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Journal of Mathematics, Computation and Statistics (JMATHCOS)
Published by Universitas Negeri Makassar
ISSN : 24769487     EISSN : 27210863     DOI : https://doi.org/10.35580/jmathcos
Core Subject : Education,
Mathematics
Fokus yang didasarkan tidak hanya untuk penelitian dan juga teori-teori pengetahuan yang tidak menerbitkan plagiarism. Ruang lingkup jurnal ini adalah teori matematika, matematika terapan, program perhitungan, perhitungan matematika, statistik, dan statistik matematika.
Arjuna Subject : Matematika - Matematika Terapan
Articles 210 Documents
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Partial Least Square Second Order dengan Pendekatan Two Stage untuk Mengukur Tingkat Adopsi Digital UMKM Sakinah, Awit; Listiani, Lina; Agustina, Nova
Journal of Mathematics, Computations and Statistics Vol. 7 No. 2 (2024): Volume 07 Nomor 02 (Oktober 2024)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v7i2.4266

Abstract

Micro, Small, and Medium Enterprises (MSMEs) in Tasikmalaya have a relatively low rate of technology adoption, particularly in e-commerce. Initial survey results show that 66% of MSMEs supported by the Tasikmalaya Kamar Dagang Industri (KADIN) have not been able to leverage e-commerce adoption. This limitation significantly impacts the growth of MSMEs and their ability to compete in a market increasingly integrated with digital technology. This study aims to analyze the factors influencing the adoption of e-commerce by MSMEs in Tasikmalaya using the Technology Acceptance Model (TAM). The analysis method employs Partial Least Square (PLS) Second Order, as the variables in TAM are multidimensional. The second-order approach selected is the two-stage method, which can minimize residuals of correlated indicators. Data were collected using simple random sampling with a rule-of-thumb sample size of 145 MSMEs. The study results indicate a significant influence of self-efficacy on perceived usefulness and perceived ease of use. There is also a significant influence of perceived ease of use on perceived usefulness. Additionally, perceived usefulness and perceived ease of use significantly and positively influence attitudes toward e-commerce adoption among MSMEs, with a total influence of 87.2%. Furthermore, there is a significant influence of perceived usefulness and attitude towards e-commerce adoption on intention to adopt e-commerce by 91.5%. The intention to use e-commerce significantly affects actual use (adoption) by 91.1%. This indicates that the more positive the perception of usefulness and ease of technology, the higher the intention and actual use of e-commerce technology by MSMEs. To increase e-commerce adoption among Tasikmalaya MSMEs, training on user-friendly e-commerce platforms and intensive mentoring is needed to enhance the self-efficacy of MSME actors in utilizing technology.
Pemodelan Pencemaran Udara sebagai Solusi Penurunan Kualitas Udara Menggunakan Generalized Space-Time Autoregressive di Kota Makassar Farhan, Muhammad; sanusi, wahidah; Ihsan, Hisyam
Journal of Mathematics, Computations and Statistics Vol. 7 No. 2 (2024): Volume 07 Nomor 02 (Oktober 2024)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v7i2.4304

Abstract

This study discusses the application of the Generalized Space-Time Autoregressive (GSTAR) model to analyze air pollution in Makassar City, focusing on NO2 and SO2 pollutants from 2017 to 2023. Data were collected from four different sampling locations: transportation, industry, residential, and office areas. This study uses inverse distance weighting and cross-correlation normalization to develop the forecasting model. The analysis results show that the GSTAR (1;0;2) model for NO2 pollutants and GSTAR (1;0;1) for SO2 pollutants are the best models, with residuals meeting the assumptions of white noise and normal distribution. Therefore, this model can be used to predict future air pollution levels.
Performance Analysis of Neighborhood Component Analysis on Support Vector Machine in Greenhouse Gas Emission Classification Gustriza Erda; Kurnia Ramadani
Journal of Mathematics, Computations and Statistics Vol. 7 No. 2 (2024): Volume 07 Nomor 02 (Oktober 2024)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v7i2.4305

