UNP Journal of Statistics and Data Science

Penerapan Partial Least Squares dan Pendekatan Robust dalam Analisis Diskriminan untuk Data Berdimensi Tinggi

Rahmadina Adityana, Dodi Vionanda, Dony Permana, Fadhilah Fitri — Sat, 30 Aug 2025 00:00:00 +0000

Analisis diskriminan klasik secara umum diketahui mengalami masalah singularitas ketika dihadapkan pada data berdimensi tinggi dan tidak kokoh terhadap pencilan yang membuat data tidak berdistribusi normal multivariat. Penelitian ini berfokus pada penyelidikan kinerja klasifikasi analisis diskriminan terhadap data berdimensi tinggi dengan menerapkan dua pendekatan yaitu pendekatan reduksi dimensi Partial Least Square (PLS) sebagai solusi dari data berdimensi tinggi dan pendekatan robust dengan teknik penduga Minimum Covariance Determinant (MCD) yang kokoh terhadap pencilan. Data yang digunakan untuk penelitian ini yaitu data Lee Silverman Voice Treatment (LSVT). PLS membentuk lima variabel laten optimal yang mewakili informasi variabel prediktor. Berdasarkan uji asumsi homogenitas kovarians antar kelompok, nilai statistik uji lebih besar dari chi-square table atau nilai p lebih kecil dari taraf signifikan yang artinya asumsi tidak terpenuhi sehingga analisis diskriminan kuadratik diterapkan. Hasil evaluasi menunjukkan bahwa model analisis analisis diskriminan kuadratik dengan pendekatan MCD pada data hasil transformasi PLS mampu mencapai akurasi sebesar 81%, presisi sebesar 71%, recall sebesar 86%, dan F1-score sebesar 77%. Nilai-nilai tersebut menandakan bahwa kedua pendekatan mampu menjaga efisiensi kinerja klasifikasi analisis diskiriminan terhadap data berdimensi tinggi dan tidak berdistribusi normal multivariat.

PayPal Usage in Indonesia with k-Nearest Neighbor Algorithm

Amannia zeze, Muhammad Ravi Azzaki, Dodi Vionanda — Sat, 30 Aug 2025 00:00:00 +0000

The development of information and digital technology has had a significant impact on the financial sector. In Indonesia, digital payment technologies such as PayPal, Gopay, Shopeepay, OVO, and DANA have become an integral part of the modern payment system. Since 2005, the implementation of the national electronic clearing system, RTGS, and ATMs have facilitated various payment methods. Sentiment analysis of PayPal provides in-depth insights into user experience, enabling improved service quality and more effective marketing strategies. Sentiment data helps PayPal in making better business decisions and managing brand reputation. This study compares PayPal usage in Indonesia from a user perspective, with a focus on sentiment analysis, to identify factors that influence the acceptance of this platform in various markets. This study aims to provide valuable insights for the development and updating of PayPal service policies.

Panel Data Model Selection and Significant Determinants of New Family Planning Participants in West Sumatra

Diah Triwulandari, Fadhilah Fitri — Sat, 30 Aug 2025 00:00:00 +0000

Population issues in Indonesia are not limited to poverty, urbanization, population explosion, or high birth rates, but also include how small families can improve and maintain their quality of life. The main objective of the Family Planning program is to create happy and prosperous families with an ideal number of children. In the West Sumatra Provincial Health Office report (2023), the focus on increasing the number of new family planning acceptors is urgent in supporting the success of maternal, child, and family planning health programs, in line with the direction of the 2020–2024 RPJMN policy. Therefore, this study will develop the best panel data model and identify the factors that significantly influence the number of new family planning participants in West Sumatra Province. The secondary data used were obtained from the Central Statistics Agency (BPS) publication titled “West Sumatra Province in Figures” from 2021 to 2024. The observation units in this study were 19 districts/cities in West Sumatra Province with a time series from 2020 to 2023. This study used panel data regression statistical methods. The results show that the best model selected is the random effect model, with the number of couples of reproductive age proven to have a significant effect on the number of new family planning participants. The R-square value of 53.11% indicates that this model can explain approximately half of the variation in the dependent variable, while the remainder is influenced by other factors not included in the model.

