Daftar Jurnal Penerbit Universitas Negeri Semarang
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Rice Price Forecasting for All Provinces in Indonesia Using The Time Series Clustering Approach and Ensemble Empirical Mode Decomposition
No full textPurpose: Accurate forecasting of rice prices is essential to ensure food security and a healthy economy for a country like Indonesia. Problems regarding time-series phenomena, such as trends or seasonality, are problematic for traditional approaches like ARIMA (Autoregressive Integrated Moving Average). This study analyzes the effect of EEMD (Ensemble Empirical Mode Decomposition) combined with time-series data clustering on forecasting accuracy.
Methods: From 2009 until 2023, the thirty-two Indonesian provincial rice prices were grouped monthly into time-series clusters using hierarchical clustering, average linkage, and DTW (Dynamic Time Warping). After clusterization, the time series were decomposed using the ensemble EEMD method to extract their IMFs (Intrinsic Mode Functions) and residual components. Each IMF was assigned an ARIMA model. The model forecast was generated by adding all individual estimates. MAPE (Mean Absolute Percentage Error) was used to measure the model\u27s performance.
Result: The prices were divided into three clusters with an optimized region. Price changes are well captured through EEMD, where the residual components contributed predominantly to the long-term trends. The validation of the prediction showed MAPE values under 10% for the majority of the provinces, which indicates a relatively accurate prediction. On the other hand, some regions had inaccuracies that were higher than others due to uncontrollable fluctuations.
Novelty: This study integrates clustering with EEMD decomposition for monthly rice price forecasting using data from 32 Indonesian provinces from 2009 - 2023, offering a novel approach that improves traditional techniques. The model can capture distinct regional price patterns and provide essential information to policymakers to manage rice supply and price stabilization. Further studies can develop external hybrid models with economic variables
Sensor Integration and ARIMA-Based Forecasting in WAQMS for Environmental Monitoring in Riau Province, Indonesia
No full textPurpose: This study aims to develop an integrated solution for real-time environmental monitoring in Riau Province, Indonesia, where air and water quality are increasingly impacted by industrial, agricultural, and climatic factors. Existing monitoring systems are often limited by their lack of real-time capabilities and predictive analytics.
Methods: To address this, we designed the Water and Air Quality Monitoring System (WAQMS), which integrates sensor-based data acquisition with the Autoregressive Integrated Moving Average (ARIMA) model for forecasting. Sensor units were deployed across three pilot locations—Kampar, Siak, and Pekanbaru—to continuously collect environmental data. The ARIMA model was applied to historical datasets to predict future trends in air and water quality, while a web-based dashboard was developed to visualize real-time data and forecasts.
Result: Calibration results showed a system accuracy of 85%, surpassing the national threshold of 75% set by the Indonesian Ministry of Environment and Forestry. This validates the use of WAQMS for Air Pollution Standard Index (ISPU) classification.
Novelty: The novelty of this study lies in the seamless integration of AQMS and WQMS within a unified predictive monitoring system, combined with a user-friendly interface for stakeholders. The results demonstrate the system\u27s potential as a decision-support tool for local governments, offering timely insights and enabling more effective and sustainable environmental management
The Empirical Best Linear Unbiased Prediction and The Emperical Best Predictor Unit-Level Approaches in Estimating Per Capita Expenditure at the Subdistrict Level
No full textPurpose: This study aims to estimate and evaluate per capita expenditure at the subdistrict level in Garut Regency by employing unit-level Small Area Estimation (SAE) techniques, specifically utilizing the Empirical Best Linear Unbiased Predictor (EBLUP) and the Empirical Best Predictor (EBP) methods.
Methods: The data used in this study are socio-economic data, specifically per capita household expenditure in Garut Regency. Socio-economic data generally skew positively rather than the normal distribution, so a method that can approximate or come close to the normal distribution is needed, for example, log-normal transformation. To improve the performance of EBLUP, which may lead to inefficient estimators because of violation of the assumption of normality, this study proposes the Empirical Best Predictor (EBP) method. It handles positively skewed data by applying log-normal transformation to sample data so that it more closely conforms to the desired distribution.
Result: The EBP results are more stable than EBLUP since EBLUP is highly sensitive to outliers, and in cases where the normality assumption is violated, it produces a significant mean square error and inefficient estimators. Evaluating the estimates with both EBLUP and EBP shows Relative Root Mean Squared Error (RRMSE) values above 25%, especially in the subdistricts of Pamulihan, Sukaresmi, and Kersamanah. This is probably due to the household samples being taken in these three subdistricts being comparatively small compared to the other.
