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Memprediksikan Indeks Pembangunan Manusia di Wilayah Indonesia Bagian Timur Menggunakan Random Forest Classification Arwini Arisandi; Syandriana Syarifuddin
Journal of Mathematics: Theory and Applications Volume 5, Nomor 1, 2023
Publisher : Program Studi Matematika Universitas Sulawesi Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31605/jomta.v5i1.2402

Abstrak. Indeks Pembangunan Manusia (IPM) merupakan salah satu indikator yang penting dalam melihat sisi lain dari pembangunan. Setiap indikator komponen penghitungan IPM dapat dimanfaatkan untuk mengukur keberhasilan pembangunan kualitas hidup manusia seperti Umur Harapan Hidup (UHH), Harapan Lama Sekolah (HLS), Pengeluaran per Kapita Disesuaikan (PKD), dan Lama Sekolah (LS). Penelitian ini bertujuan untuk mengetahui sebaran IPM di Kawasan Timur Indonesia, kemudian melakukan pemodelan data IPM dengan menggunakan regresi logistik, decision tree, dan random forest untuk mendapatkan model terbaik dalam memprediksi IPM serta mengetahui faktor-faktor yang memiliki pengaruh terhadap perubahan nilai IPM. Hasilnya menunjukkan bahwa daerah dengan kategori IPM rendah dan IPM sedang memiliki persentase sebesar 69% yang lebih tinggi dibandingkan dengan daerah dengan kategori IPM tinggi dan IPM sangat tinggi sebesar 31% untuk kawasan Timur Indonesia. Model terbaik untuk pemodelan data IPM pada Kawasan Timur Indonesia adalah model random forest dengan nilai kebaikan model sebesar 94.03% dan nilai balanced accuracy sebesar 93.33%. Hasil prediksi diperoleh sebanyak 2 kabupaten/kota atau 4.08% yang diprediksi tidak tepat. Variabel Umur Harapan Hidup memiliki pengaruh atau kontribusi yang signifikan dalam perubahan nilai IPM kabupaten/kota di Kawasan Timur Indonesia. Kata kunci: IPM, Kawasan Timur Indonesia, Random forest

Optimization of Drying Models for Various Types of Turmeric Using A Tray Dryer Jassin, Ernawati; Fitri, Muhammad; Arisandi, Arwini; Aisyah, Nur
Journal of Agriculture Vol. 3 No. 03 (2024): Research Articles November 2024
Publisher : ITScience (Information Technology and Science)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/joa.v3i03.4911

Turmeric (Curcuma domestica val) is one of the plants that has benefits including as spices, seasonings, herbs, to maintain health and beauty and utilization as traditional medicine. Drying is a method to extend the shelf life and maintain the quality of a post-harvest product before further processing. This study aims to optimize the thin layer drying model on various types of turmeric (white turmeric, black turmeric and yellow turmeric). Data obtained from drying results in the form of initial mass, mass during drying and the final mass of drying. Then processed to obtain the wet base moisture content, dry base moisture content and Moisture Ratio. The thin layer drying model is obtained by finding the constant value of k, a and n from each exponential form. Determination of constants using Solver Tool Microsoft Excel that automatically searches for constant values in each dryer model tested, then obtains the highest R² value as the best model that describes the drying model of black turmeric and yellow turmeric. From the test results of curcumin content in white turmeric, black turmeric and yellow turmeric where the content of yellow turmeric has a higher value than both turmeric because it has an almost perfect arrangement of chemical elements. In the pH test, the highest result was obtained in white turmeric, namely 4.8 where the less curcumin contained in the turmeric rhizome, the higher the level of acidity of the turmeric.

Optimization of Drying Models for Various Types of Turmeric Using A Tray Dryer Jassin, Ernawati; Fitri, Muhammad; Arisandi, Arwini; Aisyah, Nur
Journal of Agriculture Vol. 3 No. 03 (2024): Research Articles November 2024
Publisher : ITScience (Information Technology and Science)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/joa.v3i03.4911

Turmeric (Curcuma domestica val) is one of the plants that has benefits including as spices, seasonings, herbs, to maintain health and beauty and utilization as traditional medicine. Drying is a method to extend the shelf life and maintain the quality of a post-harvest product before further processing. This study aims to optimize the thin layer drying model on various types of turmeric (white turmeric, black turmeric and yellow turmeric). Data obtained from drying results in the form of initial mass, mass during drying and the final mass of drying. Then processed to obtain the wet base moisture content, dry base moisture content and Moisture Ratio. The thin layer drying model is obtained by finding the constant value of k, a and n from each exponential form. Determination of constants using Solver Tool Microsoft Excel that automatically searches for constant values in each dryer model tested, then obtains the highest R² value as the best model that describes the drying model of black turmeric and yellow turmeric. From the test results of curcumin content in white turmeric, black turmeric and yellow turmeric where the content of yellow turmeric has a higher value than both turmeric because it has an almost perfect arrangement of chemical elements. In the pH test, the highest result was obtained in white turmeric, namely 4.8 where the less curcumin contained in the turmeric rhizome, the higher the level of acidity of the turmeric.

