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All Journal Natural Science: Journal of Science and Technology JOIN (Jurnal Online Informatika) JURNAL ILMIAH MATEMATIKA DAN TERAPAN Journal of Data Analysis BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Keteknikan dan Sains (JUTEKS) ESTIMASI: Journal of Statistics and Its Application Majalah Ilmiah Matematika dan Statistika (MIMS) JAMBURA JOURNAL OF PROBABILITY AND STATISTICS DedikasiMU: Journal of Community Service Parameter: Journal of Statistics
Junaidi Junaidi
Program Studi Statistika, Universitas Tadulako
Religion Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Physics Social Sciences Transportation

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PENERAPAN METODE BOOSTING PADA CART UNTUK MENGKLASIFIKASIKAN KORBAN KECELAKAAN LALU LINTAS DI KOTA PALU Susiana, Luluk; Utami, Iut Tri; Junaidi, Junaidi
Natural Science: Journal of Science and Technology Vol 8, No 2 (2019): Volume 8 Number 2 (August 2019)
Publisher : Univ. Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (395.793 KB)

Abstract

Kota Palu sebagai ibu kota Provinsi Sulawesi Tengah dengan kecelakaan lalu lintas  yang cukup tinggi yang setiap tahunnya memiliki kematian sekitar 365 jiwa. Kecelakaan lalu lintas dipengaruhi oleh beberapa faktor, diantaranya jenis pelanggaran, jenis kecelakaan, dan lain-lain. Tujuan yang akan dicapai dalam penelitian ini adalah untuk menentukan ketepatan klasifikasi pada korban kecelakaan lalu lintas di Kota Palu dengan menggunakan metode boosting serta faktor-faktor yang mempengaruhinya. Hasil dari penelitian ini menunjukkan bahwa ketepatan klasifikasi metode boosting sebesar 82% dan  metode CART sebesar 77,9%. Hasil tersebut menunjukkan bahwa metode boosting dapat meningkatan tingkat akurasi. Sedangkan faktor-faktor yang mempengaruhi korban kecelakaan lalu lintas di Kota Palu adalah faktor jenis kecelakaan (X1), peran korban dalam kecelakaan (X4), jenis pelanggaran (X7) dan usia (X3) korban kecelakaan lalu lintas di Kota Palu.
PENERAPAN AUTOREGRESSIVE DISTRIBUTED LAG (ARDL) DALAM MEMODELKAN PENGARUH INDEKS HARGA KONSUMEN (IHK) KELOMPOK BAHAN MAKANAN DAN KELOMPOK MAKANAN JADI TERHADAP INFLASI DI KOTA PALU Tulak, Dewi Yuliastuti; Junaidi, Junaidi; Utami, Iut Tri
Natural Science: Journal of Science and Technology Vol 6, No 3 (2017): Volume 6 Number 3 (December 2017)
Publisher : Univ. Tadulako

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Abstract

Inflasi adalah suatu proses meningkatnya harga-harga secara umum dan terus-menerus berkaitan dengan mekanisme pasar. Inflasi merupakan salah satu indikator yang digunakan untuk mengukur stabilitas harga suatu barang di pasar. Indikator ini akan berdampak terhadap dinamika pertumbuhan ekonomi. Dalam penelitian ini, dilakukan analisis pengaruh indeks harga konsumen bahan makanan dan makanan jadi terhadap laju inflasi di kota Palu. Model yang digunakan adalah model Autoregressive Distributed Lag (ARDL) yaitu suatu model regresi dengan memasukkan nilai variabel yang menjelaskan nilai masa kini atau nilai masa lalu dari variable bebas sebagai salah satu variabel penjelas. Hasil penelitian ini menunjukkan bahwa tidak terdapat kointegrasi antar variabel dan model yang didapatkan yang menunjukkan bahwa harga bahan makanan berpengaruh terhadap inflasi di Kota Palu.
PENERAPAN SPATIAL DURBIN MODEL (SDM) PADA INDEKS PEMBANGUNAN GENDER DI PULAU SULAWESI Suaib, Tri Putri Andayani; Junaidi, Junaidi; Fadjryani, Fadjryani
Majalah Ilmiah Matematika dan Statistika Vol 22 No 1 (2022): Majalah Ilmiah Matematika dan Statistika
Publisher : Jurusan Matematika FMIPA Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/mims.v22i1.29581

