovery rather than permanent structural growth. This model is recommended as an initial acceleration strategy to manage undernutrition.

Background: Skin changes often occur during pregnancy, such as striae gravidarum and hyperpigmentation. Mom’s Cerij Oil is formulated to minimize the risk of allergies or skin irritation, making it suitable for sensitive skin during pregnancy. The aim of this study was to determine the effectiveness of Mom’s Cerij Oil (Centella Asiatica and Piper betle L) compared to Coconut Oil in maintaining skin moisture and preventing Striae Gravidarum in pregnant women in the second trimester at the Cikupa Health Center in Tangerang Regency. Methods: The research method was a quantitative study using a true experimental design with a pretest-post test control group approach. The study involved 66 pregnant women in their second trimester at the Cikupa Health Center in Tangerang Regency. The sampling technique used purposive sampling, with each intervention group and control group consisting of 33 respondents. Data analysis involved univariate analysis using frequency distribution and bivariate analysis using the McNemar test and the Mann–Whitney U test. The study was conducted from January to February 2025. Results: Bivariate analysis showed that Mom’s Cerij Oil was effective in maintaining skin moisture and preventing striae gravidarum in second-trimester pregnant women, with significant pretest–posttest mean differences (0.42; p=0.039 and 0.18; p=0.031). Posttest comparison between Mom’s Cerij Oil and coconut oil groups also indicated a significant difference (p=0.001). Conclusion: Mom’s Cerij Oil is more effective than coconut oil in maintaining skin moisture and preventing striae gravidarum in second-trimester pregnant women, and it can be recommended as a safe topical option during pregnancy.

Abstract Aspect-Based Sentiment Analysis (ABSA) is essential for extracting detailed sentiment polarity regarding specific aspects in product reviews, providing deeper insights into customer opinions on various product attributes. Unlike document-level sentiment analysis, ABSA allows a more granular understanding, crucial for e-commerce analytics and decision-making systems. This study investigates the effectiveness of transformer-based models, such as BERT and RoBERTa, in performing ABSA for product review mining.Purpose:This research aims to explore the application of transformer-based models for aspect-based sentiment analysis, comparing their performance with traditional deep learning models (LSTM and CNN) in the context of mining product reviews. The study evaluates how transformer-based models can more effectively capture sentiment polarity at the aspect level.Methods/Study design/approach:The study uses publicly available product review datasets from large-scale e-commerce platforms, where each review is annotated with aspect terms and sentiment polarities (positive, negative, neutral). The datasets were split into training, validation, and test sets in an 80:10:10 ratio. The models—BERT, RoBERTa, LSTM, and CNN—were fine-tuned on the ABSA task. Performance was evaluated using standard metrics: accuracy, precision, recall, and F1-score.Result/Findings:The results show that transformer-based models, especially RoBERTa, significantly outperform conventional deep learning baselines like LSTM and CNN. RoBERTa achieved the best performance with an accuracy of 0.93 and an F1-score of 0.92, while BERT achieved an accuracy of 0.91 and an F1-score of 0.90. In contrast, LSTM and CNN achieved F1-scores of 0.82 and 0.84, respectively. The transformer models excel in capturing contextual dependencies and associating sentiment polarity with the correct aspects, particularly in complex and multi-aspect sentences. This highlights the superior ability of transformers to handle long-range dependencies and complex sentence structures compared to LSTM and CNN.The findings confirm that transformer-based models are highly effective for aspect-based sentiment analysis, providing a more reliable approach for product review mining. Future research should address the efficiency and interpretability of these models, particularly for large-scale deployment in real-world e-commerce applications.

Abstract The rapid growth of financial technology (fintech) services in Indonesia has significantly transformed digital transaction behavior, with digital wallets such as DANA becoming widely adopted. Despite high usage rates, public discourse on social media frequently highlights both positive experiences and recurring concerns related to system reliability and data security. Twitter, as a real-time public communication platform, provides a rich source of user-generated content that reflects public perception toward fintech services. This study investigates public sentiment toward DANA fintech services in Indonesia through sentiment analysis of Twitter data using machine learning approaches. Purpose:This study aims to analyze public perception of DANA fintech services based on sentiment expressed on Twitter and to compare the performance of several machine learning classification algorithms in identifying positive, neutral, and negative sentiments in Indonesian-language social media texts. Methods/Study design/approach: The study employs a quantitative descriptive approach using text mining and sentiment analysis techniques. Twitter data were collected via the Twitter API over a specified period and processed through preprocessing stages including text cleaning, tokenization, stopword removal, and stemming. Feature extraction was performed using the Term Frequency–Inverse Document Frequency (TF-IDF) method. Sentiment classification was conducted using Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest algorithms. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. Result/Findings:The results indicate that public sentiment toward DANA is predominantly positive, driven by perceptions of transaction convenience and speed, followed by neutral sentiment, while negative sentiment is mainly associated with system disruptions and data security issues. Among the evaluated algorithms, SVM achieved the highest classification accuracy (91.4%), outperforming Random Forest and KNN. These findings demonstrate that SVM is more effective for sentiment classification of short Indonesian-language texts on social media platforms. Novelty/Originality/Value:This study contributes to the growing body of sentiment analysis research in Indonesia by providing empirical evidence on public perception of fintech services using machine learning techniques. By systematically comparing multiple classification algorithms in the context of Indonesian Twitter data, the study offers methodological insights and practical implications for fintech service providers in improving service quality, communication strategies, and user trust.

Perkembangan platform social commerce telah mengubah cara UMKM fashion menjangkau konsumen digital. Penelitian ini bertujuan untuk menganalisis pengaruh social commerce terhadap loyalitas pelanggan UMKM fashion online. Metode penelitian menggunakan pendekatan kuantitatif dengan survei terhadap 200 konsumen yang aktif berbelanja melalui platform social commerce seperti Instagram Shopping dan TikTok Shop. Variabel penelitian meliputi pengalaman berbelanja, interaksi sosial, dan kualitas konten sebagai variabel bebas, serta loyalitas pelanggan sebagai variabel terikat. Hasil penelitian menunjukkan bahwa social commerce secara signifikan meningkatkan loyalitas pelanggan melalui pengalaman interaktif, engagement, dan kemudahan transaksi. Penelitian ini menyimpulkan bahwa social commerce merupakan strategi efektif bagi UMKM fashion dalam membangun hubungan jangka panjang dengan pelanggan. Kata kunci: Social Commerce, Loyalitas Pelanggan, UMKM Fashion, Engagement Konsumen, Pemasaran Digital