APPLICATION OF THE ARIMA METHOD IN FORECASTING THE PRICE RED CAYENNE PEPPER IN MAKASSAR CITY Arisandi, Arwini; Gaffar, Ismail; Makkulawu, Andi Ridwan
Parameter: Journal of Statistics Vol. 4 No. 1 (2024)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/27765660.2024.v4.i1.17120

Red chili is one of the commodities with very tall cost changes. The cost variance of red chili can be caused by a huge amount of supply and request. The higher the amount of supply, the lower the cost, and the lower the amount of supply, the higher the cost. This study aims to implement the ARIMA method in forecasting red cayenne pepper prices in Makassar City. Data analysis to forecast red cayenne pepper prices used the ARIMA method with the results show that the price range of chili is from IDR 13,000 to IDR 80,000, with a mean value of IDR 38,218. The model with the minimum SSE and MSE value is ARIMA(1,1,1), so this model be used in time series data modeling for forecasting. The results of forecasting using the best model obtained a MAPE value of 15.90%, which is in the range of 10-20%, so it can be concluded that the ability of the ARIMA(1,1,1) model in forecasting the price of red cayenne pepper includes the good category.

Effect of Sugar and Juice Concentration on Vitamin C Retention and Organoleptic of Tangerine (Citrus Reticulata) Powder Ahmad, Ilham; Jassin, Ernawati; Arisandi, Arwini; Mus, Rahmi; Makkulawu, Andi Ridwan
Journal of Agriculture Vol. 4 No. 03 (2025): Research Articles, November 2025
Publisher : ITScience (Information Technology and Science)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/joa.v4i03.6951

Converting tangerine juice into powder via sucrose crystallization yields shelf?stable products with appealing flavor and nutrients. We examined the independent and interactive effects of sucrose and juice concentrations on physicochemical properties and consumer acceptance of Citrus reticulata powder. A 3×3 factorial design evaluated sucrose at 45, 55, and 65% and juice at 45, 55, and 65% (three replications). Processing included extraction and filtration, sucrose addition, controlled heating to supersaturation, crystallization during cooling, milling, and sieving. Moisture content, vitamin C (UV–Vis, 245 nm), and hedonic ratings for texture, aroma, color, and taste (nine?point scale; 30 panelists) were measured. ANOVA and Duncan’s Multiple Range Test assessed treatment effects. Moisture was 2.04–3.07% across treatments and did not differ significantly (p=0.924), meeting the SNI specification for powdered beverages (<5%). Vitamin C differed significantly (p=0.006); the 45% sucrose level consistently produced the highest values (?42.9–43.8 mg/100 g). The A1B3 formulation (45% sucrose, 65% juice) combined high vitamin C (~43.2 mg/100 g) with acceptable moisture (3.07%) and the most preferred sensory profile. Patterns suggest that reducing sucrose limits thermal/Maillard losses of ascorbic acid, while very high sucrose lowers water activity and yields intermediate retention. Overall, sucrose crystallization produced shelf?stable powders with favorable reconstitution, nutritional, and liking properties. A low?sucrose, high?juice formula optimizes the balance of quality attributes, supporting use in instant citrus drinks and functional beverages.

Akurasi Model Prediksi Menggunakan Metode Automatic Clustering Fuzzy Time Series pada Indeks Harga Konsumen di Kota Makassar Arisandi, Arwini; Hafid, Hardianti
Journal of Mathematics: Theory and Applications Vol 6 No 1 (2024): Volume 6, Nomor 1, 2024
Publisher : Program Studi Matematika Universitas Sulawesi Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31605/jomta.v6i1.3621

Indeks Harga Konsumen (IHK) adalah sebagai alat pengukuran yang digunakan untuk memantau perubahan harga barang dan jasa yang dibeli oleh rumah tangga konsumen dalam suatu periode waktu tertentu. Informasi IHK ini merupakan alat ukur yang digunakan oleh Badan Pusat Statistik (BPS) untuk mengetahui nilai inflasi pada suatu periode tertentu sehingga memprediksikan IHK dapat mengontrol laju inflasi di suatu daerah. Oleh karena itu, penelitian ini bertujuan untuk mengetahui akurasi model prediksi menggunakan metode Automatic Clustering Fuzzy Time Series pada IHK di Kota Makassar. Akurasi model prediksi diukur melalui nilai mean square error (MSE) dan mean absolute percentage error (MAPE). Data sekunder diperoleh dari BPS dengan rentang waktu Januari 2020 hingga November 2023. Hasilnya menunjukkan bahw nilai MSE yang diperoleh adalah 0,059 dan nilai MAPE sebesar 0,154%. Hal ini menunjukkan bahwa nilai MAPE berada pada rentang <10% yang disimpulkan bahwa kemampuan metode Automatic Clustering Fuzzy Time Series sangat baik dalam memprediksikan IHK di Kota Makassar.

