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All Journal EKSAKTA: Journal of Sciences and Data Analysis JURNAL DERIVAT: JURNAL MATEMATIKA DAN PENDIDIKAN MATEMATIKA BAREKENG: Jurnal Ilmu Matematika dan Terapan Delta: Jurnal Ilmiah Pendidikan Matematika Building of Informatics, Technology and Science Refleksi Pembelajaran Inovatif Jurnal Statistika dan Aplikasinya Parameter: Journal of Statistics Enthusiastic : International Journal of Applied Statistics and Data Science Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya Bulletin of Social Informatics Theory and Application Emerging Statistics and Data Science Journal

Primandari, Arum Handini

Universitas Islam Indonesia

Author-ID : 3424935

Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Implementasi Metode K-Means Clustering dalam Pengelompokan Kabupaten/ Kota di Provinsi NTB Berdasarkan Indikator Pendidikan: Implementasi Metode K-Means Clustering dalam Pengelompokan Kabupaten/ Kota di Provinsi NTB Hanifah, Salsabila; Primandari, Arum Handini
Emerging Statistics and Data Science Journal Vol. 1 No. 3 (2023): Emerging Statistics and Data Science Journal
Publisher : Statistics Department, Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/esds.vol1.iss.3.art44

Pendidikan merupakan salah satu bidang yang mempunyai peran penting dalam pembangunan suatu daerah. Pentingnya pendidikan sebagai indikator pembangunan juga terbukti dengan adanya poin Pendidikan menjadi menjadi salah satu tujuan pada Sustainable Development Goals (SDGs) yaitu “Menjamin kualitas pendidikan yang inklusif dan merata, serta mendukung kesempatan belajar seumur hidup bagi semua”. Upaya yang dapat dilakukan untuk mencapai hal tersebut adalah dengan menjalankan program wajib belajar untuk memajukan pendidikan. Data yang digunakan dalam penelitian ini adalah data indikator pendidikan SMA sederajat tahun ajaran 2021 yang meliputi Angka Partisipasi Sekolah (APS), Angka Partisipasi Kasar (APK), Angka Partisipasi Murni (APM) dan Rata-rata Lama Sekolah (RLS). Data tersebut merupakan data sekunder yang diperoleh dari website NTB Satu Data. Metode yang digunakan adalah menggunakan K-Means Clustering. K-Means clustering adalah metode pengelompokan yang berusaha mempartisi n individu dalam sebuah dataset multivariate kedalam k kelompok. Dari hasil analisis, diperoleh empat cluster. Cluster pertama terdiri dari 2 kabupaten atau kota dengan indikator pendidikan sedang, cluster kedua terdiri dari 2 kabupaten atau kota dengan indikator pendidikan tinggi, cluster ketiga terdiri dari 4 kabupaten atau kota dengan indikator pendidikan sangat rendah dan cluster keempat terdiri dari 2 kabupaten atau kota dengan indikator pendidikan yang masih rendah.

FOOD AND BEVERAGE PRODUCT SEGMENTATION BASED ON NUTRITION FACTS USING THE DBSCAN METHOD Fadlilah, Dhika Nurul; Primandari, Arum Handini
Jurnal Statistika dan Aplikasinya Vol. 9 No. 1 (2025): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.09106

Type 2 diabetes mellitus is increasingly affecting not only teenagers and adults in Indonesia but also children. This serious issue is linked to high-sugar foods, particularly candy and chocolate products consumed by children. The aim of this research is to categorize these products based on their nutritional information, specifically total fat, saturated fat, sugar, and salt (SSF) content per serving, using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method. By doing so, the study seeks to produce simplified product labels that offer clearer nutritional insights compared to conventional nutrition facts labels. Data was collected through purposive sampling from three retail stores. The clustering results, using parameters Eps 0.4 and MinPts 10, revealed two distinct clusters and 133 noise points. Cluster 1 consists of 215 products with low levels of total fat, saturated fat, sugar, and salt, while Cluster 2 includes 27 products that are high in these nutrients. The clustering quality is validated with a Silhouette Coefficient of 0.77 and a Davies-Bouldin Index of 0.345.

Forecasting Bitcoin Price Based on Blockchain Information Using Long-Short Term Method Larasati, Kinanti Dhea; Primandari, Arum Handini
Parameter: Journal of Statistics Vol. 1 No. 1 (2021)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Since its founding in 2008, Bitcoin (financial code: BTC) has emerged as a digital currency in market cap and continues to attract investors and policymakers' attention. In recent years, BTC has high price volatility, a substantial increase in 2016, followed by a significant decline in 2018. Unlike stock markets, BTC is open for 24x7 dan has no closing period. It means everyone can trade it for any time. However, this flexibility carries investment risk. This research attempts to forecast BTC's price by considering the blockchain's information to minimize the risk. We employ Long-Short Term Memory (LSTM), the artificial Recurrent Neural Network (RNN) architecture. Its model can avoid long-term problems. The data used is BTC's price and blockchain information data from August 4, 2018, to January 21, 2020. The model with 20 neurons and 500 epochs has the smallest MSE value. Then a prediction has an accuracy rate of 91.07%.

