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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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Penerapan Fuzzy Logic untuk Menentukan Minuman Susu Kemasan Terbaik dalam Pengoptimalan Gizi Auliah Khoirun Nisa; Muhammad Abdy; Ahmad Zaki
Journal of Mathematics, Computations and Statistics Vol. 3 No. 1 (2020): Volume 03 Nomor 01 (April 2020)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This applied research aims to build a model of determining the best packaged milk with consideration variables are price and nutrition. The steps used in this research are fuzzification, fuzzy rule determination, fuzzy inference with mamdani method, and defuzzification. The data used are data taken from direct field surveys conducted by researchers in one of the supermarkets in Makassar. The results of this study is sample 16 packaged milk which is the most suitable packaged milk to recommended because it has high nutrition and affordable prices.
Otokorelasi Spasial pada Prevalensi Balita Stunting, Wasting, Underweight, dan Overweight di Pulau Sulawesi Tahun 2022 Baharuddin; Yahya, Irma; Ihwal, Muhammad
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.v7i2.2408

Abstract

Prevalensi balita stunting, wasting, underweight, dan overweight setiap kabupaten/kota tidaklah sama. Ada kemungkinan bahwa angka prevalensi balita di suatu daerah terkait dengan angka prevalensi balita di daerah yang berdekatan. Penelitian ini bertujuan menguji adanya otokorelasi spasial pada prevalensi balita stunting, wasting, underweight, dan overweight di Pulau Sulawesi. Data yang dipakai adalah data sekunder berupa prevalensi balita setiap kabupaten/kota yang merupakan hasil Survei Status Gizi Indonesia (SSGI) tahun 2022. Karena beberapa kabupaten/kota terpisah oleh lautan dengan Pulau Sulawesi maka kami memakai matriks pembobot spasial berbasis k-tetangga terdekat. Hasil pengujian dengan indeks Moran menunjukkan bahwa terdapat otokorelasi spasial positif pada prevalensi balita stunting, wasting, underweight, dan overweight. Sebaran angka prevalensi membentuk pola sistematik yang mengelompok di suatu kawasan pada masing-masing provinsi.
Forecasting Analysis of Clean Water Demand at PT. Kawasan Industri Gresik Using the Time Series Method Syakiroh, Maulidah; Jawwad, Muhammad Abdus Salam; Wijaya, Rama
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.v7i2.2968

Abstract

Fulfilling the needs and quality of clean water for each tenant in an industrial area is the responsibility of the management of the industrial area, as is the case with PT. Kawasan Industri Gresik. The monthly clean water consumption of each tenant always fluctuates, because each tenant's water needs vary according to the company's business line. Therefore, a prediction or forecasting is needed regarding clean water consumption by tenants at PT. Kawasan Industri Gresik to ensure the availability of treated water in the Water Treatment Plant (WTP) unit so that it can meet the needs of each tenant for the next 12 months. Apart from ensuring the quantity of water, the industrial area must also ensure the quality of the water to be distributed so that it remains in accordance with the required quality standards. This study regarding clean water needs is based on data on clean water consumption of tenants at PT. Kawasan Industri Gresik in 2021-2023 then forecasting is carried out using the time series method. The results of forecasting showed that the best prediction method was Double Exponential Smoothing based on the smallest Mean Absolute Percentage Error (MAPE) value is 6,66%. Comparison of forecasting results and data, the production design capacity of the Water Treatment Plant (WTP) unit is still sufficient to meet the water needs of each tenant until 2024. The quality of the water that will be distributed is in accordance with water quality standards for hygiene and sanitation purposes in accordance with Minister of Health Regulations Number 2 of 2023.
Kajian Metode Simulasi Monte Carlo Br Manik, Mawar Bonita; Nasution, Putri Khairiah; Suyanto, Suyanto; Yanti, Maulida
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.v7i2.2994

