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In social life, buying and selling are fundamental mechanisms for transferring rights, beginning with an agreement. According to Articles 1313 and 1338 of the Indonesian Civil Code, agreements are legally binding acts with the force of law for the parties involved. One high-value transaction is the sale and purchase of apartment units, which involves developers as sellers. In practice, developers often fail to deliver units within the agreed timeframe. This study examines (1) the developer’s responsibility toward buyers when units are not delivered and (2) the legal protection available for buyers under such circumstances. The research applies Hans Kelsen’s Theory of Responsibility and Satjipto Rahardjo’s Theory of Legal Protection, using a normative juridical method based on library research. Primary, secondary, and tertiary legal materials were analyzed through statutory, conceptual, analytical, and case approaches, employing grammatical and systematic interpretation, legal analogy, and legal refinement. Findings reveal that developers are primarily responsible for delivering fully paid units. Failure to fulfill this obligation, due to breach of contract or negligence, triggers legal liability in the form of performance or compensation. Legal protection for buyers ensures their rights are safeguarded, and even in cases of developer negligence or bankruptcy, consumers are legally entitled to receive the apartment units they have purchased.

AImbalanced data remains a significant issue in heart disease classification using machine learning, as it tends to cause models to overestimate the majority class while ignoring minority classes with high clinical value. This can lead to a decrease in accuracy and the model's ability to accurately detect disease cases. Therefore, this study aims to assess the effectiveness of oversampling techniques, namely Random Oversampling and Synthetic Minority Oversampling Technique (SMOTE), in improving the performance of the K-Nearest Neighbors (KNN), Naive Bayes (NB), and Random Forest (RF) algorithms. The dataset used comes from Kaggle and consists of 918 data sets with 12 attributes representing patient information related to heart disease prediction. The research stages include data preprocessing, baseline model testing, and re-evaluation using the two oversampling methods. Experimental results show that oversampling can improve the performance of all algorithms. KNN achieved the best results with SMOTE, with an accuracy of 72.98% and an F1-score of 75.39%. In the Naive Bayes algorithm, both oversampling techniques produced relatively stable performance, with the highest F1-score of 73.56% using SMOTE. Meanwhile, Random Forest showed the most optimal performance when combined with Random Oversampling, with an accuracy of 79.19% and an F1-score of 81.51%. These findings confirm that the success of data balancing techniques is strongly influenced by the characteristics of the classification algorithm used, and provide a practical contribution in determining strategies for handling imbalanced data in health research.

The development of digital technology has led to an increase in the use of short video-based entertainment applications, including the Melolo application. However, the free version still has various complaints, such as inconsistent subtitles, unintuitive navigation, force close glitches, and unstable advertisements, so user satisfaction analysis is needed. This study aims to measure the level of satisfaction of users of the free version of the Melolo application using the End User Computing Satisfaction (EUCS) method, which covers five variables, namely content, accuracy, format, ease of use, and timeliness. Data was collected through an online questionnaire of 385 Melolo app users in Jambi City and analyzed using Structural Equation Modeling (SEM) with the help of SmartPLS 4. The results showed an R-Square value of 0.546, indicating that the model was able to explain 54.6% of the changes in user satisfaction levels. The variables of content and timeliness were found to have a significant effect on user satisfaction, while accuracy, format, and ease of use had no significant effect. These results indicate that content quality and system timeliness are the main factors in increasing user satisfaction. Therefore, Melolo app developers are advised to maintain content quality and improve system performance and stability to optimize the user experience.

The COVID-19 pandemic has increased public interest in investing, especially in the banking sector, which is known for its stability. However, many investors still lack an understanding of fundamental analysis. This study aims to examine the effect of Return on Asset (ROA), Return on Equity (ROE), and Net Profit Margin (NPM) on stock prices of banking companies listed on the Indonesia Stock Exchange during the 2011–2023 period. The research used a quantitative approach with purposive sampling and multiple linear regression analysis using SPSS. The results show that ROA has no significant effect on stock prices. In contrast, ROE has a significant negative effect, while NPM has a significant positive effect on stock prices. These findings indicate that investors tend to consider net profit margins more than asset efficiency, and that high ROE may be perceived as a signal of high leverage risk. This research is expected to provide insights for investors in assessing banking performance before making investment decisions.

