ANALISIS FAKTOR PROMOSI DAN KUALITAS PRODUK TERHADAP KEPUTUSAN PEMBELIAN COKELAT SULAMINA DI KABUPATEN KEPULAUAN SULA MALUKU UTARA

Tujuan dari penelitian ini adalah untuk menganalisis dampak Faktor Promosi dan Kualitas Produk terhadap Keputusan Pembelian Coklat SulaMina di Kabupaten Kepulauan Sula Maluku Utara, baik secara terpisah maupun bersamaan. Penelitian ini difokuskan pada mahasiswa Program Studi Pendidikan Ekonomi, Fakultas Keguruan dan Ilmu Pendidikan, Universitas Pattimura Ambon, dengan sampel penelitian sebanyak 40 responden. Penelitian ini menggunakan kuesioner sebagai alat pengumpul data dan analisis regresi linier berganda serta uji hipotesis untuk mengetahui pengaruh variabel independen terhadap variabel dependen, setelah terlebih dahulu melakukan uji instrumen untuk memastikan validitas dan reliabilitas data. Penelitian ini menemukan bahwa hasil analisis regresi linier berganda dan uji t menunjukkan bahwa Faktor Promosi tidak memiliki pengaruh signifikan terhadap Keputusan Pembelian, karena nilai Sig. 0,733 > 0,05 dan t hitung 1,121 < t tabel 1,68385. Sebaliknya, Kualitas Produk memiliki pengaruh signifikan terhadap Keputusan Pembelian, karena nilai Sig. 0,004 < 0,05 dan t hitung 3,067 > t tabel 1,68385. Hasil ini menunjukkan bahwa Kualitas Produk merupakan faktor yang lebih penting dalam mempengaruhi Keputusan Pembelian dibandingkan dengan Faktor Promosi. Hasil analisis menunjukkan  bahwa  Kualitas  Produk  merupakan  faktor  kunci dalam meningkatkan Keputusan Pembelian Coklat SulaMina di Kabupaten  Kepulauan  Sula  Maluku  Utara. Temuan  ini dapat  menjadi  acuan  bagi  perusahaan  untuk  meningkatkan  kualitas  produk  dan  strategi pemasarannya.&nbsp

UPAYA PEMBAHARUAN PENDIDIKAN IPS DI INDONESIA

Tujuan penulisan artikel ini adalah untuk memberikan pemahaman lebih lanjut untuk dilakukan upayah pembaharuan pendidikan IPS di Indonesia. Ini didasarkan karena pendidikan IPS di Indonesia sebagai mata pelajaran, dalam berbagai pandangan dianggap tidak penting, tidak menarik, kaku, membosankan, tidak menantang dan berbagai pandangan negatif lainnya. Perspektif buruk terkait pendidikan IPS di Indonesia ini, diakibat karena dalam proses pembelajaran IPS di sekolah tidak sepenuhnya dijalankan sesuai tujuan sebenarnya pendidikan IPS. Tujuan ini tidak diimplementasikan dalam proses pembelajaran pendidikan IPS di Indonesia, sehingga pandangan negatif tentang pendidikan IPS terus ada di kalangan siswa maupun masyarakat dan akhirnya timbul pengakuan akan tidak pentingnya mata pelajaran IPS dan siswa kurang minat dalam belajar pendidikan IPS di Indonesia. Guru yang dalam pemberian pembelajaran pendidikan IPS memiliki peranan paling utama dalam pembuatan dan penentuan metode pembelajaran, desain- desain pembelajaran yang menarik, dan antuasiasme yang tinggi untuk proses pembelajaran pendidikan IPS di Indonesia haruslah memberikan pemahaman yang tepat sesuai tujuan pendidikan IPS dan bukan hanya menunaikan kewajiban belaka, akan membuat citra pendidikan IPS menjadi baik dan dapat membuat upaya bagi pembaharuan pendidikan IPS di Indonesia. Dengan pembuatan penulisan artikel ini, diharapkan memberikan kontribusi untuk upaya pembaharuan pendidikan IPS di Indonesia dan membuat kesadaran bagi guru, siswa dan masyarakat Indonesia akan pentingnya Pendidikan IPS bagi kehidupan sosial masyarakat

OPTIMASI PENUGASAN KARYAWAN MENGGUNAKAN METODE HUNGARIAN DENGAN BANTUAN QM FOR WINDOWS (STUDI KASUS TOKO SEPATU SPECS PLAZA MEDAN FAIR)

