Edu Komputika Journal starting in 2024 migrates from OJS 2 to OJS 3 to better secure from various unwanted things, including journal hacking and so on. To submit, the author please visit the new website page of our journal at the link https://journal.unnes.ac.id/journals/edukom MIGRATION OFFICIAL STATEMENT HERE Edu Komputika Journal is an open-access, peer-reviewed journal published by the Study Program of Informatics and Computer Engineering Education, Universitas Negeri Semarang, Indonesia. It was first published in 2014 and published two issues per year, in July and December. Edu Komputika has a printed and online International Standard Serial Number: p-ISSN 2252-6811 (printed version) and e-ISSN 2599-297X (electronic version). Articles can be the results of research, scientific studies, literature reviews, and the analysis and solving of issues relevant to the fields of Education, Computer Science & IT, and Computer Engineering. Edu Komputika Journal uses Open Journal Systems (OJS) for online journal management in submission, review, copyediting, and publication. Submitted manuscripts are written in English and should follow the style of the Edu Komputika Journal. Manuscripts are original research results, or theoretical/literature study results that have never been published in other journals or are not considered for publication elsewhere. The author should follow all the provisions and processes. Accepted papers will be available online and will be charged a publication fee. This journal has been covered by following indexing and abstracting services: DIMENSION | SINTA | GARUDA
A Modified TAM-ECT Model for Evaluating User Satisfaction and Behavioral Intention in Community-Based Internet Services
This study develops and validates a modified Technology Acceptance Model–Expectation Confirmation Theory (TAM-ECT) framework to evaluate user satisfaction and behavioral intention in the context of community-based internet services (RT/RW Net). Unlike prior TAM-ECT studies predominantly conducted in commercial ISP or e-service environments, this research explicitly focuses on decentralized, community-managed internet services characterized by informal governance structures, low switching barriers, and non-contractual user relationships. Addressing the lack of research on decentralized internet service models, this study integrates external factors service quality, cost-effectiveness, system quality, and customer support and moderating factors, namely digital literacy and switching cost. A quantitative survey approach was employed, collecting valid responses from 803 active users between January and March 2025. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) and Importance-Performance Map Analysis (IPMA). The results demonstrate that perceived ease of use strongly influences perceived usefulness and behavioral intention, while perceived usefulness significantly impacts both user satisfaction and behavioral intention. Notably, and contrary to the core assumption of Expectation Confirmation Theory, user satisfaction does not significantly predict behavioral intention, indicating a context-specific deviation in community-based digital services where pragmatic usability considerations outweigh affective satisfaction. External factors such as customer support and system quality significantly affect user perceptions, highlighting the importance of technical performance and user experience in decentralized service settings. Digital literacy positively moderates the relationship between perceived ease of use and behavioral intention. The IPMA findings reveal that ease of use, service usefulness, and customer support are the most critical areas for improvement. Theoretically, this study extends TAM-ECT by demonstrating that continuance intention in community-based internet services is driven more by usability and functional value than by satisfaction-driven confirmation mechanisms commonly observed in commercial platforms. This study offers practical insights for optimizing technical quality, service functionality, and user digital competencies to foster sustainable adoption in community-managed internet infrastructures.
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Energy Supply Chain Optimization: Design of a Transportation Vendor Assessment System Using the Simple Additive Weighting Method
In the energy logistics sector, which demands high speed and efficiency, fuel transportation vendor selection is a strategic decision that significantly impacts operational smoothness. To transform the cumbersome manual selection process into digital precision, a study developed a Vendor Management Information System based on the Simple Additive Weighting (SAW) method. This system is designed to provide objective decision-making support by analyzing 2024 performance data through eight key evaluation criteria, including service quality, price, and fleet availability. After going through a normalization and weighting process in the decision matrix, the system determined Vendor A1 (PT. X) as the best provider with the highest score. The data is descriptive quantitative in nature, where the data collection process involved respondents from three departments within the company who are experts in the field of procurement, with proof of ownership of procurement certification for goods and services. A total of 23 respondents served as the basis for SAW data processing, and 5 people served as references for creating criteria for weighting in the method. This automation logic was then technically mapped through Data Flow Diagrams (DFDs) and Entity-Relationship Diagrams (ERDs) to ensure an integrated workflow. The implementation of this system marks a significant shift towards digital efficiency, which not only minimizes human error and increases transparency but also lays a strong foundation for the adoption of more sophisticated decision-making technologies in the future.
