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All Journal Journal of the Indonesian Mathematical Society Jurnal Pengabdian Kepada Masyarakat (Indonesian Journal of Community Engagement) Jurnal Pendidikan Matematika (Jupitek) Journal of Tourism Destination and Attraction Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Enthusiastic : International Journal of Applied Statistics and Data Science Indonesian Journal of Life Sciences Limits: Journal of Mathematics and Its Applications
Maria Zefanya Sampe
Universitas Prasetiya Mulya
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Mathematics Medicine & Pharmacology 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
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.
Exploring Digital Knowledge in a Rural East Java Community Sari, Faizah; Tauryawati, Mey Lista; Sampe, Maria Zefanya
Jurnal Pengabdian kepada Masyarakat (Indonesian Journal of Community Engagement) Vol 11, No 4 (2025): December
Publisher : Direktorat Pengabdian kepada Masyarakat Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jpkm.87456

Abstract

This paper presents an initiative aimed at introducing digital skills to a rural community in East Java, Indonesia. A training workshop on the use of electronic surveys was conducted through a classroom-style presentation. The session included a simple step-by-step tutorial, hands-on practice using a smartphone-based electronic survey form, and a demonstration of how the survey results could be visualized. Thirty adult participants, both men and women, from the village of Karangpakis, Kabuh Regency, East Java, took part in the workshop. The findings indicate that the community service activity highlighted both challenges and opportunities for digital empowerment, which are analyzed in three key areas: (1) the delivery of an accessible workshop that enabled participants to practice using an online survey application; (2) the significance of introducing technology to enhance communication regarding village welfare; and (3) the advancement of participants’ digital literacy through exposure to the practical benefits of the survey results. The paper concludes by discussing implications for sustainable engagement and directions for future research.
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

Show Abstract | Download Original | Original Source | Check in Google Scholar

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.
Comparative Analysis For CNN and MLP Models in Breast Cancer Diagnosis Priscilla Natalie Nurtanio; Darren Nathaniel; Temmy Sugiarto; Theresa Angelina; Raymond Tjandra; Yohana Joevanca Kurniawan; Maria Zefanya Sampe
Indonesian Journal of Life Sciences 2026: IJLS Vol 08 No.01
Publisher : Universitas Bio Scientia Internasional Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54250/ijls.v8i01.254

Abstract

Breast cancer remains one of the most common and deadly diseases affecting women worldwide, highlighting the importance of early and accurate diagnosis to improve treatment outcomes and survival rates. However, traditional mammography techniques often fall short, failing to detect up to 20% of cases, especially in women with dense breast tissue, which makes detection more difficult. In response to these limitations, this study explores the use of neural networks to enhance diagnostic accuracy in breast cancer detection, focusing on the Convolutional Neural Network (CNN) and Multilayer Perceptron (MLP). Utilizing the Wisconsin Diagnostic Breast Cancer (WDBC) dataset, a baseline CNN model is compared against an optimized CNN refined through hyperparameter tuning using randomized search, as well as two MLP models implemented via Keras and Scikit-learn, along with their optimized versions. Each model is evaluated using key classification metrics, including accuracy, precision, recall, F1-score, and AUC, with an emphasis on minimizing false negatives, as this is critical in medical diagnosis to avoid missed malignancies. The results indicate that the optimized CNN model achieved near-perfect scores across all metrics and demonstrated the best balance between training and testing data. Therefore, it outperforms the baseline CNN and MLP models in significantly reducing false negatives, showcasing the potential of a well-tuned CNN to enhance the automation and reliability of breast cancer diagnostic processes.
Integrating Customer Segmentation, Predictive Modelling, and Uplift Modelling for Retail Voucher Targeting Michael Calvin Charmelino Wijaya; Maria Zefanya Sampe
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 14 Issue 2 August 2026
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v14i2.39943

Abstract

The rapid growth of e-commerce has encouraged retail companies to optimize data-driven promotional strategies to achieve higher targeting precision and cost efficiency. Mass promotional campaigns without proper segmentation often lead to budget inefficiencies and reduced profit margins. This study aims to optimize voucher targeting strategies for transactions involving growing-up milk products during the January-December 2024 period by integrating customer segmentation, predictive modelling, and uplift modelling. Customer segmentation was performed using K-Means Clustering, resulting in seven distinct clusters representing heterogeneous purchasing behaviors. The clustering output was incorporated as an additional feature in classification models based on XGBoost and CatBoost to predict the probability of targeted voucher redemption. Model evaluation using ROC-AUC, PR-AUC, confusion matrix, and classification report indicated that XGBoost with cluster features achieved the best performance, with a ROC-AUC of 0.9972 and a PR-AUC of 0.5479. Since conventional classification does not measure the causal impact of promotions, uplift modelling with a two-model approach was implemented to estimate the incremental effect of voucher distribution. The results reveal that approximately 1,027 customers (0.46% of 222,036 total customers) belong to the persuadable segment, generating an uplift of 1.91%. These findings demonstrate that uplift-based targeting is more efficient than mass campaigns or probability-based predictive targeting alone. The integration of segmentation, predictive modelling, and uplift modelling provides a more precise and measurable framework for data-driven promotional strategies in retail e-commerce.
Co-Authors Ariawan, I Wayan Ayomi Sasmito Cathrynn, Jovanka Darren Nathaniel Eko Ariawan Faizah Sari Handa, Michelle Intan Jovanka Cathrynn Made Handijaya Dewantara Mey Lista Tauryawati Michael Calvin Charmelino Wijaya Michelle Tanaka Peni Zulandari Suroto Priscilla Natalie Nurtanio Raymond Tjandra Sasmito, Ayomi Syafrudi Tanaka, Michelle Temmy Sugiarto Theresa Angelina Yohana Joevanca Kurniawan