Penugasan karyawan yang tidak optimal dapat menyebabkan ketidakefisienan operasional pada sector ritel. Penelitian ini bertujuan untuk mengoptimalkan penugasan karyawan toko sepatu SPECS menggunakan metode Hungarian sebagai pendekatan linear programming. Data diperoleh melalui wawancara dan observasi untuk mengestimasi waktu pengerjaan setiap karyawan terhadap setiap tugas. Hasil penelitian menunjukkan bahwa metode Hungarian mampu menghasilkan kombinasi penugasan dengan total waktu minimum sehingga dapat meningkatkan efisiensi operasional

BIOEKONOMI IKAN TUNA SIRIP KUNING (Thunnus albacares) DARI LAUT BANDA

Yellowfin tuna stocks in Maluku waters have enormous potential. The catch per unit effort affects production in terms of kilograms to tons, depending on the size of the fish caught. The rate of exploitation of yellowfin tuna fisheries is greatly influenced by vessel activity and trips at sea; the higher the catch activity, the lower the CPUE value during the fishing operation. The objective of this study is to analyze the bioeconomics of yellowfin tuna fisheries in the Banda Sea. The research methods used are financial analysis and production surplus modeling. The results of the study show that the financial analysis operating profit is IDR 1,165,674,612,500; Net profit is Rp. 1,165,619,566,920 with a profit rate of 1.25% and a benefit cost ratio of 2.25; profitability is 69157; the break-even point is Rp. 111,780,032; the payback period is 2 months, which is feasible. The Schaefer model produced a regression analysis between CPUE and trips, yielding a value of α = 508.3 and a value of b = -0.0856, MSY Copt of 754,389 tons, and Eopt of 2,968 trips. In fact, the number of vessels at the research location is more than 15 vessels, which can be regulated so that the number of vessel trips and the number of vessels can be reduced to prevent the tuna stock population from being exploited to extinction

MIXED-EFFECT MODELS WITH RESTRICTED MAXIMUM LIKELIHOOD (REML), BOOT-STRAPPED REML AND BAYESIAN INFERENCE IN APPLICATION OF GAPMINDER DATA

Mixed effects model combines fixed effects and random effects, allowing for the analysis of data with both fixed and random variations. This modeling approach is widely utilized across various fields. In R, the lme4 package is commonly employed to estimate mixed effects models using Restricted Maximum Likelihood (REML). There are several methods for estimating model parameters, including Bayesian inference, which has gained prominence with ongoing research advancements. Bayesian inference using Markov Chain Monte Carlo (MCMC) is among the most widely used Bayesian methods. Bayesian inference leverages probabilistic distributions to estimate parameters.to understand the general overview of life expectancy, serving as an indicator of survival time across different continents in the Gapminder dataset, it's essential to identify relevant variables after computing mixed effects predictions using Maximum Likelihood and REML estimation. This involves predicting life expectancy by integrating both random and fixed effects, determining relevant variables after estimating the Mixed Effects Model using REML Bootstrap estimation, and identifying influential variables after estimating the Mixed Effects Model using Bayesian MCMC inference. The methods employed include REML, Bootstrapped-REML, and Bayesian MCMC. The results indicate that all inference methods can be utilized to estimate parameters, with all predictor variables influencing life expectancy, except for the population variable. Further research is recommended to utilize data with more complex predictor variables

LAPLACE TRANSFORMATION AND MITTAG-LEFFLER FUNCTION FOR THE SOLUTION OF DAMPED OSCILLATOR EQUATION WITH FRACTIONAL ORDER

Fractional calculus has emerged as an active area of research due to its ability to model complex dynamical systems with memory effects and anomalous diffusion. In particular, the Mittag–Leffler function plays a fundamental role in solving fractional differential equations. This study aims to derive the analytical solution of the Linear Fractionally Damped Oscillator using the Laplace transform and the Mittag–Leffler function, where the derivative is of Caputo type with order 0<α<1. We further extend the analysis to both homogeneous and nonhomogeneous models, the latter corresponding to the presence of an external forcing term. The results indicate that the oscillatory behavior exhibits algebraic decay and eventual convergence due to damping or dissipation effects. The decay rate is directly influenced by the asymptotic properties of the Mittag–Leffler function, which depend on the fractional order α. These findings provide a deeper understanding of fractional-order damped oscillatory systems and offer a more generalized framework for analyzing dissipative processes in engineering, physics, and control systems

