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All Journal Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA) JURNAL PENDIDIKAN TAMBUSAI Pelita Eksakta Suluah Bendang: Jurnal Ilmiah Pengabdian Kepada Masyarakat UNP Journal of Statistics and Data Science Journal of Multidisciplinary Science: MIKAILALSYS Mathematical Journal of Modelling and Forecasting
Dodi Vionanda
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Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Mathematics Medicine & Pharmacology Physics Social Sciences Other

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Classification of Poor Households in West Sumatra Province using Decision Tree Algorithm C4.5 Dinda Fitriza; Atus Amadi Putra; Dodi Vionanda; Zilrahmi
UNP Journal of Statistics and Data Science Vol. 2 No. 2 (2024): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol2-iss2/157

Abstract

The significant and increasingly complex issue of poverty poses a considerable challenge to Indonesia's development, including West Sumatra Province, with a poverty rate was 5.92% in 2022. The government has initiated programs to address poverty by focusing on the criteria of impoverished households. Data on impoverished households can be obtained through the National Socio-Economic Survey (Susenas). One method that can classify impoverished households is the decision tree. Decision tree is a flowchart that resembles a tree. The C4.5 algorithm used in this research has the ability handle discrete and continuous data, manage variables with missing values, and prune decision tree branches. The result of the analysis shows that the variables affecting the classification of poor households are the number of household members, then the age of the household head, type of house floor, type of house wall, source of drinking water, and cooking fuel. The accuracy of the test data using a confusion matrix is 69.89%, sensitivity of 71.15% for classifying regular households, and specificity of 68.72% for classifying impoverished households.
Impelementation of Subtractive Fuzzy C-Means Method in Clustering Provinces in Indonesia Based on Factors Causing Stunting in Toddlers Hariati Ainun Nisa; Admi Salma; Dodi Vionanda; Tessy Octavia Mukhti
UNP Journal of Statistics and Data Science Vol. 2 No. 2 (2024): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol2-iss2/164

Abstract

Indonesia in 2022 has a stunting rate that is still relatively high at 21.6%. For this reason, it is necessary to make various efforts to reduce the stunting rate. One of the efforts that can be made is to understand the characteristics of each province in Indonesia with cluster analysis. This study aims to cluster provinces in Indonesia based on factors that cause stunting in children under five. The method used is Subtractive Fuzzy C-Means which has advantages in terms of speed, iteration, thus producing more stable and accurate results. The results of the validity test with Silhouette Coefficient Index, the optimum number of clusters is 8 clusters with a radius (r) of 0.70. There are 8 provinces that have provided maximum handling and efforts in reducing stunting rates, namely the provinces of Bangka Belitung Islands, Riau Islands, DKI Jakarta, DI Yogyakarta, Bali, East Kalimantan, South Kalimantan, and South Sulawesi. Meanwhile, 7 provinces namely East Nusa Tenggara, South Kalimantan, Central Sulawesi, West Sulawesi, Maluku, North Maluku, and West Papua, still need special attention from the government in reducing stunting rates based on the factors that cause stunting discussed in this study.
K-Modes Analysis with Validation of the DBI in Grouping Provinces in Indonesia based on Indicators of Poor Households Syifa Azahra; Zilrahmi; Dodi Vionanda; Fadhilah Fitri
UNP Journal of Statistics and Data Science Vol. 2 No. 2 (2024): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol2-iss2/165

