Jurnal Statistika dan Komputasi

Analisis Sentimen Ulasan Pengguna BRImo Terhadap Pembaruan Fitur Aplikasi Menggunakan Naive Bayes Dengan Seleksi Fitur Chi-Square

Heppy Nur Asavia Ginasputri — 2025-12-31

Background: The BRImo app is a mobile banking service from Bank Rakyat Indonesia (BRI) with a large number of users. User reviews on Google Play Store are an important source of data for understanding user perceptions and satisfaction levels, but the number and diversity of review texts require an appropriate sentiment analysis method.

Objective: This study aims to evaluate the performance of the Naive Bayes algorithm in classifying BRImo reviews by sentiment and the effect of Chi-Square feature selection.

Methods: The research method includes text data preprocessing consisting of cleaning, case folding, tokenizing, normalization, filtering, and stemming. Next, feature weighting is performed using TF-IDF and feature selection using the Chi-Square method, followed by sentiment classification using the Naive Bayes algorithm. Model evaluation is performed using a confusion matrix with accuracy, precision, recall, and F1-score metrics.

Results: The results show that the sentiment classification model achieved an accuracy of 95%, precision of 98%, recall of 96%, and an F1-score of 97%. The high recall value indicates the model's excellent ability to detect positive sentiment in user reviews.

Conclusion: The Naive Bayes algorithm with Chi-Square feature selection is effective in analyzing BRImo application review sentiment and can be used as a basis for evaluating application development, but its performance is still limited in detecting negative sentiment due to the quantity of sentiment data.

Pengelompokan Kabupaten/Kota Di Jawa Barat Berdasarkan Indikator Ketenagakerjaan Menggunakan Metode K-Means Dan Fuzzy C-Means Dengan Evaluasi Rasio SW/SB Sebagai Validasi Klaster

Revika Inta Nur Kholifah — 2025-12-31

Background: The high dynamics of the labor market in Indonesia make employment one of the main challenges of sustainable development. Changes in economic structure, an increase in the workforce, and regional disparities in employment opportunities require adaptive strategies. This condition is particularly evident in densely populated areas with diverse economic sectors, such as West Java Province.

Objective: This study aims to group 27 districts/cities in West Java Province based on five employment indicators, namely TPAK, TPT, the number of workers in micro and small businesses, the average monthly net income of informal workers according to education, and the percentage of employment in the workforce by comparing the K-Means and Fuzzy C-Means methods.

Methods: This study uses two clustering methods, namely K-Means and Fuzzy C-Means. The best method is selected by comparing the SW and SB values.

Results: The results indicate that the K-Means method is the best method, as seen from the smaller Sw/Sb ratio of 0.076, compared to Fuzzy C-Means, which is 0.153.

Conclusion: The results of the K-Means method clustering show that the districts/cities in West Java are divided into 7 clusters, namely cluster 1 with 3 regions, cluster 2 with 8 regions, clusters 3 and 4 with 1 region, cluster 5 with 4 regions, cluster 6 with 3 regions, and cluster 7 with 7 regions. These results can serve as a basis for determining the priority of regional employment interventions.

Analisis Regresi Kuantil Dengan Pendekatan Bootstrap Pada World Happiness Report 2024

Rafif Naufal Oktiardi — 2025-12-31

Background: Happiness is a key indicator of national well-being. The World Happiness Report measures it through economic and social factors. Linear regression (OLS) is often applied but is sensitive to outliers. Quantile regression is more robust, and bootstrapping enhances estimate stability.

Objective: This study aims to analyze the influence of these factors on happiness scores using quantile regression, which is able to provide a comprehensive picture of the influence of variables at various distribution positions, especially when there are outliers and violations of classical linear regression assumptions.

Methods: Quantile regression was applied at quantiles 0.25 to 0.75, and the best model was obtained at quantile 0.4 with a pseudo-R² value of 0.6388. To improve the reliability of parameter estimates, a bootstrap approach with 1000 resampling times was used, which provided more stable confidence intervals and standard deviation estimates.

Results: The results show that social support, healthy life expectancy and freedom of choice are variables that significantly affect the level of happiness in the 0.4 quantile. Meanwhile, GDP per capita, generosity, and perception of corruption are not statistically significant in this model.

Conclusion: This study recommends the use of quantile regression with bootstrapping as a robust approach for socioeconomic data analysis, especially in the context of distributions that are not symmetric and contain outliers. The findings also provide policy implications.

Peramalan Harga Beras Mingguan di Kalimantan Timur Menggunakan Metode Singular Spectrum Analysis

Deswita Istiyanti — 2025-12-31

Background: Rice plays a central role in Indonesia's food security and inflation dynamics, making accurate price forecasts crucial for effective planning policies. In East Kalimantan, weekly rice prices represent a significant risk factor that needs to be closely monitored to anticipate potential risks to household welfare and regional inflation.

Objective: To predict rice prices in East Kalimantan over the next eight periods using the SSA method.

Methods: Singular Spectrum Analysis (SSA) to predict weekly rice prices. The SSA procedure includes embedding (window length L = 48), singular value decomposition, eigentriple clustering (trend: 1–2; seasonality: 3–35; disturbance: 36–48), diagonal averaging, and R-forecasting using the Linear Recursive Formula (LRF).

Results: MAPE of 0.084% (in-sample) and 3.299% (out-sample). Estimates indicate that rice prices will remain in the range of IDR 15,500–16,500/kg during August–October 2025.

Conclusion: The SSA is effective in capturing basic price components and producing reliable forecasts to support food reserve maintenance, distribution strategies, and inflation risk mitigation.

Penerapan Analisis Konjoin Dan Regresi Logistik Pada Preferensi Dan Keputusan Konsumen Mie Gacoan Di Yogyakarta

Nurhikmah Nurhikmah — 2025-12-31

Background: The rapid expansion of Mie Gacoan outlets in various cities, including Yogyakarta, reflects strong consumer interest in culinary products that offer diverse flavors and affordable prices. This phenomenon highlights the importance of understanding product attributes that influence consumer preferences and purchasing decisions.

Objective: This study aims to identify the attributes that affect consumer preferences and purchasing decisions for Mie Gacoan in Yogyakarta.

Methods: Data were collected from 115 respondents and analyzed using conjoint analysis and logistic regression. Five product attributes were evaluated: spiciness level, price, service, dining area, and type of noodle.

Results: The conjoint analysis indicates that spiciness level has the highest importance value (36.670%), followed by service (28.381%), type of noodle (12.132%), price (11.912%), and dining area (10.906%). Logistic regression analysis shows that spiciness level and price have a significant effect on purchasing decisions, with coefficients of 0.432 and 0.727 and odds ratios of 1.541 and 2.068, respectively.

Conclusion: Overall, the findings demonstrate that spiciness level and price are the most influential attributes in actual purchasing decisions, even though other attributes also shape consumer preferences.