OJS UNPATTI Publication Center (Universitas Pattimura)
Not a member yet
8549 research outputs found
Sort by
COMPARISON BETWEEN BAYESIAN QUANTILE REGRESSION AND BAYESIAN LASSO QUANTILE REGRESSION FOR MODELING POVERTY LINE WITH PRESENCE OF HETEROSCEDASTICITY IN WEST SUMATRA
Get PDFThe poverty line is the threshold income level below which a person or household is considered to be living in poverty. The poverty line is a representation of the minimum rupiah amount needed to meet the minimum basic food needs equivalent to 2100 kilocalories per capita per day and basic non-food needs. According to data from the Central Bureau of Statistics (BPS), although the poverty rate in West Sumatra has decreased in recent years, the issue of poverty is still very relevant to be discussed and addressed. The issue of the poverty line is important to discuss because it is directly related to the welfare of people and the development of a country. For modeling the poverty line and its influencing factors, appropriate statistical methods are needed. This research is about the comparison of two methods, namely the Bayesian quantile regression method and Bayesian LASSO quantile regression. The two methods are compared with the aim of seeing which method produces the smallest error. Bayesian quantile regression is one method that can model data assuming heteroscedasticity violations. This study compares the ordinary Bayesian quantile regression method with penalized LASSO. These two methods are applied in modeling the poverty line in West Sumatra. The purpose of this study is to see the best method for modeling data. The data used amounted to 133 data points from BPS in the years 2017 and 2023. Model parameters were estimated using MCMC with a Gibbs sampling approach. The results show that the Bayesian LASSO method is superior to the method without LASSO. This is evidenced that the superior method produces the smallest MSE value, 0.208, at quantile 0.5. Model poverty line in West Sumatra is significantly influenced by per capita spending ), Gross Regional Domestic Product ), Human Development Index ), Open Unemployment Rate , and minimum wages
GRID SEARCH AND RANDOM SEARCH HYPERPARAMETER TUNING OPTIMIZATION IN XGBOOST ALGORITHM FOR PARKINSON’S DISEASE CLASSIFICATION
Get PDFParkinson's disease is a neurodegenerative disorder affecting motor abilities, with a prevalence of 329 cases per 100,000 individuals. Early diagnosis is crucial to prevent complications. This study classifies Parkinson's disease using the Extreme Gradient Boosting (XGBoost) algorithm with hyperparameter tuning via Grid Search and Random Search. The dataset from Kaggle consists of 2105 records from 2024 and includes 32 clinical and demographic features such as age, gender, BMI, medical history, and Parkinson's symptoms. The XGBoost method effectively manages large and complex data and reduces. Tuning was performed with 5-fold cross-validation for result validity. After tuning with Grid Search, the model achieved 93.35% accuracy in 44 minutes 51 seconds, with optimal parameters gamma=5, max depth=3, learning rate=0.3, n estimators=100, and subsample=0.7. Meanwhile, Random Search with 50 iterations achieved 93.97% accuracy in 3 minutes 4 seconds with optimal parameters gamma=5, max depth=3, learning rate=0.262, n estimators=58, and subsample=0.631. Random Search also shows better time efficiency than Grid Search, although with relatively similar accuracy. The results of this study confirm that hyperparameter tuning using Random Search not only produces competitive accuracy performance but also minimizes computation time, making it a more optimal choice for Parkinson's disease classification
IMPLEMENTATION OF THE DBSCAN ALGORITHM FOR CLUSTERING STUNTING PREVALENCE TYPOLOGY IN WEST JAVA, CENTRAL JAVA, AND EAST JAVA REGIONS
Get PDFStunting, a condition where children are malnourished for a long period, causes growth failure in children. West Java, Central Java, and East Java are the 3 provinces with the highest prevalence of stunting in 2021. This study aims to group districts/cities in these provinces based on factors that influence stunting using the DBSCAN method (there has been no previous research using this method for this case), so the typology of stunting prevalence is implied. The group results can be valuable input for policy priorities in overcoming stunting. The study used the DBSCAN (Density-Based Spatial Clustering of Application with Noise) method, which can also detect noises (outliers). The determination of eps and MinPts is based on the average value of the distance from each data to its closest neighbor. The distance obtained then was used in the KNN algorithm to determine eps and MinPts parameters. Clustering is done using standardized data and DBSCAN parameters obtained from the k-dist plot, eps is 1.92, and MinPts is 2. The validation test used is the silhouette coefficient to determine the goodness of the cluster results. The clustering results show that there are 2 clusters and 1 noise that have special characteristics related to factors that influence the prevalence of stunting. Cluster 1 consisted of 97 districts/cities and was characterized by a high percentage of infants under 6 months receiving exclusive breastfeeding and the lowest average per capita household expenditure. Cluster 2 (Bekasi City and Depok City) was characterized by the lowest percentage of households with proper health facilities and infants aged 0-59 months receiving complete immunization. The noise (high stunting prevalence) in Bandung City is characterized by the lowest percentage of households having proper sanitation