Novelty: In this research, we use EBP to improve the performance of EBLUP, which produces inefficient estimators when the normality assumption is violated
Comparative Analysis of High School Student and AI-Generated Essays Using IndoBERT and Linguistic Features
No full textPurpose: The purpose of this study is to address the growing challenge of distinguishing between essays written by humans and essays generated by AI, particularly in the context of high school education in Indonesia. This study aims to analyze the semantic and linguistic differences between student-written and ChatGPT-generated in Indonesian language.
Methods: The study employs an IndoBERT-based semantic model trained with triplet loss to generate paragraph-level embeddings, allowing the measurement of semantic similarity within and between essay classes. Additionally, linguistic features such as lexical diversity, word count, modal usage, and stopword ratio were extracted to capture stylistic and structural differences. These three key features are combined and used as input to a neural network classifier.
Result: The IndoBERT-based semantic model successfully grouped student-written and ChatGPT-generated essays into distinct clusters. The similarity scores within student essays ranged from 0.7 to 0.9, while the similarity between classes was mostly negative with a few outliers, reflecting the cosine similarity metric used in this study, which has a range of -1 to 1. The classification model showed a 90.55% accuracy and an AUC of 0.9999 when evaluated on the independent test set defined in the Data Preparation stage. These results suggest that student-written and ChatGPT-generated essays form distinct semantic clusters. Students’ essays show more linguistic diversity, while ChatGPT essays show consistency in the coherence and formality aspects of the essays.
Novelty: This study provides empirical insights of semantic similarities and linguistic features to differentiate between human and AI-generated essays in the Indonesian language. It contributes to supporting academic integrity efforts and highlighting the need for further research across different writing models and contexts
Development of Employee Management Information System UI/UX Using a User Centered Design Approach
No full textPurpose: This study aims to develop a user interface and user experience (UI/UX) prototype for an Employee Management Information System (EMIS) at PT. Galsoft. The research focuses on addressing the manual administrative processes that are still widely used in the company\u27s daily operations. The primary objective is to produce an interface design that aligns with user needs and improves the efficiency of work processes.
Methods: This research adopts a User Centered Design (UCD) approach, consisting of four main stages: understanding the context of use, specifying user requirements, designing solutions, and evaluating the design. The prototype developed focuses solely on the UI/UX aspects without involving full system implementation. Usability evaluation was conducted using the System Usability Scale (SUS), involving 38 respondents from various divisions within the company.
Result: The evaluation results show that the developed UI/UX prototype achieved an average SUS score of 89.4. This score indicates that the design has a high level of usability, is easy for users to operate, and supports the administrative workflows required by the organization. These findings demonstrate that the UCD approach effectively contributed to creating a design that is both functional and responsive to user needs.
Novelty: This study contributes to the field of administrative information system prototyping in workplace environments that have yet to adopt digital solutions. The novelty of this research lies in the comprehensive application of the UCD approach, combined with SUS based usability evaluation, to produce a relevant and functional design that is ready to be further developed into a fully implemented system
Smart Rupiah Recognition: A Mobile Machine Learning Approach for Visually Impaired Users
No full textPurpose: Despite advances in assistive technology, low-connectivity areas lack reliable solutions for visually impaired individuals, prompting this study to enhance financial autonomy in cash-based economies. This research addresses high fraud risks and the limitations of online tools like Be My Eyes, which fail in areas with only 40% internet access, by developing a 3MB MobileNetV2 model for offline Rupiah denomination recognition on low-end Android devices.
Methods: A MobileNetV2-based Convolutional Neural Network, optimized to 3MB via TensorFlow Lite quantization, was trained on 10,855 augmented images (rotation ±30°, flipping, Gaussian noise, σ=0.1). The Kotlin-based application integrates CameraX for 720p video and Bahasa Indonesia text-to-speech, with a “no object” class. The model was tested on 4–8GB RAM devices, validated through usability evaluations with diverse stakeholders.
Result: The model achieves 90% accuracy (F1-score 0.90) at 1000 lux, 85% at <50 lux, 80% at >60° angles, and 88% for “no object,” with 10ms latency. Self-supervised learning (SimCLR) on 2,000 worn notes improves accuracy by 3% (p < 0.05). Usability evaluations yield 95% session success, with TTS and UI Likert scores of 4.2 and 4.0..
Novelty: The 3MB MobileNetV2 model, with 10ms latency and 15% false positive reduction, outperforms YOLOv5 (500MB, 50ms), Vision Transformer (1GB, 200ms), and YOLOv8 (200MB, 30ms). This model shows potential for cross-currency detection throught preliminary exploration (e.g., USD and euro), which may advance edge AI and financial inclusion in developing nations
Performance Analysis of Machine Learning Models using RFE Feature Selection and Bayesian Optimization in Imbalanced Data Classification with Shap-Based Explanations
No full textPurpose: This research aims to evaluates the performance of Random Forest (RF) and Light Gradient Boosting Machine (LightGBM) models integrated with Recursive Feature Elimination (RFE) for feature selection, Bayesian Optimization (BO) for hyperparameter tuning, and three imbalanced data handling techniques Random Undersampling (RUS), Random Oversampling (ROS), and SMOTENC. Identifying key determinants of household food insecurity in Papua using SHAP for transparent feature interpretation.