The problem faced is the decline in the performance of a 650 liter/minute centrifugal pump due to wear on components, especially the impeller, rolling bearings, and mechanical seals in supplying process water and clean water in industrial production systems. The planning objective is to obtain a maintenance schedule for a 650 liter/minute centrifugal pump for the operational period of 2026 and the ratio between maintenance costs and profits generated by the machine. The maintenance planning method includes collecting maintenance data from previous maintenance periods, reviewing centrifugal pump specifications, applying the inspection, replace, repair, and overhaul (IRRO) approach, estimating the age and price of components that are expected to be damaged, estimating the cost and duration of dismantling and installing components that have been repaired in accordance with the provisions of the requirements for usable components or replacement parts, scheduling maintenance and repairs, estimating maintenance and repair costs for the 2026 period, and determining the ratio of maintenance costs to profits. The planning results are in the form of a maintenance schedule for the 2026 period worth IDR 4,290,000,-, a maintenance cost to profit ratio of 7.44% and the implications indicate that the machine is still suitable for use and prospective for operations in the next few years.  

The COVID-19 pandemic significantly affected the financial performance of PT Kereta Api Indonesia (Persero), as reflected in the shift from profit in 2020 to a substantial pre-tax loss in 2021. This change had direct implications for the company’s tax components, particularly current tax and deferred tax, in accordance with PSAK 46 on Income Taxes. This study aims to analyze the changes in current tax and deferred tax between the two reporting periods and to examine the role of deferred tax benefits in reducing the company’s net loss. The research employs a quantitative descriptive approach with a comparative analysis method using secondary data from the company’s interim consolidated financial statements. The findings indicate that in 2021 the company recognized a deferred tax benefit that converted total income tax into a net tax benefit, thereby reducing the company’s net loss by approximately 15.8 percent. These results demonstrate that deferred tax does not merely arise from temporary differences but can function as an instrument of loss mitigation during periods of financial distress. The implications of this study highlight the importance of accurate application of PSAK 46, especially in times of economic downturn, and emphasize the need for realistic assessments of future taxable profits to ensure the reliability of deferred tax asset recognition.

This study aims to systematically analyze the influence of Work Family Conflict (WFC) and Work Life Balance (WLB) on employee performance through a Systematic Literature Review (SLR) approach. The increasing demands of work and family roles in modern organizational settings make these variables highly relevant in organizational behavior research. The SLR procedure was conducted based on guidelines by Kitchenham and Wahono, consisting of planning, conducting, and reporting stages. Literature searches were performed across several academic databases, including Google Scholar, Garuda Ristek, ResearchGate, and university journal portals, covering publications from 2020 to 2025. From a total of 5,992 identified articles, 28 studies met all inclusion and quality criteria and were reviewed in depth. The findings indicate that Work Family Conflict consistently exerts a negative effect on employee performance by increasing stress, reducing concentration, and impairing psychological well-being. Conversely, Work Life Balance demonstrates a significant positive influence on performance, as employees who successfully balance their professional and personal responsibilities tend to exhibit higher productivity, job satisfaction, and engagement. Furthermore, factors such as organizational support, flexible working arrangements, and job satisfaction are shown to mediate or moderate the relationship between WFC, WLB, and employee performance. This study contributes by mapping the latest empirical patterns and providing recommendations for organizations to develop supportive work policies that enhance employee well-being and sustainable performance.