Weakly Supervised Sentiment Analysis of Indonesian Rural Tourism Reviews: A TF-IDF Baseline for Melung Tourism Village
This study investigates sentiment classification of Indonesian-language tourist reviews from the rural destination of Melung Tourism Village. A total of 724 user-generated reviews from 546 unique users are preprocessed using Indonesian-specific text cleaning, stopword filtering, and stemming, then weakly labeled through a stemmed positive–negative lexicon. TF-IDF unigram–bigram features are extracted from the preprocessed texts and used to train three classical classifiers: Naive Bayes, linear Support Vector Machine (SVM), and Logistic Regression. To address class imbalance, RandomOverSampler is applied only to the training data, and model evaluation combines stratified 5-fold cross-validation with a held-out test set, using weighted F1-score as the primary metric. Logistic Regression achieves the best performance on the test set (weighted F1 = 0.8799, accuracy = 0.8828), closely followed by SVM, while Naive Bayes lags behind. The results show that, even with a modest, weakly supervised dataset, a carefully designed classical pipeline can yield reliable sentiment indicators to support data-driven management of rural tourism destinations.
A Comparison of Machine Learning and Deep Learning Methods for Temperatures Predictions on Java Island
Climate change is a global long-term change in temperatures and weather. Climate change is a worldwide issue that requires proper handling to reduce the negative impact on humans and the environment. Analyzing historical data is beneficial for studying climate change. Machine learning and deep learning methods are useful tools for data analysis. The goal of this paper is to find the best model for forecasting temperatures, a case study in Java Island. Java Island is the most densely island and the central economy and business in Indonesia. Climate change research in Java Island is important for sustainability. It runs several algorithms i.e., Gradient Boosting, AdaBoost, XGBoost, CatBoost, Light GBM, Random Forest, Support Vector Regression, Extreme Learning Machine, Long Short-Term Memory, Gated Recurrent Unit, Bidirectional Long Short-Term Memory, and Bidirectional Gated Recurrent Unit. The experiment uses a historical daily time series of temperatures from 1 January 1990 to 31 December 2024. In general, the experimental results show that Gradient Boosting produces the highest average coefficient of determination R2 scores of 0.34 and the lowest Mean Absolute Error scores of 0.69. Long Short-Term Memory and Gated Recurrent Units are the deep learning models that also work well for forecasting. According to the experimental results, in some cases, machine learning models outperform deep learning models and vice versa.
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Enhancing Waste Classification with MobileNetV2: Adding a Plastic Sachets Class for Sustainable Management
The issue of waste management remains a critical concern due to its adverse impact on the environment. This research enhances a deep learning-based waste classification model by introducing a new class, namely plastic sachets, to broaden the classification scope and increase the model's relevance to waste types commonly found in the community. The dataset used is an extended version of a previous open-source dataset, comprising 2,968 images divided into seven classes. Data preprocessing steps include stratified data splitting, data augmentation to increase image diversity, and pixel normalization. The model adopts the MobileNetV2 architecture through a transfer learning approach, utilizing 2D Global Average Pooling and Dense layers with softmax activation for multi-class classification. Evaluation using precision, recall, and F1-score demonstrated strong performance, with an overall accuracy of 97%. While the model performs well across most classes, further improvement is needed for minority classes such as plastic sachets. This study highlights the promising potential of deep learning in supporting automated waste sorting to promote sustainable waste management practices in Indonesia.
Integration of Skyline Query with the PROMETHEE MCDM Method: A Case Study on Structural Official Selection
The selection of structural officials within higher education institutions is a strategic and complex process that demands objectivity, transparency, and a data-driven approach. However, the increasing number of candidates and the diversity of evaluation criteria, such as years of service, rank, education, age, and performance, pose significant challenges in ensuring fair and efficient decision-making. Addressing this gap, this study proposes a hybrid method by integrating Skyline Query with the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE), offering a novel contribution to multi-criteria decision-making (MCDM) in public sector human resource selection. Skyline Query is employed as a preselection mechanism to eliminate 161 dominated candidates from an initial dataset of 228, allowing only the 67 most non-dominated candidates to advance to the ranking stage. PROMETHEE is then applied to generate rankings based on leaving and entering flow values. To evaluate the consistency and validity of this combined approach, the resulting rankings are compared with those from the pure PROMETHEE method using Spearman’s Rank Correlation. The analysis yields a high correlation coefficient of ρ = 0.967, indicating a very strong agreement between the two methods and confirming that the Skyline filtering does not distort ranking quality. The findings demonstrate that the Skyline+PROMETHEE integration significantly enhances the efficiency of the selection process by reducing computational complexity while preserving decision accuracy. Moreover, this approach strengthens the transparency and accountability of structural official selection, particularly in the context of the University of Mataram, and can be generalized to other institutional decision-making scenarios.
