ZERO: Jurnal Sains, Matematika dan Terapan
Journal title : ZERO: Jurnal Sains, Matematika dan Terapan Subject : Mathematics, Statistic, Computer Sciences, & Application Language : English ISSN : 2580-5754 (online), 2580-569X (print) Frequency : 2 issues per year (June & December) DOI : Prefix 10.30829 by Crossref Publisher : Department of Mathematics, UIN Sumatera Utara Medan Cit. Analysis : SINTA 2, DOAJ, Garuda, Google Scholar Information : 📞 +6285270009767 ✉️ zero_journal@uinsu.ac.id ZERO: Jurnal Sains, Matematika dan Terapan is a blind reviewed academic journal, established in 2017 as part of the State Islamic University of North Sumatra Medan, dedicated to the publication of scholarly articles in various Sciences, mathematics, and its applications, by which exchanges of ideas as research findings and contemporary issues are facilitated. ZERO: Jurnal Sains, Matematika dan Terapan welcomes contributions of articles in such fields as Mathematics, Statistics, Computer Science, Information System, and its applications.
Authors who publish with this journal agree to the following terms: Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlike 4.0 International License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
Bifurcation Analysis of Cubic Type Nonlinear Schrödinger Equation with Dispersion and Attenuation in Optical Fiber
This study investigates the bifurcation of the cubic Nonlinear Schrödinger Equation (NLSE) in optical fiber media by considering dispersion and attenuation effects. The NLSE models light pulse propagation, with γ representing the strength of nonlinearity. Analytical derivations yield stationary solutions, while numerical simulations using the Newton–Raphson method and eigenvalue analysis verify stability. Results show a critical bifurcation at γ=0: for γ ≤ 0, the system exhibits one unstable fixed point, whereas for γ > 0, three fixed points appear, with simulations confirming that only the two nontrivial branches are stable. This corresponds to a pitchfork bifurcation and a stability transition governed primarily by nonlinearity. Although attenuation is included in the model, its contribution is negligible, indicating that bifurcation behavior is dominated by γ. Compared with previous studies focusing on dispersion-only NLSE or fractional/extended models, this work highlights the decisive role of nonlinearity in determining fixed points and stability in optical fibers.
Copyright (c) 2025 Rika Saputri, Marwan Ramli, Muhammad Ikhwan, Harish Abdillah Mardi http://creativecommons.org/licenses/by-sa/4.0
Evaluation of Best-Fit Probability Distribution Models for Monthly Rainfall in the Lake Toba Region
Understanding rainfall’s statistical distribution is crucial for effective water resource management, disaster mitigation, and climate adaptation in tropical regions. This study identifies the best-fit probability distributions for monthly rainfall in the Lake Toba region, Indonesia, based on long-term data from 34 rain gauge stations. Ten commonly used probability distributions were evaluated, with parameters estimated via Maximum Likelihood Estimation (MLE). The Kolmogorov–Smirnov (KS) test was applied to assess model goodness-of-fit at each station and month. Results indicate that the Generalized Extreme Value (GEV), Gamma, and Weibull distributions consistently provide the best fit for most stations and regencies, while Exponential and Inverse Gaussian distributions perform poorly. Spatial analysis reveals notable variation in best-fit models among regencies, emphasizing the influence of local topography and microclimate. These results highlight the need to select flexible probability models for hydrological planning and climate risk assessment in complex tropical regions. The findings provide valuable references for rainfall modeling and bias correction elsewhere.
Copyright (c) 2025 Syukri Arif Rafhida, Sri Nurdiati, Retno Budiarti, Mohamad Khoirun Najib http://creativecommons.org/licenses/by-sa/4.0
Designing an Optimal Route Network for the Synchronized Trans Gadjah Mada Electric Bus (TGMEB) using Max-Plus Algebra
We develop an optimal route network and synchronized timetable for the Trans Gadjah Mada Electric Bus (TGMEB), designed to cover the entire campus with 4 buses and 28 stops. Using literature review, field observation, and online mapping, we create three network candidates—one-, two-, and three-terminal—and model each as a max-plus linear discrete-event system. Service period and periodic departures are derived from eigenvalue–eigenvector analysis and implemented in Scilab 5.5.2 with a max-plus toolbox. The two-terminal layout performs best: its average inter-stop travel time is 32% faster than the other alternatives while keeping departures synchronized and coverage intact. The results confirm that jointly selecting the network architecture and its timetable yields superior campus operations. This is the first campus-scale study that co-designs a multi-terminal electric-bus route network and synchronized timetable via max-plus algebra, optimizing departure throughput and average inter-stop travel time. Unfortunately, this design has not been tested on the field.