Comparison of Expectation-Maximization (EM) Algorithm and Kmeans for District/City Clustering in West Sumatera Province Based on Breadfruit Production

Mayrita Addila Putri Mayrita, Fadhilah Fitri — Sat, 30 Aug 2025 00:00:00 +0000

Buah sukun (Artocarpus altilis) merupakan sumber pangan penting yang kaya nutrisi dan memainkan peran strategis di Provinsi Sumatera Barat. Namun, tantangan seperti hama, penyakit, dan kendala pemasaran mempengaruhi budidaya dan produktivitasnya. Studi ini menggunakan metode pengelompokan K-means dan expectation-maximisation (EM) untuk mengklasifikasikan wilayah berdasarkan karakteristik budidaya buah sukun. Metode elbow mengidentifikasi tiga kluster optimal untuk K-means dan tujuh untuk EM. Evaluasi kualitas kluster menggunakan koefisien siluet menghasilkan nilai 0,47 dan 0,37 untuk EM dan K-Means masing-masing, menunjukkan bahwa EM menghasilkan kluster yang lebih rapat dan terdefinisi dengan jelas. Hasil ini menyarankan bahwa EM adalah metode yang lebih efektif untuk menggambarkan variasi produksi buah sukun di Sumatera Barat. Dengan demikian, penelitian ini diharapkan dapat memberikan masukan untuk pengambilan keputusan strategis yang bertujuan meningkatkan produktivitas dan nilai tambah tanaman buah sukun di wilayah tersebut.

Nonparametric Regression with Local Polynomial Kernel on Relationship Between Schooling Years and Unemployment Rate in Banten

Bunga Miftahul Barokah, Fadhilah Fitri, Chairina Wirdiastuti — Sat, 30 Aug 2025 00:00:00 +0000

Tingkat Pengangguran Terbuka (TPT) merupakan indikator utama dalam menilai kinerja ekonomi di Provinsi Banten. Salah satu faktor yang diduga memengaruhi TPT adalah pendidikan, yang diukur melalui rata-rata lama sekolah. Penelitian ini bertujuan untuk menganalisis hubungan antara rata-rata lama sekolah dan TPT menggunakan metode Regresi Nonparametrik Kernel Polinomial Lokal pada periode 2017–2024. Metode ini dipilih karena fleksibel dalam memodelkan hubungan nonlinier tanpa memerlukan asumsi ketat terhadap data. Parameter bandwidth optimal untuk pemulusan ditentukan menggunakan metode Direct Plug-In (DPI) melalui fungsi dpill pada perangkat lunak R. Hasil penelitian menunjukkan bahwa model nonparametrik memiliki koefisien determinasi (R²) sebesar 0,2841, lebih tinggi dibandingkan model regresi linear Ordinary Least Squares (OLS) yang hanya sebesar 0,1710. Hal ini menunjukkan bahwa pendekatan nonparametrik lebih mampu menangkap hubungan kompleks antara pendidikan dan pengangguran. Namun, rendahnya nilai R² pada kedua model mengindikasikan adanya faktor lain yang turut memengaruhi tingkat pengangguran, seperti kondisi ekonomi, struktur pasar tenaga kerja, dan kebijakan pendidikan. Oleh karena itu, peningkatan rata-rata lama sekolah saja tidak cukup untuk menurunkan tingkat pengangguran secara signifikan. Diperlukan kebijakan yang lebih komprehensif, seperti peningkatan keterampilan kerja, pelatihan vokasional, serta strategi ekonomi yang berorientasi pada penciptaan lapangan kerja. Temuan dalam penelitian ini diharapkan dapat memberikan masukan yang bermanfaat bagi para pembuat kebijakan dalam merumuskan strategi yang lebih efektif untuk mengatasi pengangguran di Provinsi Banten.