Ontology Engineering for Modeling National Student Achievements in Higher Education
The need for structured and semantically rich data in higher education underscores the role of ontology-based knowledge modeling. This study develops an ontology to represent national-level student achievements, covering key aspects such as institution, achievement field, category, year, level, and student status. Using a formal ontology engineering approach, the ontology was developed in Protégé and encoded in OWL. Evaluation involved technical validation and reasoning tests including class subsumption, consistency checking, instance classification, and rule-based inference to assess logical soundness and semantic correctness. Description Logic (DL) queries were also executed based on competency questions to evaluate the ontology’s ability to support semantic querying. The results demonstrate that the ontology effectively supports knowledge inference and structured data retrieval, offering strong potential for integration within semantic web environments. This provides a foundation for data interoperability and knowledge sharing across educational systems at the national level. Future work includes expanding the ontology to incorporate dynamic achievement updates and linking with external educational data sources.
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Benchmarking YOLOv3 and SSD: A Performance Comparison for Multi-Object Detection
Multiple object detection remains a significant challenge in the field of computer vision. One of the key factors affecting detection performance is the feature extraction process, especially when objects are relatively small or positioned closely together. This study aims to compare the effectiveness of two popular object detection models, YOLO (You Only Look Once) and Single Shot MultiBox Detector (SSD), in detecting multiple objects within images. These models were selected due to their reported high accuracy and real-time processing capabilities, outperforming traditional methods such as the Hough Transform, Deformable Part-based Models (DPM), and conventional CNN architectures. The models were evaluated using a subset of the PASCAL VOC dataset, which includes object categories such as aircraft, faces, cars, and others, with a total of 1,447 annotated images used in training and testing. The evaluation metric used was mean Average Precision (mAP) to assess detection accuracy. Experimental results indicate that YOLO achieves a mAP of 82.01%, while SSD achieves 70.47%. These findings demonstrate that YOLO provides better performance in detecting multiple objects under the same conditions. Overall, this study confirms the advantages of YOLO in scenarios requiring fast and accurate multi-object detection, highlighting its potential for deployment in real-time applications such as autonomous vehicles, surveillance systems, and robotics. The main contribution of this study lies in providing a comparative performance benchmark between YOLO and SSD on a standard multi-object dataset to guide practical model selection in real-time computer vision tasks.
Implementation of Equivalence Partitioning Techniques and ISO/IEC 25010 Functional Suitability Standards for Testing SCM Applications in Small-Scale Automotive Industries
A Supply Chain Management (SCM) application prototype for PT XYZ has been developed. Before its release, the testing phase is crucial to minimize functional failures. This study aims to conduct comprehensive testing to verify that all functions operate as intended, deliver the expected results, and identify potential errors in the application. Unlike previous research, this study not only uses Equivalence Partitioning as a functional testing technique but also combines it with the ISO/IEC 25010 standard, which guides Functional Suitability and software quality more comprehensively. This approach addresses the limitations of Equivalence Partitioning, which typically focuses only on input class division without ensuring coverage of specific quality standards. Furthermore, integrating testing techniques with the internationally recognized ISO/IEC 25010 standard enhances the relevance and applicability of this research in software development for industries, particularly in supply chain information processing automation. By combining both methods, the testing process becomes more thorough and structured, as evidenced by the 254 test cases generated. Additionally, this study provides quantitative data in the form of valid (83%) and invalid (17%) test case percentages, which can serve as indicators of testing effectiveness and the prototype's quality.