Klasifikasi Penggunaan Teknologi Pada Petani Milenial di Sulawesi Selatan Menggunakan Density Based Spatial Clustering Algorithm With Noise Hafid, Hardianti; Arisandi, Arwini
Journal of Mathematics: Theory and Applications Vol 6 No 1 (2024): Volume 6, Nomor 1, 2024
Publisher : Program Studi Matematika Universitas Sulawesi Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31605/jomta.v6i1.3623

This research examines the use of agricultural technology by millennial farmers in South Sulawesi Province by applying the Density-Based Spatial Clustering Algorithm with Noise (DBSCAN) to the agricultural census data of South Sulawesi Province Phase 1 in 2023. The secondary data used includes the number of millennial farmers aged 19-39 who use or do not use digital technology, divided by district/city and gender. The analysis process begins with preprocessing to prepare the data, followed by clustering using the DBSCAN algorithm, determining the optimal values for the Eps and minPts parameters, and evaluating the quality of the formed clusters using the silhouette coefficient and elbow method. The results of the study indicate that the combination of Eps value of 1.000 and minPts value of 7 produces optimal clustering with 2 clusters formed and 92 data points clustered, while 4 other data points are considered as noise. Evaluation using the silhouette coefficient and elbow method also indicates that the optimal data grouping is k=2.

Akurasi Nilai Peramalan Harga Cabai Rawit Merah di Kota Makassar dengan Metode Single Exponential Smoothing Arisandi, Arwini
Journal of Mathematics: Theory and Applications Vol 7 No 1 (2025): Volume 7, Nomor 1, 2025
Publisher : Program Studi Matematika Universitas Sulawesi Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31605/jomta.v7i1.4780

Red cayenne pepper is a food commodity that is widely cultivated in Indonesia. This chili is in demand from domestic to foreign markets, so the price of chili can fluctuate under certain conditions. This is a concern that forecasting chili prices is important as a step for policymakers in making policies. Data obtained from the Makassar City Food Security Service amounted to 671 data (days) modeled using the single exponential smoothing forecasting method, which aims to assess the ability of forecasting results for the price of red cayenne pepper in Makassar City. The results show that descriptively the lowest chili price occurred in September 2023 at IDR 13,000 to IDR 15,000, while the highest chili prices will occur at the end of 2023 at IDR 70,000 to IDR 80,000. The resulting MAPE value is 6.50% < 10%, so it is concluded that the single exponential smoothing model provides excellent forecasting capabilities in forecasting the price of red cayenne pepper in Makassar City.

Integration of Local Moran's I and Geographically Weighted Regression for Early Detection of Potential Stunting Crisis Areas: A Case Study of South Sulawesi Province Reski Wahyu Yanti; Nurul Azizah Muzakir; Arwini Arisandi; Samsinar
SAINSMAT: Jurnal Ilmiah Ilmu Pengetahuan Alam Vol. 15 No. 1 (2026): Volume 15 Nomor 1 (Maret 2026)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/c9asjt27

Stunting is a chronic nutritional problem that has long-term impacts on physical growth, cognitive development, and the quality of human resources. South Sulawesi Province still faces a high prevalence of stunting with variations in conditions between districts/cities. This study aims to conduct early detection of areas with potential stunting crises through the integration of Local Moran's I spatial analysis and the Geographically Weighted Regression (GWR) method. The research data consists of secondary data from the Statistics Indonesia (BPS) in 2024 in 24 districts/cities with variables related to nutritional, socioeconomic, and environmental factors. The analysis stage begins with OLS regression and classical assumption tests, followed by the Local Moran's I test to identify spatial autocorrelation patterns, indicating clustering of at-risk areas. Therefore, GWR modeling is used to map variations in the influence of local determinants of stunting. The results show that the GWR model outperforms OLS based on increased R² values and coefficient variations between regions, with significant variable differences in each location. These findings emphasize the importance of region-specific stunting management policies as a basis for early detection and more effective intervention in South Sulawesi Province.

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