Abstract

The heatwave phenomenon has hit several countries in various parts of the world, caused by climate change. Climate change leads to greenhouse gas emissions increasing beyond the limits set by the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report Global Warming Potentials. This final project uses a combination of Neighborhood Component Analysis (NCA) and Support Vector Machine (SVM) methods with linear, polynomial, Radial Basis Function (RBF), and sigmoid kernel functions. The purposes of this final project are to evaluate the performance of NCA on SVM and to determine the best kernel function in this combination. Based on the analysis, it was found that classification using a combination of NCA and SVM methods can reduce variables, with the best kernel function being the Polynomial kernel function. This is because the analysis using the Polynomial kernel function achieved the highest accuracy values for training data, testing accuracy, and F1-Score, which are 98,96%, 99,15%, and 98,98% respectively. Additionally, the training analysis time and testing analysis time were the shortest at 0,15 seconds and 0,04 seconds.
Prediksi Risiko Emisi Karbon Dioksida Melalui Pemodelan GSTAR Kriging di Wilayah Asia Utriweni Mukhaiyar; Dianti, Naila Ratu; Rezeki, Elizabet Sri; Richardo, Nicholas Ramos
Journal of Mathematics, Computations and Statistics Vol. 7 No. 2 (2024): Volume 07 Nomor 02 (Oktober 2024)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v7i2.4309

Abstract

This study analyzes the variability of carbon dioxide emissions across Asia, revealing that China, India, and Indonesia are the primary contributors with extremely high average emissions and standard deviations. The GSTAR(3;1,1,1) model has been shown to be optimal for forecasting future emissions, based on lower RMSE and MAPE values. Ordinary Kriging analysis using the isotropic spherical semivariogram model provides the most accurate predictions for unobserved areas, with contour maps indicating that the northeastern region of Asia will continue to face high emission concentrations until 2027. While countries such as Brunei and Armenia have managed to keep emission levels low, the instability of emission trends across Asia underscores the need for emission reduction strategies tailored to the specific context of each country.
Peramalan Inflow dan Outflow Uang Kartal Menggunakan X-13 ARIMA-SEATS Herdiantini , Rizka Fitria; Kartikasari, Mujiati Dwi
Journal of Mathematics, Computations and Statistics Vol. 7 No. 2 (2024): Volume 07 Nomor 02 (Oktober 2024)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v7i2.4331

Abstract

Currency is an important part of Indonesian society's economic transactions. In order to effectively manage the amount of currency in circulation, Bank Indonesia must carefully plan and estimate its currency needs. One way to estimate this need is by looking at Bank Indonesia's inflow and outflow. Therefore, forecasting currency inflow and outflow is crucial for future planning. Inflow and outflow data are included in the time series that is affected by calendar variations. Traditional forecasting methods, such as exponential smoothing and ARIMA, cannot handle these variations. Therefore, this study uses the X-13 ARIMA-SEATS method, which is able to forecast time series data with the effect of calendar variations. Based on monthly data on currency inflow and outflow from January 2015 to December 2022, the results show that the X-13 ARIMA-SEATS method is effective when used with the mean absolute percentage error (MAPE) criteria.
Penerapan Metode ARIMA dalam Meramalkan Kebutuhan Energi Listrik di Kota Makassar Maya Sari Wahyuni; Zaki, Ahmad; Hidayat, Syarif; Pratama, Muhammad Isbar
Journal of Mathematics, Computations and Statistics Vol. 7 No. 2 (2024): Volume 07 Nomor 02 (Oktober 2024)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v7i2.4388

Abstract

. Tingkat pemakaian energi listrik di Kota Makassar tiap bulannya pada tahun 2018 sampai dengan 2022 berbeda-beda. Oleh karena itu, diperlukan peramalan agar dapat dilakukan pengelolaan dan perencanaan kebutuhan energi listrik dengan baik. Tujuan penelitian ini untuk mengetahui model ARIMA terbaik yang digunakan dalam peramalan kebutuhan energi listrik di Kota Makassar 12 bulan berikutnya. Tahapan untuk menentukan model ARIMA dimulai dari identifikasi kestasioneran data, indentifikasi model sementara, estimasi dan uji signifikansi parameter, uji asumsi residual, pemilihan model terbaik, melakukan peramalan serta uji ketepatan model peramalan. Hasil dari penelitian ini diperoleh model terbaik yaitu ARIMA(1,0,0) dengan nilai MAPE sebesar 0,4735% yang berarti bahwa model sangat baik dan layak untuk digunakan dalam peramalan. Kata Kunci: Energi Listrik, ARIMA, MAPE The level of electricity consumption in Makassar City every month from 2018 to 2022 is different. Therefore, forecasting is needed so that the management and planning of electrical energy needs can be carried out properly. The purpose of this study was to determine the best ARIMA model to be used in forecasting electricity demand in Makassar City for the next 12 months. The steps for determining the ARIMA model start with identifying the stationarity of the data, identifying temporary models, estimating ang testing the significance of parameters, testing the residual assumptions, selecting the best model, making forecasts and testing the accuracy of the forecasting model. The results of this study were obtained by the best model, ARIMA(1,0,0) with a MAPE value of 0,4735% which said that the model was very good and feasible to use in forecasting. Keywords: Electrical Energy, ARIMA, MAPE
Penerapan Metode Rantai Markov dalam Memprediksi Hasil Panen Tanaman Padi di Kabupaten Bulukumba Dhian Eka Wijaya; Rasyid, Nur Ahniyanti
Journal of Mathematics, Computations and Statistics Vol. 7 No. 2 (2024): Volume 07 Nomor 02 (Oktober 2024)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v7i2.4432