Abstract

The Monte Carlo Simulation Method is one of the forecasting methods that uses random numbers, specifically through the use of a Linear Congruential Generator and mathematical equations for prediction, forecasting, estimation, and risk analysis. The Monte Carlo Simulation Method with one iteration has a high level of accuracy, as evidenced by previous research. The more iterations used, the more accurate the forecasting results. Therefore, the author is interested in examining how well the Monte Carlo Simulation Method with N iterations performs in forecasting. The study of the Monte Carlo Simulation Method with N iterations will be conducted on the forecast of the number of visitors to Fort Rotterdam. The aim of this research is to determine the accuracy of the Monte Carlo Simulation Method with N iterations for forecasting the number of visitors to Fort Rotterdam. The MAPE values from 2013 to 2018 using the Monte Carlo Simulation Method with N iterations sequentially are 16%, 13%, 13%, 12%, 1008%, and 31%. The forecasting ability from 2013 to 2016 falls into the good category, the forecasting for 2017 falls into the poor category, and the forecasting for 2018 falls into the fair category.
Prediksi Jumlah Kunjungan Pasien Mental disorder Pelayanan Mentari Puskesmas Kecamatan Kalideres menggunakan ARCH, GARCH dan Box-Jenkins Arifin, Alicia; Wiyanti, Wiwik
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.v7i2.3043

Abstract

Pelayanan Mentari adalah pelayanan pasien mental disorder di Puskesmas kecamatan Kalideres, yang mana memiliki sub pelayanan Polania (poli anti aniaya) untuk menangani permasalahan bullying pada. Penelitian ini dilakukan untuk memprediksi jumlah kunjungan pasien di pelayanan Mentari. Data yang ditemukan menunjukkan bahwa 26,7% remaja di Indonesia mengalami gangguan cemas dan total prevalensi masalah kesehatan mental mencapai 46,8%, penting untuk mengidentifikasi pola data agar pemerintah bisa mengatasi masalah mental disorder sejak dini. Jenis penelitian yang digunakan adalah metode kuantitatif. Tujuan dari penelitian ini adalah menggambarkan kondisi kesehatan mental di daerah Kalideres dan bisa memproyeksikan jumlah pasien mental disorder di daerah Kalideres, sehingga pemerintah bisa membuat kebijakan untuk menurunkan masalah mental disorder. Data yang digunakan adalah data bulanan kunjungan pasien mental disorder pelayanan Mentari tahun 2020-2023. Metode analisa time-series yang digunakan adalah Box-Jenkins, ARCH dan GARCH. Metode yang terbaik adalah metode Box-Jenkins dengan ARIMA(1,1,1) yang mempunyai nilai MAPE sebesar 9,7%. Prediksi di bulan januari 2024 adalah 270,60 (95% CI, 204,97-336,23) dan bulan Februari 2024 sebanyak 269.31 (95% CI, 200,77 - 337,86). Hasil prediksi oleh model ARCH(1) sebesar 200,97(95% CI, -206,28-608,22), model GARCH(1,1) sebesar 191,95 (95% CI, -215,30-599,20). Kata kunci: mental disorder, forecast, arima, arch, garch
The Impact of Covid-19 on Stunting Cases in Indonesia: A Bayesian Spatial Modeling Approach Ankaz As Sikib; Aswi, Aswi; Ruliana
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.v7i2.3065

