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NISKY IMANSYAH YAHYA

Universitas Negeri Gorontalo

Author-ID : 1764220

Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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APPLICATION OF BINARY LOGISTICS REGRESSION AND RANDOM FOREST TO CIGARETTE CONSUMPTION EXPENDITURE IN GORONTALO REGENCY 2022 Hamani, Mohamad Taufik; Isa, Dewi Rahmawaty; Nasib, Salmun K.; Panigoro, Hasan S.; Hasan, Isran K.; Yahya, Nisky Imansyah
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 13, No 1 (2025): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.13.1.2025.14-22

The goal of this research is to predict or identify an object's class using its available attributes through classification. The aim of this research is to use the random forest method to develop a classification model and the binary logistic regression method to discover significant determinants in cigarette consumption expenditure in Gorontalo Regency. The findings indicated that the size of the home, the number of family members, and the head of the household's educational attainment all had a considerable impact. Only the household head's educational attainment, however, consistently influences the model and satisfies the goodness of fit requirements. In contrast, the random forest model outperformed binary logistic regression in the classification analysis when classification characteristics including accuracy, precision, recall, and f1-score were assessed. Consequently, random forest was found to be the most effective classification model in this investigation.

Klasifikasi Preferensi Mahasiswa dalam Pemilihan Laptop Menggunakan Analisis Diskriminan Kernel Gaussian Meilan Sigar; Lailany Yahya; Salmun K. Nasib; Nisky Imansyah Yahya; Djihad Wungguli
Bilangan : Jurnal Ilmiah Matematika, Kebumian dan Angkasa Vol. 3 No. 5 (2025): Oktober : Bilangan : Jurnal Ilmiah Matematika, Kebumian dan Angkasa
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62383/bilangan.v3i5.804

Rapid developments in information technology have made laptops an essential device for students, especially those in their final year of study. Choosing the right laptop plays an important role in supporting academic productivity, such as writing theses, analyzing data, and developing software. This study aims to classify the preferences of mathematics students at Gorontalo State University in choosing laptops based on usage characteristics and factors that influence purchasing decisions. The method used is Kernel Discriminant Analysis (KDA) with a Gaussian kernel function and an optimal bandwidth of 0.8. The research data involved 268 respondents divided into training and testing data. The analysis results show that the KDA model has an accuracy rate of 60% on the training data and 52% on the testing data, which indicates the model's ability to recognize student preference patterns despite a decrease in accuracy on new data. Based on the kernel density estimation results, Acer is the most widely used laptop brand, while Zyrex and Apple are rarely chosen. The most influential factor in purchasing decisions is processor specifications, with a contribution of 35.739%, followed by brand, warranty, and price. These findings indicate that hardware characteristics are the main consideration in laptop selection, with most students choosing laptops with Intel Core i5 processors, a minimum of 8GB of RAM, and SSD storage. The results of this study can also be used by universities to provide recommendations for selecting laptops that suit students' academic needs.

Determination of Premium Price for Rice Crop Insurance in Gorontalo Province Based on Rainfall Index with Black Scholes Method Nadiyyah, Ana; Rahmi, Emli; Nasib, Salmun K.; Nuha, Agusyarif Rezka; Yahya, Nisky Imansyah; Nashar, La Ode
Pattimura International Journal of Mathematics (PIJMath) Vol 3 No 2 (2024): Pattimura International Journal of Mathematics (PIJMath)
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/pijmathvol3iss2pp51-62

With its complex topography, Gorontalo Province experiences significant rainfall variations that impact the agricultural sector, particularly rice crops. These variations can cause substantial losses for farmers. One way to address uncertain probabilities caused by rainfall is through agricultural insurance. This research aims to calculate the value of agricultural insurance premiums based on the rainfall index. The Black- Scholes method is used to calculate the premiums, while the Burn Analysis method is employed to determine the rainfall index. The research results classify the rainfall index values in Gorontalo Province into 7 (seven) percentiles. The lowest is at the 20th percentile, with 17.37 mm and a premium value of IDR 1,574,190, while the highest is at the 80th percentile, with 17.65 mm and a premium value of IDR 2,154,574. This indicates that the higher the rainfall, the greater the premium to be paid.

