Contemporary Mathematics and Applications (ConMathA)

Pattern-Based Identification of Priority Sectors for Greenhouse Gas Emission Control in Indonesia Using Self-Organizing Map

Aisyah Tur Rif’atin Nurdini, Siti Amiroch — Sun, 07 Sep 2025 00:00:00 +0700

Indonesia is one of the countries that ratified the Paris Agreement, a legally binding international treaty under the United Nations Framework Convention on Climate Change (UNFCCC) regarding greenhouse gas emissions. In line with this commitment, Indonesia is expected to prioritize emission control in sectors that contribute significantly to national emission levels. This study applies the Self-Organizing Map (SOM), a type of neural network, to cluster emission data by sector based on similarity patterns, aiming to identify priority sectors for emission control in Indonesia. The results indicate that the highest-emitting sectors are: Processes for Carbon Dioxide (CO₂), Transport for Methane (CH₄), Processes for F-Gases, and Agriculture for Nitrous Oxide (N₂O). These findings can inform government efforts to prioritize emission control policies in the Processes, Transport, and Agriculture sectors, tailored to each dominant gas type. Such recommendations are essential to support data-driven decision-making, improve national emission control strategies, and strengthen Indonesia’s position in meeting its Nationally Determined Contributions (NDCs) under the Paris Agreement. Model validation using Quantization Error (QE) produced values of 0.0218 for CO₂, 0.0207 for CH₄, 0.0040 for F-Gases, and 0.0171 for N₂O. These low values indicate high mapping accuracy and confirm that SOM is effective in capturing the distribution patterns of emission data, thus providing a scientific basis for designing more targeted mitigation strategies.

Analysis of Land Surface Temperature Changes in East Lombok Regency Using the Cloud-Based Platform Google Earth Engine

Sun, 07 Sep 2025 00:00:00 +0700

This study uses Google Earth Engine, a cloud computing platform, to examine variations in land surface temperature in East Lombok Regency between 2010 and 2020. This study uses MODIS satellite data and is analyzed using Google Earth Engine by adapting a mathematical method—namely, the Random Forest Algorithm from discrete mathematics. According to the results, the temperature increased from its lowest point in 2010 (14.32°C) to its maximum point in 2020 (36.19°C), rising to 16.67°C and 37.74°C. Due to residential growth, Wanasaba, Pringgabaya, and Sambelia saw the biggest increases. Comprehensive temperature monitoring is made possible by Google Earth Engine, which facilitates the effective processing of large-scale spatial data. These results offer a scientific foundation for policy decisions and are essential for environmental management and climate change mitigation. In addition to supporting environmental monitoring initiatives, this study provides a reference for related studies in other areas dealing with issues related to land use and climate change. This research is important for the Central Lombok government as a basis for mitigation efforts in responding to temperature changes and their implications for land use change.

Comparison of Double Exponential Smoothing and Double Moving Average for Forecasting Lost Vehicle Registration Certificates in Pamekasan

Nia Ramadani, Faisol, Kuzairi, Rica Amalia — Sun, 07 Sep 2025 00:00:00 +0700

The government issues the Vehicle Registration Certificate (STNK), an official document that certifies a motorized vehicle's identity and authenticity. Pamekasan Regency is one of the regencies in East Java Province that frequently suffers losses associated with Vehicle Registration Certificates (STNK). Consequently, it is essential to predict the amount of car registration losses so that the Pamekasan regional administration can use the information to lower the losses. The Double Exponential Smoothing and Double Moving Average techniques were used in this study to forecast the amount of vehicle registration losses. According to the research findings, the smoothing parameters 𝑎 = 0.3 and 𝛾 = 0.025 had the lowest MAPE value from the Double Exponential Smoothing method, with a MAPE value of 49.4082%. The double moving average method's smallest MAPE, 𝑘 = 3, has a MAPE value of 31.53215%. The twofold moving average approach is the best way to forecast the loss of car registration in Pamekasan, according to the comparison's findings.

Numerical Invariants Of Nilpotent Graphs In Integer Modulo Rings

Sun, 07 Sep 2025 00:00:00 +0700

Graph theory offers a robust framework for examining algebraic structures, especially rings and their elements. This paper focuses on the nilpotent graph of rings of the form Z_pk, where p is a prime and k∈N, investigating both their structural and numerical properties. We begin by characterizing the nilpotent elements in these rings and examining their relationship to ring ideals. The study then presents theoretical results on key graph invariants, including connectivity, chromatic number, clique number, and specific subgraph configurations. To complement these, we also analyze numerical invariants such as edge count and degree distribution, which reveal deeper connections between ring-theoretic and graph-theoretic properties. Our results highlight consistent structural patterns in nilpotent graphs of Z_pk and provide a concrete contribution to algebraic graph theory by bridging properties of commutative rings and their associated graphs.

Efficiency Measurement of Subsidized Fertilizer Use on Rice Crop Yields in Bangsri District using Data Envelopment Analysis

Hafidh Khoerul Fata, Keysa Andinar Maharani — Sun, 07 Sep 2025 00:00:00 +0700

This study evaluates the technical efficiency of subsidized fertilizer use in rice production across villages in Bangsri Sub-district, Jepara Regency, using the output-oriented CCR model of Data Envelopment Analysis (DEA). The research was motivated by the crucial role of fertilizers in sustaining rice productivity and the persistent issue of inefficient allocation and utilization of subsidized inputs. Secondary data for 2023 were obtained from the Balai Penyuluh Pertanian of Bangsri Sub-district and the Badan Pusat Statistik of Central Java. Input variables included the quantity of subsidized urea, NPK, and granular organic fertilizers, as well as cultivated land area; the output variable was rice production, calculated under the assumption of constant provincial average productivity of 49.26 quintals/ha. All villages in the sub-district were treated as Decision-Making Units (DMUs). The DEA results revealed that only Kedungleper achieved full efficiency (score = 1.00), while Jerukwangi was near the frontier (score = 0.96). Most villages scored between 0.80 and 0.89, indicating substantial room for optimization. The observed inefficiencies may be attributed to mismatches in fertilizer dosage and composition, suboptimal timing and application methods, and local agronomic constraints. Recommended strategies include benchmarking efficient villages, revising fertilization packages and schedules based on site-specific conditions, and strengthening plot-level extension services to ensure that inputs are effectively translated into yield gains. These findings provide actionable insights for improving resource-use efficiency and guiding targeted fertilizer subsidy policies.

Forecasting the Consumer Price Index in Banyumas Regency Using Double Exponential Smoothing with Proportional Integral Derivative Controller

Nurcahya Yulian Ashar, Maulana Fatih Abiyyin — Sun, 07 Sep 2025 00:00:00 +0700

The Consumer Price Index (CPI) is a crucial measure of inflation and the cost of living within a specific region. Accurate CPI forecasts are essential for policymakers, businesses, and stakeholders to make informed decisions. This study utilizes the Double Exponential Smoothing (DES) method to forecast the CPI for Banyumas Regency in January 2025, employing monthly CPI data from January 2020 to December 2024. The DES method was selected due to the observed upward trend in historical CPI data. Python programming was employed to optimize the smoothing parameters α and β, and the results were evaluated using Mean Absolute Deviation (MAD), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE). The forecasted CPI for January 2025 is 106.36, with high accuracy indicators, including a MAPE of 0.26%, demonstrating that DES is a reliable model for CPI forecasting in Banyumas Regency.