Abstract

Poverty is the most pressing social problem in Indonesia. Efforts to alleviate poverty are to group provinces in Indonesia based on indicators of poor households using the K-modes algorithm. The data used is data from the 2017 Indonesian Demographic and Health Survey (IDHS) on the Household List. The analysis includes data noise detection, data clustering using K-Modes algorithm, and cluster validation with Davies Bouildin Index (DBI). Based on the clustering that has been done, two clusters are obtained, where cluster 1 consists of 26 provinces and cluster 2 consists of 8 provinces. cluster 1 is a cluster that fulfills 9 indicators of poor households and cluster 2 only a few indicators of poor households. So that the government can prioritize these 8 provinces to overcome poverty in Indonesia. For the DBI value obtained is 1.89 which means that 2 clusters are already well used in the algorithm.
Artificial Neural Networks to Forecasting the Retail Price of Beras Solok in Padang City using Backpropagation Algorithm Putri Rivani; Tessy Octavia Mukhti; Dodi Vionanda; Dina Fitria
UNP Journal of Statistics and Data Science Vol. 2 No. 2 (2024): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol2-iss2/168

Abstract

Strengthening rice production is an important step as the population continues to grow. Padang City is only able to meet 30% of the community's needs, so to fulfill the community's needs, rice is also imported from Solok. Forecasting can be done especially in order to see the movement of the average retail price of Anak Daro Solok Rice in Padang City which has decreased and increased in rice prices due to the lack of rice availability in Padang City. In this research, the forecasting method that will be used is the Artificial Neural Network Backpropogation Algorithm. Artificial Neural Networks are widely used for forecasting nonlinear time series data. Based on the results of the research that has been done, forecasting the average retail price of Anak Daro Solok Rice in Padang City using the Backpropagation Algorithm Artificial Neural Network obtained the optimal network architecture has the best model, namely BP (1,6,1) which model produces a MAPE of 0.03121%, indicating that the network performance of the model that has been formed shows very good results because it manages to achieve an accuracy rate (MAPE) of less than 10%. Artificial Neural Network Model based on Backpropagation Algorithm can be applied to predict the average retail price of Anak Daro Solok Rice in Padang City. Comparison of the results of forecasting the average retail price of Anak Daro Solok Rice in Padang City for the next 12 months period, namely an increase from the previous 12 months period.
Sentiment Analysis of Twitter User Government Official of Indonesia Vacancy in 2024 Using Naive Bayes Classification Larissa, Dwika; Vionanda, Dodi
Jurnal Pendidikan Tambusai Vol. 9 No. 1 (2025)
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai, Riau, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jptam.v9i1.25497

Abstract

Pengumuman seleksi CPNS merupakan momen penting yang selalu ditunggu-tunggu oleh masyarakat Indonesia setiap tahunnya. Hal ini tidak terlepas dari tingginya animo masyarakat untuk menjadi bagian dari Aparatur Sipil Negara. Penelitian ini menganalisis sentimen masyarakat terhadap pengumuman seleksi CPNS tahun 2024 dengan menggunakan metode klasifikasi Naive Bayes. Data dikumpulkan dari 2001 tweet di Twitter yang berkaitan dengan Lowongan CPNS 2024, dan dilakukan preprocessing sebelum dilakukan analisis sentimen. Hasil penelitian menunjukkan bahwa mayoritas respon masyarakat adalah netral dengan 1788 tweet, sedangkan 94 tweet positif, dan 10 tweet negatif. Ketidakpastian mengenai jumlah formasi, proses seleksi, persyaratan, dan kebijakan lainnya menjadi faktor utama yang membuat sebagian besar masyarakat cenderung netral. Hasil analisis juga menunjukkan bahwa model klasifikasi Naive Bayes memiliki akurasi sebesar 92%, menunjukkan kemampuan yang baik dalam mengkategorikan data sentimen. Penelitian ini memberikan masukan yang berharga bagi pemerintah dan lembaga terkait dalam merancang kebijakan yang lebih transparan dan jelas untuk meningkatkan dukungan masyarakat terhadap pembukaan lowongan CPNS di masa mendatang.
Comparison of the Fuzzy Time Series Chen Model and the Heuristic Model in Forecasting the Number of International Tourists in West Sumatra Rizki Akbar; Fitri, Fadhilah; Vionanda, Dodi; Mukhti, Tessy Octavia
Mathematical Journal of Modelling and Forecasting Vol. 2 No. 1 (2024): June 2024
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/mjmf.v2i1.20

