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All Journal FORUM STATISTIKA DAN KOMPUTASI Jurnal Media Infotama IJID (International Journal on Informatics for Development) Journal of Mathematics, Computation and Statistics (JMATHCOS) Indonesian Journal of Statistics and Its Applications Diophantine Journal of Mathematics and Its Applications
Cici Suhaeni
Institut Pertanian Bogor (IPB)
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Industrial & Manufacturing Engineering Mathematics Other

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PENDUGAAN SELANG KEPERCAYAAN BOOTSTRAP BAGI ARAH RATA-RATA DATA SIRKULAR (Bootstrap Confidence Interval Estimation of Mean Direction for Circular Data) Cici Suhaeni; I Made Sumertajaya; Anik Djuraidah
FORUM STATISTIKA DAN KOMPUTASI Vol. 17 No. 2 (2012)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (570.579 KB)

Abstract

The confidence interval is an estimator based on the sampling distribution. When the sampling distribution can not be derived from population distribution, the bootstrap method can be used to estimate it. Three methods used to estimate the bootstrap confidence interval for circular data were equal-tailed arc (ETA), symmetric arc (SYMA), and likelihood-based arc (LBA). In this study, three methods were evaluated through simulation study. The most important criterion to evaluate them were true coverage and interval width. The simulation results indicated in all methods, the interval width shortened when the concentration parameter increased. True coverage approached confidence level when the concentration parameter were one or more. For small concentration parameter, all three methods appeared unstable. Based on the true coverage, SYMA was the best, while in terms the interval width, LBA was the best one. For both criterion could be summarized that ETA is the best result. ETA applicated for estimate the period of Dengue Fever outbreaks in Bengkulu. The estimation showed that Dengue Fever outbreaks in 2009 were October through January. In 2010, it were January through March, and in 2011, it were June through September.Keywords : Circular, Bootstrap confidence interval, Equal-tailed arc, Symmetric arc, Likelihood-based arc.
Perbandingan Hasil Pengelompokan menggunakan Analisis Cluster Berhirarki, K-Means Cluster, dan Cluster Ensemble (Studi Kasus Data Indikator Pelayanan Kesehatan Ibu Hamil) Cici Suhaeni; Anang Kurnia; Ristiyanti Ristiyanti
Jurnal Media Infotama Vol 14 No 1 (2018)
Publisher : UNIVED Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (398.364 KB) | DOI: 10.37676/jmi.v14i1.469

Abstract

Pengelompokan merupakan kegiatan di bidang riset yang banyak digunakan hingga saat ini. Terlebih di era big data seperti sekarang. Banyak metode yang berkembang untuk keperluan tersebut. Penelitian ini membandingkan hasil pengelompokan menggunakan metode cluster hierarki, k-means cluster, dan cluster ensemble pada pengelompokan provinsi di Indonesia berdasarkan indikator pelayanan kesehatan ibu hamil. Hasil analisis menunjukkan bahwa cluster ensemble merupakan metode yang paling tepat dalam mengelompokkan provinsi-provinsi tersebut. Cluster yang dihasilkan adalah 3 (tiga) cluster. Kata Kunci: analisis cluster, cluster ensemble, cluster hierarki, k-means cluster.
KAJIAN SIMULASI PENDUGAAN SELANG KEPERCAYAAN BOOTSTRAP BAGI ARAH MEDIAN DATA SIRKULAR Cici Suhaeni; I Made Sumertajaya; Anik Djuraidah
Indonesian Journal of Statistics and Applications Vol 2 No 1 (2018)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v2i1.64

Abstract

The median direction is one of central tendency of circular data. The estimation process usually requires information about sampling distribution of statistic that want to be used as a parameter estimate. Theoretically, sampling distribution derived from population distribution. But, it is not easy to get sampling distribution of median although the population distribution is known. When the sampling distribution cannot be derived easily from population distribution, the bootstrap method can be an alternative to handle it. This study wants to evaluate the effect of increasing concentration parameter to the performance of bootstrap confidence interval estimation for median direction through simulation study. Three methods were used to estimate the interval which are equal-tailed arc (ETA), symmetric arc (SYMA), and likelihood-based arc (LBA). The most important criterion to evaluate them were true coverage and interval width. The simulation results that in general, the increasing of concentration parameter followed by more narrow interval. For small concentration parameter (k<1), all methods give unstable true coverage and interval width. The authors also identify that those three methods produce intervals with identical width when the parameter concentration is 20 or more. In terms of coverage and interval width, the best method was ETA.
Identifikasi Tema Perbincangan Masyarakat Tentang Vaksinasi Covid-19 di Media Sosial Cici Suhaeni; , Bagus Sartono
Diophantine Journal of Mathematics and Its Applications Vol. 1 No. 1 (2022)
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/diophantine.v1i1.25216