Forecasting Inflation Rate in Indonesia Using Autoregressive Integrated Moving Average Method

Lathifa Putri, Zilrahmi — Sat, 30 Aug 2025 00:00:00 +0000

Inflasi menjadi indikator penting dalam menilai stabilitas ekonomi suatu negara. Peningkatan inflasi yang terus terjadi menjadi hambatan pada pertumbuhan ekonomi. Oleh karena itu, peramalan tingkat inflasi yang akurat sangat penting untuk perencanaan ekonomi jangka menengah hingga jangka panjang. Studi ini dilakukan untuk memperkirakan tingkat inflasi di Indonesia selama 12 periode ke depan, mulai Januari 2025 hingga Desember 2025. Penelitian ini menggunakan metode ARIMA, karena model ARIMA fleksibel untuk semua pola data time series, meskipun data tidak stasioner. Hasil menunjukkan bahwa ARIMA (2,0,2) merupakan model terbaik dengan nilai akurasi MAPE sebesar 25,21%. Model ini dapat memprediksi tingkat inflasi yang stabil di Indonesia untuk 12 periode ke depan, dengan rata-rata 1,861%. Hasil ini menunjukkan bahwa kenaikan harga barang dan jasa secara umum di Indonesia selama periode ini akan stabil tanpa fluktuasi, yang merupakan tanda positif bagi stabilitas ekonomi makro dan daya beli masyarakat.

Comparison of Nadaraya-Watson Method with Local Polynomial in Modeling HDI and Poverty Relationship in Java Island

Yoli Marda Novi, Fadhilah Fitri, Zamahsary Martha — Sat, 30 Aug 2025 00:00:00 +0000

Poverty remains a critical issue in Indonesia, with the number of poor people reaching 24.06 million in September 2024. The Human Development Index (HDI), which indicates the level of human resource quality, is one of the factors influence poverty. This analysis focuses on the correlation involving HDI also this number of poor people in districts/cities in Java Island by comparing two kernel regression methods, namely Nadaraya-Watson Estimator and Local Polynomial Estimator. Nonparametric regression was chosen thus it does not necessitate this presumption of a certain form of connection among variables, so it is more flexible in capturing complex relationship patterns. Secondary data from Statistics Indonesia (BPS) in 2024 was used in this study. Initial exploration shows, the data distribution does not have a clear pattern, so nonparametric methods are more suitable for use. Modeling is done using the optimal bandwidth obtained through the dpill function in R software. The analysis results show that the local polynomial estimator produces smoother regression curves and lower MSE values. In addition, comparison of different polynomial degrees shows that higher polynomial degrees tended to improve model performance. Among the tested polynomial degrees, the local polynomial with degree five (p=5) produced the lowest MSE value and the highest coefficient of determination. Therefore, the local polynomial estimator with degree 5 is the best method for modeling the relationship between the HDI and poverty levels in Java in 2024.

Aplication Algorithm Learning Vector Quantization for Classification of Hypertention in Padang Laweh Health Center

Riska Harpidna Harpidna, Chairina Wirdiastuti, Yenni Kurniawati — Sat, 30 Aug 2025 00:00:00 +0000

arena itu, dilakukan penelitian untuk mengklasifikasikan risiko hipertensi berdasarkan diagnosis hipertensi di Pusat Kesehatan Padang Laweh, Kabupaten Dharmasraya, menggunakan Algoritma Learning Vector Quantization (LVQ). Keunggulan LVQ adalah kemampuannya untuk mencapai akurasi tinggi dalam memproses data dengan fitur numerik dan kategorikal yang banyak. Hasil analisis menunjukkan bahwa penggunaan Algoritma Learning Vector Quantization pada data uji menghasilkan akurasi yang sangat baik, yaitu 95,17% klasifikasi yang benar untuk pasien hipertensi.

