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All Journal Indonesian Journal of Artificial Intelligence and Data Mining SINTECH (Science and Information Technology) Journal Jurnal INTECH Teknik Industri Universitas Serang Raya Jurnal Telematika
Ivan Michael Siregar, Ivan Michael
President University
Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering Transportation

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ALGORITMA TPDA DAN TPDA Π SEBAGAI ALTERNATIF STRUKTUR BAYESIAN NETWORK Siregar, Ivan Michael
Jurnal Telematika Vol. 5 No. 1 (2009)
Publisher : Yayasan Petra Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61769/telematika.v5i1.29

Abstract

Knowledge discovery in databases (KDD) merupakanproses pencarian pengetahuan bermanfaat dari data menggunakanteknik komputasi. Salah satu langkah khusus dalam KDD adalahdata mining, yaitu aplikasi algoritma spesifik untuk mengekstrakpola/model dari data. Salah satu representasi model data miningadalah Bayesian Network (BN). BN digunakan untukmerepresentasikan pengetahuan tentang hubungankebebasan/kebergantungan diantara variabel. BN terdiri daristruktur yang merepresentasikan pengetahuan secara kualitatifdan parameter yang merepresentasikan pengetahuan secarakuantitatif. Ada dua poendekatan untuk mengkonstruksi strukturBN dari data, yaitu metode analisis dependensi dan metode search& scoring. Tujuan utama tugas akhir ini adalah melakukan studidan implementasi dari Algoritma TPDA dan TPDA-π untukmengkonstruksi struktur BN dari data.
Penggunaan Jaccard Similarity Coefficient dalam Optimasi Proses Rekrutmen Karyawan Berbasis Profil dan Kompetensi Siregar, Ivan Michael; Pratama, Daniel; Himawan, Cindy
SINTECH (Science and Information Technology) Journal Vol. 7 No. 2 (2024): SINTECH Journal Edition Agustus 2024
Publisher : Prahasta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/sintechjournal.v7i2.1617

Abstract

Rekrutmen karyawan yang efektif sangat penting untuk menemukan karyawan yang tepat sesuai kebutuhan perusahaan. Salah satu cara untuk mencapainya adalah dengan menggunakan algoritma yang bisa mengidentifikasi kandidat potensial berdasarkan kompetensi yang dibutuhkan. Meski ada berbagai metode untuk mengefisiensikan rekrutmen, seperti penyaringan resume dan optimasi iklan lowongan, belum banyak penelitian yang fokus pada metode berbasis kemiripan untuk mengurangi subjektivitas. Penelitian ini bertujuan mengembangkan model Machine Learning menggunakan Jaccard similarity coefficient untuk menentukan kandidat potensial berdasarkan kesamaan profil dan kompetensi dengan karyawan yang ada. Model ini terdiri dari tiga tahap: pertama, mengidentifikasi karyawan dan kandidat dengan keahlian sesuai kebutuhan; kedua, menggunakan Jaccard similarity coefficient untuk menghitung skor kemiripan profil dan mengelompokkan mereka; ketiga, menghitung skor kemiripan kompetensi dan memprediksi kandidat yang lolos. Hasil menunjukkan akurasi 75%, presisi 71%, recall 62%, dan f1 score 67%, dengan stabilitas terbaik pada dataset 509 karyawan. Akurasi 75% menunjukkan bahwa model dapat memprediksi kandidat yang tepat dengan tingkat ketepatan 75%, cukup baik untuk mengurangi subjektivitas, meningkatkan efisiensi, dan membantu perusahaan menemukan kandidat terbaik.
Deep Learning Based Recommendation System for Employee Retention Using Bipartite Link Prediction Siregar, Ivan Michael; Othman, Zulaiha Ali; Bakar, Azuraliza Abu
Jurnal INTECH Teknik Industri Universitas Serang Raya Vol. 11 No. 1 (2025): Juni
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/intech.v11i1.10069

Abstract

The Human Resources (HR) department faces significant challenges in employee retention. Traditional methods, such as performance evaluations and career development using regression, association, and clustering, have been widely used and have yielded positive results. However, these approaches are limited in predicting changes in employee behaviour and capturing complex relationships between variables. In this study, we leverage AI advancements to enhance predictive analysis by utilizing deep learning’s ability to identify patterns and complex relationships while continuously adapting to employee behavior changes. Specifically, we integrate Graph Convolutional Network (GCN) deep learning-based and bipartite graph-based approaches to construct a robust link prediction model. The bipartite employee-training network serves as input to the GCN, where each convolutional layer aggregates information from neighboring nodes, leveraging observed link information at each hidden layer. During the evaluation phase, the model iteratively aggregates information until an optimal state is reached, uncovering hidden relationship patterns that facilitate employee skill development. Empirical results on a benchmark dataset demonstrate significant performance improvements, with precision, recall, and AUC metrics exceeding 80%, highlighting the model's effectiveness in enhancing employee retention.
Enhancing Product Recommendation Accuracy Using Bipartite Link Prediction and Long Short-Term Memory in Retail Industry Siregar, Ivan Michael; Rosdiana, Firlie Resti
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 2 (2025): July 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i2.36052

Abstract

As competition in the retail sector intensifies, the demand for accurate customer-product recommendation systems has grown. Traditional similarity-based approaches such as common neighbor, Jaccard, Adamic Adar, preferential attachment, and resource allocation have been widely adopted in many business applications. However, these methods often struggle with capturing complex purchasing behaviors, product heterogeneity, temporal demand variations, and scalability challenges. This study introduces a deep learning-based recommendation model that integrates bipartite link prediction networks with Long Short-Term Memory (LSTM) to improve predictive accuracy. The bipartite network represents customer-product interactions, while the LSTM model captures sequential purchasing patterns to forecast future transactions. Experimental evaluation on a real-world building materials retail dataset comprising 389,087 transactions demonstrates the effectiveness of the proposed approach, achieving a Precision of 0.8223, Recall of 0.8034, F1-score of 0.8128, NDCG of 0.8601, and overall prediction accuracy of 0.854. The results indicate that the proposed model significantly outperforms similarity-based techniques, offering a robust solution for enhancing recommendation performance in dynamic retail environments.
Co-Authors Bakar, Azuraliza Abu Brian Sumali, Brian Himawan, Cindy Othman, Zulaiha Ali Pratama, Daniel Rosalina Rosalina Rosdiana, Firlie Resti