Abstract

The Gender Development Index (GDI) is a development index of the quality of human life that is more concerned with gender status. GDI can be used to determine human development between males and females. This study uses the Spatial Durbin Model (SDM) method. The SDM method was formed due to the spatial influence on the dependent and independent variables. The purpose of this study is to determine the GDI model in Sulawesi Island and the factors that influence it. The factors that have a significant effect on the Gender Development Index (GDI) in Sulawesi Island using the Spatial Durbin Model (SDM) are Life Expectancy, per capita contests, average years of schooling, and labor force participation.Keywords: GDI, AIC, SDMMSC2020: 62H11
Analisis Sensitivitas Model Regresi Linier Berganda Menggunakan Pendekatan Bayesian (Distribusi Prior Normal) Junaidi Junaidi; Mohammad Fajri; Yandi Ristawan
Journal of Data Analysis Volume 3, Number 1, June 2020
Publisher : Department of Statistics, Syiah Kuala University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/jda.v3i1.18358

Abstract

Metode regresi linier berganda merupakan metode yang memodelkan hubungan antara peubah respon (y) dan beberapa peubah predictor (x). Pada metode Bayesian parameter yang digunakan merupakan variabel random yang dilkukan dengan mengalikan Likelihood dengan distribusi prior. Distribusi prior adalah distribusi subyektif berdasarkan pada keyakinan seseorang dan dirumuskan sebelum data sampel diambil. Tujuan penelitian ini adalah  untuk menganalisis sensitivitas dari parameter-paremeter pada model regresi linier berganda yang akan dilakukan dengan menggunakan prior berdistribusi Normal. Selanjutnya, penerapan model pada data aset bank di Indonesia dengan hasil estimasi parameter yaitu , , , , , dan , dengan selang kepercayaan 95%  untuk setiap parameter yang dihasilkan yaitu==       (-1,427 ; 3,594),  =(-5,07;0,3061), =(, , dan  = (-0,5955 ; 2,487). Nilai estimasi parameter yang diperoleh dengan pendekatan Bayesian mendekati nilai parameter yang diperoleh dengan Frequantis. Selang kepercayaan yang diperoleh juga mendekati dengan hasil frequentis yang memiliki interval lebih sempit dibandingkan nilai interval dengan metode OLS. Hal ini menunjukkan bahwa metode Bayesian merupakan suatu pendekatan yang dapat digunakan untuk mengestimasi parameter pada analisis regresi linier berganda. The multiple linear regression method is a method that models the relationship between the response variable (y) and several predictor variables (x). In the Bayesian method, the parameters used are random variables which are conducted by multiplying the likelihood with the prior distribution. The prior distribution is a subjective distribution based on a person's beliefs and is formulated before the sample data is taken. The purpose of this study is to analyze the sensitivity of the parameters in the multiple linear regression model that will be carried out using prior normal distribution. Furthermore, the application of the model to the data on bank assets in Indonesia with the results of parameter estimation is β0 = 23.06, β1 = 1.05, β2 = -2,379, β3 = -0,4786, β4 = -0.03796, and β5 = 0.9075, with a 95% confidence interval for each resulting parameter, namely β0 = (6,052; 40,200), β1 = (-1,427; 3,594), β2 = (- 5.07; 0, 3061), β3 = (0.9896; 0.03289), β4 = (- 1,224; 1.139), and β5 = (-0.5955; 2.487). The parameter estimate value obtained by the Bayesian approach is close to the parameter value obtained by Frequantis. The confidence interval obtained is also close to the frequentis result which has a narrower interval than the interval value with the OLS method. This shows that the Bayesian method is an approach that can be used to estimate parameters in multiple linear regression analysis.
Penerapan Model Analisis Regresi Linier Berganda dengan Pendekatan Bayesian pada Data Aset Bank di Indonesia Ahmad Mursyid Ainul; Junaidi Junaidi; Iut Tri Utami
Jurnal Keteknikan dan Sains (JUTEKS) Vol. 1 No. 1 (2018): Jurnal Keteknikan dan Sains - Juni 2018
Publisher : LPPM Universitas Hasanuddin

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (523.445 KB)