Process Capability Analysis of OPC Cement Production Using Statistical Process Control and IMR Method: Blaine Test Evaluation

Wafiq Alya Aufa, Yenni Kurniawati, Admi Salma, Darwas — Sat, 30 Aug 2025 00:00:00 +0000

The main challenge in cement production at PT Semen Padang is maintaining consistent product quality, particularly the fineness of cement particles measured by the Blaine test. Variations in raw materials and the production process can cause fluctuations in quality, which affect the performance of the final product. Therefore, it is crucial to monitor and control process stability and capability to consistently meet product specifications. Based on the Statistical Process Control (SPC) analysis using Individuals and Moving Range (I-MR) control charts on 28 observations of Ordinary Portland Cement (OPC) Blaine values from February 2025, one out-of-control point was detected on the Moving Range chart between observations 16 and 17, indicating a significant variation. However, all points on the Individuals chart remained within control limits, suggesting that the individual process values were still under control. After revising the outlier data, the process was confirmed stable. Process capability analysis showed a Cp value of 2.17 and a Cpk value of 1.98, indicating that the production process is not only statistically stable but also highly capable of meeting quality specifications. Therefore, despite some variation between data points, the cement production process at PT Semen Padang can be considered stable and capable. Nevertheless, periodic evaluations are recommended to maintain consistent product quality and provide strategic recommendations for the Quality Assurance division in implementing data-driven quality control.

Comparison of Kernel and Spline Nonparametric Regression (Case Study: Food Security Index of Jambi Province 2023)

Rosa Salsabila Azarine, Septrina Kiki Arisandi, Fadhilah Fitri, Yenni Kurniawati — Sat, 30 Aug 2025 00:00:00 +0000

Food security is one of the issues that plays an important role in national development, especially in regions with varying levels of economic welfare such as Jambi Province. One of the main factors affecting food security is food expenditure, which reflects the economic capacity of households to access food. The complex and non-linear relationship between Food Security Index (FSI) and Food Expenditure requires a flexible modeling approach in the analysis.

This study aims to compare the performance of nonparametric regression Kernel ans Spline regression methods, namely the Nadaraya-Watson Estimator (NWE) and Local Polynomial Estimator (LPE) for Kernel Regression as well as Smoothing Spline and B-Spline for Spline Regression. The analysis was conducted using secondary data obtained from the Food Security and Vulnerability Map (FSVA) of 2023, with a total of 141 subdistricts in Jambi Province. The response variable is the Food Security Index (FSI), while the predictor variable is Food Expenditure. Model evaluation was conducted using the Mean Squared Error (MSE) and the coefficient of determination (R²).

The results showed that the NWE method had the best performance with the smallest MSE value of 24.47690 and the highest R² value of 0.3332, meaning that approximately 33.32% of the variation in FSI could be explained by Food Expenditure. The LPE method showed nearly comparable performance, while Smoothing Spline and B-Spline exhibited higher prediction error rates. Therefore, the NWE method can be recommended as an effective nonparametric regression approach for modeling the relationship between food expenditure and food security.

Comparison of Nadaraya-Watson and Local Polynomial Methods in Analyzing the Relationship Between Consumer Price Index and Inflation in South Kalimantan

Salwa Hifa Fadilah, Fadhilah Fitri, Fenni Kurnia Mutiya — Sat, 30 Aug 2025 00:00:00 +0000

This study aims to compare the performance of Nadaraya-Watson and Local Polynomial regression methods in analyzing the relationship between the Consumer Price Index (IHK) and inflation in South Kalimantan Province. Given the potential non-linear nature of this relationship, nonparametric regression approaches were employed as they offer more flexibility compared to traditional parametric models. Data from the Central Statistics Agency (BPS) from January 2022 to December 2024 were used, and missing values in the inflation variable were handled using mean imputation. The optimal bandwidth for both methods was determined using the direct plug-in method (dpill) to ensure consistent comparison. The results show that the Nadaraya-Watson method produces a more fluctuating curve, indicating high sensitivity to local data variations but also a higher risk of capturing noise. In contrast, the Local Polynomial method yields smoother and more stable curves, better capturing the overall trend without being overly affected by local fluctuations. The findings suggest that the choice of method should be based on the analysis objective, whether to emphasize local detail or overall trend.