Copyright (c) 2025 Franciscus Budi Pranatta, Marcellinus Andy Rudhito, Dewa Putu Wiadnyana Putra http://creativecommons.org/licenses/by-sa/4.0
Bidirectional GRU for Aspect-Based Sentiment Classification in Multi-Dimensional Review Analysis
Traditional markets in Yogyakarta face mounting pressure from modernization and digital retail competition, yet user-generated reviews remain underutilized. This study applies Aspect-Based Sentiment Analysis (ABSA) with a Bidirectional Gated Recurrent Unit (BiGRU) on 9,222 annotated reviews from nine markets (2016–2024). BiGRU was chosen not only for its efficiency but also for its robustness in low-resource, multilingual settings with informal expressions, where transformer models often require larger datasets and compute. The best configuration with 64 GRU units and a 70:15:15 split achieved 83.4% accuracy (95% CI: ±1.2%) and an F1-score of 0.813, surpassing baselines such as Naïve Bayes (74.5%) and SVM (77.2%). At the aspect level, security yielded the highest F1-score (0.944), followed by cleanliness (0.904) and culinary (0.838), while “others” scored lowest (0.676). Practically, the findings reveal positive sentiment toward pricing and product availability but highlight concerns about cleanliness and accessibility, offering actionable guidance for market policy.
Copyright (c) 2025 Sri Redjeki, Basanto Joshid, Alfonso Situmorang, Muhammad Guntara, Sri Rezeki Candra Nursari, Dara Kusumawati http://creativecommons.org/licenses/by-sa/4.0
The Impact of Using A Linear Model for the Ordinal Response of Mixture Experiments
In a sensory test, the response is a Likert scale, which belongs to the ordinal scale. The ordinal response can be analyzed using a linear model approach; however, this approach can be misleading. This research aims to compare three different methods for ordinal response: the average score, the second-order Scheffe model, and the ordinal logistic model. The case study focused on the response to the taste of cookies resulting from the mixture experiment. The mixture experiment is one type of experimental design which is commonly used for product formulation. The research involved three ingredients with different lower bonds. The D-optimal design which also the {3,2} simplex-lattice design was chosen for the experiment. The three methods were conducted, and they all yielded the same results for the optimum composition; however, the ordinal model provided more information about the data's characteristics. The optimal formulation of each ingredient was 10%, 20%, 70%.
Copyright (c) 2025 Utami Dyah Syafitri, Erfiani Erfiani, Agus M Soleh, Aji Hamim Wigena http://creativecommons.org/licenses/by-sa/4.0
Analyzing Criteria Count Impact on SAW and TOPSIS Stability in Decision Support Systems
This study investigates how increasing the number of decision criteria (5–30) affects the ranking stability and computational efficiency of Simple Additive Weighting (SAW) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Previous studies compared these methods in domains such as scholarship selection and food assistance but did not examine how rankings evolve under greater complexity. Using a synthetic dataset of five fixed alternatives with multiple random seeds, results show that SAW is more prone to ranking fluctuations, while TOPSIS demonstrates greater stability. Kendall’s Tau reveals variability across scenarios, and sensitivity tests confirm that agreement depends on data generation. Computationally, SAW exhibits quasi-linear growth in processing time (≈0.002–0.008 s), whereas TOPSIS remains efficient (≈0.002–0.004 s) with minimal variance. These findings highlight a context-dependent choice SAW offers simplicity in low-dimensional settings, while TOPSIS provides scalability and robustness for complex, high-stakes decision support.
Copyright (c) 2025 Alif Catur Murti, Muhammad Imam Ghozali, Indra Lina Puta, Ali Ikhwan http://creativecommons.org/licenses/by-sa/4.0
Electroencephalogram Signal Analysis for Impulsivity Detection in Drug User Using K- Nearest Neighbor
Drug abuse disrupts normal brain activity and contributes to recurrent impulsive behaviors. While various machine learning approaches have been investigated for analyzing brain signals, studies focusing on the use of the K-Nearest Neighbor (KNN) method for impulsivity detection in drug users remain limited. In this research, KNN was implemented to classify Electroencephalography (EEG) signals based on neuroelectric features that reflect impulsive tendencies. EEG recordings were collected from individuals with a history of drug use while performing cognitive tasks designed to trigger impulsive responses and were compared with recordings from a healthy control group. The classification results showed that KNN achieved an accuracy of 95% in identifying neural patterns associated with impulsivity. This work introduces a novel application of EEG analysis integrated with KNN for objective and precise detection of impulsivity in drug users. The findings highlight the potential of this approach to serve as a supportive tool in rehabilitation programs through reliable neuropsychological monitoring.