Methods: The research used 2022 SUSENAS data from Papua Province. Exploring data composition and variable characteristics, and aggregating individual data into household data. Data were split using random sampling (80% training, 20% testing). Eighteen experimental scenarios were created by combining feature selection or no feature selection, three imbalance handling methods, and default or hyperparameter tuning. RF and LightGBM were evaluated over 50 iterations using accuracy, sensitivity, specificity, and G-Mean, with SHAP applied to the best-performing models for interpretability.
Result: LightGBM achieved the highest accuracy and stability, particularly when combined with SMOTENC and RFE+BO. RF showed better performance in maintaining G-Mean when paired with RUS, with the highest G-Mean (0.756) obtained by RF + BO + RUS. Three-way ANOVA proved that model type, imbalance handling, feature selection, and their interaction significantly affected the G-Mean value. SHAP analysis shows that health, financial, and educational limitations can increase the risk of food insecurity.
Novelty: This research offers a new integration between feature selection, hyperparameter tuning, and imbalanced data handling within an interpretable machine learning framework, thereby providing a robust solution for food vulnerability classification on imbalanced datasets
EMPTY BOX AS SYMBOLIC ACTION: YOUNG VOTERS’ REJECTION OF THE SOLE CANDIDATE IN GRESIK’S LOCAL
No full textThis study explores the symbolic meaning behind the choice of the "empty box" (kotak kosong) among young voters in the 2024 regional election (Pilkada) in Gresik, where only one candidate was on the ballot. Rather than seeing this choice as political apathy, this research interprets it as a conscious act of resistance and a demand for genuine political representation. Using a qualitative descriptive approach and symbolic interactionism theory, the study examines how young voters construct political meaning through their decision to vote for the empty box. Data were collected through in-depth interviews with informants aged 17–25 who reside in Gresik and intentionally voted for the empty box. The findings reveal that their decisions were shaped by critical reflection, peer discourse, and disillusionment with the lack of electoral competition. The act of selecting the empty box is understood as a symbolic expression of frustration, political identity, and expectations for democratic renewal. This study contributes by offering an empirical explanation of how symbolic political choices function as a form of youth resistance, a dimension rarely discussed in previous research on uncontested elections in Indonesia. It also provides a nuanced interpretation of the empty-box vote as a meaning-making process rather than a mere protest action
Effectiveness of PowerPoint-Based Digital Learning Media "Animals Education" in Terms of Retention Ability of Students
No full textThis study aims to examine the effectiveness of PowerPoint-based digital learning media "Animals Education" in improving the retention ability of Phase A students at SDN 1 Kuta, Central Lombok. This quasi-experimental study used quantitative data analysis. Data were gathered using tests and examined with paired sample t-tests, independent samples t-tests, N-gain analysis, and effect size tests. The findings are as follows: (1) The paired sample t-test for the experimental group showed a significance value of 0.000 < 0.05, indicating a significant difference in students\u27 retention scores before and after the intervention; (2) the independent samples t-test showed a significance value of 0.000 < 0.05, suggesting a significant difference in retention between the experimental and control groups; (3) the N-gain score for the experimental group was 0.47, classified as moderate; (4) the effect size for the experimental group was 3.05, indicating that the use of digital "Animals Education" media is highly effective in enhancing student retention
Can We Trust AI to Assess Writing? An Analysis of Scoring Reliability and Feedback Consistency
No full textThis study analyzes AI-generated writing assessments\u27 scoring reliability and feedback consistency using ChatGPT. Adopting a mixed-methods approach, 23 student descriptive texts were evaluated across three assessment rounds. Quantitative findings showed high scoring reliability, with an Intraclass Correlation Coefficient (ICC) of 0.93, indicating excellent consistency across repeated evaluations. Qualitative analysis revealed that ChatGPT consistently addressed five core writing criteria—content, organization, vocabulary, language use, and mechanics. However, the feedback varied in focus and detail across rounds, and the absence of reference to prior feedback limited its support for revision as a recursive process. The findings suggest that although ChatGPT demonstrates reliable scoring and generally stable feedback themes, it lacks the continuity to facilitate sustained writing development. To enhance its pedagogical value, AI-based feedback systems should be designed to build upon previous responses, thereby enabling more effective support for students\u27 progressive improvement in writing