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All Journal Jurnal Statistika Universitas Muhammadiyah Semarang PYTHAGORAS: Jurnal Program Studi Pendidikan Matematika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Indonesian Journal of Applied Statistics BAREKENG: Jurnal Ilmu Matematika dan Terapan Jambura Journal of Mathematics METIK JURNAL Jurnal Matematika UNAND Variance : Journal of Statistics and Its Applications ILKOMNIKA: Journal of Computer Science and Applied Informatics Jurnal Statistika dan Aplikasinya JAMBURA JOURNAL OF PROBABILITY AND STATISTICS Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Research in the Mathematical and Natural Sciences Prosiding Seminar Nasional Pendidikan Matematika, Universitas Mulawarman Pattimura International Journal of Mathematics (PIJMath) Jurnal Riset Mahasiswa Matematika The Indonesian Journal of Computer Science Journal of Mathematics, Computation and Statistics (JMATHCOS) Indonesian Journal of Computational and Applied Mathematics
Isran K Hasan
Universitas Negeri Gorontalo
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Journal : JAMBURA JOURNAL OF PROBABILITY AND STATISTICS

 1 
Perbandingan Fuzzy Time Series Lee untuk Meramalkan Nilai Tukar Petani di Provinsi Gorontalo Alvitha Habibie; Lailany Yahya; Isran K. Hasan
Jambura Journal of Probability and Statistics Vol 4, No 1 (2023): 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.v4i1.17453

Abstract

Gorontalo Province is one of the provinces in Indonesia where 60% of the population are farmers and fishermen. As much as 28,66% of PDRB in Gorontalo Province in 2020 was contributed by the agricultural sector. Farmer's Exchange Rate is a measurement capability of agricultural products in producing goods or services. Therefore, NTP forecasting is needed so that it becomes a reference in the future in making a decision to increase the agricultural sector. In this study, a comparison was made of the Holt Winters Exponential Smoothing method with Lee's Fuzzy Time Series to find out which is the best forecasting method for predicting NTP in Gorontalo Province. Based on the forecasting results, the accuracy value obtained from FTS Lee has a mape value of 0,65557% for FTS Lee order 1 and 0,55607%. While the accuracy value obtained by the multiplicative Holt Winters Exponential Smoothing is 5.92509% and the additive Holt Winters Exponential Smoothing is 6,14574%. From the forecasting results obtained, it can be concluded that the best method for predicting NTP in Gorontalo Province is the FTS Lee Order 2 method. 
PENERAPAN MODEL SPASIAL DURBIN DENGAN UJI LANJUTAN LOCAL INDICATOR OF SPATIAL AUTOCORRELATION UNTUK MELIHAT PENYEBARAN STUNTING DI KABUPATEN BONE BOLANGO HASIRU, LISA SYAHRIA; DJAKARIA, ISMAIL; HASAN, ISRAN K
Jambura Journal of Probability and Statistics Vol 3, No 1 (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.v3i1.13083

Abstract

One of the spatial regression analysis used is the spatial durbin model (SDM). This model can be applied to obtain the relationship between X and Y variables and their spatial effects. This research was continued by testing the local spatial autocorrelation, namely the local indicator of spatial autocorrelation (LISA) which aims to provide information on the pattern of spatial relationships of each observation area in Bone Bolango regency. Stunting cases in Gorontalo province, especially in Bone Bolango regency, are in a status that needs to be addressed immediately due to the prevalence rate in Bone Bolango regency in 2019 above 20% based on the WHO standard. The results showed that the factors that significantly affected stunting in 2019 in Bone Bolango regency were exclusive breastfeeding, the  proper sanitation and poverty. Meanwhile, based on the spatial effect, the factors that significantly affected stunting in 2019 in Bone Bolango regency were the percentage of exclusive breastfeeding, the percentage of LBW, the number of children with CBI and poverty. Based on result from the LISA, the observation areas of stunting cases showed that the percentage of exclusive breastfeeding, the number of children with CBI and povertu had a spatial autocorrelation or forming a grouping on the distribution of the stunting cases, the number of children with IDL and poverty, there are sub-districts that have spatial autocorrelation.
Implementasi Deep Learning dalam Pengklasifikasian Wajah Menggunakan Library Tensorflow pada Algoritma Convolutional Neural Network (CNN) Usman, Rahmat Setiawan; Hasan, Isran K.; Isa, Dewi Rahmawaty
Jambura Journal of Probability and Statistics Vol 4, No 2 (2023): Jambura Journal Of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjps.v4i2.18264

Abstract

The convolutional neural network is a deep learning method that functions to recognize and classify objects in an image. An example of its application is a facial recognition system which consists of a detection and classification process. Facial recognition by computers can be influenced by many things such as lighting, expressions, and the amount of dataset provided. This study aims to find out how to implement CNN to identify faces using Tensorflow with the Python programming language. The number of datasets used is 120 data and 10 respondents in total with different lighting conditions and shooting angles. Apart from the dataset, this study also uses several different scenarios in the training process, namely the difference in the number of epochs and the difference in the number of learning rates. Based on the results of the discussion, two models were obtained. In the first model, the results obtained an accuracy of 100% in the training process and 65% in the testing process. In the second model, the results obtained are 100% accuracy in the training process and 75% in the testing process. performance of the model made in this study can be said to be optimal in recognizing objects in several lighting conditions and image angles.
Co-Authors Abubakar, Agung Sucipto Agusyarif Rezka Nuha Alaina, Silvana Rahmayanti Alvitha Habibie Armayani Arsal Asriadi Asriadi Asriadi Asriadi Buhungo, Rayhanul Jannah Demas Novaleda Abdul Karim Dewi Rahmawaty Isa Djihad Wungguli Djuma, Clara Anggriani Doholio, Nadya Pratiwi Emiro, Awalia Hamani, Mohamad Taufik Harmain, Ismail Saputra R. Harun, Trieke Nurfadilah Hasan S. Panigoro HASIRU, LISA SYAHRIA Hinelo, Ikrar Prasetyo I Wayan Can Aryasandi Indrawati Lihawa Ismail Djakaria Ivan Novri Jefriyanto Ibrahim Karim, Finansiya S. Abd. Kasim, Afrianto Pratama Khairun Nisa Humolungo Kilo, Nur Ain Kurniawan Kurniawan La Ode Nashar Lailany Yahya Lapitung, Putri Mohamad, Ramla Mokodompit, Marcela Nadhilah, Farhah Nanda Arista Rizki NISKY IMANSYAH YAHYA Novianita Achmad Nur Falaq Nurdin, Sri Ayu Nurul Afifah Nurwan Nurwan Nurwan, Nurwan Pakaya, Desya Neydi Putri Pomahiya, Saiful Rahmi, Emli Resmawan Resmawan Salmun K. Nasib Sampe, Nofia Sigar, Tesya Siti Nurmardia Abdussamad Suharmin, Wandayana Nur'Amanah Sumarno Ismail Thesalonica Graina Barung Ulopo, Asrul S Usman, Rahmat Setiawan Wewengkang, Hanz Franklyn Bachruddin Winni A. Pakaya wolah, Meitasya