Abstract

The high number of COVID-19 cases has impacted various sectors. One of the notable consequences of the COVID-19 pandemic is its effect on food security and nutrition. Social restrictions implemented to curb the spread of the virus have resulted in worsening economic conditions, limited access to healthcare facilities, difficulties in obtaining nutritious food, and school closures. Changes in the routines and activities of COVID-19 patients may contribute to an increase in the prevalence of stunting in Indonesia. While research has been conducted on the impact of COVID-19 on the rise of stunting cases in Indonesia, previous studies have typically focused on individual provinces and have not utilized the Bayesian Conditional Autoregressive (CAR) model. This study aims to investigate the relationship between COVID-19 and the increase in stunting cases across Indonesia. We analyze data on stunting cases in each Indonesian province and the number of COVID-19 patients between March 23, 2020, and December 31, 2021. To assess the relationship, we employ the Bayesian spatial CAR Leroux model with several Inverse-Gamma hyperpriors. We compare these models using various fit criteria. The results indicate that the Bayesian spatial CAR Leroux model with Inverse-Gamma hyperpriors (0.1;0.1) performs best, as it yields the smallest Deviance Information Criterion (DIC) and Watanabe-Akaike Information Criterion (WAIC) values. In conclusion, our analysis reveals a positive correlation between the number of COVID-19 cases and the increase in stunting cases in Indonesia. Approximately 50% of the regions in Indonesia face a high relative risk of stunting, with Nusa Tenggara Timur having the highest relative risk, followed by Kalimantan Barat and Sulawesi Barat
Penerapan Metode Dynamic Programming dan Fuzzy Linear Programming Dalam Mengoptimasi Keuntungan serta Jumlah Produksi Padi   Simbolon, Haris Miller; Nasution, Putri Khairiah; Mardiningsih, Mardiningsih; Zahedi, Zahedi
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.v7i2.3263

Abstract

The Kebun Sayur Farmers Group, located in Sub-District Sidoarjo 2 Ramunia, Deli Serdang Regency, North Sumatra Province, is a group focused on rice production. Currently, Farmers groups are experiencing a lack of capital which results in limited resources. To overcome these challenges by optimizing inventory and production quantities so as to enable farmers to maximize their profits. Therefore, the methods used in this research are dynamic programming and fuzzy linear programming, with the application of these two methods can overcome existing problems.. The dynamic programming method increases the production of Ciherang rice by 3580 kg, Inpari 32 rice by 2430 kg, and Inpari 49 rice by 2130 kg, with an increase in production of Ciherang rice by 20.87%, Inpari 32 rice by 17.37%, and Inpari 49 rice by 14.76%, with a probability of profit of 8.33%. Using the fuzzy linear programming method, for a tolerance limit of 5%, the profit obtained is Rp 154.573.935,12 with a profit percentage of 4.89% and a λ-cut value of 0.488. If the tolerance limit is set to 10%, the profit obtained is Rp 156.888.371,28 with a profit percentage of 6.46% and a λ-cut value of 0.5  
Analisis Dinamik Model Predator-Prey dengan Efek Pemanenan Pada Populasi Ikan Nike (Awaous Melanocephalus) di Provinsi Gorontalo Usman, Nunung; Nento, Abdul Djabar; Hendri, Excel Muhammad; Biga, Azril Saputra; Sigar, Leidi; Nuha, Agusyarif Rezka
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.v7i2.3461

Abstract

Gorontalo Province is one of the areas directly adjacent to Tomini Bay, which is known for its rich natural resources, especially in the fisheries sector such as Nike fish (Awaous melanocephalus). Nike fish is the main food commodity for people in the Gorontalo region. However, intensive fishing activities can pose an extinction threat to this species. This study aims to understand the dynamics of the Nike fish population using a predator-prey model that integrates the effects of harvesting on the prey population. The research process includes formulation of assumptions, model building, model validation, and data analysis through mathematical analysis and numerical simulation. The analysis showed that the model has four equilibrium points, each with different stability conditions. Numerical simulations showed that an increase in harvesting rate and natural mortality of prey, as well as a decrease in birth rate, could threaten the viability of the Nike fish population. Thus, appropriate management efforts are needed to maintain ecosystem balance and the sustainability of the Nike fish population. The calculation results show that the Nike fish production threshold in Gorontalo Province is 8.470.858 kg per year. However, the amount of Nike fish production in Gorontalo Province in 2023 only reached 56,75% of the threshold. This indicates that the production of Nike fish in Gorontalo can still be increased as long as the harvest limit is observed to prevent extinction.
Klasifikasi Menggunakan Metode Support Vector Machine (SVM) Multiclass pada Data Indeks Desa Membangun (IDM) di Provinsi Maluku Latuconsina, Fidyah; Noya van Delsen, Marlon Stivo; Yudistira
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.v7i2.3624

