Multimedia Nusantara University

Marriage contracts are often accompanied by local phenomena in the form of additional requirements proposed by either party. The goal of the research is to analyze the additional Conditions in Marriage Contracts in North Kalimantan based on the legal Perspective, normative conflicts, and its impact to the socio legal aspect. This study employs a doctrinal legal approach (juridical-normative) to examine the validity of additional conditions in marriage contracts based legal materials including Law Number 1 of 1974, the Indonesia Civil Code (KUHPerdata), the Compilation of Islamic Law, and Government Regulation No. 9/1975. The result of the research is in the practice of including additional conditions in marriage contracts in North Kalimantan illustrates the profound impact of local wisdom. For example, the Bugis community enforces uang panai (dowry), several traditional rituals among the Bulungan community, and also Tidung cultural traditions. From a legal perspective, the additional conditions in marriage contracts should be viewed in light of Law No. 1 of 1974 on Marriage, Government Regulation No. 9 of 1975, Compilation of Islamic Law (KHI), and Civil Code or KUHPerdata. Conditions such as uang panai in Bugis tradition or symbolic rituals in Bulungan and Tidung communities may be recognized as cultural practices, but they do not carry binding legal force under civil law unless incorporated into a formal marriage agreement that complies with the provisions of KUHPerdata. Furthermore, additional requirements in a marriage contract not only impact the immediate couple involved but can also influence broader social dynamics.

The rapid expansion of digital technologies has reshaped higher education, creating both opportunities and pressures for lecturers, particularly in resource-constrained regions. This study explores the phenomenon of technostress and its impact on lecturers' self-efficacy in Terdepan, Terluar, dan Tertinggal (3T) regions. Based on the Person–Environment Fit Theory and self-efficacy theory, the research investigates how lecturers experience and respond to digital demands amid infrastructure limitations. Using a qualitative phenomenological design, nine lecturers from diverse disciplines participated in in-depth interviews. Thematic analysis revealed five interrelated dimensions: techno-overload, techno-complexity, techno-uncertainty, the mediating role of self-efficacy, and institutional and cultural support. Digitalisation intensified workloads, platform complexity challenged senior lecturers lacking formal training, and unstable systems increased uncertainty. Higher self-efficacy fostered adaptive coping, whereas lower self-efficacy encouraged avoidance strategies. Findings highlight that technostress in underdeveloped higher education settings is not only an individual struggle but also a systemic issue shaped by weak infrastructure, inconsistent institutional support, and cultural dynamics. While self-efficacy strengthens resilience, it cannot offset structural inequities. The study extends the technostress literature in underdeveloped regional contexts and highlights the need for sustained infrastructure investment, structured training, and culturally grounded mentors to support inclusive digital transformation.

The Javan Gibbon (Hylobates moloch) is a frugivorous primate that relies on fruit as the main component of its diet. Environmental conditions can influence food availability for these species, which in turn affects their behavior and survival. However, detailed data in how seasonal variations impact the gibbon’s diet remain limited, making it challenging to design effective conservation strategies. Thus, understanding these diet preferences is crucial. To address these knowledge gaps, this study aims to analyze the differences in diet composition and feeding preferences of the Javan Gibbon during different seasons at the Cikaniki Resort, Gunung Halimun Salak National Park (GHSNP). We used scan sampling with a 10-minute interval to record the activity of gibbons continuously from January to December 2022, and we found that the diet composition of Javan Gibbons did not differ significantly between the rainy and dry seasons. A total of 73 plant species from 34 families were consumed by the Javan Gibbon throughout 2022, with the dietary composition consisting of 69% fruit, 19% young leaves, 10% flowers, and 2% invertebrates. Javan Gibbon’s food preference in both seasons continued to show fruit as the primary source. The most frequently consumed plants were from the Moraceae family, particularly Ficus villosa and Ficus heteropleura.

<title>Abstract</title> Diabetes Mellitus (DM) is a chronic metabolic disease with increasing prevalence, and it remains a major health problem worldwide. This study investigated the anti-diabetic potential of Indonesian traditional herbal plants ( <italic>Curcuma amada</italic> Roxb., <italic>Curcuma longa</italic> , and <italic>Allium cepa</italic> L. var. <italic>aggregatum</italic> ) using a network pharmacology approach combined with molecular docking. Active compounds were collected from IJAH Analytics and KNApSAcK, screened using oral bioavailability and drug-likeness criteria, and then mapped to predicted protein targets using SwissTargetPrediction. Diabetes-related targets were obtained from OMIM, UniProt, MalaCards, and GeneCards, and overlapping targets were selected for network analysis. A protein--protein interaction network was built using STRING v12.0, followed by centrality analysis (degree, betweenness, closeness, and eigenvector). The Skyline query algorithm was applied to prioritize key proteins, and the top five proteins were selected as receptors for docking. Molecular docking was performed using AutoDock Vina against nine candidate ligands. The docking results showed that Tropeoside B1 consistently produced the strongest and most frequent binding across the selected receptors, followed by Progesterone and Peonidin-3-Arabinoside. Overall, this workflow helps narrow down promising herbal compounds and protein targets, providing candidates for further experimental validation.