SMP Negeri 5 Merlung is a public junior high school in Merlung Subdistrict that has utilized the DAPODIK system for online data processing management, enabling efficient sending and receiving of information to the government. This research analyzes IT governance on the DAPODIK system using the COBIT 5 framework, specifically the MEA01 domain (Monitor, Evaluate and Assess Performance and Conformance), which focuses on monitoring and evaluating performance and conformance. The research background is based on the need to maximize the utilization of the system at the school level. The main objectives are to determine the current and expected capability levels, as well as to provide improvement recommendations to achieve higher process maturity. The research method applies Assessment Process Activities, covering observation, interviews, identification of findings, gap analysis, and recommendations. The results show that the current capability level is at level 3 (established process), while the expected capability level is directed toward level 4 (predictable process). The implications of these findings provide practical recommendations such as routine monitoring enhancements, staff training, and integration of automation tools to bridge the capability gap, thereby improving the effectiveness of IT governance at SMP Negeri 5 Merlung sustainably.

This study conducts sentiment analysis of Indonesian user reviews of the CapCut application using IndoBERT and compares two evaluation schemes: a single 80/20 train–test split and stratified 5-fold cross-validation (k=5). A total of 1,048,575 reviews were collected from the Google Play Store through web scraping and labeled into three sentiment classes based on rating: negative (1–2), neutral (3), and positive (4–5). After preprocessing—cleaning, case folding, banned-word removal, normalization—and duplicate removal, 517,962 reviews were retained. IndoBERT Base P1 was fine-tuned using fixed hyperparameters (batch size 32, learning rate 2e-5, up to 4 epochs, early stopping patience 2), while undersampling was applied to the training set to address class imbalance. Performance was assessed using accuracy, precision, recall, F1-score, and ROC-AUC, supported by confusion matrix and ROC-curve visualizations. The single split achieved an accuracy of 0.756, whereas cross-validation produced a mean accuracy of 0.740. Across both schemes, the positive class achieved the best performance (F1-score 0.850; ROC-AUC 0.918–0.919), while the neutral class remained the most challenging (precision 0.198–0.206; F1-score 0.280–0.283). Overall, cross-validation is recommended for reporting because it reduces dependence on a single partition and provides a more representative estimate across multiple splits.

Chronic Kidney Disease (CKD) is a heterogeneous disorder that gradually affects the structure and function of the kidneys, is difficult to recover, and causes the body to be unable to maintain metabolism and fail to maintain fluid and electrolyte balance, leading to increased urea levels. Chronic kidney disease data was obtained from Kaggle, in this study a comparison was made between two classification algorithms, namely Naïve Bayes Classifier (NBC) and Random Forest because it is not yet known what algorithm is best in classifying chronic kidney disease (CKD). Both algorithms are evaluated based on performance metrics such as accuracy, precision, recall, and confusion matrix. The results of the evaluation showed that in a dataset of 400 samples, the performance  of the Naïve Bayes Classifier (NBC) algorithm obtained an accuracy of 94%, while Random Forest had an accuracy of 93%. Then in the small dataset (158 data), Random Forest got a better accuracy score with 87% compared to the Naïve Bayes Classifier (NBC) of 78%. Based on the results of the evaluation, Random Forest has a more stable performance on small datasets, while Naïve Bayes Classifier (NBC) provides higher performance on larger datasets in the context of chronic kidney disease classification.

Jambi Province is one of the largest natural rubber producing regions in Indonesia; however, rubber factories under GAPKINDO Jambi still face productivity issues, particularly the gap between production capacity and actual output, and productivity assessment that is still conducted manually by GAPKINDO Jambi. This study employs Decision Tree, Random Forest, KNN, and SVM algorithms within a structured pipeline involving preprocessing, feature selection, standardization, data balancing using SMOTE, and hyperparameter tuning. The proposed solution applies productivity level classification both individually and through paired combinations (ensemble voting). The results show that the Decision Tree + Random Forest model achieves the best performance with an accuracy of 0.84 and an F1-score of 0.83, confirming the effectiveness of ensemble methods in supporting productivity improvement decisions.

