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All Journal International Journal Of Computer, Network Security and Information System (IJCONSIST) Priviet Social Sciences Journal Jurnal Pengabdian West Science
Nuchaila Ainiyah
Universitas Telkom
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Law, Crime, Criminology & Criminal Justice Public Health Social Sciences

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Business Process Monitoring Using a Robust Max-Half-Mchart Developed with Fast S Estimator Awang Putra Sembada R; Muhammad Ahsan; Sischa Wahyuning Tyas; Muhammmad Galang Satrio Wicaksono; Nuchaila Ainiyah
Priviet Social Sciences Journal Vol. 6 No. 6 (2026): June 2026
Publisher : Privietlab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55942/pssj.v6i6.1845

Abstract

In a business environment, ensuring production processes plays a crucial role in a company's quality and stability. One tool that can be used to monitor the quality of business processes is a control chart. Control charts are useful tools for quickly monitoring a business process. Multivariate control charts are control charts that monitor several quality variables simultaneously. This is more effective than monitoring variables individually. There are control charts that can control the mean and covariance matrix of the process simultaneously, the tool used is a simultaneous multivariate control chart. Some commonly used methods are Max-Mchart, Max-MEWMA, Max-Half-Mchart. In addition to the method, it is also important to pay attention to the data in the business process. Data in business processes can contain outliers that cause classification errors. Therefore, a strong estimator is needed combined with a control chart to be resistant to outliers. The Fast S estimator is a robust estimator that has the ability to handle data containing outliers and combined with Max-Half-Mchart, a simultaneous control chart is good at detecting shifts in the production process. The results show that the Fast S estimator can detect six more out-of-control data points than the conventional method, which only detects two. There is a significant difference in detection rates between the Robust and non-Robust methods. These results indicate that the developed method is more sensitive than the method without the Robust estimator.
Application of the Random Forest Classifier Method in Grouping Patients with Intellectual Disabilities Nuchaila Ainiyah; Muhammad Afifudin; Reyhan Dela Masyhuri; Muhamad Hakam Fardana; Sischa Wahyuningtyas; Awang Putra Sembada R; Muhamad Liswansyah Pratama
IJCONSIST JOURNALS Vol 7 No 1 (2025): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v7i1.162

Abstract

This research explores the effectiveness of the Random Forest Classifier method in grouping mental retardation patients based on their level of severity. Medical record data from mental hospitals is collected and processed to train a classification model. The preprocessing process is applied to ensure data quality before use. Model evaluation is carried out by measuring the accuracy of the scores. The research results showed that the Random Forest Classifier succeeded in classifying mental retardation patients with an accuracy of 84%. These findings show the potential of the Random Forest Classifier method as a clinical tool for doctors in determining appropriate interventions for mental retardation patients based on their level of severity.
Optimalisasi Pengelolaan Website Sekolah dan Penguatan Literasi Data Melalui Pelatihan Pembuatan Konten Amalia Nur Alifah; Ahmad Wali Satria Bahari Johan; Nuchaila Ainiyah; Hafshah Hafshah; Faza Nur Aulia Suraya; Feysha Kamila Pracilya; Clairine Anargya Athallah; Talitha Shafa Azzahra
Jurnal Pengabdian West Science Vol 5 No 07 (2026): Jurnal Pengabdian West Science
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/jpws.v5i07.3650

Abstract

Optimasi situs web sekolah sangat penting untuk menyebarluaskan informasi dan meningkatkan citra lembaga pendidikan di era digital. Namun, banyak sekolah yang tidak mampu mengelola situs webnya secara aktif karena keterbatasan kompetensi guru dalam pembuatan konten digital dan literasi data. Program pengabdian masyarakat ini bertujuan untuk meningkatkan kemampuan guru dalam mengelola konten situs web serta memperkuat literasi data melalui pendekatan pelatihan partisipatif dan kontekstual. Kegiatan ini dilaksanakan pada tanggal 4 Juni 2026, melibatkan 13 guru melalui ceramah, demonstrasi, studi kasus, dan praktik langsung. Evaluasi menggunakan tes awal dan tes akhir menunjukkan peningkatan nilai rata-rata dari 55,54 menjadi 74,92, yang dikonfirmasi oleh uji-t berpasangan (t = 3,689; p = 0,003). Seluruh peserta berhasil menghasilkan konten situs web berkualitas tinggi dalam hal struktur, keterbacaan, kelengkapan data, dan kategorisasi. Program ini meningkatkan kompetensi teknis sekaligus menumbuhkan kesadaran literasi data serta praktik pengelolaan informasi yang akuntabel di lingkungan sekolah.
Co-Authors Ahmad Wali Satria Bahari Johan Alifah, Amalia Nur Awang Putra Sembada R Awang Putra Sembada R Clairine Anargya Athallah Faza Nur Aulia Suraya Feysha Kamila Pracilya Hafshah Hafshah Muhamad Hakam Fardana Muhamad Liswansyah Pratama Muhammad Afifudin Muhammad Ahsan Muhammmad Galang Satrio Wicaksono Reyhan Dela Masyhuri Sischa Wahyuning Tyas Sischa Wahyuningtyas Talitha Shafa Azzahra