Inflation Prediction In Indonesia Using Extreme Learning Machine and K-Fold Cross Validation

Wahda Aulia Assara, Zamahsary Martha, Dony Permana, Dina Fitria — Sat, 30 Aug 2025 00:00:00 +0000

Peramalan tingkat inflasi merupakan aspek penting dalam mendukung kebijakan ekonomi dan pengendalian harga oleh pemerintah. Penelitian ini bertujuan untuk mengevaluasi kinerja algoritma Extreme Learning Machine (ELM) dalam meramalkan tingkat inflasi di Indonesia dan memberikan hasil prediksi inflasi untuk tahun 2025. Data yang digunakan adalah data historis tingkat inflasi Indonesia periode 2003–2024. Proses analisis diawali dengan normalisasi data untuk memastikan skala yang seragam, dilanjutkan dengan partisi data menggunakan 10-Fold Cross Validation. Model ELM dibangun dengan 30 neuron tersembunyi, fungsi aktivasi sigmoid, dan parameter regularisasi sebesar 0,8. Hasil pengujian menunjukkan bahwa algoritma ELM memiliki kinerja yang unggul. Hal ini dibuktikan dengan nilai rata-rata MAPE sebesar 1,71%, RMSE sebesar 0,0359, dan koefisien determinasi (R²) sebesar 0,9833, yang menunjukkan akurasi yang sangat tinggi. Prediksi inflasi untuk periode Januari hingga Desember 2025 berada pada kisaran 1,03%–2,0%, dengan rata-rata mendekati 1,5%, menunjukkan pola yang relatif stabil sepanjang tahun. Berdasarkan hasil ini, algoritma ELM dapat digunakan sebagai metode alternatif yang efektif untuk meramalkan data deret waktu, terutama dalam konteks inflasi. Penelitian ini diharapkan dapat menjadi referensi bagi pemerintah dalam menetapkan kebijakan pengendalian inflasi dan bagi peneliti lain yang tertarik menerapkan model kecerdasan buatan dalam analisis ekonomi.

Forecast Accuracy Comparison Between Holt’s Method and the Box-Jenkins Approach: The Case of Madiun City Labor Force Participation Rate

Sat, 30 Aug 2025 00:00:00 +0000

Pacitan district was ranked first with the highest Labor Force Participation Rate (LFPR) in Eastern Java Province. Meanwhile, Madiun city which is one of the largest cities in East Java Province, is ranked only 34 out of 39 cities in 2023. It is a matter of concern to the Madiun city government, especially the employment service, to deal with this problem. So that when the productive age to work can work. The percentage is issued annually by the provincial Central Bureau of Statistics (CBS) or the city or district. LFPR is a timeseries data, so to predict the percentage of LFPR in the next year can use the Double Exponential Smoothing Holt and ARIMA Box-Jenkins methods. On this study obtained that ARIMA (1,0,1) was better at predicting Madiun city LFPR than other methods. Madiun's forecast LFPR result is 67,19% in 2024, 67,20% in 2025 and 67,21% in 2026. MSE value is 14,48; RMSE is 3,80 and MAPE value is 4,75%.

Applications of Panel Data Analysis on Human Development Index Indicators in Districts/Cities of Lampung 2022 – 2024

Rahmad Wanizal Pastha, Zilrahmi, Zamahsary Martha — Sat, 30 Aug 2025 00:00:00 +0000

