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All Journal SAINSMAT Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA) BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Varian Inferensi Jurnal Darma Agung Quantitative Economics and Management Studies Panrannuangku Jurnal Pengabdian Masyarakat Parameter: Journal of Statistics Journal of Mathematics: Theory and Applications SAINSMAT: Jurnal Ilmiah Ilmu Pengetahuan Alam VARIANSI: Journal of Statistics and Its Application on Teaching and Research English Language, Linguistics, and Culture International Journal (ELSTIC-IJ) Ininnawa: Jurnal Pengabdian Masyarakat Prosiding Seminar Nasional Dies Natalis Universitas Negeri Makassar Jurnal Pengabdian Masyarakat Indonesia Jurnal Hasil-Hasil Pengabdian dan Pemberdayaan Masyarakat (JHP2M) SMART: Jurnal Pengabdian Kepada Masyarakat Journal of Mathematics, Computation and Statistics (JMATHCOS) JAMP
Hardianti Hafid
Universitas Negeri Makassar
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Transportation Other

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Journal : Journal of Mathematics, Computation and Statistics (JMATHCOS)

 1 
Implementation of K-Median Algorithm for the Regencies Clustering in South Sulawesi Province Based on Food Commodity Yields Hardianti Hafid; Sitti Masyitah Meliyana
Journal of Mathematics, Computations and Statistics Vol. 7 No. 2 (2024): Volume 07 Nomor 02 (Oktober 2024)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v7i2.3674

Abstract

Abstrak. Ketahanan pangan berkaitan dengan tentang potensi produksi di berbagai wilayah di Indonesia. Penelitian ini bertujuan untuk mengidentifikasi pola distribusi produksi komoditas pangan di Provinsi Sulawesi Selatan dengan menggunakan metode K-Median clustering. Data sekunder yang diperoleh dari Badan Pusat Statistik Provinsi Sulawesi Selatan digunakan untuk menganalisis produksi padi, jagung, ubi jalar, ubi kayu, kacang tanah, dan kedelai. Hasil clustering menunjukkan bahwa pengelompokan untuk produksi padi, ubi jalar, ubi kayu, kacang tanah, dan kedelai terbentuk dengan baik dimana nilai Silhouette Coefficient masing-masing sebesar 0,53, 0,56, 0,69 dan 0,66, menunjukkan kesamaan yang signifikan dalam setiap cluster. Namun, pengelompokan produksi jagung menunjukkan kualitas cluster yang lebih lemah dengan nilai 0,46. Hal ini menunjukkan adanya keragaman yang lebih besar dalam distribusi produksi jagung di berbagai wilayah Kabupaten/Kota Provinsi Sulawesi Selatan. Hasil penelitian ini diharapkan dapat memberikan dasar yang kuat bagi pembuat kebijakan untuk merancang strategi peningkatan produksi dan distribusi pangan yang lebih terarah, serta mendukung perencanaan kebijakan berbasis data yang lebih efisien di Provinsi Sulawesi Selatan. Kata Kunci : Clustering, K- Median, Komoditas Pangan Abstract. Abstract: Food security is closely related to the production potential in various regions of Indonesia. This study aims to identify the distribution patterns of food commodity production in South Sulawesi Province using the K-Median clustering method. Secondary data obtained from the Central Statistics Agency of South Sulawesi Province were used to analyze the production of rice, corn, sweet potatoes, cassava, peanuts, and soybeans. The clustering results indicate that the clusters formed for rice, sweet potatoes ,cassava, peanuts, and soybeans were well-defined, with Silhouette Coefficient values of 0,53, 0,56, 0,69 dan 0,66, respectively, showing significant similarity within each cluster. However, the clustering of corn production showed weaker cluster quality with a coefficient of 0.46, indicating greater variability in the distribution of corn production across the districts/cities in South Sulawesi Province. The findings of this study are expected to provide a strong foundation for policymakers to design more targeted strategies for improving food production and distribution, as well as to support more efficient data-driven policy planning in South Sulawesi Province.
Penerapan K-Fold Cross Validation untuk Menganalisis Kinerja Algoritma K-Nearest Neighbor pada Data Kasus Covid-19 di Indonesia Hafid, Hardianti
Journal of Mathematics, Computations and Statistics Vol. 6 No. 2 (2023): Volume 06 Nomor 02 (Oktober 2023)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The Covid-19 pandemic has been a global challenge in recent years. This virus has impactedmost aspects of human life, including health, the economy, and society. Indonesia is one of the affectedcountries that has now entered an endemic phase. This research aims to apply the K-Fold Cross Validationmethod to analyze the performance of the K-Nearest Neighbor (K-NN) algorithm on Covid-19 cases datain Indonesia, in order to measure how accurately the K-NN model can predict Covid-19 cases. The resultsobtained using 30-Fold cross-validation with a value of k=5 show an accuracy rate of 68.65% and a kappavalue of 0.5123. These results indicate that the K-NN model is capable of providing adequate predictionswith a higher level of agreement. This research provides a deeper understanding of the performance of theK-NN algorithm in the context of Covid-19 cases data in Indonesia, which can be used as a foundation forfurther improvements in modeling and understanding Covid-19 case data.
Implementation of DBSCAN for Earthquake Clustering in Indonesia with Potential Surface Damage Hardianti Hafid; Rahmat Hidayat; Rahmat H. S.
Journal of Mathematics, Computations and Statistics Vol. 8 No. 1 (2025): Volume 08 Nomor 01 (April 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i1.7318