STATISTICAL MODELING FOR DOWNSCALING USING PRINCIPAL COMPONENT REGRESSION AND DUMMY VARIABLES: A CASE OF SIAK DISTRICT

Indonesia, as a tropical country, is characterized by two primary seasons: the rainy season and the dry season. It is evident that meteorological shifts can exert considerable influence on the agricultural sector, a notable example being the cultivation of palm oil. Consequently, the ability to predict rainfall has emerged as a pivotal element in the broader endeavor to mitigate the adverse effects of climate change. This study employs statistical downscaling using the Principal Component Regression (PCR) approach to model rainfall predictions. The issue of multicollinearity, a common occurrence in Global Circulation Model (GCM) data, is addressed through the use of Principal Component Regression (PCR). This method has been demonstrated to stabilize the model structure and reduce variance in the regression coefficients. The data utilized encompass observed rainfall from LIBO Estate, which is owned by PT SMART Tbk (SMART Research Institute), for the period from 2013 to 2022. This data serves as the response variable, while the CMIP6 GCM simulation output data functions as the predictor variable. The findings indicated that the initial PCR model exhibited an RMSE value ranging from 97.06 to 131.69, along with an R² value ranging from 14.25% to 20.49%. The incorporation of dummy variables into the model resulted in a substantial enhancement in its performance, as evidenced by a decline in RMSE to 24.46–35.83 and an increase in R² to 89.02%–90.24%. The findings indicate that the use of PCR with dummy variables is an effective approach for enhancing the accuracy of rainfall modeling through statistical downscaling

LEVERAGING XGBOOST, LIGHTGBM, AND CATBOOST FOR ENHANCED CUSTOMER SEGMENTATION IN THE AUTOMOTIVE INDUSTRY

This study evaluates the performance of three gradient boosting algorithms, XGBoost, LightGBM, and CatBoost, for customer segmentation in the automotive industry. Utilizing a dataset of 8,068 training and 2,627 testing observations with 11 demographic and behavioral variables, the research aims to classify customers into four segments. The methodology includes preprocessing (handling missing values, encoding), hyperparameter tuning via Randomized Search Cross-Validation, and evaluation using ROC AUC. Results indicate that XGBoost outperforms other models, achieving an AUC of 0.5837 on testing data with significant variables, while LightGBM and CatBoost scored 0.5834 and 0.5759, respectively. Key findings highlight the importance of feature selection, with Age, Profession, and Spending Score being the most influential predictors. The study concludes that XGBoost is the most robust for segmentation tasks, though all models exhibit challenges in distinguishing overlapping classes. These insights can guide data-driven marketing strategies in automotive and related sectors

A COMPARATIVE EVALUATION OF SARIMA AND FUZZY TIME SERIES CHEN MODELS FOR RAINFALL FORECASTING IN MAKASSAR

High rainfall intensity in Makassar often leads to flooding. Therefore, forecasting the amount of rainfall is necessary as a reference for taking appropriate mitigation measures. This study was conducted to select the best model between the SARIMA and Fuzzy Time Series (FTS) Chen based on a comparison of their forecasting accuracy, as well as to forecast the amount of rainfall in Makassar for 2024 using the best model. For this study, monthly rainfall data covering the period from January 2014 to December 2024 were collected from the official website of the Central Statistics Agency (BPS) Makassar. Based on the analysis results, SARIMA(7,2,3)(1,1,1)12 was selected as the best model, with an MAE value of 2.654 and an RMSE value of 3.846. The contribution of this study lies in providing an empirical comparison between SARIMA and FTS Chen for rainfall forecasting in tropical regions. However, the limitation of this study is that the forecasting relies solely on historical rainfall data, without incorporating other meteorological variables that may influence rainfall patterns

USE OF GLUE VALUE AT RISK FOR OPTIMAL PORTFOLIO RISK MEASUREMENT WITH THE SINGLE INDEX MODEL METHOD

Creating an optimal portfolio and measuring risk are ways that can be used to reduce losses and maximize returns in an investment. In this study, the optimal portfolio is formed using the Single Index Model method, which assumes stock returns are influenced only by market returns. The stocks used are stocks that are consistently included in the IDX30 index during the period October 24, 2022-October 25, 2024 and provide positive expected returns, so that based on the Single Index Model method, 5 stocks are included in the optimal portfolio with the proportion of each stock as follows, PT Indofood Sukses Makmur Tbk (INDF) 30%, PT Barito Pacific Tbk (BRPT) 8%, PT Bank Mandiri Tbk (BMRI) 35%, PT Bank Central Asia Tbk (BBCA)17%, and PT Bank Negara Indonesia Tbk (BBNI) 10%. The risk of the optimal portfolio can be calculated using the Glue Value at Risk method, which provides a more accurate and coherent measure of risk. In this study with a confidence level of  and  and used a high distortion function   and , the Glue Value at Risk amount for the optimal portfolio was obtained at Rp1,996,926. The backtesting results show that Glue Value at Risk provides valid and accurate results for measuring risk at this level of confidence