Abstract

Indeks Desa Membangun (IDM) merupakan konsep dasar dalam melakukan pembangunan dan pengembangan potensi desa. IDM terdiri dari Indeks Ketahanan Sosial (IKS), Indeks Ketahanan Ekonomi (IKE), dan Indeks Ketahanan Lingkungan (IKL). Status kemajuan dan kemandirian suatu desa dibagi menjadi beberapa tingkatan yaitu desa mandiri, desa maju, desa berkembang, desa tertinggal, dan desa sangat tertinggal. Penelitian ini bertujuan untuk mengetahui penerapan metode Support Vector Machine (SVM) Multiclass pada klasifikasi IDM di Provinsi Maluku, serta mengetahui tingkat akurasi pengklasifikasian pada IDM di Provinsi Maluku yang dihasilkan menggunakan metode SVM Multiclass. Berdasarkan hasil penelitian, klasifikasi SVM Multiclass dengan menggunakan fungsi kernel yang terbaik terdapat pada kernel linear dengan nilai akurasi sebesar 97,75%. Adapun hasil pengelompokan IDM Provinsi Maluku dengan metode SVM Multiclass fungsi kernel linear adalah sebagai berikut: 75 desa dengan kategori mandiri, 291 desa dengan kategori maju, 585 desa dengan kategori berkembang, 245 desa dengan kategori tertinggal, dan 4 desa dengan kategori sangat tertinggal.
Implementation of K-Median Algorithm for the Regencies Clustering in South Sulawesi Province Based on Food Commodity Yields Hardianti Hafid; Sitti Masyitah Meliyana
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.v7i2.3674

Abstract

Abstrak. Ketahanan pangan berkaitan dengan tentang potensi produksi di berbagai wilayah di Indonesia. Penelitian ini bertujuan untuk mengidentifikasi pola distribusi produksi komoditas pangan di Provinsi Sulawesi Selatan dengan menggunakan metode K-Median clustering. Data sekunder yang diperoleh dari Badan Pusat Statistik Provinsi Sulawesi Selatan digunakan untuk menganalisis produksi padi, jagung, ubi jalar, ubi kayu, kacang tanah, dan kedelai. Hasil clustering menunjukkan bahwa pengelompokan untuk produksi padi, ubi jalar, ubi kayu, kacang tanah, dan kedelai terbentuk dengan baik dimana nilai Silhouette Coefficient masing-masing sebesar 0,53, 0,56, 0,69 dan 0,66, menunjukkan kesamaan yang signifikan dalam setiap cluster. Namun, pengelompokan produksi jagung menunjukkan kualitas cluster yang lebih lemah dengan nilai 0,46. Hal ini menunjukkan adanya keragaman yang lebih besar dalam distribusi produksi jagung di berbagai wilayah Kabupaten/Kota Provinsi Sulawesi Selatan. Hasil penelitian ini diharapkan dapat memberikan dasar yang kuat bagi pembuat kebijakan untuk merancang strategi peningkatan produksi dan distribusi pangan yang lebih terarah, serta mendukung perencanaan kebijakan berbasis data yang lebih efisien di Provinsi Sulawesi Selatan. Kata Kunci : Clustering, K- Median, Komoditas Pangan Abstract. Abstract: Food security is closely related to the production potential in various regions of Indonesia. This study aims to identify the distribution patterns of food commodity production in South Sulawesi Province using the K-Median clustering method. Secondary data obtained from the Central Statistics Agency of South Sulawesi Province were used to analyze the production of rice, corn, sweet potatoes, cassava, peanuts, and soybeans. The clustering results indicate that the clusters formed for rice, sweet potatoes ,cassava, peanuts, and soybeans were well-defined, with Silhouette Coefficient values of 0,53, 0,56, 0,69 dan 0,66, respectively, showing significant similarity within each cluster. However, the clustering of corn production showed weaker cluster quality with a coefficient of 0.46, indicating greater variability in the distribution of corn production across the districts/cities in South Sulawesi Province. The findings of this study are expected to provide a strong foundation for policymakers to design more targeted strategies for improving food production and distribution, as well as to support more efficient data-driven policy planning in South Sulawesi Province.

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