Abstract

Indonesia is one of the countries with the highest seismic activity in the world due to its location at the convergence of three major tectonic plates. Understanding earthquake distribution patterns is crucial for disaster mitigation efforts and policy planning. This study applies the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to cluster earthquake data in Indonesia based on magnitude and depth. The data used is secondary data from the Meteorology, Climatology, and Geophysics Agency (BMKG) for the period of January–December 2023. The research stages include data collection and preprocessing, applying the DBSCAN algorithm with the selection of Eps and MinPts parameters, and evaluating the clustering results using the silhouette coefficient and Davies-Bouldin Index (DBI). The results show that the combination of Eps = 0.5 and MinPts = 5 produces clusters with a silhouette coefficient of 0.3959 and a DBI of 0.7384, indicating a fairly good cluster structure. Visualization results reveal high-density clusters in active seismic zones and several smaller clusters representing specific earthquake characteristics. This study provides insights into the earthquake distribution patterns in Indonesia and demonstrates that DBSCAN effectively identifies complex cluster structures. The findings can serve as a reference for seismological studies and support earthquake disaster mitigation efforts.
Classification Poverty Levels in Indonesia Using Discriminant Analysis Muthahharah, Isma; Hafid, Hardianti
Journal of Mathematics, Computations and Statistics Vol. 8 No. 1 (2025): Volume 08 Nomor 01 (April 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i1.6816

Abstract

Poverty is a complex global challenge affecting countries like Indonesia that seek to improve the welfare of their citizens. Although the number of Indonesia's poor has fluctuated over the past few years, the study shows a decline in 2022. Using Multivariate Discriminant Analysis, this study aims to classify poverty levels in Indonesian provinces. Previous findings highlighted the relationship between the poverty depth index and average and duration of schooling. Through the development of classification models, this research seeks to provide a better understanding of poverty factors and support more effective policymaking in combating poverty in various regions. Using secondary data from the Central Bureau of Statistics in 2022, this research is quantitative research that produces important insights for the formulation of poverty eradication policies and programs in Indonesia. The result is the low provincial group of 20 provinces only 10 provinces are correctly predicted, the remaining 10 are predicted in the high province group. The same thing happened in the high province group of 13 provinces, only 9 provinces were correctly predicted, while the remaining 4 were predicted in the low group.
Implementation of Random Forest Algorithm for Shallot Price Forecasting in Makassar City Hardianti Hafid; Arwini Arisandi; Reski Wahyu Yanti
Journal of Mathematics, Computations and Statistics Vol. 8 No. 2 (2025): Volume 08 Nomor 02 (Oktober 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i2.9477

Abstract

This study aims to implement the Random Forest algorithm for forecasting shallot prices in Makassar City using monthly historical data from January 2018 to December 2024, obtained from the Statistics Indonesia (Badan Pusat Statistik) of South Sulawesi Province. The analysis begins with identifying significant lags through the Partial Autocorrelation Function (PACF) plot, resulting in seven input variable schemes. Each scheme was tested using training and testing datasets. Model performance was evaluated using the Mean Absolute Percentage Error (MAPE). The results show that Scheme 1 (Lag 1) achieved the best performance with a MAPE value of 13.08%, which falls into the “good” category. Price forecasts for January–December 2025 using the best scheme indicate a price range of IDR 23,200 – 24,300 per kilogram, with peak prices in March, July, and November, and the lowest prices in April, August, and December. Although the model successfully captures historical price patterns, real-world fluctuations driven by seasonal factors, supply disruptions, and distribution costs may cause prediction deviations. This study recommends integrating exogenous variables and real-time data to improve forecasting accuracy and support local food price stabilization policies.
Co-Authors Abd. Hafid Abdul Rahman Ahmad Talib Ahmad Yasir Aras Ahmar, Ansari Saleh Arwini Arisandi Awaluddin Muin Ayu Pebriyanti Bungatang DH, Satriani Fahmuddin S., Muhammad Fahmuddin, Muhammad Fahmuddin, Muhammad Hafid, Abd. Haryono, Andri Herdawati, Herdawati Isma Muthahharah Mar'ah, Zakiyah Mar’ah, Zakiyah Meliyana R, S.Pd, M.Si, Sitti Masyitah Meliyana R, Sitti Masyitah Meyliana, Sitti Masyitah Moeh Zainal Khairul Muh. Fahmuddin Muhammad Fahmuddin S Muhammad Isbar Pratama Muhammad Nusrang Muhammad Nusrang Muhammad Yusuf Muhsin Efendi Muthahharah, Isma Muthahharah, Isma Mutiara Nur Agus Salim, Nur Agus Nusrang, Muhammad Nusrang, Muhammad Rahmanda, Lalu Ramzy Rahmat H. S. Rahmat Hidayat Rais, Zulkifli Reski Wahyu Yanti Rezky Amalia Hamka Rezky, Akhmad Rezky Ramadhana T Risqi, Shopia Rosmalah Rosmalah Rosmalah Rosmalah, Rosmalah Ruliana Rustam, Sitti Nail Satriani DH Selvi Annisa Sitti Masyitah Meliyana R. Sitti Nailah Rustam Sudarmin Yati Samsyuddin Zakiyah Mar'ah Zakiyah Mar’ah Zulkifli Rais