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All Journal Sodality: Jurnal Sosiologi Pedesaan Media Kedokteran Hewan BAREKENG: Jurnal Ilmu Matematika dan Terapan Eksakta : Berkala Ilmiah Bidang MIPA Jurnal Kesehatan Ibu dan Anak International Journal of Advances in Data and Information Systems Majalah Ilmiah Matematika dan Statistika (MIMS) Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Jurnal Veteriner dan Biomedis Engagement: Jurnal Pengabdian Kepada Masyarakat Scientific Journal of Informatics Indonesian Journal of Statistics and Its Applications International Journal of Oil Palm
Sachnaz Desta Oktarina
Statistics And Data Science Study Program, School Of Data Science, Mathematics, And Informatics, IPB University, Indonesia
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Development of Situ Gede Lake Tourism and Local MSMEs through Web-Based Digital Promotion and Data Analysis Meavi Cintani; Zamrah Mutmainah; I Gusti Ngurah Sentana Putra; Sabrina Adnin Kamila; Lisa Amelia; Sachnaz Desta Oktarina; Anang Kurnia; Agus Mohamad Soleh; Akbar Rizki
Engagement: Jurnal Pengabdian Kepada Masyarakat Vol. 10 No. 2 (2026): May 2026
Publisher : Asosiasi Dosen Pengembang Masyarajat (ADPEMAS) Forum Komunikasi Dosen Peneliti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29062/engagement.v10i2.2161

Abstract

Background: The development of tourism at Situ Gede Lake and local MSMEs through a web-based digital promotion and data analysis approach addresses the need for local tourism to compete more effectively. This community service focuses on empowering local MSMEs within the tourism ecosystem using digital technology. Purpose of the Study: This study aims to develop a web-based platform that integrates digital promotion and data analysis to support tourism digitalization at Situ Gede Lake, enhance MSME empowerment, and introduce data-driven visitor trend analysis using the Long Short-Term Memory (LSTM) deep learning method. Methods: The platform was developed using daily visitor data from the Situ Gede Village Office. The LSTM model was applied to forecast tourist numbers through 2026. Socialization and training were conducted on July 3, 2025, with 31 participants from POKDARWIS, MSME actors, village officials, and the community. The web application (https://situgede-ssmi.ipb.ac.id/) integrates a visitor statistics dashboard, MSME catalog, interactive map, event schedule, and waste management education. Results: The dashboard reveals an average of 53 visitors per day, 17 active communities, and estimated daily revenue of 2.5 million rupiah. LSTM predictions indicate a seasonal surge in mid-2026 potentially exceeding 400 visitors per day. MSME actors showed readiness to utilize the digital catalog, and participants responded positively to improved information access. Early findings demonstrate that combining web-based digital promotion and data analysis enhances MSME visibility, supports sustainable tourism development, and strengthens environmental awareness at Situ Gede Lake.
Comparative Performance of Gradient Boosting Algorithms for Household Food Resilience Classification during The COVID-19 Nabila Syukri; Sachnaz Desta Oktarina; Septian Rahardiantoro
EKSAKTA: Berkala Ilmiah Bidang MIPA Vol. 27 No. 04 (2026): Eksakta : Berkala Ilmiah Bidang MIPA (E-ISSN : 2549-7464) In Progress
Publisher : Faculty of Mathematics and Natural Sciences (FMIPA), Universitas Negeri Padang, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/eksakta/vol27-iss04/667

Abstract

The need for data-driven analysis to understand socio-economic vulnerability, particularly in relation to food security, has intensified due to the pandemic. The growing volume of survey data demands analytical methods that can capture multidimensional relationships. Over time, food security evolves into the concept of food resilience, reflecting a household's capacity to withstand or recover from adverse conditions. This study uses machine learning techniques to categorize household food resilience, based on data from the World Bank's High Frequency Phone Survey (HFPS), covered 2,868 households in Indonesia. A comparative evaluation of gradient boosting algorithms (XGBoost, LightGBM and CatBoost) was conducted. Model performance was evaluated using accuracy, sensitivity, specificity, and AUC across ten repeated train-test splits, with statistical significance assessed using Friedman and Wilcoxon tests. The results show that CatBoost performed best and most consistently, achieving mean accuracy of 0.7592 and mean AUC of 0.8331, which is significantly higher than that of competing models. SHAP analysis further indicates that baseline vulnerability, financial concerns, income capacity, food price shocks and unmet healthcare needs are important features for identifying resilient households. These findings demonstrate that gradient boosting, particularly CatBoost, provides strong predictive power and interpretability to support data-driven decision-making in classifying food resilience.
Winsorization for Outliers in Clustering Non-Cyclical Stocks with K-Means and K-Medoids: Winsorization untuk Penanganan Pencilan dalam Penggerombolan Saham Sektor Consumer Non-Cyclical dengan K-Means dan K-Medoids Naura Tirza Ardhani; Khairil Anwar Notodiputro; Sachnaz Desta Oktarina
Indonesian Journal of Statistics and Applications Vol 9 No 1 (2025)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v9i1p46-60

