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All Journal Jurnal Gaussian Journal of Nutrition Science Theta: Journal of Statistics
Aulia Ikhsan
Jurusan Agribisnis, Fakultas Pertanian, Universitas Sultan Ageng Tirtayasa
Agriculture, Biological Sciences & Forestry Chemistry Health Professions Immunology & microbiology Medicine & Pharmacology Nursing Public Health Veterinary Other

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ESTIMASI RISIKO PORTOFOLIO SAHAM PERUSAHAAN PERKEBUNAN DI BURSA EFEK INDONESIA MENGGUNAKAN VALUE AT RISK NON-NORMAL Aulia Ikhsan; Tatang Sutisna; Siti Widiati
Jurnal Gaussian Vol 12, No 1 (2023): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.12.1.146-158

Abstract

Stock investment portfolio aims to minimize the investment risk. However, problems of the portfolio formation are determining funds allocation for each stock and measuring its risk. Fund allocation is determined using the Mean-Variance Efficient Portfolio method, while risk measurement is carried out using Value at Risk (VaR). Nevertheless, problem on VaR is determining a fit distribution which would be involved to obtain quantile values at certain probability. This study discusses way of funds allocation determination and VaR value calculation that is aimed to analyze their impact in estimating the VaR value. The study used stock price return rate data of plantation companies listed on Indonesia Stock Exchange such as Astra Agro Lestari Tbk. (AALI), BISI International Tbk. (BISI), and PP London Sumatra Indonesia Tbk. (LSIP). The result showed BISI stock has high volatility so that its funds allocation is relatively smaller. The distribution identified for portfolio return rate is Logistics Distribution with the estimated parameters  0.0001187447 and 0.008810698. Portfolio VaR value at the 95% confidence level is -0.02582382. We conclude the determination of funds allocation does not minimize risk and the calculation of VaR with distributions do not match the data result a relatively higher VaR value.
Analysis of Oil Palm Farmer Households Food Security and Nutrition Based on the Share of Food Expenditures and Energy Consumption Siti Widiati; Tatang Sutisna; Aulia Ikhsan; Suherman Suherman
Journal of Nutrition Science Vol 4, No 1 (2023): May, 2023
Publisher : Universitas Teuku Umar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35308/jns.v4i1.7647

Abstract

Compared to farmers who cultivate food and horticultural commodities, oil palm farmers have different business characteristics and incomes. The income of the oil palm farming farmer will affect the food security of the farmer's household family. This study aims to determine the level of food security in the households of smallholder oil palm plantation farmers through a review of the share of food expenditure as well as energy adequacy and minimum energy consumption per capita. The research uses a quantitative descriptive method using structured interview data collection techniques and survey techniques. The respondents were oil palm farmer households who joined oil palm farmer groups in Cipuendeuy Village, Malingping District, Lebak Regency, Banten Province. Quantitative data analysis uses analysis of the Adequacy of Energy Rate (AKE) and Adequacy of Protein (AKP), to analyze the level of household food security. Analysis of the share of food expenditure showed the largest food expenditure of households of oil palm farmers was allocated for grains as much as 31.3%, while the smallest food expenditure was tubers at 1.59%. Based on the share of food expenditure, households of oil palm farmers are in a food secure condition, with 75% of households with a share of food expenditure <60%. The average household energy consumption of oil palm farmers is 1,707.39 kcal per capita/day with a household energy adequacy level of 83.79%. The average protein consumption per capita/day is 80.79 grams with a protein adequacy level of 80.63%. Analysis of the level of food security shows that 41.66% of households are at the level of food secure, 33% are at the level of food insecure, 8.33 are at the level of food vulnerability and 16.66% of households are food insecure.
Customer Segmentation Analysis of Maxim Application Based on RFM Model and K-Means Clustering as the Basis for Marketing Strategy Zilda Ainun Tazkia; Zahra Mahendra Putri; Atira Keisha Belva Armanda Fadhilla; Atia Sonda; Aulia Ikhsan; Putri Dina Sari
Theta: Journal of Statistics Vol 2, No 1 (2026): Available Online in March 2026
Publisher : Faculty of Engineering, Univesitas Sultan Ageng Tirtayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62870/tjs.v2i1.39360

Abstract

The rapid development of online transportation services requires a data-driven understanding of customer behavior. This study aims to segment Maxim application customers using the Recency, Frequency, and Monetary (RFM) model combined with the K-Means clustering method among students of the Faculty of Engineering, Sultan Ageng Tirtayasa University. This research employs a descriptive quantitative approach with a sample of 100 respondents. The optimal number of clusters was determined using the Elbow method, resulting in four customer segments: Inactive Customers, Occasional Customers, Loyal Customers, and Priority Customers. The segmentation analysis was conducted separately for Maxim Bike and Maxim Car services. The results indicate that the Priority cluster has the highest transaction frequency and expenditure value despite consisting of relatively few customers, while the Inactive cluster shows the lowest level of transaction activity. In the Maxim Bike category, the Priority cluster represents the largest proportion of customers and shows the most recent transaction activity. In addition, the distribution of study programs indicates the dominance of Statistics students in the Loyal and Priority clusters across both service categories. Descriptive statistical analysis further shows that respondents' perceptions of Maxim services fall into the positive category, with average indicator scores above 3.20.
Application of the TARCH Model for Stock Price Prediction: Evidence from PT Bank Rakyat Indonesia (BRI) Tbk Putri Dina Sari; Faula Arina; Aulia Ikhsan; Isnaini Mahuda; Syarif Abdullah; Patricia Pingkan Kumenap; Regina Dwirahma Alisya
Theta: Journal of Statistics Vol 1, No 2 (2025): Available Online in September 2025
Publisher : Faculty of Engineering, Univesitas Sultan Ageng Tirtayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62870/tjs.v1i2.35930

Abstract

Stock price volatility is a crucial aspect in capital market analysis because it can influence investment decisions. The GARCH model is commonly used to model volatility, but this model assumes that positive and negative shocks affect volatility symmetrically. In practice, particularly in banking stocks, asymmetric effects are often observed, with negative shocks having a greater impact on volatility than positive shocks. To address this issue, this study uses the Threshold ARCH (TARCH) model, which is capable of capturing asymmetric effects. The research data consists of the daily closing prices of PT Bank Rakyat Indonesia (BRI) Tbk shares from January 2, 2015, to September 12, 2025. The results show that the TARCH model is more appropriate than the symmetric GARCH model, as the asymmetry coefficient is significant, indicating the presence of leverage in BRI shares. Therefore, the TARCH model can be used to forecast BRI stock volatility and provide more accurate information for investors and analysts in anticipating market risks.
Co-Authors Arina, Faula Atia Sonda Atira Keisha Belva Armanda Fadhilla Isnaini Mahuda Patricia Pingkan Kumenap Putri Dina Sari Regina Dwirahma Alisya Suherman, Suherman Syarif Abdullah Tatang Sutisna Widiati, Siti Zahra Mahendra Putri Zilda Ainun Tazkia