Development of Service Satisfaction Instrument Application Based on 5 Stars Rating Using PHP, SQL, and Javascript on FT UNNES Website
Service satisfaction instruments are tools used to measure the level of user satisfaction with services provided by an institution, company, or organization. Service satisfaction instruments can be in the form of forms, applications, interviews, and many more. Service satisfaction instruments in the form of website applications have more advantages than others. Applications can be designed to suit various user needs. The service satisfaction instrument application at the Faculty of Engineering, UNNES, was designed as a form of renewal of the instrument that previously used a form. The instrument application has many advantages over the previously used form. The service satisfaction instrument application can present data in real-time after the user inputs an assessment. The application design is designed to be as attractive as possible so that users can use and receive information from the application easily. The application was developed using the agile software development method. This method is used because it has many advantages such as good flexibility and adaptability.In addition, the program is designed with a good structure to facilitate the development and maintenance process. With an organized structure, the code becomes more modular, clearly separated between components, and follows the principles of clean code.
Feature Extraction Implementation in the Forecasting Method to Predict Indonesian Oil and Gas Exports and Imports
Future export and import predictions can use data mining and forecasting applications of data mining. Then, normalisation is carried out using datasets taken at the centre of the statistical agency using a mix-max scaler. The normalisation results are then calculated using several forecasting methods, such as Exponential Smoothing, SARIMAX, XGBoost, and CatBoost. The accuracy of this method can be improved by using feature extraction decomposition. They are decomposing, such as trend, residue, and seasonal. The results of the decomposition then become new features that are entered into the prediction model. The prediction results are evaluated using the root mean square error (RMSE). The smaller the RMSE, the better the results. The prediction results without using the method obtained by the Exponential Smoothing method have the best level of accuracy with an average RMSE value of 0.111 and the SARIMAX method with an average RMSE value of 0.146. Meanwhile, the prediction results using the CatBoost and XGBoost feature extraction methods have the best level of accuracy with an RMSE value of 0.046. From the results of the comparison of predictions, the addition of decomposition features to most forecasting methods can significantly increase the accuracy of the calculation.
Decision Support System for Service Development Priority Selection at Pitcar Service Using the ELECTRE Method
The increasing competition in the automotive industry is pushing companies like Pitcar Service, a car workshop in South Purwokerto, to develop efficient and quality services. However, this workshop faces limitations in assets, materials, and capabilities. Therefore, Pitcar Service requires a Decision Support System (DSS) to assist in determining the priority for service development. The DSS helps identify and prioritize the most important services or those that have the greatest impact on business growth. By focusing on strategic development that provides significant added value, the company can achieve its strategic goals in line with the company's vision. The method used in this study is ELECTRE (Elimination and Choice Translation Reality), a suitable algorithm for designing a decision support system. This method allows pairwise comparisons between alternatives based on relevant criteria. The assessment is done by comparing each criterion individually to determine whether an alternative dominates other alternatives. With this method, the assessment of workshop services can be done objectively. The service alternatives considered include Workshop Service, Pick Up Service, Emergency Service, Home Service, Variations, and B2B Services. Meanwhile, the criteria used for the assessment include the number of requests, average income per job, tool usage, the number of mechanics, and processing time. The calculation results show that B2B Service is the main priority for service development with a dominant aggregation matrix result is 2.
Ecofy App UX Design using User-Centered Design Approach for Waste Management
Indonesia faces a significant waste management challenge, particularly with plastic waste, which poses severe environmental risks and has low recycling rates. This study aims to address these challenges by designing and developing the Ecofy application using the User-Centered Design (UCD) approach. The focus is on creating a solution that not only meets user needs but also effectively facilitates the waste management process, making it more accessible and efficient for the general population. The UCD methodology was employed to ensure that the application’s features are intuitive, user-friendly, and tailored to the local waste bank model, which encourages recycling at the community level. The research involved multiple stages, including user needs assessment, iterative design, and usability testing, to refine the application. The findings demonstrate that the Ecofy application significantly enhances the effectiveness of waste management by offering practical tools for users to sort, manage, and recycle waste. Furthermore, the implementation of this application is projected to reduce the burden on Final Processing Sites (TPA), lower environmental pollution, and increase recycling rates, especially for non-biodegradable materials like plastic. The success of Ecofy could serve as a model for other regions facing similar waste management issues, contributing to broader sustainable development goals in Indonesia.