Abstract

Analisis regresi merupakan salah satu teknik analisis data yang seringkali digunakan untuk mengkaji hubungan antara beberapa variabel. Salah satu penerapan regresi dapat ditemukan pada bidang ekonomi yakni penentuan faktor-faktor yang mempengaruhiaset bergantung pada Suku Bunga Dasar Kredit (SBDK). Tujuan penelitian ini adalah mengestimasi parameter dan menentukan faktor-faktor yang mempengaruhi aset bankmenggunakan metode regresi linier berganda dengan pendekatan Bayesian. Aplikasi WinBUGSdigunakan dalam iterasi algoritma. Variabel bebas yang digunakan dalam penelitian adalah        Aset (Y), Kredit korporasi (X1), Kredit ritel (X2), Kredit mikro (X3), Kredit komsumsi KPR (X4), Kredit konsumsi non KPR (X5). Sebanyak 5000 iterasidengan penerapan metode MCMC dan hasil estimasi parameter yaitu :yˆ = 2,836+0,2836x +0,2634x +0,1953x +0,2718x +0,2617x 1i 2i 3i 4i 5i dengan selang kepercayaan 95% untuk masing-masing penduga parameter berturut-turut adalah(1.383;4.791), (0,135;0,479), (0,123;0,447), (0,092;0,333), (0,13;0,468)dan (0,126;0,439).Kata Kunci :Bayesian, MCMC, suku bunga, WinBUGSABSTRACT Regression analysis is a technique of statistical data analysis to investigate the relationship between several variables. One of the application of the regression can be found in the economic field to determine factors that affect the asset which depends on the Basic Interest Rate of Credit (SBDK). The purpose of this study is to estimate the parameters and determining factors that affect bank assets using multiple linear regression method with Bayesian approach. The WinBUGS is used in algorithm iteration. The independent variables used in the research are Assets (Y), Corporate Credit (X1), Retail Credit (X2), Micro Credit (X3), KPR Consumption Loan (X4), Non-KPR Consumption Loans (X5). A total of 5000 iterations with the application of the MCMC method and parameters estimation showing that the regression equations are: yˆ =2,836+0,2836x +0,2634x +0,1953x +0,2718x +0,2617x 1i 2i 3i 4i 5i with 95% confidence intervals for each parameterized predictor are (1,383,4,791), (0,135; 0,479), (0,123; 0,447), (0,092; 0,333), (0,13; 0,468) and (0,126; 0.439). Our research reveals that the 5 independent variables affect the asset Keywords : Bank Asset, Bayesian, MCMC, Regression Analysis, WinBUGS  
Automatic Plant Watering System for Local Red Onion Palu using Arduino Iman Setiawan; Junaidi Junaidi; Fadjryani Fadjryani; Fika Reski Amaliah
JOIN (Jurnal Online Informatika) Vol 7 No 1 (2022)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v7i1.813

Abstract

Central Sulawesi Province in Indonesia has great potential for horticultural commodities, namely local red onion Palu. In the current climate change, local farmers are still watering plants in the conventional way. The automatic watering system simplifies the work of local farmers. This device uses a soil moisture sensor as a soil moisture detector and Arduino as a program brain. This study aims to determine the position of soil moisture sensor, the optimal length of watering time and analyze the quality of data stored. The experiment was carried out using a Completely Randomized Design (CRD). The position of the soil moisture sensor was analyzed by Profile Analysis. The optimal length of watering time was determined by Analysis of Variance (ANOVA) and Least Significant Difference (LSD). The quality of data stored was determined by a number of missing values and frequency of watering. The results showed that in soil planting media the position of soil moisture sensor had no significant effect, while in others planting media (water and combination of water and soil) the position of the sensor had a significant effect. The optimal watering time was 3 seconds. The stored data has low quality in terms of missing values and lack of consistency.
PENGARUH INTERAKSI GENOTIP DAN LINGKUNGAN TERHADAP PENINGKATAN PRODUKTIVITAS TANAMAN BAWANG MERAH MENGGUNAKAN METODE SEM-AMMI Ghina Rizqa Raihanah; Junaidi Junaidi; Fadjryani Fadjryani
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 15 No 1 (2021): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (564.046 KB) | DOI: 10.30598/barekengvol15iss1pp115-126

Abstract

Stable and adaptive superior varieties play an important role in increasing plant productivity. The technological innovation was carried out by studying the yield GEI. However, if only paying attention to yield GEI would not be enough in selecting stable and adaptive varieties, so this research used a combination of AMMI and SEM methods. Through the SEM-AMMI, GEI modeling was carried out by taking into account the physiological processes of growth and genotype development which explained the relationship between yield GEI components and how it affected yield GEI. The results of the AMMI biplot showed that genotypes were adaptable and relatively stable were planted in five planting locations, namely Biru Lancor and Tinombo. SEM test results showed that the yield component has an effect on production yield, where the tuber weight above the average will give relatively more onion yields and genotypes planted in relatively low locations, soil pH above 6 and dusty clay soil conditions will produce relatively more red onions and quality.
PEMODELAN HASIL PRODUKSI PADI DI PROVINSI SULAWESI TENGAH MENGGUNAKAN FIXED EFFECT MODEL (FEM) Nurul Fiskia Gamayanti; Junaidi Junaidi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 15 No 2 (2021): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (400.961 KB) | DOI: 10.30598/barekengvol15iss2pp347-354

