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INDONESIA
JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI
Published by Universitas Hasanuddin
ISSN : 18581382     EISSN : 26148811     DOI : -
Core Subject : Education,
Mathematics
Jurnal ini mempublikasikan paper-paper original hasil-hasil penelitian dibidang Matematika, Statistika dan Komputasi Matematika.
Arjuna Subject : -
Articles 496 Documents
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Target prediction of compounds on jamu formula using nearest profile method Nur Hilal A Syahrir; Sumarheni Sumarheni; Supri Bin Hj Amir; Hedi Kuswanto
Jurnal Matematika, Statistika dan Komputasi Vol. 17 No. 2 (2021): JANUARY 2021
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/jmsk.v17i2.11616

Abstract

Jamu is one of Indonesia's cultural heritage, which consists of several plants that have been practiced for centuries in Indonesian society to maintain health and treat diseases. One of the scientification efforts of Jamu to reveal its mechanism is to predict the target-protein of the active ingredients of the Jamu. In this study, the prediction of the target compound for Jamu was carried out using a supervised learning approach involving conventional medicinal compounds as training data. The method used in this study is the closest profile method adopted from the nearest neighbor algorithm. This method is implemented in drug compound data to construct a learning model. The AUC value for measuring performance of the three implemented models is 0.62 for the fixed compound model, 0.78 for the fixed target model, and 0.83 for the mixed model. The fixed compound model is then used to construct a prediction model on the herbal medicine data with an optimal threshold value of 0.91. The model produced 10 potential compounds in the herbal formula and its 44 unique protein targets. Even though it has many limitations in obtaining a good performance, the closest profile method can be used to predict the target of the herbal compound whose target is not yet known.
Effect of Variability on Cronbach Alpha Reliability in Research Practice Muhammad Amirrudin; Khoirunnisa Nasution; Supahar Supahar
Jurnal Matematika, Statistika dan Komputasi Vol. 17 No. 2 (2021): JANUARY 2021
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/jmsk.v17i2.11655

Abstract

This study aims to describe the effects of variability through data simulation to determine which aspect of variability that maximizes coefficient of Cronbach Alpha reliability. Cronbach Alpha is widely used for estimation of reliability, in recent still. This study served a conceptual and practical simulation for estimating the profound aspect of Cronbach Alpha coefficient relating to the variability of the data. This study carried out with data simulated using the rand between method by Microsoft Excel then simulate different categorical data responses to different range of items by manipulating sample size, range, number of items, variance and standard deviation. The results show that number of variance and standard deviation of data had the most profound aspect of Cronbach Alpha's reliability other than range. The increasing number on some aspect shows that standard deviation and variance has the stability to shows the positive correlation with the coefficient of Cronbach Alpha reliability other than range.
Workforce Classification in West Java 2018 With Random Forest Ahmad Safrian Fauzi; Muh. Rizki; Rendy Rendi; Ria Nurul; Tika Novitasari; Rani Nooraeni
Jurnal Matematika, Statistika dan Komputasi Vol. 17 No. 2 (2021): JANUARY 2021
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/jmsk.v17i2.11680

Abstract

Pengangguran di Indonesia merupakan masalah yang serius. Tingginya angka pengangguran di Indonesia tersebut dikarenakan jumlah lapangan kerja yang tersedia tidak sebanding dengan jumlah angkatan kerja yang terus meningkat. Berdasarkan data BPS, Provinsi Jawa Barat sebagai penyumbang terbesar jumlah pengangguran di indonesia, dengan angka tingkat pengangguran terbuka sebesar 8,52 persen. Tujuan penelitian ini untuk melakukan klasifikasi penduduk angkatan kerja kedalam kelompok berstatus pengangguran atau bukan pengangguran (bekerja) di Provinsi Jawa Barat tahun 2018 dengan metode random forest menggunakan pendekatan machine learning. Model random forest ini dibentuk dengan 80 persen dari data total atau sebanyak 16.059 data untuk data training dan 20 persen dari data total atau sebanyak 4.015 data untuk data testing. Penelitian ini menggunakan data Sakernas 2018 dan terdapat tujuh variabel yang digunakan dalam penelitian, yaitu klasifikasi wilayah, jenis kelamin, umur, status perkawinan, tingkat pendidikan, pelatihan, dan pengalaman kerja. Dalam model random forest yang terbentuk, variabel status pernikahan dan tingkat pendidikan seseorang memiliki kontribusi besar dalam menentukan status pengangguran.
Penerapan Metode Random Forest dalam Pengklasifikasian Penerima Kartu BPJS Kesehatan Penerima Bantuan Iuran (PBI) di Kabupaten Karangasem, Provinsi Bali 2017 Qonita Raihananda; I Wayan Edy Darma Putra; Monica Seftaviani Sijabat; Sifa Rofatunnisa; Ibnu Maruf; Hermarwan Hermarwan; Rani Nooraeni
Jurnal Matematika, Statistika dan Komputasi Vol. 17 No. 2 (2021): JANUARY 2021
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/jmsk.v17i2.11710