Development of Learning Media for Hydroponic Systems Based on the Internet of Things
Advances in information and communication technology have changed the paradigm in the agricultural sector, especially in the application of the Internet of Things (IoT). An IoT-based monitoring system is an innovative step as there is an increasing need for technology in the agricultural sector and the development of the world of education. SMK Negeri 3 Salatiga, majoring in agribusiness, has great potential in hydroponic agriculture of lettuce plants, but there is no use of IoT technology in it. Hydroponic learning activities at SMK Negeri 3 Salatiga still use manual hydroponic learning media, which results in the competence possessed by teachers and students not being competent in IoT technology, especially its application to hydroponics. Meanwhile, if adjusted to learning outcomes, students should be able to fertilize plants in a modern way. The purpose of this study is to develop and implement hydroponic monitoring learning media based on Internet of Things technology. The application of learning media aims to improve the understanding or competence of teachers and students at SMK Negeri 3 Salatiga. The research methods used are Research and Development with the ADDIE development model, as well as one group pre-test and post-test design research design with 31 samples as a competency improvement test. This study resulted in 26 participants out of 31 participants experiencing an increase in competency in the results of the post-exam exam. So, it can be concluded that there is an increase in the competence of using hydroponic IoT by 83% among teachers and students.
Implementation of Project-Based Visual Programming Modules on Problem-Solving Skills Information Technology Education students to Support the SDG’s
Students in the Information Technology Education program need to master programming languages as an essential part of their competencies to support educational goals. One of the courses that require programming proficiency is Visual Programming or desktop-based programming. Problem-solving skills are crucial in the development of desktop-based applications. This study aims to analyze the visual programming problem-solving abilities of PTI students in supporting the SDGs through the implementation of the project-based visual programming module. This research employs a quantitative approach with a pre-experimental design of the One-Group Pretest-Posttest type. The sampling technique used is purposive sampling, involving 15 students currently taking the object-oriented programming course. The data used in this study consists of the test results of problem-solving abilities through the final project. Based on the analysis, the project-based visual programming module has been proven effective in improving students' problem-solving skills, as indicated by the paired sample t-test with a significance value of 0.002 < 0.05. In conclusion, there is a significant difference in students' problem-solving abilities before and after using the project-based module. The improvement in students' problem-solving skills through this module is considered high, with an N-Gain score of 0.839.
Analysis of the Use of the Merdeka Mengajar Platform Using the Hedonic Motivation System Adoption Model
The success of the implementation of the Merdeka Mengajar Platform (PMM) is measured by its acceptance and sustained use by teachers. This study will measure the factors influencing the acceptance and continued use of PMM by 53 teachers as respondents, from four high schools in the Rumbai District of Pekanbaru City, Riau Province, each of whom was given 21 questions. To accomplish this, the Hedonic Motivation System Adoption Model was used, which includes the following variables: Perceived Ease of Use (X1), Perceived Usefulness (X2), Curiosity (X3), Joy (X4), Control (X5), Behavioral Intention to Use (Y1), and Focused Immersion (Y2). Data analysis was conducted using Validity Tests, Reliability Tests, Normality Tests, Linearity Tests, Correlation Tests, Partial Tests, Simultaneous Tests, and Hypothesis Tests. The aspects derived from the use of PMM indicate that the hedonic factors for ease of use (X1) and curiosity (X3) in relation to intention (Y1) have correlation values of 0.560 and 0.661, respectively. Ease of use (X1) has full control over joy (X4) in relation to intention (Y1), with correlation values of 0.560 and 0.657. The factors influencing the success of PMM acceptance are curiosity (X3) and joy (X4). The higher the ease of use combined with curiosity and joy, the stronger the intention to use the application.
The Effectiveness of Flipbook-Based Learning Media Using Problem-Based Learning on Students’ Interest in UX Design Course
User Experience Design at the State University of Malang encounters significant challenges in students' grasp of essential concepts such as Design Thinking, User Experience (UX), and User Interface (UI) design, primarily due to the lack of specialized learning resources. This study addresses these challenges by developing a Flipbook-based learning resource integrated with Problem-Based Learning (PBL) strategies, aiming to enhance student engagement and motivation. The research follows the Research and Development (R&D) method, utilizing the ADDIE model, which includes Analyze, Design, Development, Implement, and Evaluate phases. The resulting media, a UX Design flipbook with a .html extension, underwent rigorous validation. Material expert validation achieved a score of 81.90%, while media expert validation scored 87.82%, both categorized as very feasible. Furthermore, small and large group trials yielded very feasible ratings of 86.23% and 87.38%, respectively. The student interest test indicated a high score of 86.30%, reflecting very high student interest. These findings demonstrate that the developed media is well-suited as supplementary material in UX Design education, promoting a more interactive, engaging, and effective learning environment. This innovative approach holds promise for improving student understanding and interest in the UX Design course.