Abstract

The Fuzzy Time Series Chen and Heuristic are two forecasting methods based on fuzzy logic used to predict values in time series. The FTS Chen and Heuristic models have almost identical forecasting processes, but the main difference lies in how they develop fuzzy logical relationships. The FTS Chen model uses Fuzzy Logical Relationship Groups obtained from the results of Fuzzy Logical Relationships for the forecasting process. On the other hand, the FTS Heuristic model uses Fuzzy Logical Relationships directly in the forecasting process. Fuzzy Logical Relationships are a collection of fuzzy logical relationships used to connect values in time series. By using Fuzzy Logical Relationships, the Heuristic model can predict values in time series more accurately and effectively. The forecasting is done to plan the development of tourism infrastructure, determine service needs, and optimize tourism promotion. The data shows that the number of foreign tourists visiting West Sumatra has continued to grow from 2006 to 2023. The comparison of the accuracy of the forecasting results of FTS Chen and Heuristic models for foreign tourists in West Sumatra yielded a MAPE of 0.241% for FTS model Chen and 0.194% for FTS model Heuristic. This indicates that the best forecasting model for foreign tourists is the Heuristic model due to its lower MAPE value.
Nagari Tanjung Balik Menuju Digitalisasi Data Syafriandi, Syafriandi; Amalita, Nonong; Vionanda, Dodi; Fitria, Dina; Zilrahmi, Zilrahmi; Yarman, Yarman
Suluah Bendang: Jurnal Ilmiah Pengabdian Kepada Masyarakat Vol 22, No 3 (2022): Suluah Bendang: Jurnal Ilmiah Pengabdian kepada Masyarakat
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/sb.03280

Abstract

Kegiatan pembangunan hendaknya dilaksanakan dengan menggunakan perencanaan yang tepat. Perencanaan ini memerlukan informasi yang diperoleh dengan adanya data.  Nagari Tanjung Balik Kecamatan X Koto Singkarak merupakan salah satu nagari yang termasuk dalam Kecamatan X Koto Singakarak, Kabupaten Solok. Untuk perencanaan pembangunan, nagari ini memerlukan adanya informasi dan data. Namun, nagari ini tidak memiliki akses ke data sektoral yang terhimpun di BPS. Di sisi lain, nagari ini juga dihadapkan pada keterbatasan sumber daya yang memiliki pengetahuan tentang Statistika. Oleh karena itu, tim pengabdi melaksanakan Kegiatan Pengabdian kepada Masyarakat di Nagari Tanjung Balik untuk membantu mengatasi kedua masalah di atas.  Dari kegiatan pengabdian ini, pemerintah Nagari Tanjung Balik memiliki database yang terbaru, akurat, dan mudah diakses yang bisa digunakan untuk mengetahui informasi yang detail tentang masyarakat nagari ataupun untuk memetakan potensi dan masalah di nagari. Begitu pula, dari kegiatan ini,  pemerintah nagari telah memiliki kader yang bisa melakukan pengumpulan data di waktu yang akan datang dengan menggunakan aplikasi RSN dan mengelola database yang telah dibangun.
Vector Autoregressive Exogenous Modelling to Forecast Rice Prices Based on Inflation and Rice Production in West Sumatra Province Khairisa Putri, Nadya; Kurniawati, Yenni; Vionanda, Dodi; Martha, Zamahsary
Jurnal MSA (Matematika dan Statistika serta Aplikasinya) Vol 14 No 1 (2026): VOLUME 14 No 1, 2026
Publisher : Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/msa.v14i1.66522