Abstract

BPJS Kesehatan is a social security facility provided by the government to all people who are registered as members. BPJS Kesehatan membership is divided into two, namely BPJS for Contribution Assistance Recipients (BPJS PBI) and BPJS Non-Contribution Assistance Recipients (BPJS Non-PBI). In 2019, Bali Province is targeted to achieve Universal Health Coverage of 95 percent so that the Bali Provincial Government has budgeted funds worth IDR 945 billion to finance JKN - KBS services which are integrated with JKN - KIS. Karangasem is one of the four districts in Bali Province that received the most percentage of financing, which is 51 percent of the total budget needed when compared to other areas. This study aims to classify the BPJS-PBI recipient community based on education variables, employment indicators, age, and per capita expenditure in Karangasem Regency in 2017. The classification method used in this study is the random forest method. The results showed that the per capita expenditure variable had the largest contribution in classifying the status of PBI participants. The model that is formed produces an accuracy of 0.8017. This means that the model can predict 80.17 percent testing data correctly.
Kestabilan Model Mangsa Pemangsa dengan fungsi respon Holling tipe IV dan penyakit pada pemangsa A. Muh. Amil Siddik; Syamsuddin Toaha; Andi Muhammad Anwar
Jurnal Matematika, Statistika dan Komputasi Vol. 17 No. 2 (2021): JANUARY 2021
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/jmsk.v17i2.11716

Abstract

Stability of equilibrium points of the prey-predator model with diseases that spreads in predators where the predation function follows the simplified Holling type IV functional response are investigated. To find out the local stability of the equilibrium point of the model, the system is then linearized around the equilibrium point using the Jacobian matrix method, and stability of the equilibrium point is determined via the eigenvalues method. There exists three non-negative equilibrium points, except , that may exist and stable. Simulation results show that with the variation of several parameter values infection rate of disease , the diseases in the system may become endemic, or may become free from endemic.  
PEMODELAN SPASIAL AREA PADA DATA COVID-19 PULAU JAWA BERBASIS R-SHINY WEB FRAMEWORK Rokhana Dwi Bekti; Yudi Setyawan; Enik Laksminiasih
Jurnal Matematika, Statistika dan Komputasi Vol. 17 No. 3 (2021): May, 2021
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v17i3.11743

Abstract

The Covid-19 in Indonesia has had an impact on almost all lives, especially at economic, social, education, and health.. Efforts to prevent and reduce the number of cases are still ongoing. Likewise, research on the causes of the emergence of the Covid-19 pandemic outbreak, drugs, vaccines, and the factors that influence it are still being carried out. This study analyzes the effect of Covid-19 on inflation and the effect of population density on Covid-19 in Java. The method used is area spatial modeling. To make it easier for researchers to analyze data, this study also developed a web application based on the R shiny framework. This application has displayed valid output from the results of its use and is in accordance with existing theories, and is able to make it easier for users to carry out Covid-19 analysis in Java using the area spatial model method. The estimation results of the Spatial Durbin Model (SDM) show that the variable that has a significant effect on inflation is the inflation lag in the model with cumulative positive cases (α = 10%). This shows that the inflation of a province tends to be influenced by other neighboring provinces. Meanwhile, population density is also significant for Covid-19 positive cases (α = 5%).
Penerapan Metode Hybrid Nonlinear Regression With Modified Logistic Growth Model - Double Smoothing Exponensial untuk Peramalan Kasus Covid-19 di Indonesia dan Armenia Andy Rezky Pratama Syam Arez
Jurnal Matematika, Statistika dan Komputasi Vol. 17 No. 2 (2021): JANUARY 2021
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/jmsk.v17i2.11747