This study analyzes creditworthiness assessment and predicts non-performing loan (NPL) risk using the Naïve Bayes algorithm at BPR Ukabima Lestari, Jambi Branch. A quantitative data mining approach with probabilistic classification is applied. The dataset includes borrower attributes such as age, occupation, income, loan amount, tenor, collateral, and repayment history. Research stages comprise data preprocessing, model development, and performance evaluation using accuracy, precision, recall, and F1-score implemented in RapidMiner. The results indicate that the Naïve Bayes model achieves 99.58% accuracy, demonstrating strong capability to predict potential problem loans accurately and efficiently, supporting data-driven credit decisions and strengthening credit risk management in microbanking institutions.

The Indonesia Pintar Program (PIP) is an educational assistance program for students from underprivileged families, but determining the eligibility of recipients still faces obstacles in the form of subjectivity and data imbalance. This study aims to classify the eligibility of high school students receiving PIP in Jambi City using data mining methods. The SMOTE technique was applied to overcome class imbalance, and Gain Ratio feature selection was used to determine important attributes. The dataset used consisted of 19,596 student data with a training data distribution of 70% and testing data of 30%. The classification process used the Naïve Bayes, Decision Tree (J48), and Random Forest algorithms with the Use Training Set, 5-Fold, and 10-Fold Cross Validation testing schemes. The results show that SMOTE improves model performance, but feature selection in some cases reduces accuracy. Overall, Random Forest without feature selection provides the best results with an accuracy of 93.33% and is recommended as the most effective model for objectively determining PIP recipient eligibility.

This study analyzes the synergy between human resource development (HRD), work culture, and decision-making systems in enhancing productivity and organizational performance. The approach used is a descriptive qualitative literature review, with sources from peer-reviewed articles from 2021-2025 obtained through databases such as Google Scholar and ScienceDirect. The research findings indicate that HRD can improve employee competence by up to 28.7%, while work culture contributes 41.4-55% to motivation and collaboration within teams. Decision-making systems, such as AHP (Analytical Hierarchy Process) and BI (Business Intelligence), play a role in optimizing operational efficiency by up to 65%. The synergy of these three elements forms a more adaptive organization to change. One example of the implementation of this synergy can be seen in PT X Jakarta, which experienced a 28% increase in output and employee satisfaction reached 85%. However, the main challenge faced is resistance to change, which can be overcome through effective communication. The practical implications of this study include recommendations for holistic organizational transformation, especially in Indonesia, to face the challenges of global disruption.

Amidst the current changing global conditions, it is important for a country to achieve the Sustainable Development Goals (SDGs) to face challenges in sustainable development, social inequality, and strengthen economic and environmental resilience. This study aims to analyze the influence of environmental performance and political stability on the SDG scores of ASEAN countries for the 2020-2024 period, moderated by economic growth. Researchers used a quantitative method, processed using multiple linear regression with SPSS. The regression process was conducted twice, before and after using moderating variables. The findings suggest that economic growth can alter the influence of environmental performance and political stability on SDG scores. Political stability has a positive impact on the SDGs after economic growth has moderated. While environmental performance has a negative impact after being moderated by economic growth. Economic growth promotes political stability and sustainable growth. Conversely, with high growth, improvements in environmental performance are indicated to shift priorities from sustainability to exploitation.

This study aims to classify customer payment methods at 17 Coffee & Eatery using machine learning algorithms, namely Naïve Bayes and Support Vector Machine (SVM). The increasing use of digital and non-cash payments has generated large volumes of transaction data that are rarely analyzed optimally, even though such data contain valuable information for business decision making. This research used secondary transaction data collected from January to March 2025, consisting of 10,147 transaction records. The dataset included several attributes such as order time, payment time, transaction type, total sales, number of items, and payment method. Data preprocessing was performed through data cleaning, feature engineering, normalization, and label encoding before being divided into training and testing sets with an 80:20 ratio. The Naïve Bayes and SVM models were then trained and evaluated using accuracy, precision, recall, F1-score, and ROC–AUC metrics. The results show that both algorithms were able to classify payment methods effectively, but SVM achieved higher accuracy and more stable performance than Naïve Bayes. These findings indicate that SVM is more suitable for handling complex and heterogeneous transaction patterns. The implementation of machine learning for transaction classification can support more efficient financial management and data-driven decision making for small and medium enterprises in the culinary sector.

