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All Journal Journal of the Indonesian Mathematical Society Jurnal Pendidikan Matematika (Jupitek) Journal of Tourism Destination and Attraction Enthusiastic : International Journal of Applied Statistics and Data Science Limits: Journal of Mathematics and Its Applications
Maria Zefanya Sampe
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Mathematics Other

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Predictive Analysis of Employee Loyalty: A Comparative Study Using Logistic Regression Model and Artificial Neural Network Sampe, Maria Zefanya; Ariawan, Eko; Ariawan, I Wayan
Journal of the Indonesian Mathematical Society Volume 25 Number 3 (November 2019)
Publisher : IndoMS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22342/jims.25.3.825.325-335

Abstract

Employee turnover is a common issue in any company. A high turnover phenomenon becomes a big problem that will certainly affect the performance of the company. Therefore, measuring employee turnover can be helpful to employers to improve employee retention rates and give them a head start on turnover. A study to analyze for employee loyalty has been carried out by using Logistic Regression (LR) and Artificial Neural Networks (ANN) model. Response variables such as satisfaction level, number of projects, average monthly working hours, employment period, working accident, promotion in the last 5 years, department, and salary level are used to model the employee turnover. Parameters such as accuracy, precision, sensitivity, Kolmogorov-Smirnov statistic, and Mean Squared Error (MSE) are used to compare both models.
EKSPLORASI PENGALAMAN TERHADAP RISIKO BERWISATA PADA KONSUMEN WISATA PEREMPUAN DI INDONESIA Peni Zulandari Suroto; Maria Zefanya Sampe; Made Handijaya Dewantara
Journal of Tourism Destination and Attraction Vol 8 No 2 (2020): Journal of Tourism Destination and Attraction
Publisher : Fakultas Pariwisata Universitas Pancasila

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35814/tourism.v8i2.1649

Abstract

This study aims to explore the experiences of Indonesian female tour consumers regarding risks they face when traveling domestically. Data was collected through focus group discussions (FGD), to nine female tour consumer informants who traveled in Indonesia. Qualitative data were analyzed descriptively. The opposite grouping of keywords is done by creating a code and make narration. The results showed that there were four motivations for traveling, and two of them were related to risk. Female tour consumers want to explore new destinations, have high sense of curiosity, are interested in enjoying natural, cultural, and culinary richness. Forms of risk experienced by female tour consumers include natural conditions, geography, racial discrimination, verbal harassment, and physical injury. Female tour consumers anticipate risks by looking at various references, for making decisions, travel partners, and seeing publications from trusted sources. Although index and risk management have not been standardized in Indonesia, due to a travel ban in a cultural context, Indonesian female tour consumers tend to take risks to travel and repeat it several times. Behind the risk, they get important things such as unbeatable views, new experiences and knowledge, excitement, satisfying curiosity, and enjoyment of local wisdom. The findings on Indonesian female tour consumers are important input for tourism destination stakeholders.
ONLINE MATHEMATICS LEARNING STRATEGY APPROACH: TEACHING METHODS AND LEARNING ASSESSMENT Sampe, Maria Zefanya; Syafrudi, Syafrudi
Jurnal Pendidikan Matematika (JUPITEK) Vol 7 No 1 (2024): Jurnal Pendidikan Matematika (JUPITEK)
Publisher : Program Studi Pendidikan Matematika FKIP Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/jupitekvol7iss1pp42-55

Abstract

The primary focus of this research is to develop strategies aimed at identifying effective methods applicable to online mathematics instruction and assessment. Teaching through distance education has many challenges, requiring educators' adeptness in utilizing e-learning platforms for both instructional delivery and assessment purposes. This study adopts a literature review methodology, drawing upon various previous studies relevant to this subject matter. The research findings reveal several online mathematical strategies and assessment techniques envisioned to adequately measures students' capacity to discern complex issues and employ critical thinking to resolve mathematical problems. The important role of the educator as a facilitator is underscored in crafting learning materials synchronized to the needs of students navigating the online platform. In this context, online mathematics instructional strategies are intricately intertwined with pedagogical dimensions and the appropriateness of utilized media and assessment modalities. Emphasizing interactive, creative, and innovative learning emerges as a critically important characteristic, particularly within the realm of online mathematics instruction
Modified Snow Avalanches Algorithm untuk Vehicle Routing Problem Ayomi Sasmito; Jovanka Cathrynn; Michelle Tanaka; Maria Zefanya Sampe
Limits: Journal of Mathematics and Its Applications Vol. 21 No. 3 (2024): Limits: Journal of Mathematics and Its Applications Volume 21 Nomor 3 Edisi No
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

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Abstract

Metaheuristic algorithms are often used to tackle various optimization problems. In recent years, many new metaheuristic algorithms have been developed, such as the snow avalanches algorithm (SAA), which is inspired by natural snow avalanches. SAA consists of four avalanche phases: avalanches due to steep mountain slopes, human factors, local weather conditions, and it only has one control parameter. Like most metaheuristic algorithms, SAA has the potential to get trapped in local optima due to having only one control parameter. Therefore, this study presents a modification of SAA, called modified SAA (mSAA), which integrates the opposition-based learning (OBL) method with SAA to enhance the optimization process. To validate the performance of mSAA, tests were conducted on various OBL techniques to determine the best combination for solving complex and nonlinear problems, specifically the vehicle routing problem (VRP) on three types of VRP datasets (D01, D02, and D03 datasets). The results were then compared with the snow avalanches algorithm (SAA), hiking optimization algorithm (HOA), teaching learning-based optimization (TLBO), and grey wolf optimizer (GWO). Based on the average value, standard deviation, and best value, the mSAA method performed well and effectively in solving VRP using a combination of Quasi OBL and S_i=0.6+0.4 rand.
Analyzing Sentiments on IISMA Discontinuation Rumors with SVM, Random Forest Classifier, and XGBoost Classifier Handa, Michelle Intan; Sampe, Maria Zefanya; Syafrudi
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 5 Issue 2, October 2025
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol5.iss2.art5

Abstract

Indonesian International Student Mobility Award (IISMA) is a government-run student exchange program. Recently, rumors regarding its discontinuation have sparked various public opinions. This study aims to analyze these public sentiments and evaluate which machine learning model is most suitable for classifying sentiment labels in the dataset. The models tested included support vector machine (SVM), random forest classifier (RFC), and extreme gradient boosting (XGBoost) classifier. The dataset consisted of 630 tweets scraped from Twitter and was split into an 80:20 ratio, with 80% allocated for training and 20% for testing. The results indicated that both SVM and RFC were the most effective models, achieving the highest accuracy of 85.44%. Sentiment analysis reveals that the majority of public opinion is positive, suggesting that most people agree with the discontinuation of the IISMA program because the program is perceived as nonurgent and not a current national priority. These findings provide insights into public sentiment and highlight the utility of machine learning models in classifying such sentiment data effectively.
Co-Authors Ariawan, I Wayan Ayomi Sasmito Eko Ariawan Handa, Michelle Intan Jovanka Cathrynn Made Handijaya Dewantara Michelle Tanaka Peni Zulandari Suroto Syafrudi Syafrudi, Syafrudi