Abstract

Since the first cases of Covid-19 (Corona Virus Disease-19) infection were officially recognized and recorded in Indonesia on March 2, 2020 and March 1, 2020 in Armenia, the addition of new cases has not shown any indication of sloping. The relatively high number of new cases indicates that Indonesia has not yet passed the peak of the pandemic. As for Armenia, the addition of new cases indicates a new pandemic peak to be faced. In these conditions, an important question for decision makers (the Government) to find answers to is when and at what level of total cases will the COVID-19 pandemic end in Indonesia or the second wave in Armenia. Based on this, the forecasting method of Hybrid Nonlinear Regression With Modified Logistic Growth Model - Double Smoothing Exponential and Classical methods is used to predict the Covid-19 cases that occur in Indonesia and Armenia. Based on the model formed, the peak of Covid-19 cases in Indonesia is predicted to occur on November 26, 2020, with the number of cases reaching 5968 cases. As for Armenia, the peak of Covid-19 cases will occur on November 15, 2020, with the number of cases reaching 3098 cases. Covid-19 in both countries is predicted to decline and be constant in 2021. For the country, Indonesia is predicted to begin to stabilize and be controlled in July - August 2021. As for Armenia, Covid-19 is predicted to be under control and approaching 0 cases in February - March 2021.
Optimization of CV.Amanda Makassar Production Planning in the Time of Covid-19 Using Multiple Goal Linear Program Model Astri Aksan; Aidawayati Rangkuti; Agustinus Ribal
Jurnal Matematika, Statistika dan Komputasi Vol. 17 No. 2 (2021): JANUARY 2021
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/jmsk.v17i2.11793

Abstract

A research has been conducted on the use of multiple-goal linear program model to solve multi goals by taking the case of optimization of production planning at CV. Amanda Makassar during the Covid-19 period. In this research, four goals were formulated, that were (i) the fulfillment of the number of market demand, (ii) maximizing income, (iii) minimizing production costs, and (iv) maximizing working hours. Then for the optimal solution using LINGO 18 software. Based on the research results, the optimal production plan during the Covid-19 period resulted from the two different models for original brownies products where the results of the dual-purpose linear program model without target priority produced 16.118 original brownies and 32.400 packages from the dual-purpose linear program model with priority target with weight. For cream cheese brownies, there are 3.000 packages, 18.000 packages of sarikaya pandan brownies, 3.600 packs of choco marble brownies, pink marble brownies, tiramishu marble brownies, roasted brownies, and 1.800 packs of cappuccino marble brownies. Chocolate bananas bolen, pineapple molen, and chocolate ganache in 840 packages. Then for 15.000 packs of blueberry brownies, 960 packs of strawberry brownies, 360 packs of dry brownies, 2.400 banana cheese brownies, 300 packs of cheese bananas bolen, 600 packs of peanut butter, and 9.000 packs of pandan cake for a month. The maximum revenue obtained by the company with a multiple-purpose linear program model without target priority is Rp.628.602.000.- and the minimum production cost that the company must pay is Rp.495,048,300,-. Then for the multiple-purpose linear program model with target priority accompanied by a weight of Rp.4.299.480.000.- and the minimum production cost is Rp.3.394.366.000. The result shows that optimization using a multiple goal linear program model with goal priority provide optimal production which results in greater profit compared to the process (optimization) carried out by the company so far, which is only based on the number of demand.
Forecasting Bank Indonesia Currency Inflow and Outflow Using ARIMA, Time Series Regression (TSR), ARIMAX, and NN Approaches in Lampung Laila Qadrini; Asrirawan Asrirawan; Nur Mahmudah; Muhammad Fahmuddin; Ihsan Fathoni Amri
Jurnal Matematika, Statistika dan Komputasi Vol. 17 No. 2 (2021): JANUARY 2021
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/jmsk.v17i2.11803

Abstract

There are various types of data, one of which is the time-series data. This data type is capable of predicting future data with a similar speed as the forecasting method of analysis.  This method is applied by Bank Indonesia (BI) in determining currency inflows and outflows in society. Moreover, Inflows and outflows of currency are monthly time-series data which are assumed to be influenced by time. In this study, several forecasting methods were used to predict this flow of currency including ARIMA, Time Series Regression (TSR), ARIMAX, and NN. Furthermore, RMSE accuracy was used in selecting the best method for predicting the currency flow. The results showed that the ARIMAX method was the best for forecasting because this method had the smallest RMSE.
MODEL SPATIO TEMPORAL DATA CURAH HUJAN MENGGUNAKAN KALMAN FILTER DAN ALGORITMA EKSPEKTASI-MAKSIMISASI Amran Amran; Muh. Idil Islami; A. Kresna Jaya; Bambang Bakri
Jurnal Matematika, Statistika dan Komputasi Vol. 17 No. 2 (2021): JANUARY 2021
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/jmsk.v17i2.11918

Abstract

Location and time dimension data modeling, also known as spatial-temporal data, generally has high complexity. This study analyzes a spatial-temporal model of rainfall data and climate variables, namely temperature, and humidity. The complexity of the relationship between variables and parameters in the spatial-temporal model is simplified by a hierarchical approach. The parameter estimation of the ratio-temporal model uses the Kalman Filter approaches and the Expectation-Maximization (EM) method combined with the bootstrap method to calculate the standard error estimation. Implementation of the spatial-temporal model on rainfall data in South Sulawesi Province with temperature and humidity shows that there is a relationship between rainfall and temperature and humidity.

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