Abstract

Tanaman padi merupakan salah satu komoditas pangan utama yang diproduksi di Indonesia. Kabupaten Bulukumba merupakan salah satu kabupaten di Sulawesi Selatan, Indonesia yang memiliki potensi besar dalam memproduksi padi. Namun, petani di Kabupaten Bulukumba masih menghadapi tantangan terkait ketidakpastian hasil panen. Hal ini disebabkan oleh berbagai faktor yang mempengaruhi hasil panen, sehingga sulit untuk memprediksi hasil akhir panen dan mengantisipasi fluktuasi produksi padi. Dengan mempertimbangkan faktor-faktor stokastik tersebut, maka diperlukan suatu analisis yang mampu memodelkan dan meramalkan perilaku dinamis dari sistem produksi padi di Kabupaten Bulukumba. Salah satu metode analisis yang dapat diterapkan adalah Rantai Markov. Metode ini merupakan suatu proses stokastik yang menggambarkan urutan peristiwa yang mungkin, di mana probabilitas setiap kejadian hanya bergantung pada keadaan sebelumnya. Penelitian ini bertujuan untuk mengetahui hasil panen tanaman padi di Kabupaten Bulukumba pada tahun 2024-2026. Data yang digunakan pada penelitian ini adalah data hasil panen padi pada sepuluh Kecamatan di Kabupaten Bulukumba tahun 2014-2023. Hasil penelitian menunjukkan prediksi total panen padi seluruh kecamatan di Kabupaten Bulukumba adalah 278.245,30 ton pada tahun 2024; 278.306,90 ton pada tahun 2025; dan 278.319,10 ton pada tahun 2026. Nilai Mean Absolute Percentage Error (MAPE) sebesar 8,445%, dengan kondisi steady state diperkirakan terjadi pada tahun 2028.
Deteksi informasi hoaks vaksin covid-19 Di media sosial twitter menggunakan jaringan syaraf tiruan backpropagation Syam, Rahmat; Sanusi, Wahidah; SYahnur, Andi Aulia
Journal of Mathematics, Computations and Statistics Vol. 7 No. 2 (2024): Volume 07 Nomor 02 (Oktober 2024)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v7i2.4433