Development of Android Application for Ordering Vegetables and Food Condiments
The growth of internet users continues to increase rapidly each year, supported by the growth of accompanying devices. Mobile applications are the main drivers of the increasing number of internet users. In Indonesia, internet users have reached 47% of the total population. Based on this data, around 46% use it to visit online trading sites. The phenomenon of the rise of online buying and selling is certainly very beneficial, especially for the residents of Malang City, where 34% of its population are traders. Supporting applications for conducting trade are essential for both merchants and consumers to facilitate the business process of buying and selling. This research will study the development of a buying and selling application between sellers and consumers, emphasizing ease of use. The development of this application uses the Mobile-D development model as a reference. The Mobile-D method was adopted because offers a development process well-suited for users who are not directly alongside the developers. Additionally, Mobile-D's iterative nature allows it to accommodate changing requirements, making it ideal for gathering needs directly from the application's end users. Based on the results of the study, user evaluations of the application showed a score more than 70, leading to the conclusion that the developed application is user-friendly and acceptable to users.
Creation of a Virtual Laboratory for Collision Dynamics Educational Tool with Integrated Collision Algorithm
Conventional physics laboratories often suffer from limitations in terms of equipment availability and safety, which hinders optimal learning of collision dynamics concepts. This research aims to develop a virtual laboratory based on collision algorithm to simulate perfect collision as an alternative solution in physics learning. The development uses the ADDIE model, which includes the stages of analysis, design, development, implementation, and evaluation. The collision algorithm was implemented using ActionScript 3, with interpolation allowing for more accurate collision detection at high speeds. The validation results show that the simulation is in line with the law of conservation of momentum and kinetic energy and is consistent with analytical solutions from MATLAB and Python. Functionality testing was conducted by 20 students, and the results showed that the use of this virtual laboratory significantly improved their concept understanding, with the average improvement ranging from 24% to 56%. Students also reported that this virtual laboratory is more interactive and interesting, thus increasing their learning motivation. The conclusion of this study is that the collision algorithm-based virtual laboratory is effective as a physics learning media and can be adopted more widely in technology-based education, especially to understand complex physics concepts more deeply.
Sentiment Analysis of Visitor Reviews on Baturaden Tourist Attraction Using Machine Learning Methods
This study evaluates the performance of four machine learning models: Support Vector Machine (SVM), Random Forest, K-Nearest Neighbors (KNN), and Naive Bayes in analyzing visitor reviews of the Lokawisata Baturaden tourist attraction. Using 5-fold cross-validation, the study aims to determine which machine learning model best suits sentiment analysis on the Baturaden review data. This study was conducted through several stages, including data preprocessing, feature extraction, and the data training process. Case folding, text cleaning, tokenization, stopword removal, and stemming were performed during the data preprocessing stage. The feature extraction method used was TF-IDF. SMOTE was applied to increase data variation and address the data imbalance in the dataset. The results show that SVM provides the best performance with an accuracy of 0.937, an F1-score of 0.937, a precision of 0.943, and a recall of 0.937. Random Forest also performs well with an accuracy of 0.918 and an F1-score of 0.918, though slightly below SVM. KNN shows the lowest performance with an accuracy of 0.651 and an F1-score of 0.544, while Naive Bayes performs adequately with an accuracy of 0.845 and an F1-score of 0.841. Based on this evaluation, SVM is recommended as the best model for sentiment analysis of reviews, followed by Random Forest as a good alternative. The KNN model is not recommended due to its lower performance, while Naive Bayes can be considered for its speed and simplicity, although its results are not as good as SVM and Random Forest. These conclusions guide the selection of the optimal model to enhance understanding and visitor experience at the Baturaden tourist attraction.