Abstract

Informasi mengenai vaksin covid-19 dan program vaksinasi pemerintah merupakan isu yang mendapat perhatian besar masyarakat dan menjadi perbincangan utama di media sosial, termasuk twitter. Beragam tema dan sudut pandang telah disampaikan oleh masyarakat, dan penelitian ini berupaya mengidentifikasi opini apa saja yang berkembang. Pengetahuan ini dapat menjadi masukan bagi pemerintah dan pemangku kepentingan lain untuk secara bersama membantu proses pemulihan dampak pandemi. Identifikasi opini masyarakat mengenai vaksin covid-19 dilakukan menggunakan metode text clustering terhadap tweets hasil crawling data di Twitter dalam kurun waktu 1 s.d 7 Agustus 2021. Hasil analisis menunjukkan terdapat enam tema besar yang menjadi isu perbincangan yaitu: (1) Kepercayaan terhadap efek vaksin, (2) keikutsertaan dalam vaksinasi untuk mencegah terpapar covid, (3) vaksinasi sebagai upaya herd immunity, (4) keampuhan vaksin melawan virus, (5) jenis-jenis vaksin (6) riset medis tentang.
LDA Topic Modeling Analysis of Public Discourse on Indonesia’s Free Nutritious Meals Program (MBG) Cici Suhaeni; Mualifah, Laily Nissa Atul; Wijayanto, Hari
IJID (International Journal on Informatics for Development) Vol. 14 No. 1 (2025): IJID June
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

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Abstract

This study investigates public discourse on Indonesia's Free Nutritious Meals (Makan Bergizi Gratis/MBG) program through Latent Dirichlet Allocation (LDA) topic modeling of YouTube comments. Filling a research gap on online public opinion regarding the MBG policy, this study identifies dominant themes and discursive patterns in public perception. A three-topic model, validated through coherence score evaluation and pyLDAvis visualization, reveals key topics: concerns over food prices and distribution, perceived benefits for children and society, and emotionally and politically driven reactions. The findings provide valuable insights into public opinion, while also highlighting challenges in processing Indonesian-language text, such as informal language and noisy data. This study contributes to understanding public perceptions of social policies in digital environments and recommends future research directions, including improved text preprocessing and alternative topic modeling approaches. By shedding light on online public discourse, this research informs policymakers and stakeholders about the effectiveness and potential areas for improvement in the MBG program.
Performance Comparison of Random Forest and XGBoost Optimized with Cuckoo Search Algorithm for Coconut Milk Adulteration Detection Using FTIR Spectroscopy I Gusti Ngurah, Sentana Putra; Kusman Sadik; Agus Mohamad Soleh; Cici Suhaeni
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.v8i2.7817

Abstract

Coconut milk has emerged as a strategic food commodity in the global tropical region, with market demand growing at 7.2% per annum since 2021. This increasing demand has led to sophisticated adulteration practices, including dilution with water. Such adulteration not only reduces the nutritional value but also poses serious health risks, including food poisoning and allergic reactions. This study developed an innovative detection method combining Fourier Transform Infrared (FTIR) spectroscopy with a sophisticated machine learning algorithm. We analyzed 719 coconut milk samples (wavelength range 2500-4000 nm) consisting of traditional market products and instant commercial products. This study aims to develop an FTIR-based coconut milk adulteration detection model by optimizing RF and XGBoost parameters using CSA and evaluating the comparative performance of the two models in identifying different types of adulterants. The spectral data underwent rigorous preprocessing using a combination of Standard Normal Variate (SNV) and Savitzky-Golay (SG) techniques to overcome the effects of noise and light scattering, which significantly improved feature extraction. The results show that CSA-optimized XGBoost achieves superior performance with 92% accuracy and 91% F1 score, outperforming Random Forest in all evaluation metrics. The model shows particular strength in precision (98%), indicating its outstanding ability to minimize false positives in adulteration detection. Stability tests through 30 experimental repetitions reveal that the combination of XGBoost+CSA maintains consistent performance with minimal variance, confirming its reliability for industrial applications. Comparative analysis shows that the combination of SNV+SG preprocessing improves the accuracy of the baseline model by 9-12%, while CSA optimization provides an additional performance improvement of 10-15%. This research makes significant contributions to food science and safety. This study demonstrates the effectiveness of CSA in optimizing spectroscopic models, achieving 19.5% higher precision. The combination of SNV+SG preprocessing improves the baseline accuracy by 9-12%, while CSA optimization provides an additional performance improvement of 10-15%. This study not only provides a rapid and non-destructive adulteration detection solution but also proves the effectiveness of the CSA approach in optimizing the spectroscopic model. These findings have important implications for strengthening food safety regulations and developing real-time quality control systems in the coconut milk industry.
Co-Authors Agus Mohamad Soleh Anang Kurnia Anik Djuraidah Bagus Sartono Hari Wijayanto I Gusti Ngurah, Sentana Putra I Made Sumertajaya Kusman Sadik Mualifah, Laily Nissa Atul Ristiyanti Ristiyanti