Copyright (c) 2025 Delima Sitanggang, Michael Siahaan, Mertfil Tampubolon, Erdani Agustina Ginting, Mardi Turnip http://creativecommons.org/licenses/by-sa/4.0
Analysis of Ruin Probability in Insurance Companies Using the Cramer-Lundberg Model with Variations in Claim Distributions
Ruin risk is a critical concern in the insurance industry, reflecting a company’s ability to fulfill long-term claim obligations. This study estimates ruin probabilities using the Cramer-Lundberg model, with Monte Carlo simulation applied to three claim distributions: exponential, lognormal, and gamma. Simulations vary initial capital while holding the premium rate, claim intensity, and distribution parameters constant. Results show that the lognormal distribution, due to its heavy tail, leads to higher ruin probabilities compared to exponential and gamma distributions. The gamma distributions produce intermediate outcomes, while the exponential shows the lowest risk. These findings highlight the importance of considering distributional characteristics when assessing solvency risk. The study provides practical insights for actuaries and risk managers in evaluating capital adequacy, stress-testing portfolios, and developing adaptive pricing and reinsurance strategies.
Copyright (c) 2025 ALFI KHAIRIATI, Retno Budiarti, Mohamad Khoirun Najib http://creativecommons.org/licenses/by-sa/4.0
Performance Comparison of WSN Topologies in IoT-Based Water Quality Monitoring Systems
This study, we quantify how WSN topology shapes QoS for IoT water-quality monitoring and derive deployment rules. Five topologies (Hybrid Star–Mesh, Cluster Tree, Full Mesh, Ring, ZigBee Star; 20 nodes) were simulated in NS-3 for 10 independent runs with random seeds. Our mathematical contribution is a compact QoS model set—latency LLL, packet-loss PlossP_{\text{loss}} Ploss, bandwidth usage UBU_BUB, and throughput TTT—used to compare topologies and compute relative/absolute improvements. Statistics report mean±SD with 95% confidence intervals from Student’s t-distribution; pairwise Mann–Whitney tests with Benjamini–Hochberg FDR control (α=0.05) yield compact-letter displays; Cliff’s δ quantifies effect sizes. Results: Hybrid Star–Mesh minimizes latency/loss while maximizing throughput; Ring is consistently inferior; Cluster Tree and ZigBee Star are mid-range; Full Mesh trades redundancy for delay and bandwidth. These models produce actionable guidance for aquaculture (real-time dissolved-oxygen) and urban drinking-water safety, and motivate multi-objective optimization (latency–throughput–energy) toward Pareto-optimal designs.
Copyright (c) 2025 Muhammad Imam Ghozali, Alif Catur Murti, Wibowo Harry Sugiharto, Klaus Roder http://creativecommons.org/licenses/by-sa/4.0
Comparison of User Satisfaction of Mobile-Based Document Scanner Applications on Camscanner and Simple Scanner Using the TAM Method
CamScanner and Simple Scanner users were not very satisfied with the services and performance of their applications. Complaints included annoying ads, technical difficulties, limited features, and problems with the paid version. To analyze user satisfaction, this study used the Technology Acceptance Model (TAM) involving five main variables: Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Attitude Toward Using (ATU), Behavioral Intention to Use (BI), and Actual Use (AU). This study aimed to compare user satisfaction of both applications. The results showed that for CamScanner, four hypotheses were accepted and one was rejected, while for Simple Scanner, all hypotheses were accepted. This indicates that for CamScanner, 80% of the variables influence user satisfaction, and for Simple Scanner, 100%. In general, this study confirms that perceived ease of use and usefulness of both applications are important factors that shape user attitudes, intentions, and behavior in using document scanning applications.