Wind Speed Category Characteristics in Bone Bolango Regency: A Markov Chain Approach Using the Beaufort Scale and Metropolis-Hastings Algorithm Pomahiya, Saiful; Nurwan, Nurwan; Yahya, Nisky Imansyah; Nasib, Salmun K.; Hasan, Isran K.; Asriadi, Asriadi
Pattimura International Journal of Mathematics (PIJMath) Vol 3 No 2 (2024): Pattimura International Journal of Mathematics (PIJMath)
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/pijmathvol3iss2pp63-68

This study models daily wind speed transitions in the Bone Bolango Regency using the Markov Chain Monte Carlo (MCMC) method and the Metropolis-Hastings algorithm, employing the Beaufort scale for wind speed classification. The research aims to predict the steady-state distribution of wind speeds and evaluate their temporal stability. Daily wind speed data from 2023, provided by the Meteorology, Climatology, and Geophysics Agency (BMKG), were categorized into three levels: calm, light breeze, and fresh breeze, based on the Beaufort scale. Transition probabilities were estimated using the Beta distribution, and simulations via the Metropolis-Hastings algorithm yielded the steady-state distribution. Results show a significant tendency for transitions from calm and light breeze categories to fresh breezes, with varying probabilities. Notably, calm conditions exhibit a 69% likelihood of transitioning to a light breeze. This research contributes to improving wind speed prediction models by integrating statistical algorithms with meteorological classifications. The findings have implications for enhancing short-term weather forecasts and developing predictive systems for regions with similar weather patterns.

Implementation of Fuzzy Time Series Markov Chain Method using Kernel Smoothing in forecasting the Stock Price of PT. Elnusa Tbk. Mokodompit, Marcela; Nasib, Salmun K; Djakaria, Ismail; Yahya, Nisky Imansyah; Hasan, Isran K.
Indonesian Journal of Computational and Applied Mathematics Vol. 1 No. 1: February 2025
Publisher : Gammarise Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64182/indocam.v1i1.9

This research aims to apply the Fuzzy Time Series Markov Chain combined with Kernel Smoothing in forecasting stock prices. The Kernel Smoothing technique is used to smooth stock data before the fuzzification process, resulting in more accurate predictions. The research stages include Data Smoothing, Fuzzy interval formation, Fuzzy Logical Relationship and Fuzzy Logical Relationship Group formation, and forecasting using Markov Chain Transition Matrix. Evaluation using MAPE shows a low prediction error rate, with a value of 0.005974257%, so this method is effective for volatile stock data. The implementation of this model is expected to be a reference for investors and analysts in understanding and predicting future stock price movements.

Co-Authors Afifah Farhanah Akadji Afifah Farhanah Akadji Agusyarif Rezka Nuha Akadji, Afifah Farhanah Amelia T. R. Sidik Armayani Arsal Aruchamy, Pradeepa Asriadi Asriadi Asriadi Asriadi Bertu Rianto Takaendengan Cindy Aisa Putri Noor Dennynatalis Taha Dewi Rahmawaty Isa Dhandapani, Prasantha Bharathi Djihad Wungguli Fatmawati, Ainun Ganesan, Gomathi Hamani, Mohamad Taufik Harmain, Ismail Saputra R. Harun, Trieke Nurfadilah Hasan S. Panigoro Imran, Nurain Indrawati Lihawa Ismail Djakaria Isran K Hasan Jusuf, Anryan K. Nasib, Salmun Kai, Ferawati Karim, Finansiya S. Abd. Karina Anselia Mamonto Kasim, Afrianto Pratama Khairun Nisa Humolungo Kiayi, Fuji Fauzia La Ode Nashar Lailany Yahya Lailany Yahya Lakisa, Narti Lakoro, Tiara Mahagaonkar, Pralahad Meilan Sigar Melisa Huntala Mita Sari Mohamad Rivaldi Moha Mokodompit, Marcela Muhammad Rezky F. Payu Muhammad Rifai Katili Nadiyyah, Ana Narti Lakisa Novianita Achmad Novria Fatmawati Lakutu Nurmardia Abdussamad, Siti Nurwan Nurwan Nurwan Nurwan, Nurwan Pomahiya, Saiful Pranata, Widya Eka Prasetyo, Deny Ardika Rahim, Delvira Masita Rahmi, Emli Randi Mooduto Rauf, Dewi Nur Angriani Resmawan Resmawan Salmun K. Nasib Salmun K. Nasib Sari, Septi Rahmita Sembiring, Rinawati Siti Nurmardia Abdussamad Sri Lestari Mahmud Suharmin, Wandayana Nur'Amanah Sumarno Ismail Taha, Dennynatalis Tahir, Fauzia D Tahir, Fauzia D. Taufik, Mohamad Alfiransyah Wahab, Nur Dhea Wahdania A.T. Ja’a Wewengkang, Hanz Franklyn Bachruddin Zulkifli Alamtaha

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