Development of the RPS Module in the Portal-Prodi System at the University of Lampung using the RAD Method
The development of the RPS Module in the University of Lampung Study Program Portal System aims to streamline and standardize the creation of Semester Learning Plans (RPS) in various study programs. In 2020, the ministry issued a policy ranking state universities based on key performance indicators (KPI). The KPI assessment has eight indicators, including KPI 7: Collaborative and Participatory Class. In this indicator, the Semester Learning Plan (RPS) is one of the main pieces of evidence for universities stating that lectures taught by lecturers are carried out using the Project Based Method or Case-Based Method. This research uses the Rapid Application Development (RAD) methodology. The iterative nature of the RAD method facilitates efficient requirements gathering, user design, construction, and implementation stages. The main features of the semester learning plan module have been successfully developed based on the features that have been created, namely, submitting RPS, validating RPS, viewing RPS, and printing RPS by generating RPS into a PDF file. Based on testing the semester learning plan module using the black box method, the test results followed the expected results and went well. This study contributes to ongoing efforts to integrate digital solutions in higher education administration.
Performance Evaluation of Machine Learning Models for Soil Fertility Classification Based on the Indian Soil Fertility Dataset
Rice farming productivity worldwide has been declining due to improper soil management practices, including excessive chemical fertilizer use and irregular irrigation. The main challenge lies in accurately classifying soil fertility levels to support optimal land use and reduce resource waste, especially when dealing with imbalanced datasets. This study aims to compare the performance of single classifiers and ensemble classifiers in classifying soil fertility. The single classifiers used include K-Nearest Neighbor (KNN), Naive Bayes, Decision Tree, Support Vector Machine (SVM), and Artificial Neural Network (ANN), while the ensemble classifiers include Random Forest and XGBoost. The Indian Soil Fertility Dataset, obtained from Kaggle, contains 880 samples with 12 features and 1 output class. The research methodology involved data acquisition, preprocessing, data splitting, standardization, and classification, with performance evaluation conducted using a confusion matrix. The results show that ensemble classifiers, particularly Random Forest and XGBoost, outperform single classifiers in imbalanced datasets, achieving accuracy, precision, recall, and F1-score values exceeding 92%-95% across all split scenarios. The findings conclude that Random Forest and XGBoost can serve as reliable models for assisting farmers and agricultural experts in evaluating soil conditions, minimizing unnecessary fertilizer usage, and improving rice farming productivity globally.
Utilisation of Analytic Hierarchy Process in Determining Natural Disaster Evacuation Points
The positive impact of the dry season in Indonesia is that the tourism sector, which prefers to travel to beaches, islands and other tourist attractions, becomes busier and activities such as surfing and diving are usually better during the dry season. Another negative impact is the natural disaster of forest fires and dry peatlands which are highly flammable and cause damage to the natural environment, reduce air quality and threaten wildlife habitats. The most frequent natural disasters in Indonesia are floods, which have a significant impact on health problems because polluted flood water can cause water diseases such as diarrhea, skin infections and even threaten people's lives. Determining the best evacuation point in the context of disaster mitigation is a complex and critical decision. The Analytic Hierarchy Process (AHP) method is a decision analysis method that makes it possible to compare several different criteria that are relevant in decision making, very helpful for determining the most effective alternative. The existing calculation results are added from each existing sub-criteria, the Supermarket alternative result is 0.27, the Hill alternative result is 0.45, the Health Center alternative result is 0.03, the Place of Worship alternative result is 0.30, the Government Building alternative result is 0.28. If the largest value is ranked to the smallest value, Alternative Bukit is the best evacuation place in the event of a natural disaster.
Analysis of Forecasting Methods on Rice Price Data at Milling Level According to Quality
Rice is a primary source of carbohydrates for many Indonesians, and its prices often surge due to uncontrolled demand. Therefore, the government is crucial in monitoring rice prices to maintain stability. Information technology, particularly data mining such as forecasting, is essential for providing accurate information on future rice prices. It will assist various stakeholders in making informed pricing policy decisions. This study employs Random Forest Regression and Gradient Boosting Regressor methods to predict rice prices using a dataset that includes monthly average rice prices at milling levels, categorized by quality (Premium and Medium), spanning from January 2013 to April 2024. The dataset consists of 136 rows, each representing a unique combination of year, month, and quality, and is stored in CSV format. Methodological steps include data collection, preprocessing, modeling, and model evaluation using monthly average rice prices at milling levels based on quality, including premium and medium grades. The results from Random Forest Regression indicate Root Mean Square Error (RMSE) values of 24.90 for premium rice and 25.47 for medium rice. The study reveals that Random Forest Regression outperforms Gradient Boosting Regressor in this context. Future research should explore additional prediction methods and consider other variables influencing rice prices to enhance model accuracy.