Copyright (c) 2025 Restu Apri Yanto, Zarnelly Zarnelly, Megawati Megawati, M.Afdal M.Afdal http://creativecommons.org/licenses/by-sa/4.0
Forecasting Patchouli (Pogostemon Cabin Benth) Production in North Kolaka Using ARIMA, LSTM and Hybrid ARIMA-LSTM
Patchouli (Pogostemon cablin Benth.) is one of Indonesia’s most economically valuable essential oil commodities. This study forecasts patchouli production using ARIMA, LSTM, and a hybrid ARIMA–LSTM model, emphasizing the novelty of applying machine learning techniques to essential oil production forecasting, an understudied area. Weekly production data from ARS Atsiri North Kolaka (January 2022–July 2025, 187 records) were analyzed. ARIMA was applied to capture linear patterns, LSTM to model nonlinear dynamics, and the hybrid model to combine both characteristics. Model performance was evaluated using MSE, RMSE, and MAPE. The ARIMA (2,1,1) model performed best among the linear approaches, while LSTM with normalization, windowing, and 50 hidden units achieved the highest overall accuracy (MSE = 251.22, RMSE = 321.67, MAPE = 0.238%). The hybrid model did not outperform LSTM, likely due to the limited dataset and the dominance of nonlinear patterns, thus confirming LSTM as the most effective model.
Copyright (c) 2025 Sri Muslihah Bakhtiar http://creativecommons.org/licenses/by-sa/4.0
A Spatio-Temporal Panel Data Model for Coronavirus Deaths: Evidence from Europe
The spatial panel data model is a model in which the independent variables are estimated to be influenced by place, time, and explanatory variables. Almost all countries have been attacked by the coronavirus, starting in China in February 2020. Some of the affected residents died, some recovered, and some are still under surveillance. This study aims to identify the most accurate model with the response variable of population deaths due to coronavirus and the independent variables of the number of cases with coronavirus and the number of tests. This study uniquely compares multiple spatial models for pandemic analysis. The four models are SAR, SEM, GSM, and Temporal Spatial Panel Data. The fourth model enables more accurate analysis of COVID-19 mortality by capturing both spatial and temporal dependencies. As a result, the spatial temporal panel data model is the best model with a coefficient of determination of 63.9%.
Copyright (c) 2025 Asep Rusyana http://creativecommons.org/licenses/by-sa/4.0
Optimal Control of Cardiovascular Disease Using Pontryagin’s Maximum Principle
Cardiovascular disease poses serious global health and economic challenges. This study develops a dynamic model by adapting the infectious-disease SEIR framework to a non-communicable disease context and extending it with a hospitalized compartment. Three control strategies: curative intervention (u1), lifestyle modification (u2), and preventive screening–education (u3) are incorporated and optimized using Pontryagin’s Maximum Principle. Numerical simulations, calibrated with data from Rinkendiknas, World Health Organization, National Health Insurance program, BMC Public Health, National Library of Medicine, and RS Roemani Muhammadiyah Semarang, show that curative control rapidly reduces early case burden, while lifestyle modification and education sustain long-term declines. With combined controls, the exposed, infected, and hospitalized compartments decrease by 10%, 20,8%, and 7,4%, respectively, while costs are reduced by 28,5% compared to single interventions. The integration of epidemiological models, health-system dynamics, and multi-control optimization offers both methodological novelty and practical value for cost-effective cardiovascular disease policy design.
Copyright (c) 2025 Arista Fitri Diana, Tarita Intan Soraya, Mia Siti Khumaeroh http://creativecommons.org/licenses/by-sa/4.0
Modeling E-Payment Adoption and its Impact on MSME Financial Inclusion in North Kolaka
Micro, Small, and Medium Enterprises (MSMEs) play a crucial role in Indonesia’s economic development; however, the adoption of e-payment systems in North Kolaka Regency remains relatively low. This study aims to model and analyze the adoption dynamics of e-payment among MSMEs using an SEIR (Susceptible–Exposed–Infected–Recovered) framework. Based on survey data from 485 MSMEs, simulations were conducted using Python to determine the spread and sustainability of adoption behavior. The results reveal that a majority of MSMEs are still unfamiliar with e-payment systems, while the proportion of active users remains limited. The numerical simulation yields a basic reproduction number of R₀ = 0.4939, indicating that e-payment adoption has not yet reached a sustainable level. To enhance the growth of e-payment usage and strengthen financial inclusion, policy strategies should focus on improving digital financial literacy, providing continuous incentives, and integrating MSMEs into broader go-digital programs.
