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All Journal Instal : Jurnal Komputer Jurnal Nasional Teknologi Komputer Jurnal Sistem Informasi dan Teknologi Jaringan
Rido Favorit Saronitehe Waruwu
Universitas Pembangunan Panca Budi
Computer Science & IT Decision Sciences, Operations Research & Management

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Classification of Success Factors for Job Training Participants at LPK Ziona Gunungsitoli Using Support Vector Machine and Decision Tree C4.5 Rido Favorit Saronitehe Waruwu; Muhammad Irfan Sarif; Rian Farta Wijaya
Bahasa Indonesia Vol 18 No 4 (2026): Instal : Jurnal Komputer
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jurnalinstall.v18i4.512

Abstract

ABSTRACTThis study aims to classify the success factors of job training participants at LPK Ziona Gunungsitoli by comparing Support Vector Machine (SVM) and Decision Tree C4.5. The dataset used in this study was obtained from participant records for 2022-2025 and contains tracer study outcomes, gender, education level, training type, program type, and year of graduation. Personal identifiers such as names, national identity numbers, addresses, and telephone numbers were excluded during preprocessing. The tracer study variable was transformed into a binary target: participants who were working or continuing study were categorized as successful, while participants who were still looking for work were categorized as not yet successful. From 376 raw records, 372 valid records were processed. The data were encoded using one-hot encoding and evaluated using an 80:20 stratified train-test split. The results show that Decision Tree C4.5 achieved an accuracy of 81.33%, precision of 94.83%, recall of 83.33%, and F1-score of 88.71%. Meanwhile, SVM achieved an accuracy of 77.33%, precision of 94.55%, recall of 78.79%, and F1-score of 85.95%. The most influential attributes in the Decision Tree model were year, training type, gender, education, and program type. These results indicate that Decision Tree C4.5 is more suitable for this dataset because it provides both higher performance and interpretable rules for institutional decision support.
Prediction of Digital Marketing Campaign Success Using Deep Neural Network Models Mayang Modelina Cynthia; Rahma Syahri; Muhammad Hafizh Al-Ghifari Rangkuti; Muhammad Akbar Firdaus; Rido Favorit Saronitehe Waruwu; Yasoziduhu Halawa; Tita Ritonga
Jurnal Sistem Informasi dan Teknologi Jaringan Vol 6 No 2 (2025): September
Publisher : CV. ADMITECH SOLUTIONS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63703/sisfotekjar.v6i2.33

Abstract

The rapid growth of digital advertising platforms has generated large volumes of complex and nonlinear campaign performance data, making accurate prediction of campaign success increasingly challenging. Traditional machine learning approaches often struggle to fully capture these nonlinear relationships. Therefore, this study proposes a Deep Learning approach using a Deep Neural Network (DNN) to predict the success of digital marketing campaigns based on key performance indicators such as impressions, clicks, CTR, CPC, CPM, engagement rate, and conversions.This research follows the CRISP-DM framework, including data understanding, preprocessing, model development, training, and evaluation. The dataset was obtained from digital advertising platform performance reports and processed through data cleaning, feature scaling, and train–test splitting. The proposed DNN model consists of multiple fully connected layers with ReLU activation functions and is optimized using the Adam optimizer. Model performance was evaluated using accuracy, precision, recall, F1-score, and ROC-AUC.The experimental results show that the proposed Deep Learning model achieves an accuracy of 87.6%, precision of 86.9%, recall of 85.8%, F1-score of 86.3%, and ROC-AUC of 0.91, indicating strong predictive performance. These findings demonstrate that Deep Learning effectively captures complex patterns in digital marketing data and provides reliable insights to support data-driven marketing decision-making.
Perancangan Arsitektur Database Terdistribusi pada Sistem Manajemen Data Universitas Pembangunan Panca Budi Rido Favorit Saronitehe Waruwu
Jurnal Nasional Teknologi Komputer Vol 6 No 3 (2026): Juli 2026
Publisher : CV. Hawari

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Universitas Pembangunan Panca Budi (UNPAB) Medan mengelola ekosistem akademik yang dinamis dengan populasi mahasiswa mencapai 14.508 jiwa di berbagai fakultas dan jenjang pendidikan. Tingginya lonjakan beban lalu lintas data pada periode krusial—seperti pengisian Kartu Rencana Studi (KRS), pembayaran termin, dan pendaftaran mahasiswa baru (PMB)—berpotensi menimbulkan masalah kelambatan respons (high latency) dan single point of failure (SPOF) pada basis data terpusat. Penelitian ini bertujuan merancang arsitektur basis data terdistribusi (Distributed Database Management System / DDBMS) berbasis Hybrid Cloud-On Premise yang mengintegrasikan server lokal fakultas (Linux Debian & Mikrotik) dengan infrastruktur Amazon Web Services (AWS RDS & EC2). Metode perancangan menerapkan fragmentasi horizontal berdasarkan predikat fakultas, fragmentasi vertikal pada data master mahasiswa, serta strategi replikasi data terencana. Pemrosesan transaksi diatur menggunakan protokol Two-Phase Commit (2PC) dan Saga Pattern, sedangkan optimasi kueri terdistribusi menerapkan teknik Semi-Join. Hasil pemodelan matematis menunjukkan bahwa implementasi arsitektur DDBMS ini mampu meningkatkan ketersediaan (availability) data akademik hingga 99,995% dan menekan biaya transmisi jaringan secara signifikan. Arsitektur ini memberikan solusi yang skalabel, andal, dan aman bagi efisiensi sistem manajemen data di UNPAB.
Co-Authors Mayang Modelina Cynthia Muhammad Akbar Firdaus Muhammad Hafizh Al-Ghifari Rangkuti Muhammad Irfan Sarif Rahma Syahri Rian Farta Wijaya Tita Ritonga Yasoziduhu Halawa