Abstract

Padi merupakan komoditas pangan utama di Indonesia. Tingkat konsumsi padi mayarakat di Sulawesi Tengah sebesar 111,4 kg perkapita pertahun yang lebih tinggi jika dibandingkan dengan masyarakat Sulawesi Selatan yaitu 106,9 kg perkapita pertahun. Diperlukan model yang dapat memprediksi hasil produksi padi di Sulawesi tengah untuk menjaga stok kebutuhan pangan masyarakat. Fixed effect Model dapat digunakan untuk melihat faktor apa saja yang dapat mempengaruhi hasil produksi padi di Sulawesi Tengah dengan menggunakan pendekatan data penelitian data panel. Fixed effect Model adalah cara mengestimasi data panel dengan menggunakan variabel dummy untuk memperoleh perbedaan intersep yang diinginkan. Dari hasil penelitian ini diperoleh bahwa faktor yang mempengaruhi hasil produksi padi di Sulawesi tengah adalah luas panen dengan setiap kenaikan luas panen sebesar 1 % akan meningkatkan hasil produksi padi sebesar 0,6764%. Dari hasil analisis diperoleh nilai R2 sebesar 98.15%.
PENERAPAN PETA KENDALI T^2 HOTELLING ALGORITMA FAST MINIMUM COVARIANCE DETERMINANT PADA PENGENDALIAN KUALITAS BAWANG MERAH VARIETAS LEMBAH PALU Puja Lestari Marulu; Junaidi Junaidi; Fadjryani Fadjryani
Jambura Journal of Probability and Statistics Vol 3, No 2 (2022): Jambura Journal Of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjps.v3i2.15522

Abstract

The physical condition of the Palu Valley shallots variety greatly affects the quality of the fried onions that are obtained. The poor quality of shallots will affect the product that will sale by the farmers. Therefore, it is necessary to monitor by conducting the quality control analysis of the physical condition of shallots. In this study we use the quality control method of the  Hotelling control map with the fast-MCD algorithm. This method is used because the outlier in the data to be analyzed. The purpose of this study is to produce average vector estimates and variance-covariance matrix estimates in the formation of the  Hotelling control map. From the calculation by using the mean vector and the variant-covariant matrix with fast-MCD estimation, 93 data were obtained that experienced out of control on the  Hotelling control map with the fast-MCD algorithm where the observations that experienced out of control were more than the usual of  Hotelling control map. This shows that the  Hotelling control map with the fast-MCD algorithm is more effective in detecting observations which contain outliers. The value of the multivariate  process capability analysis is less than one showing the process is uncapable.
ANALISIS SPASIAL PENYEBARAN PENYAKIT SCHISTOSOMIASIS MENGGUNAKAN INDEKS MORAN UNTUK MENDUKUNG ERADIKASI SCHISTOSOMIASIS DI PROVINSI SULAWESI TENGAH BERBASIS WEB DASHBOARD Nur Sakinah; Wawan Saputra; Nurfitra Nurfitra; Satriani Satriani; Junaidi Junaidi
Jambura Journal of Probability and Statistics Vol 3, No 2 (2022): Jambura Journal Of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjps.v3i2.16580

Abstract

 Schistosomiasis is a parasitic disease which is caused by worm infection with worms from the Schistosoma class. This disease is zoonotic, consequently the source of transmission is not only infected on mammals but also on humans. The method used in this study is spatial autocorrelation. This is conducted to determine the presence or absence of global or local spatial autocorrelation as well as the pattern distribution of Schistosomiasis cases in Poso Regency by using Moran's I. The result in this study showed that the p-value of positive global autocorrelation is 2,2 × 10-16. This result is smaller than the 5% of significance level and also smaller than the Moran's I value (0,66).  The Moran’s I value lies in the interval  indicating that each adjacent area has the same number of Schistosomiasis cases. Meanwhile, the local spatial autocorrelation test (LISA) for Schistosomiasis cases in Poso Regency, such as villages at Lore Utara, Lore Timur and Lore Peore has the LISA value 1 determining the correlation is strong and positive. The distribution pattern of Schistosomiasis cases in Poso Regency forms a group pattern, namely disease prone areas (HH), disease spread areas (HL), disease alert areas (LH) and disease safe areas (LL) 
Co-Authors Abdi Abdi Ahmad Mursyid Ainul D Wahyuli Diana Wahyuli Fadjriyani Fadjryani, Fadjryani Fika Reski Amaliah Ghina Rizqa Raihanah Hajra Rasmita Ngemba I W Sudarsana I Wayan Sudarsana Ikram Ikram Ikram Iman Setiawan Iut Tri Utami Iut Tri Utami J Khasmawati Julia Khasmawati Mohammad Fajri Mohammad Fajri N Mutmainna Nur Sakinah Nurfitra Nurfitra Nurpati Nurpati Nurul Fiskia Gamayanti Nurul Mutmainna Puja Lestari Marulu S Hendra Satriani Satriani Suaib, Tri Putri Andayani Susiana, Luluk Tulak, Dewi Yuliastuti Wawan Saputra Yandi Ristawan Yayang Matira