Copyright (c) 2025 Nurul Aulia Bohari http://creativecommons.org/licenses/by-sa/4.0
Predicting Malaria Incidence Using LSTM and Environmental Variables
Climate change is exacerbating malaria risk in Indonesia, especially in Papua. This study proposes a Bidirectional Long Short-Term Memory (LSTM) model to forecast malaria incidence using climate variables. The dataset comprises monthly malaria and climate records (rainfall, temperature, humidity) from four high-endemic provinces between 2014 and 2024. Key methodologies included data augmentation to address data imbalances and a grouped time-series cross-validation for robust model evaluation. An ARIMA model was implemented as a validation baseline to benchmark the proposed approach. The Bi-LSTM model delivered superior performance, achieving an average test R² of 0.7210 and SMAPE of 11.02%. the model demonstrated excellent generalization with no evidence of overfitting, significantly outperforming the ARIMA baseline. The findings validate the use of deep learning models as effective tools for public health surveillance, providing reliable early warnings to support timely interventions. Future work will apply SHAP interpretability techniques and expanding the model's geographic scope.
Copyright (c) 2025 wellie sulistijanti http://creativecommons.org/licenses/by-sa/4.0
Development of a Mathematical Model for the Optimization of Natural Resources in Villages
This study integrates Linear Programming (LP) and Geographic Information System (GIS) to optimize land and water resource utilization in Indonesian rural areas. The proposed multi-period LP model aims to maximize farmers’ net profit under land, water, and labor constraints, where decision variables represent the land area (in tumbak) allocated to each crop per period. Conducted in three agricultural villages of Garut Regency with 60 farmer respondents selected through stratified sampling, the model was solved using Excel Solver and spatially visualized through GIS. Results show an optimal cultivated area of 453.202 tumbak, utilizing 16,412.49 m³ of water and 5,319.098 man-days of labor, producing Rp 2,078,047,500 in gross revenue and Rp 26,493,556 in net profit. Land-use efficiency improved by 64.8% from baseline conditions, with land as the main binding constraint. The model offers a practical decision-support tool for policymakers to plan crop rotation, irrigation scheduling, and sustainable land-use strategies.
Copyright (c) 2025 Ikhsan Faturohman http://creativecommons.org/licenses/by-sa/4.0
Classification of Numeracy Achievement of Junior High School Educational Units Based on National Assessment Data using Random Forest
This study classifies numeracy achievement in Indonesian junior high schools using 2023 National Assessment data from 11,399 schools. The Random Forest algorithm was applied because it is able to capture nonlinear relationships and complex interactions between heterogeneous predictors, while simultaneously reducing variance through bagging and out-of-bag validation techniques. Two models were developed, one without and one with literacy variables. The addition of literacy increased accuracy from 82.97% to 90.0% and increased the ROC-AUC value from 0.8986 to 0.9609. Based on Gini importance, literacy was the most influential predictor, followed by religiosity, learning experience, gender equality, and class size. Government policies need to integrate literacy and numeracy improvements within a unified curriculum framework and promote gender equality and contextual learning in schools. Furthermore, utilizing data-driven analysis from the National Assessment is crucial for guiding targeted interventions and equitable resource allocation for numeracy improvement.
Copyright (c) 2025 Angelin Ica Pramesti, Chatarina Enny Murwaningtyas http://creativecommons.org/licenses/by-sa/4.0
Polygon Area Board Game for Building Thinking Skills in Area Conservation and Measurement
This study aimed to develop a board game to enhance fifth-grade students’ understanding of area concepts and the law of conservation of area in polygons. The research employed the ADDIE model, covering analysis, design, development, implementation, and evaluation stages. Expert validation and classroom trials were conducted to assess validity, practicality, and effectiveness. The results showed that the board game achieved a validity score of 89% (very feasible) and improved students’ test performance by 23%. Students exhibited high engagement and active participation during learning activities. The study highlights the importance of integrating design elements such as layout, color, and illustration to support conceptual understanding. It concludes that well-designed board games can effectively foster critical thinking and motivation in elementary mathematics learning.