Artikel ini bertujuan untuk mengidentifikasi faktor-faktor yang memengaruhi Indeks Pembangunan Manusia (IPM) di Provinsi Lampung, Indonesia, selama periode 2022–2024 dengan menggunakan regresi data panel. Lampung secara konsisten berada di antara provinsi dengan skor IPM terendah di Sumatera, yang menunjukkan adanya kesenjangan pembangunan antarwilayah. Penelitian ini menggunakan data sekunder dari 15 kabupaten/kota dan mencakup variabel seperti angka harapan hidup, harapan lama sekolah, rata-rata lama sekolah, dan pengeluaran per kapita. Model regresi data panel yang digunakan meliputi fixed effect, random effect, dan common effect, yang dievaluasi melalui uji Chow, Hausman, dan Lagrange Multiplier untuk menentukan model yang paling tepat. Model random effect dipilih, didukung oleh nilai R-Squared yang tinggi sebesar 92,71% yang menunjukkan daya jelas yang kuat. Hasil analisis menunjukkan bahwa angka harapan hidup dan rata-rata lama sekolah berpengaruh signifikan terhadap IPM, sedangkan harapan lama sekolah dan pengeluaran per kapita tidak berpengaruh secara statistik dalam model ini. Analisis ini menunjukkan bahwa pemerataan akses terhadap layanan kesehatan dan pendidikan berkontribusi signifikan terhadap peningkatan pembangunan manusia. Penelitian selanjutnya disarankan untuk menggabungkan pendekatan kualitatif serta menggunakan variabel yang lebih mutakhir guna memperkaya analisis.

Grouping Regencies/Cities in West Sumatra Province Based on People’s Welfare Indicator Using Biplot Analysis

Maya Ifra Shobia, Yenni Kurniawati — Sat, 30 Aug 2025 00:00:00 +0000

The level of community welfare is a crucial reflection of the success of development in a region. Welfare is assessed based on eight aspects: poverty, employment, education, housing, consumption patterns, health, population, and other social factors. In West Sumatra Province, the level of community welfare still requires improvement across all indicators. The determination of community welfare levels can be achieved by reviewing all dimensions based on the linear relationships between districts/cities, thereby providing insights into the indicators that still need enhancement. This effort can assist the West Sumatra Provincial Government in formulating regional policies and programs for equitable distribution and improvement of community welfare across all districts/cities. The data used in this study are secondary data obtained from the West Sumatra Provincial BPS website in 2024. The grouping of districts/cities was conducted using Principal Component Analysis based on singular value decomposition biplot analysis. The analysis results formed four groups with distinct characteristics of community welfare indicators. The groups that need to be prioritized for improvement are groups 1 and 3, which exhibit low levels of community welfare. Group 2 consists of districts/cities with high community welfare characteristics in terms of population, education, and housing. Meanwhile, group 4 includes districts/cities with high community welfare characteristics regarding consumption patterns, poverty, and labor indicators.

Factors Affecting Households Program Keluarga Harapan Recipients in West Sumatra: Binary Logistic Regression Analysis

Sonia Ardhi, Dodi Vionanda, Yenni Kurniawati, Tessy Octavia Mukhti — Sat, 30 Aug 2025 00:00:00 +0000

Poverty is still a complex issues in Indonesia. Poverty rate in West Sumatra province has increased over the past 3 years. One of the government's initiatives to address poverty is the Program Keluarga Harapan (PKH), which is a social protection program that provides conditional cash transfers to poor and vulnerable Keluarga Penerima Manfaat (KPM) on condition that they are registered in the Data Terpadu Kesejahteraan Sosial (DTKS). Although PKH has a positive impact on poverty alleviation and enhanced access to health, education, and social welfare, the implementation still faces major challenges such as data inaccuracies, particularly in targeting accuracy. Therefore, an analysis is needed to determine the factors that significantly affects PKH recipient households in West Sumatra Province. This research used variables from the DTKS variable group contained in SUSENAS 2024 with 11,600 observations consisting of 1,790 receiving PKH and 9,810 not receiving PKH. The dependent variable is PKH recipient status (Yes = 1, no = 0). Data were analyzed using binary logistic regression with a significance level of 5%. Based on the results of the analysis, it can be concluded that floor area of the house, age of the household head, household size, education level of the household head, and floor material of the house have a significantly effect on PKH recipient households. Household size has the most influence on PKH receipt with a 40,3% probability of receiving PKH.