This study developed a machine learning-based model to predict academic concentration selection among information systems students at Universitas Multimedia Nusantara (UMN). A survey of 125 students from the 2024 cohort revealed that 90% experienced difficulties in choosing a specialization, primarily due to limited information on course relevance, unclear academic pathways, and career uncertainty. While the survey provides a contextual background, the predictive model was trained using historical academic performance data from the 2021–2023 cohorts. The three classification algorithms, Decision Tree, Random Forest, and Extreme Gradient Boosting (XGBoost) were implemented following the CRISP-ML methodology. To address class imbalance in the dataset, the Synthetic Minority Over-sampling Technique (SMOTE) was applied, followed by hyperparameter tuning and feature selection. The Random Forest model demonstrated superior performance, achieving an accuracy of 78.08% on the 2021–2022 cohort data, outperforming Decision Tree and XGBoost across all experimental settings. This result highlights Random Forest's robustness in this context, particularly after the integration of SMOTE and optimization procedures. The main contribution of this study lies in the application of machine learning for academic pathway prediction in an Indonesian higher education setting, providing a data-driven decision support tool to assist students in making informed and personalized specialization choices.

Electricity consumption, production, and supply based on fossil fuels have increased due to population growth, urbanization, and technological development, leading to environmental damage in countries like Indonesia. In response to this issue, electricity forecasting is essential. This study applies to a Nonlinear Autoregressive with Exogenous Input (NARX) neural network to forecast one year ahead of electricity generation using MATLAB. Two algorithms are used for comparison: Levenberg-Marquardt and Bayesian Regularization. The data is classified using a standard method of 70%−30% split, with 30 hidden layers and a standard delay of 2-time steps. The results show that both algorithms achieve an R² value above 0.9 and a Mean Absolute Percent Error (MAPE) of under 3%, with the Levenberg-Marquardt algorithm demonstrating marginally superior performance. These results indicate that the model provides valuable insights for forecasting annual electricity generation in Indonesia over a short timeframe.

Selecting the right wood Supplier is a critical factor in ensuring smooth production processes and maintaining the quality of raw materials used by a company. Mistakes in Supplier selection can lead to production delays, increased costs, and a decline in final product quality. Therefore, this study aims to determine the best wood Supplier using a Multi-Criteria Decision Making (MCDM) approach. This research employs a descriptive quantitative approach involving four alternative Suppliers and seven evaluation criteria: wood price, wood quality, delivery punctuality, Supplier distance, supply capacity, payment system, and Supplier reputation. Data were collected through structured interviews with company decision makers and a review of relevant internal documents. The analytical methods used are Weighted Product (WP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to rank Suppliers based on the established criteria. The results indicate a difference in Supplier rankings between the two methods due to differing calculation approaches and evaluation concepts. The WP method emphasizes the consistency of Supplier performance across all criteria, while the TOPSIS method assesses the proximity of an alternative to the ideal solution. Based on the analysis, the Weighted Product method is considered more appropriate and is recommended for determining the best wood Supplier for the company.

This research aims to address the challenges of student motivation and engagement in fundamental programming education by implementing the Octalysis Gamification Framework within an Intelligent Learning System. Traditional learning methods often fail to visualize abstract concepts or provide personalized feedback, leading to student demotivation. To overcome this, a platform named "Starcoder" was designed and built, integrating two conceptual pillars: the eight core drives of the Octalysis Framework and an AI-supported Intelligent Tutoring System (ITS). The system employs the Next.js framework and integrates the Gemini AI API (M.E.C.H.A.) to provide real-time, adaptive feedback and remedial learning paths. The effectiveness of the platform was evaluated using the Hedonic-Motivation System Adoption Model (HMSAM) with 54 respondents, comparing the gamified platform against traditional classroom methods. Evaluation results demonstrate that the platform significantly outperforms traditional methods, achieving an 86.44% score in Perceived Usefulness and an 85.56% score in Curiosity. Notably, Behavioral Intention to Use increased by 15.56% compared to the baseline. These findings demonstrate that the comprehensive integration of gamification frameworks with generative AI agents effectively enhances student motivation and immersion in technical education. Future work should focus on expanding the AI's capability to dynamically adjust gamification elements in real-time based on student performance.

The health and welfare of stray dogs remains a significant problem in Indonesia, primarily due to malnutrition, limited healthcare, and low public awareness of responsible pet ownership. This community service activity was carried out at the Maria Stray Home dog shelter, South Tangerang, with the aim of improving the physical and emotional well-being of shelter dogs while raising awareness among the surrounding community. The implementation method of the activity was carried out through three main approaches, namely dog ​​hygiene care through bathing activities, fulfilling nutritional needs through providing nutritious food according to a structured schedule, and counseling to the community about animal welfare and the importance of adoption. The activity was carried out through preparation, implementation, and evaluation stages with an observational and reflective approach together with the shelter manager. The results of the activity showed an improvement in the physical condition of the dogs, marked by increased cleanliness of their fur and skin and better activity responses after regular care and feeding. Emotionally, the dogs showed a decrease in aggressive and anxious behavior observed through an increase in positive responses to human interaction. The outreach activities also resulted in increased community involvement and acceptance of the shelter's existence. This activity demonstrates that structured and sustained community interventions can significantly contribute to improving the welfare of shelter dogs and strengthening public awareness and support for animal welfare efforts.

In recent years, companies have experienced growing competition, financial inefficiencies, and operational instability, which have contributed to declining profitability. Digital transformation is found to increase operational efficiency while minimizing risk of insolvency. PT Dwi Family Investama, an offset and printing company, experiences similar problems from its manual record of orders and invoices, which causes decreased efficiency, errors, and complicated handling of customer information. Such inefficiencies slow down business strategy formulation and decision-making. This research designs a web-based Enterprise Resource Planning (ERP) system integrated with business intelligence dashboard to facilitate better decision-making. The system automates business processes and delivers real-time operational insights through an interactive dashboard. Performance assessments record an increase in operational efficiency by 52.3%, proving the system’s effectiveness in improving business operations and decision-making.