Copyright (c) 2025 Karlimah - Karlimah, Dindin Abdul Muiz Lidinillah, Supriadi Supriadi, Ika Fitri Apriani, Sofi utiara Insani http://creativecommons.org/licenses/by-sa/4.0
GSTAR (1;1) Transfer Function Model for Forecasting Chili Prices with Rainfall Effect
Chili price fluctuations are strongly influenced by climate variability, distribution inefficiencies, and spatial. The ARIMA–TF and GSTARX models can address these issues. However, GSTARX is generally limited to linear responses of exogenous variables, while ARIMA–TF does not capture spatial heterogeneity. This study proposes an integration of the GSTAR and Transfer Function approaches. This model combines spatial-temporal dependencies with the external factors such as rainfall. The modeling process begins with GSTAR modeling, followed by parameter estimation of the transfer function using the Marquardt algorithm. The data used are rainfall and chili prices in three locations in West Kalimantan. The model data estimation results show very good accuracy, with MAPE values of 15%, 8%, and 14%. These results confirm that rainfall is a crucial factor influencing chili price variations and demonstrate the reliability of GSTAR–TF in forecasting agricultural commodities in the face of climate variability.
Copyright (c) 2025 Yundari Yundari, Asri Rahmawati, Yuyun Eka Pratiwi http://creativecommons.org/licenses/by-sa/4.0
IoT-Based Automatic Fire Protection System (SIPEKA) with Android Application for Fast Monitoring and Response
Fire hazards cause serious damage, especially in densely populated areas lacking rapid-response systems. This study introduces SIPEKA, an IoT-based automatic fire suppression system integrated with an Android application for real-time monitoring and control. The system uses DHT22 and MQ-2 sensors connected via Wi-Fi to detect fire indicators and activate suppression devices such as pumps or sprinklers. Testing was conducted in a controlled 3×3 m residential-like environment. Results show an average fire detection time of 2.8 s, a notification delay of 1.5 s, and a suppression success rate of 94.7%. The Android interface provides reliable manual override and remote monitoring with 99% uptime. Unlike conventional IoT fire systems that only issue alerts, SIPEKA combines automatic suppression, mobile control, and real-time monitoring, offering an effective, low-cost, and intelligent solution to improve household fire safety.
Copyright (c) 2025 jenie sundari, yunita yunita, sifa fauziah, popon handayani, hardiyan hardiyan http://creativecommons.org/licenses/by-sa/4.0
Hybrid GSTAR-Machine Learning Model for Forecasting Tourists Numbers in Yogyakarta
Tourism management in DI Yogyakarta is vital to ensure tourism benefits local communities. A key challenge lies in the uncertainty and spatial interdependence of tourist visits among neighboring regions. While the GSTAR model captures spatial relationships, its accuracy decreases with outliers, non-linearity, and assumption violations. To overcome these issues, this study integrates GSTAR with machine learning. Using 168 observations of tourist visits across DI Yogyakarta’s regencies/cities (January 2010–December 2023), GSTAR-GLS-XGBoost model achieved 22–34% lower RMSE than other models. Tourist numbers fluctuate greatly, with peaks in May, June, July, and December. Practically, these findings can help local governments and stakeholders optimize resource allocation, plan promotions, and prepare facilities during peak seasons for sustainable tourism management in DI Yogyakarta.
Copyright (c) 2025 Gama Putra Sohibien, Annisa Nurul Azmi, Wahyuni Andriana Sofa, Cucu Sumarni, Rindang Bangun Prasetyo, Christiana Anggraeni Putri http://creativecommons.org/licenses/by-sa/4.0
Constructing Efficient Frontiers in Cryptocurrency Market Using Long-Run GARCH Volatility
Cryptocurrency portfolio optimization faces challenges from persistent volatility. This study aims to construct efficient frontiers using long-run GARCH (1,1) volatility estimates to improve portfolio stability in crypto markets. Daily prices of Bitcoin (BTC), Ethereum (ETH), and Solana (SOL) from January 2023 to December 2024 are analyzed. After confirming stationarity and modeling conditional volatility, GARCH-based risks are applied in portfolio optimization. Results show BTC is the least volatile (25.54%), SOL the most (43.56%), and ETH exhibits strong volatility persistence but a weaker risk-adjusted return. The maximum Sharpe ratio portfolio favors BTC and SOL (Sharpe ratio = 3.692), while the minimum variance portfolio concentrates on BTC. Compared with traditional variance-based methods, the GARCH approach produces more stable and realistic efficient frontiers. These findings suggest that volatility-aware modeling enables investors to design more resilient crypto portfolios under persistent structural risk.