Abstract

Rice prices in West Sumatra Province tend to be high despite high production levels, making forecasting essential to support food security. This study aims to forecast rice prices in traditional and modern markets using a Vector Autoregressive with Exogenous Variables (VARX) model, incorporating inflation and rice production as exogenous variables. The data used consists of monthly secondary data covering the period from January 2019 to December 2024, sourced from PIHPS and the West Sumatra Provincial Statistics Agency. The analysis includes the Augmented Dickey-Fuller stationarity test, determination of the optimal lag based on the Akaike Information Criterion, parameter estimation using Ordinary Least Squares, as well as tests of stability, parameter significance, and residual diagnostics. Forecast performance is evaluated using the Mean Absolute Percentage Error (MAPE). The results show that the VARX (3,3) model is the best, with an MAPE of 1.95\% for traditional markets and 1.56\% for modern markets, indicating very high forecasting accuracy. This study demonstrates that incorporating external factors into the VARX model improves rice price forecasting accuracy, providing a basis for the government to formulate policies to maintain food price stability in West Sumatra Province.
Implementation of XGBoost Algorithm for Sentiment Classification of Public Opinions on the Rupiah Redenomination Policy Andinie Rachmah Basri; Fadhilah Fitri; Dodi Vionanda
Journal of Multidisciplinary Science: MIKAILALSYS Vol 4 No 3 (2026): Journal of Multidisciplinary Science: MIKAILALSYS
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mikailalsys.v4i3.11949

Abstract

Although rupiah redenomination has long featured in Bank Indonesia’s monetary policy discourse as a means of simplifying currency denominations without altering the exchange rate or real purchasing power, public concerns about declining purchasing power and price rounding underscore the need for effective policy communication. This study analyzes Indonesian public sentiment toward rupiah redenomination using the Extreme Gradient Boosting (XGBoost) algorithm to classify YouTube comments. Data were collected through the YouTube Data API v3 from 1,763 comments posted on the Tribunnews video titled Purbaya Targets Rupiah Redenomination Bill to Be Completed in 2027. Following text cleaning and tokenization, 1,169 comments were retained and transformed using Term Frequency–Inverse Document Frequency (TF-IDF). Manual labeling classified 657 comments as positive and 512 as negative. The XGBoost model, trained using optimized hyperparameters, achieved an accuracy of 73.39%, with F1-scores of 0.772 for positive sentiment and 0.680 for negative sentiment. These results indicate that the model classified positive sentiment more effectively, although ambiguous comments remained challenging. The findings reveal the distribution of public responses to the proposed policy and emphasize the need for intensive public outreach to mitigate potential resistance and misconceptions. This study contributes a data-driven basis for developing more effective monetary policy communication and anticipating the social dynamics associated with rupiah redenomination.
Co-Authors Afdhal Rezeki Afdhal Andini Yulianti Andinie Rachmah Basri Aprilla Suhada Atus Amadi Putra Bahri Annur Sinaga defal aditya defran Dina Fitria Dina Fitria Dina Fitria, Dina Dinda Fitriza Dony Permana Fadhilah Fitri Fadhillah Fitri Fashihullisan Fayza Annisa Febrianti FAZHIRA ANISHA Febri Ramayanti Findri Wara Putri Fitri, Fadhilah Fitria Panca Ramadhani Hariati Ainun Nisa Ichlas Djuazva Ihsanul Fikri Isra Miraltamirus Jimmi Darma putra Khairisa Putri, Nadya Larissa, Dwika Lifia Zullani Mardhiatul Azmi Muhammad Tibri Syofyan nabillah putri Nazifatul Azizah Nindi Syahfitrri Nonong Amalita Nurul Afifah Putri Rivani rama novialdi Rizki Akbar Salma, Admi Seif Adil El-Muslih Shavira Asysyifa S Sherly Amora Jofipasi Silvia Agustina Syafriandi Syafriandi Syafriandi Syifa Azahra Tessy Octavia Mukhti Tessy Octavia Mukhti Yarman Yarman, Yarman Yenni Kurniawati Yunistika Ilanda Zamahsary Martha Zilrahmi, Zilrahmi