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All Journal Jurnal VOK@SINDO Journal of Development Research Indonesian Journal of Electrical Engineering and Computer Science
I Dewa Made Widia
Universitas Brawijaya
Humanities Civil Engineering, Building, Construction & Architecture Education Social Sciences Other

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Cluster Validity Index to Determine the Optimal Number Clusters of Fuzzy Clustering for Classify Customer Buying Behavior I Dewa Made Widia; Salnan Ratih Asriningtias; Sovia Rosalin
Journal of Development Research Vol. 5 No. 1 (2021): Volume 5, Number 1, May 2021
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Nahdlatul Ulama Blitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/jdr.v5i1.134

Abstract

One of the strategies in order to compete in Batik MSMEs is to look at the characteristics of the customer. To make it easier to see the characteristics of customer buying behavior, it is necessary to classify customers based on similarity of characteristics using fuzzy clustering. One of the parameters that must be determined at the beginning of the fuzzy clustering method is the number of clusters. Increasing the number of clusters does not guarantee the best performance, but the right number of clusters greatly affects the performance of fuzzy clustering. So to get optimal number cluster, we can measured the result of clustering in each number cluster using the cluster validity index. From several types of cluster validity index, NPC give the best value. Optimal number cluster that obtained by the validity index is 2 and this number cluster give classify result with small variance value
APLIKASI PENGADAAN BAHAN BAKU BATIK MENGGUNAKAN METODE FUZZY TSUKAMOTO DAN FUZZY ANALYTICAL HIERARCHY PROCESS Salnan Ratih Asriningtias; Novita Rosyida; I Dewa Made Widia; Eka Ratri Wulandari
VOK@SINDO : Jurnal Ilmu-Ilmu Terapan dan Hasil Karya Nyata Vol 10, No 1 (2023)
Publisher : Fakultas Vokasi Universitas Brawijaya

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Abstract

UMKM menjadi perhatian untuk dioptimasi mengingat UMKM adalah usaha kecil menengah dengan modal yang tidak terlalu besar. Segala upaya efisiensi harus terus diupayakan untuk membantu kinerja dan penekanan biaya produksi. Pentingnya efisiensi dalam proses order mempengaruhi penghematan biaya yang harus dikeluarkan untuk persediaan. Faktor utama yang mempengaruhi efisiensi dalam proses order diantaranya adalah pemilihan pemasok yang tepat dan penentuan jumlah order yang tepat. Pada penelitian ini digunakan pengembangan aplikasi  pengadaan bahan baku batik yang menerapkan metode Fuzzy Tsukamoto dan Fuzzy AHP guna memperoleh efisiensi dan efektifitas proses order. Fuzzy Tsukamoto untuk menentukan jumlah order dan Fuzzy AHP untuk pemilihan pemasok. Aplikasi pengadaan bahan baku batik dapat merekomendasikan pemasok untuk beli bahan baku, jumlah pemesanan bahan yang harus dipesan oleh manager beserta total biaya yang dikeluarkan dengan nilai MAPE 8.85% yang menujukkan bahwa tingkat akurasinya tinggi.Kata Kunci: Fuzzy AHP, Fuzzy Tsukamoto, Pemasok, UMKM
Hybrid machine learning framework for anomaly detection in industrial IoT environments I Dewa Made Widia; Toni Anwar
Indonesian Journal of Electrical Engineering and Computer Science Vol 43, No 1: July 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v43.i1.pp345-354

Abstract

The industrial internet of things (IIoT) has become a core component of Industry 4.0, enabling highly connected and data-driven industrial systems while simultaneously increasing exposure to cyber threats. Conventional intrusion detection systems (IDS), especially rule-based and signature-driven approaches, often struggle to cope with the dynamic, high-dimensional, and heterogeneous nature of IIoT traffic. This study proposes a hybrid anomaly detection framework that integrates autoencoder, isolation forest, and long short-term memory (LSTM) models using a weighted decision fusion strategy. Each component contributes complementary capabilities, including nonlinear feature learning, efficient outlier detection, and temporal pattern modeling. The framework is evaluated on the botnet of things (BoT-IoT) dataset and further validated using IoT-23. Experimental results show that the proposed hybrid approach achieves a precision of 0.999, recall of 0.970, and an F1-score of 0.985, while maintaining a false-negative rate below 0.001%. Although its area under the curve (AUC) is slightly lower than that of a standalone light gradient boosting machine (LightGBM) baseline, the hybrid framework consistently reduces missed detections, making it well suited for reliable real-time IIoT security monitoring.
Co-Authors Eka Ratri Wulandari Novita Rosyida Salnan Ratih Asriningtias, Salnan Ratih Sovia Rosalin Toni Anwar