MODELING TOTAL FERTILITY RATE IN INDONESIA: A COMPARISON OF FOURIER SERIES REGRESSION AND ELASTIC NET REGRESSION
Get PDFThe Total Fertility Rate (TFR) describes population growth and socioeconomic development of a country. This statistic plays an important role in predicting future social and economic conditions. Indonesia has experienced a steady decline in TFR over the past few decades, which can be a serious problem if this trend continues. Therefore, the factor influencing the decline must be found. The independent variables include the percentage of women graduating high school, percentage of the poor population, poverty gap index, poverty severity index, prevalence of inadequate food consumption, proportion of people living below 50 percent of median income, unemployment rate, infant mortality rate, child mortality rate, and percentage of ever-married women aged 15–49 years using contraception methods. The aim of this study is to compare both Fourier Series Regression and Elastic Net Regression models to see which approximation can capture the TRF phenomenon that occurs in Indonesia and identify the causes of its decline. Fourier Regression is chosen because there is a repetition of patterns in several variables. Moreover, this data is experiencing multicollinearity; hence, Elastic-net Regression is the best way because this method overcomes the limitations of each Ridge and Lasso approach. These models are compared to see which is more suitable to capture the relationships between these factors and TFR. The best model obtained will provide a clearer understanding of Indonesia's underlying drivers of fertility decline. The result is that the Fourier Series Regression can model all variables better than the Elastic-net Regression, and the independent variables can explain the proportion of variance in the dependent variables by 97.91%, with all the independent variables significantly affecting the Total Fertility Rate
PERFORMANCE COMPARISON OF SOME TYPES OF WAVELET TRANSFORMS FOR TOURISM DATA PATTERN APPROXIMATION
Get PDFTourism is an economic sector that significantly supports the country's foreign exchange, including in West Nusa Tenggara Province (NTB). Data on tourist visits to an area, including to NTB, is a representation of time series data. The wavelet method is one of the tools that is quite reliable for modeling time series data. This study aims to model the number of tourist visits to NTB Province using discrete wavelet transformation decomposition to estimate data. Several Wavelet functions such as Haar wavelet, Symlet, Coiflet, Daubechies, Best-localized Daubechies, Fejér-Korovkin, and Bi-orthogonal Splines at various orders and levels of decomposition became the basis for simulation in modeling data. Based on the Root of Mean of Square Error (RMSE) indicator, this study compares the performance of each wavelet function against the modeling performance at various orders and levels of decomposition. Numerically, for the data on the total number of tourists visiting NTB Province, the best approximation was given by the Fejér-Korovkin wavelet order 4-th (fk4) and the best-localized Daubechies wavelet order 7-th (bl7) at the 2-nd level with an RMSE value of 2.2993 × 10-11. Partially, the best approximation of the data on the number of foreign tourist visits was given by the Bi-orthogonal Splines wavelet type order 2.6 (bior2.6) at the 2nd decomposition level with an RMSE value of 1.1718 × 10-11 and for the data on domestic tourist visits was given by the Fejér-Korovkin wavelet type order 4-th (fk4) and the best-localized Daubechies wavelet order 7-th (bl7) at the 2nd level with an RMSE value of 1.3352 × 10-11
SECURING INFORMATION CONFIDENTIALITY: A MATHEMATICAL APPROACH TO DETECTING CHEATING IN ASMUTH-BLOOM SECRET SHARING
Get PDFThe Secret Sharing Scheme (SSS) based on the Chinese Remainder Theorem (CRT) is a crucial method for safeguarding confidential information. However, this scheme is vulnerable to collaborative cheating involving multiple participants. This study aims to modify the Asmuth-Bloom scheme by introducing two detection mechanisms: Threshold Range Detection and Detection Parameter Verification, to identify and prevent collaborative fraudulent activities. The research design is based on mathematical algorithms and tests the effectiveness of detection against predetermined cheating scenarios using structured parameters. The results indicate that the proposed modifications can accurately detect the manipulation of secret fragments, even in cases involving participant collusion. This robustness is achieved through the mathematical structure of the CRT, which enables the detection of inconsistencies during the secret reconstruction process. In addition to maintaining the efficiency of the original Asmuth-Bloom scheme, these modifications enhance the reliability of the scheme in protecting sensitive data. The study concludes that the implementation of dual detection mechanisms significantly strengthens the security of the SSS, particularly in applications prone to dishonest participant collaboration. Future research is recommended to explore computational efficiency and the implementation of this scheme in real-world environments, such as financial systems and blockchain technology
PREDICTION OF AVERAGE TEMPERATURE IN BANYUWANGI REGENCY USING SARIMA