Abstract

Hoaks di bidang kesehatan adalah hal yang berbahaya terutama pada suatu pandemi Covid-19 di mana keadaan masih belum pasti. Hoaks terkait vaksin Covid-19 banyak tersebar di media sosial salah satunya di media sosial Twitter. Pembaca umumnya dapat melakukan pendeteksian terhadap suatu pesan Twitter yang termasuk hoaks secara manual seperti dengan membaca berita di media massa resmi. Namun dengan kecepatan dan banyaknya informasi yang tersebar di Twitter membuat cara manual sulit dilakukan. Karena itu, deteksi informasi hoaks secara otomatis dapat menjadi solusi untuk kesulitan tersebut. Penelitian ini mendeteksi informasi hoaks vaksin Covid-19 menggunakan metode jaringan syaraf tiruan backpropagation dengan mengklasifikasi teks pesan Twitter ke dalam dua kelas yaitu hoaks dan bukan hoaks. Tujuan dari penelitian ini yaitu untuk mengetahui model arsitektur jaringan syaraf tiruan backpropagation dalam mendeteksi hoaks vaksin Covid-19 di media sosial Twitter dan tingkat akurasi yang didapatkan dari model tersebut menggunakan 7,130 data tweet yang dikumpulkan dengan data scraping dengan bahasa pemrograman Python dan dilabeli secara manual kemudian diterapkan preprocessing data dan pembobotan TF-IDF sebelum teks tweet diproses ke dalam model. Hasil penelitian ini menunjukkan bahwa model dengan performa paling baik dimiliki oleh metode backpropagation model pembagian 20% data latih dan 80% data uji yang menggunakan dua hidden layer (3, 5) yaitu mencapai tingkat akurasi sebesar 77.12% dengan tingkat error sebesar 22.88%, di sisi lain nilai AUC dari kurva ROC yang dihasilkan sebesar 0.7414 yaitu masuk pada kategori klasifikasi cukup. Kata Kunci: Hoaks, Twitter, Vaksin Covid-19, Jaringan Syaraf Tiruan, Backpropagation Hoaxes in the health field are dangerous, especially in a Covid-19 pandemic where the situation is still uncertain. Hoaxes related to the Covid-19 vaccine are widely spread on social media, one of which is Twitter. Readers can generally detect a Twitter message that is a hoax manually, such as by reading news in the official mass media. However, the speed and amount of information spread on Twitter makes manual methods difficult to do. Therefore, automatic detection of hoax information can be a solution to this difficulty. This research detects hoax information about the Covid-19 vaccine using the backpropagation artificial neural network method by classifying Twitter message text into two classes, namely hoaxes and non-hoaxes. This study aims to determine the backpropagation artificial neural network architecture model in detecting Covid-19 vaccine hoaxes on Twitter social media and the level of accuracy obtained from the model used 7,130 tweet data collected by data scraping using Python programming language and manually labeled then applied data preprocessing and TF-IDF weighting before the tweet text is processed into the model. The results of this study show that the model with the best performance is owned by a backpropagation model method of a division of 20% training data and 80% test data using two hidden layer (3, 5), which achieves an accuracy rate of 77.12% with an error rate of 22.88%, on the other hand, the AUC value of the resulting ROC curve is 0.74 that is included in the fair classification category. Keywords: Hoax, Twitter, Covid Vaccine, Artificial Neural Network, Backpropagation
Eksplorasi Transaksi Pembelian Produk Dengan Metode Asosiasi Apriori Data Mining Siti Maryam; Asep Saepuloh; Awit Marwati Sakinah
Journal of Mathematics, Computations and Statistics Vol. 7 No. 2 (2024): Volume 07 Nomor 02 (Oktober 2024)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v7i2.4458

Abstract

In an increasingly digital era and increasingly fierce business competition, businesses must become more innovative to find out how consumers usually buy products. This can be achieved by collecting data from every transaction in which a consumer purchases a product. This research analyzes exploration of product purchase transaction data using the apriori association method in data mining, with primary data obtained from PT employees. Sinar Sosro Tasikmalaya during February 2024. Limitations of this research include a limited data period so the sample size used is small, which can affect the generalization of the results. However, the choice of parameter values ​​of support of 0.1 and confidence of 1 helps to compensate for the limited sample size while still providing relevant and high-quality rules. The results of this research produce 3 association rules that can be used to support decisions, such as more efficient product placement and sales bundles. Where, the results of associations that have a strong product connection can be bundled with products that are less popular, and products that are often purchased together can be placed closer to the position of the product in the warehouse to make it easier to pick up the product.
Analisis Regresi Data Panel dalam Memodelkan Faktor yang Mempengaruhi Stunting di Indonesia Ardiana Fatma Dewi; Hartina Husain
Journal of Mathematics, Computations and Statistics Vol. 7 No. 2 (2024): Volume 07 Nomor 02 (Oktober 2024)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v7i2.4493

Abstract

Most nations deal with stunting to some extent, but emerging nations and those in the lower middle class, like Indonesia, have it the worst. Accordingly, the stunting epidemic remains the top priority for the Indonesian government at the present time. Because of its many potential future effects, stunting is a major worry for the government. Determining what variables may impact stunting in Indonesia is one approach to bolstering this initiative. Panel data will be used in this study due to the fact that different provinces in Indonesia exhibit different features on an annual basis. In 2020 and 2022, researchers in 34 of Indonesia's provinces used panel data regression analysis to identify the variables that impact stunting rates. Based on these findings, a model using a fixed effect estimator was determined to be the most effective. The results of the Chow and Hausman tests support this idea, respectively showing that fixed-effect models outperform common-effect models and random-effect models. Both full and partial tests show that the number of stunted children under five in Indonesia is significantly affected by the variables of poverty rate and gross regional product per capita. The panel data regression model using the poverty rate and GDP per capita provides a strong explanation for the prevalence of stunting in children less than five years old, as shown by the model's coefficient of determination of 94.29%.

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