Abstract

Non-cyclical consumer sector stocks are often chosen by investors because the products in this sector are essential products that always in demand by society. Therefore, the demand for these products tends to be stable and defensive or less affected by economic shocks. However, it does not guarantee that every stock in this sector has good performance, thus it is necessary to group stocks based on their fundamental indicators in the form of financial ratios. This research aims to identify the best method by considering outliers and determining the clusters with the best fundamental performance as a recommendation for investors to make the right investment decisions. The data used in this study is secondary data with observations in the form of 50 non-cyclical consumer sector stocks. The variables used are Earning per Share, Return on Equity, Return on Assets, Debt to Equity Ratio, Price to Earnings Ratio, and Price to Book Value. The clustering results indicated that K-Medoids is the best clustering method, both on the data before and after handling extreme outliers with winsorization approach. However, the optimum number of clusters before and after winsorization are different, with 3 and 6 clusters. Considering the influence of extreme outliers and to get a more informative clustering result, the clustering result after the application of winsorization technique was chosen, which resulted in 6 clusters. Cluster 1, which consists of AALI, GGRM, INDF, and SGRO can be recommended because it has excellent fundamental performance, especially in terms of Earning per Share in 2022.
Optimization of Fuzzy C-Means Clustering with Particle Swarm Optimization on Socioeconomic Indicators of ASEAN Countries Cindy Indriyani; Siti Arbaynah; Ananda Putra Wijaya; Lusi Oktaviani; Fadhilah Yumna; Norashida Othman; Sachnaz Desta Oktarina; Rahma Anisa
Indonesian Journal of Statistics and Applications Vol 9 No 2 (2025)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v9i2p274-288

Abstract

Grouping data based on similarity in characteristics is commonly applied in various exploratory analyses. The Fuzzy C-Means algorithm offers flexibility through the degree of membership of data points in each cluster, but it is vulnerable to poor cluster center initialization, which increases the risk of getting trapped in local optima. To enhance the performance of Fuzzy C-Means, this study integrates the Particle Swarm Optimization method for determining cluster centers. The evaluation is conducted by comparing Fuzzy C-Means and Fuzzy C-Means-Particle Swarm Optimization across several cluster counts using three internal validation metrics, namely the silhouette coefficient, partition coefficient, and Xie-Beni Index. The results show that Fuzzy C-Means-Particle Swarm Optimization consistently yields higher silhouette coefficient and partition coefficient values, along with lower Xie-Beni Index values, compared to standard Fuzzy C-Means. This indicates that the integration of Particle Swarm Optimization can improve clustering quality in terms of cluster compactness and separation. This hybrid approach demonstrates significant potential in complex data clustering scenarios.
Co-Authors Agus Mohamad Soleh Akbar Rizki Alpharofi, Deswita Nur Ananda Putra Wijaya Anang Kurnia Anisa, Rahma Bagus Sartono Cindy Indriyani Elke Frida Rahmawati Erfiani Erfiani Fadhilah Yumna Faizal Rafiq Farid Yafi Suwandi Fredian Tonny Nasdian Hapsari, Linda Dwi Hari Wijayanto Hasanah, Delita Nur I Gusti Ngurah Sentana Putra Indra Mahib Zuhair Riyanto Irdayanti Khairil Anwar Notodiputro Laily Nissa Atul Mualifah Laras Suprapti Lisa Amelia Lusi Oktaviani Maziah, Lily Meavi Cintani Megawati - Mualifah, Laily Nissa Muzakki, Faiz Aji Nabila Syukri Najdmuddin, Muhammad Tsaqif Naura Tirza Ardhani Norashida Othman Notodipuro, Khairil Anwar Pandansari, Niken Rahardiantoro, Septian Rahma Anisa Ratnawati Nurkhoiry Rizky Kurniawan Sabrina Adnin Kamila Salsabila Fayiza Sari, Jefita Resti Sipayung, Tungkot Siti Arbaynah Sri Wahyuni Ulfa Aulia, Risnayanti Yarah, Helena Ramadhini Zamrah Mutmainah