Get PDFClimate change due to human activity has significantly impacted increasing global average temperatures, including in Banyuwangi Regency, East Java. The impact is felt in several sectors, such as agriculture, tourism, and health. As a preventive measure to minimize the adverse effects that will occur in the future, an accurate prediction of the average temperature of Banyuwangi Regency is needed. This research used secondary data from the official website of the Central Statistics Agency (BPS) of Banyuwangi Regency per month from January 2012 to December 2023. Predictions are made using the seasonal autoregressive integrated moving average (SARIMA) approach. The best model is selected based on its fulfillment of stationarity, the significance of its parameters, and compliance with the assumptions of normality and white noise. From this method, the best model obtained to predict the average temperature of Banyuwangi Regency is the probabilistic SARIMA (1,0,0)(0,1,1)12. The probabilistic SARIMA model treats both parameters and forecasts as probability distributions. The average temperature of Banyuwangi Regency is obtained for the next year, namely from January 2023 to December 2023, with a MAPE of 1.63%. With an accuracy rate of 98.37%, it can be said that the probabilistic SARIMA (1,0,0)(0,1,1)12 model is accurate in predicting the average temperature of Banyuwangi Regency in the future. Thus, the prediction of the average temperature of Banyuwangi Regency is expected to help the community and government manage the impact of erratic climate change to improve the welfare of all Banyuwangi people
IMPLEMENTASI PEMBELAJARAN STEAM BERBASIS PROYEK UNTUK MENINGKATKAN KREATIVITAS SISWA SEKOLAH MENENGAH ATAS
Get PDFPenelitian ini bertujuan untuk mengkaji penerapan pembelajaran STEAM (Science, Technology, Engineering, Arts, and Mathematics) berbasis proyek dalam meningkatkan kreativitas siswa pada materi energi terbarukan. Studi dilakukan di SMA Negeri 57 Maluku Tengah, yang menghadapi dua permasalahan utama: belum optimalnya penerapan pembelajaran STEAM berbasis proyek dan rendahnya kreativitas siswa dalam memahami konsep energi terbarukan. Penelitian menggunakan pendekatan kuantitatif dengan desain pre-eksperimental (one-group pretest-posttest). Data dikumpulkan melalui tes kreativitas dan lembar observasi berdasarkan indikator kelancaran, fleksibilitas, orisinalitas, dan elaborasi. Hasil menunjukkan peningkatan signifikan pada kreativitas siswa setelah penerapan model pembelajaran tersebut. Siswa menjadi lebih aktif, mampu mengembangkan ide orisinal, dan menunjukkan pemahaman yang lebih baik melalui kerja proyek kolaboratif. Temuan ini menunjukkan bahwa pembelajaran STEAM berbasis proyek efektif dalam mendorong keterampilan berpikir tingkat tinggi. Disarankan pendekatan ini diintegrasikan dalam kurikulum dan pelatihan guru untuk mendukung pendidikan abad ke-21
Forecasting Palm Oil Production in North Sumatera Using the Adaptive Neuro Fuzzy Inference System Method
Get PDFIndonesia is an agricultural and maritime country because it is the country that has the largest agriculture and plantations in ASEAN. One of them is palm oil production, because palm oil is believed to not only be able to produce various types of butter, cooking oil or soap, but can also be a substitute for fuel oil. In the province of North Sumatra itself, oil palm is a crop that has potential and produces very high profits. Therefore, forecasting is used to determine future palm oil production results using the ANFIS method in order to increase or catalyze palm fruit. The data source used in this research comes from the Central Statistics Agency (BPS) of North Sumatra. The aim of this research is to determine the results of forecasting palm oil production in North Sumatra using the ANFIS model. So we got results from forecasting palm oil production in North Sumatra which experienced fluctuations throughout the period January 2023 to December 2024 with a forecasting accuracy level of 92% and a MAPE value of 12.778179% with MAPE criteria of 10% - 20% which was considered 'Good '. So it can be concluded that the forecasting results were carried out well and can be used for future forecasting
Input Teknologi Pada Budidaya Tanaman Kelapa Di Kecamatan Taniwel Timur Kabupaten Seram Bagian Barat Provinsi Maluku
Get PDFKelapa (Cocos nucifera) merupakan salah satu tanaman yang memiliki nilai ekonomi dan pemanfaatan yang tinggi di masyarakat. Provinsi Maluku termasuk dalam sepuluh besar provinsi sentra kelapa di Indonesia, dengan luas areal perkebunan kelapa mencapai 115,16 juta hektar pada tahun 2020. Namun demikian, produksi kelapa di Maluku mengalami penurunan, dari 0,52% menjadi 103,77 juta ton pada tahun yang sama. Produktivitas yang rendah menjadi salah satu permasalahan utama dalam pengembangan kelapa di Indonesia. Salah satu upaya yang dapat dilakukan untuk meningkatkan produktivitas kelapa adalah melalui pengelolaan input usahatani secara lebih optimal. Penelitian ini bertujuan untuk mengetahui tingkat penggunaan teknologi dalam budidaya tanaman kelapa di Kecamatan Taniwel Timur, Kabupaten Seram Bagian Barat. Metode yang digunakan dalam penelitian ini adalah metode survei dan observasi langsung terhadap petani kelapa di wilayah tersebut. Hasil penelitian menunjukkan bahwa tingkat penggunaan teknologi dalam budidaya tanaman kelapa, mulai dari tahap persiapan lahan, pembibitan, penanaman, pemeliharaan, panen, hingga pascapanen di Desa Walakone, tergolong dalam kategori rendah. Budidaya tanaman kelapa umumnya masih dilakukan berdasarkan pengetahuan tradisional yang diwariskan secara turun-temurun, sehingga belum memenuhi kaidah budidaya yang baik dan benar