Applying Robust Spatial Autoregressive Model to Analyze the Determinants of Open Unemployment in West Java

Sat, 30 Aug 2025 00:00:00 +0000

Open unemployment is a critical macroeconomic challenge in developing regions like West Java, Indonesia, where spatial disparities and data anomalies complicate traditional analysis. This study addresses these limitations by employing a Robust Spatial Autoregressive (RSAR) model with M-Estimator, integrating spatial dependence and outlier resilience to enhance estimation accuracy. Using 2024 district-level data from Indonesia’s Central Bureau of Statistics (BPS) and Open Data Jabar, the research examines determinants such as labor force participation, education, and regional GDP. The methodology begins with Ordinary Least Squares (OLS) to identify initial predictors, followed by spatial diagnostics (Moran’s I, Lagrange Multiplier tests) to confirm spatial autocorrelation. A customized Queen contiguity weight matrix captures neighborhood effects, while robust M-Estimation mitigates outlier distortions. Results reveal that the RSAR model achieves superior explanatory power (R² = 0.8626) compared to OLS and standard Spatial Autoregressive (SAR) models, with labor force participation (X₄) emerging as a significant negative predictor of unemployment. Spatial effects (ρ = 0.337) though modest, underscore the importance of inter-regional dynamics. The study concludes that RSAR offers a more reliable framework for regional labor analysis, combining spatial rigor with robustness against data irregularities. Policy-wise, the findings advocate targeted interventions to boost labor participation and address localized disparities, emphasizing the need for spatially informed, outlier-resistant methodologies in economic planning.

Forecasting Consumer Price Index in Personal Care Sector in Bukittinggi Using SVR with Grid Search and Radial Basis Function Kernel

khairunnisa Pane, Fadhilah Fitri, Dina Fitria — Sat, 30 Aug 2025 00:00:00 +0000

Inflasi, yang diukur menggunakan Indeks Harga Konsumen (IHK), memiliki peran penting dalam menjaga stabilitas ekonomi dan perumusan kebijakan. Di Kota Bukittinggi, sektor Perawatan Pribadi dan Jasa Lainnya menunjukkan fluktuasi IHK yang signifikan, sehingga mempersulit proses peramalan secara akurat.Penelitian ini menggunakan metode Support Vector Regression (SVR) untuk memprediksi data IHK bulanan pada sektor tersebut selama periode 2020 hingga 2024. Data diperoleh dari Badan Pusat Statistik dan dinormalisasi menggunakan teknik Min-Max Normalization untuk meningkatkan akurasi model serta menghindari distorsi skala.Fitur lag ditambahkan guna menangkap ketergantungan waktu, dan data dibagi menjadi dua bagian, yaitu data latih (80%) dan data uji (20%). Model SVR linear pertama kali diterapkan, namun menunjukkan performa yang terbatas karena sifat data yang non-linear. Oleh karena itu, digunakan kernel Radial Basis Function (RBF), dengan parameter hiper (C, sigma, epsilon, dan folds) yang dioptimalkan menggunakan Grid Search dan cross-validation.Pengaturan optimal (C=32, sigma=2, epsilon=0.1, k=10) menghasilkan nilai RMSE terendah sebesar 0.1099 pada cross-validation dan 0.0767 pada data uji. Hasil penelitian menunjukkan bahwa model SVR-RBF mampu menangkap pola non-linear IHK secara efektif dan mengungguli model linear. Evaluasi dilakukan menggunakan metrik RMSE, MSE, dan MAE. Penelitian ini menyimpulkan bahwa SVR yang dikombinasikan dengan Grid Search merupakan metode peramalan yang andal untuk sektor-sektor dengan perilaku IHK yang kompleks, serta mendukung perencanaan ekonomi lokal di Bukittinggi. Penelitian selanjutnya dapat mengkaji model hibrida dan dataset yang lebih besar untuk meningkatkan akurasi prediksi serta adaptabilitas terhadap perubahan pasar.