This study aims to analyze the application of the franchising system on business performance in the beverage franchise business through a case study of Mitra Minuman Siap Saji. The approach used is qualitative with a case study design, with data collection techniques in the form of in-depth interviews, operational observations, and supporting documentation. The results show that the implementation of standardized Standard Operating Procedures (SOPs), franchisor support in the form of training, raw material supplies, and periodic monitoring contribute significantly to improving business stability, product quality consistency, and customer satisfaction. However, there are limitations in flexibility and several communication obstacles that have the potential to affect the effectiveness of the partnership. The relatively strict contract structure also impacts partners' perceptions of local innovation space, although it is generally still viewed as providing business security and business model clarity. These findings emphasize that a balance between franchisor control and partner autonomy, accompanied by open communication and fair contract design, is a key factor in creating sustainable business performance in a franchising system.

Traffic congestion is a persistent challenge in urban areas in Indonesia, where increasing vehicle density creates the need for intelligent traffic monitoring systems. This study aims to develop a real-time vehicle parking system using the YOLOv8 object detection model to provide efficient traffic analysis from live CCTV broadcasts and recorded videos. This study uses a quantitative experimental approach with the implementation of the YOLOv8m model using the Ultralytics library in Python, tested on data collected from CCTV cameras A TCS Dishub Medan and additional footage from mobile devices. Vehicles are detected and counted in two directions up (Up) and down (Down) using virtual detection lines on the video frame. The system performance is evaluated by automatic detection counting with manually recorded ground truth data. The results show that on live CCTV broadcasts, the YOLOv8m model achieves an average precision of 98.96%, a recall of 96.59%, and an F1 score of 97.74% for upstream traffic, while for downstream traffic it achieves 100% precision, 95.64% recall, and an F1 score of 97.730/0. On the other hand, on high-quality recorded videos, all performance metrics achieve 100%, indicating perfect detection accuracy. These findings confirm the effectiveness of YOLOv8 in real-time traffic monitoring, but also indicate that video quality and stream stability affect detection performance. In conclusion, the developed system shows strong potential to support smart city traffic management solutions. Future research should focus on performance optimization under low-resolution live streaming conditions to improve accuracy in practical applications.  

This study presents a high-accuracy real-time soft failure detection framework for large-scale fiber-to-the-home(FTTH) optical access network using a hybrid ensemble of Isolation Forest and One-Class Support Vector Machine (OCVSM). The proposed model was trainde and validated on a real-word multivariate performance dataset comprising more than 1.8 million samples collected at 5-minute intervals from 50 Optical Line Terminal (OLTs) and over 3,000 Optical Network Terminals (ONTs) across a five-month periode(June-October 2025). Ground-truth validation was performed using 111 confirmed network incidents in October 2025 affecting 12,990 customer. The hybrid ensemble achieved Precision 0.940, Recall 0.982, with an average detection delay of only 7.8 minutes-representing an 87.7% reduction compared to conventional manual response (63.5 minutes). The framework significantly outperforms traditional threesholding and recent ML-based methods while demonstrating practical deployability in live operational enviroments.

The purpose of this community service is to help provide direction and counseling to the Principal, Vice Principal, Administrative Staff, and Teachers' Council of Citra Madinatul Ilmi Banjarbaru High School , regarding the introduction of elements of human resource management. Counseling in order to increase insight and knowledge for the Principal, Vice Principal, Administrative Staff, and Teachers' Council. The method of implementing this community service is carried out in several activities, namely the survey stage, namely socialization is carried out by compiling various things that will be conveyed during the community service activities that will be carried out which include: preparing the material to be provided, preparing the schedule for providing materials and surveying the community service location. The socialization stage, namely before the community service activities are carried out, a socialization stage is carried out, namely conducting a friendly meeting with the school to convey the intent and purpose of this community service. At this stage, cooperation is also established and the community service activity schedule is determined.