Copyright (c) 2025 Staenly Staenly http://creativecommons.org/licenses/by-sa/4.0
Oil Spill Detection and Verification in Nothern Bintan
The Riau Islands Province, particularly northern Bintan Island, is strategically located near countries such as Singapore, making it a gateway to regional and international markets but also vulnerable to oil spills. This study aims to detect oil spill areas using GLCM texture analysis, adaptive thresholding, and various machine and deep learning models, followed by look-alike verification. The XGBoost model achieved the best performance with an accuracy of 0.9772, detecting oil spill areas of 5,400,241 m² and look-alike regions covering 1,333,045 m² on March 23, 2024. The findings also indicate that inland waters are often misidentified as spills, highlighting the importance of verification. This study is the first to integrate several of these methods for Sentinel-1 based oil spill detection in Bintan waters, as a new approach to accurate and efficient regional monitoring.
Copyright (c) 2025 Brigitta Aurelia Putri Suhendi, Waris Marsisno http://creativecommons.org/licenses/by-sa/4.0
Spectral Clustering-Based Segmentation Framework for TikTok Influencer Classification
This study presents a data-driven segmentation model for TikTok influencers using Spectral Clustering on 120 verified beauty influencers from FastMoss TikTok Analytics (2024–2025). Five engagement metrics views, likes, comments, shares, and followers were selected via variance thresholding, explaining 92.6% of behavioral variance. A similarity graph with a Radial Basis Function (RBF) kernel (σ = 0.5) and k = 3 clusters yielded a Silhouette Score of 0.9473, indicating highly cohesive and well-separated clusters. Compared to K-Means and Hierarchical Clustering, Spectral Clustering achieved 7.8% higher cohesion, capturing complex, nonlinear engagement patterns. Principal Component Analysis (PCA) confirmed clear distinctions among Micro–Mid, Macro, and Mega influencers. Results show that influencer impact depends more on interaction dynamics than follower count, offering a graph-based approach to optimize brand strategies effectively.
Copyright (c) 2025 Rizky Ageng Saputra, Joko Purwadi http://creativecommons.org/licenses/by-sa/4.0
Routh-Hurwitz Stability Analysis of the Predator-Prey Model with Prey Population Harvesting in Polluted Aquatic Ecosystems
Environmental pollution and overharvesting are critical external factors that disrupt predator–prey balance in aquatic ecosystems. This study develops a two-dimensional nonlinear predator–prey model incorporating both toxicity and harvesting. Local stability is analyzed using the Routh–Hurwitz criterion, and findings are validated through numerical simulations under varied initial conditions. The system yields four equilibria: E0, E1 and E1 are unstable extinction states, while the interior equilibrium E*= (0.4146, 1.0899) is locally stable, with Tr(J)=-1.3052 and det(J)=0.4177. Stability is preserved as long as the combined toxicity–harvesting parameter remains below approximately 4.1-4.2 day-1. The novelty of this work lies in explicitly quantifying threshold effects of harvesting and toxicity, showing that coexistence is achievable under moderate external pressures. These results highlight that sustainable management requires keeping exploitation and pollution below critical thresholds to ensure long-term persistence of both prey and predator.
Copyright (c) 2025 Nissa Fiska, Muhammad Ikhwan, Nurmaulidar Nurmaulidar, Oky Askal http://creativecommons.org/licenses/by-sa/4.0
Egg Quality Classification Using Support Vector Machine Based on Image and Non-Image Fusion
Egg production and consumption in Indonesia continue to rise, highlighting the need for accurate egg quality assessment. This study evaluated egg quality using a Support Vector Machine (SVM) model that integrates image and non-image features through feature-level fusion. A total of 750 eggs were analyzed based on external characteristics (shell color, cleanliness, texture, weight, and images) and internal characteristics (odor, albumen, yolk, black spots, images). Image data were reprocessed through grayscale conversion, resizing, and texture extraction using the Gray Level Co-occurrence Matrix (GLCM). Both linear and polynomial SVM kernel with varying degrees were tested, and the polynomial kernel (degree 6) achieved the best, with 86% accuracy, 91% precision, and 87% recall. These results demonstrate that integrating image and non-image features significantly enhances egg quality classification compared to using either data type alone. These findings provide valuable insights for developing automated egg grading system in the poultry industry.
Copyright (c) 2025 Faizal Abrolillah, Irwan Budi Santoso, Totok Chamidy http://creativecommons.org/licenses/by-sa/4.0