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KLASTERING WILAYAH DI JAWA TIMUR BERDASARKAN FAKTOR UNMET NEED MENGGUNAKAN FUZZY GUSTAFSON-KESSEL Chrysilla Citra Windyadari; Aviolla Terza Damaliana; Mohammad Idhom
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 8 No. 2 (2025): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v8i2.3976

Abstract

The Family Planning Program is an effort to control the rate of population growth by regulating desired pregnancies. In its realization, the family planning program faces challenges in the form of unmet need (couples of childbearing age who do not use contraception). East Java Province in 2023 was recorded as the province with the third highest number of unmet need cases in Java. One method that can be used to analyze the phenomenon of unmet need is clustering analysis. Clustering analysis will help identify areas in East Java based on the priority level of the family planning program. Fuzzy Gustafson-Kessel (FGK) is one of the clustering methods developed as a refinement of the Fuzzy C-Means method. This study implements the Fuzzy Gustafson-Kessel (FGK) method with and without Principal Component Analysis (PCA) to cluster regions in East Java based on unmet need and determinant factors such as the availability of family planning facilities and resources. The results showed that the best model was obtained when using FGK with PCA, with the highest FSI value of 0.668 and XB of 0.235 at configuration c = 4 and m = 3.5. The clusters formed consist of 5 medium priority areas, 12 low priority areas, 9 high priority areas, and 12 developing priority areas. The results of this clustering can be used as a basis for policy makers in designing more effective intervention strategies to address unmet need in East Java.
IMPLEMENTASI SISTEM PENGENALAN WAJAH UNTUK PENDATAAN KEHADIRAN BERBASIS PYTHON Risky Pratama, Daffa; Mohammad Idhom
Journal of Data Science Theory and Application Vol. 5 No. 1 (2026): JASTA
Publisher : LP3M Universitas Putra Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32639/be3krg76

Abstract

Maraknya pertemuan daring membuka peluang besar dalam penerapan sistem otomatis untuk pendataan kehadiran. Penelitian ini bertujuan mengimplementasikan sistem pengenalan wajah berbasis Python untuk mendukung proses absensi peserta video meeting secara otomatis dan efisien. Sistem dikembangkan menggunakan algoritma Convolutional Neural Network (CNN) dengan arsitektur FaceNet sebagai ekstraktor fitur wajah dan Haar Cascade sebagai metode deteksi wajah awal. Aplikasi dilengkapi antarmuka grafis berbasis pustaka customtkinter sehingga pengguna dapat mengoperasikan seluruh fungsi sistem secara interaktif tanpa perintah berbasis teks. Metode pengembangan yang digunakan adalah Software Development Life Cycle (SDLC) dengan model Incremental yang terdiri dari empat modul utama, yaitu deteksi wajah, pengenalan wajah, antarmuka pengguna, dan penyimpanan data. Pengujian dilakukan menggunakan dua tangkapan layar video meeting yang memuat total 30 wajah peserta. Hasil evaluasi menunjukkan nilai precision sebesar 100%, recall 80%, accuracy 80%, dan F1-score 88,8%. Hasil ini menunjukkan sistem mampu melakukan pendataan kehadiran secara akurat meskipun dipengaruhi oleh pose wajah dan kondisi pencahayaan.