IMPLEMENTATION OF THE STEP FUNCTION INTERVENTION AND EXTREME LEARNING MACHINE FOR FORECASTING THE PASSENGER’S AIRPORT IN SORONG Faizin, Nur; Fauzan, Achmad; Primandari, Arum Handini
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

This study aims to forecast the number of passengers departing at the domestic departure terminal at Domine Eduard Osok Sorong Airport in 2022 using the Autoregressive Integrated Moving Average (ARIMA) method, ARIMA with Step Function Intervention, and Extreme Learning Machine (ELM). The knowledge of the number of passengers can help the airport prepare facilities. The residual ARIMA model (0,1,0) has no serial correlation (random walk) based on the Ljung-Box test. The MAPE value of the ARIMA model (0,1,0) is 65.47% which means poorly fitted. Because of it, the researchers propose an intervention in the ARIMA model. The RMSE and MAPE ARIMA Intervention (1,0,0) (0,1,0) [12] were 9,027.671 and 35.86%, respectively. Besides, this study also employed the ELM method, which has a MAPE error measurement value of 30.64%. The ELM method has the lowest error measurement results among the three methods. Therefore, the ELM method is suitable for forecasting the number of passengers with predicted values from June to September 2022 as follows: 47985, 37821, 31247, and 33578. On the other hand, intervention in ARIMA can reduce MAPE by 45%.

Analysis of Changes in Atmospheric CO2 Emissions Using Prophet Facebook Primandari, Arum Handini; Thalib, Achmad Kurniansyah; Kesumawati, Ayundyah
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 2 Issue 1, April 2022
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol2.iss1.art1

CO2 emissions have been an environmental issue for decades. The trigger for the increasing concentration of CO2 in the atmosphere is the growth of industries related to burning fossil fuels for coal, natural gas, and petroleum. For nearly a century, several attempts have been made to suppress the rapid growth of CO2 . This study uses daily atmospheric CO2 levels observed in Mauna Loa laboratories. The method used is a Prophet that can handle seasonality and mark the change points. Almost 20% of data was missing value, which was then imputed using spline interpolation. Based on the analysis results, CO2 levels have an upward trend throughout the year and seasonality. There is no point of change in the last ten years that shows a decrease in CO2 levels. Using forward chaining cross-validation evaluation and error measurement, the prophet model can follow the pattern of CO2 levels well. The average RMSE value is less than 2.0, with an MAPE value bellow 0.5%.

Analyzing the Impact of the Pandemic on Indonesia’s Economic Growth Using Dynamic Time Warping Primandari, Arum Handini; Kusuma Arum, Widya
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 4 Issue 1, April 2024
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol4.iss1.art7

Asia’s GDP experienced the most drastic decline during the COVID-19 compared to other economic crises. This study collected data on economic indicators for each province/city to observe economic growth in Indonesia, such as Gross Regional Domestic Product (GRDP), unemployment rate, and economic growth. The clustering method on time series data found several provinces/cities with similar economic growth patterns to observe the pandemic's impact on their economies. Knowing the pattern of economic growth will help the regulation holder support provinces with the right policy. For this purpose, we utilized the Dynamic Time Warping (DTW) distance with the k-medoids procedure. The DTW is an algorithm for measuring the similarity between two temporal sequences. The clustering of the three economic indicators had three clusters with the most optimal validation index. Each cluster had almost the same pattern since the trend tended to increase from before the pandemic and then decrease during the pandemic. The decrease in GRDP was less significant than the minimal data on GRDP that happened before the pandemic. Most provinces had negative economic growth during the pandemic, which skyrocketed even for the first quarter of 2023, almost the same as before the pandemic.

Segmentasi Produk Minuman Tidak Termasuk Produk Susu Berdasarkan Informasi Nilai Gizi Menggunakan Metode DBSCAN Rachmatia, Baiq Wita; Primandari, Arum Handini
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7255

Approximately 28.7% of Indonesians consume sugar, salt, and fat (SSF) in amounts that exceed the Ministry of Health's recommended limits. Over the past two decades, sweetened drink (MBDK: minuman berpemanis dalam kemasan) consumption has surged, making Indonesia the third highest in Southeast Asia for MBDK consumption. To mitigate this, consumers need clear information about GGL content, but nutritional labels are often complex and underutilized. Product segmentation can help consumers make healthier drink choices and support health interventions aimed at reducing risky consumption. Data on GGL values were collected from MBDK sold in three store types and analyzed using the DBSCAN method, which handles diversity and outliers without predefining cluster numbers. Descriptive statistics showed most products had low fat but higher sugar content, nearing 15 grams. After standardizing the data using z-scores, the DBSCAN clustering revealed two clusters and some noise. The evaluation indicated a silhouette coefficient of 0.396 and a Dunn index of 0.137, with t-tests showing significant differences between the clusters.

Co-Authors Achmad Fauzan, Achmad Ayundyah Kesumawati Dita Anies Munawwaroh Fadlilah, Dhika Nurul Hanifah, Salsabila Ikasakti, Nur Aini Iskandar , Shafa Amalia Kesumawati, Ayundyah khusnul khotimah Kusuma Arum, Widya Larasati, Kinanti Dhea Lestari, Indri Fauzi Mustika Noor Mifrahi Nabilla Wardah Bonitta Nur Faizin Perihatini, Denisha Intan Rachmatia, Baiq Wita Thalib, Achmad Kurniansyah

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