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All Journal InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Jurnal Penelitian Pendidikan IPA (JPPIPA) Journal of Computer Networks, Architecture and High Performance Computing Jurnal Nasional Teknologi Komputer Pengabdian Deli Sumatera
Doughlas Pardede
Universitas Deli Sumatera
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Economics, Econometrics & Finance Education Materials Science & Nanotechnology Physics Social Sciences

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Enhancing Multi-Layer Perceptron Performance with K-Means Clustering Doughlas Pardede; Aulia Ichsan; Sugeng Riyadi
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i1.3600

Abstract

Machine learning plays a crucial role in identifying patterns within data, with classification being a prominent application. This study investigates the use of Multilayer Perceptron (MLP) classification models and explores preprocessing techniques, particularly K-Means clustering, to enhance model performance. Overfitting, a common challenge in MLP models, is addressed through the application of K-Means clustering to streamline data preparation and improve classification accuracy. The study begins with an overview of overfitting in MLP models, highlighting the significance of mitigating this issue. Various techniques for addressing overfitting are reviewed, including regularization, dropout, early stopping, data augmentation, and ensemble methods. Additionally, the complementary role of K-Means clustering in enhancing model performance is emphasized. Preprocessing using K-Means clustering aims to reduce data complexity and prevent overfitting in MLP models. Three datasets - Iris, Wine, and Breast Cancer Wisconsin - are employed to evaluate the performance of K-Means as a preprocessing technique. Results from cross-validation demonstrate significant improvements in accuracy, precision, recall, and F1 scores when employing K-Means clustering compared to models without preprocessing. The findings highlight the efficacy of K-Means clustering in enhancing the discriminative power of MLP classification models by organizing data into clusters based on similarity. These results have practical implications, underlining the importance of appropriate preprocessing techniques in improving classification performance. Future research could explore additional preprocessing methods and their impact on classification accuracy across diverse datasets, advancing the field of machine learning and its applications
Analysis of Logistic Regression Regularization in Wild Elephant Classification with VGG-16 Feature Extraction Aulia Ichsan; Sugeng Riyadi; Doughlas Pardede
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 2 (2024): Articles Research Volume 6 Issue 2, April 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i2.3789

Abstract

The research article explores the intersection of image-based wildlife classification and logistic regression regularization, focusing on the classification of wild elephant species. It begins by highlighting the significance of ecological research in biodiversity monitoring and conservation and introduces Convolutional Neural Networks (CNNs) as potent tools for feature extraction from images. The VGG-16 model is particularly emphasized for its ability to capture hierarchical representations of visual features crucial for classification tasks. The integration of VGG-16 feature extraction with logistic regression regularization is proposed as a compelling approach, offering a balance between sophisticated feature representation and efficient classification algorithms. The literature review delves into image-based wildlife classification, emphasizing the role of CNNs, especially VGG-16, in extracting discriminative features. It discusses the fusion of VGG-16 features with logistic regression and the challenges in this field, such as dataset annotation and environmental variability. The method section outlines the dataset acquisition, feature extraction using the VGG-16 architecture, and model configuration using logistic regression with lasso and ridge regularization. The process of finding the optimal regularization parameter (lambda) and model evaluation through cross-validation is detailed. Results showcase the optimal lambda values for lasso and ridge regularization and compare the performance of logistic lasso and logistic ridge models. Misclassification analysis reveals factors influencing classification accuracy, including feature variability and contextual complexity. The discussion reflects on the implications of the findings, emphasizing the importance of lambda selection and addressing challenges in wildlife classification. It suggests avenues for further research, such as advanced modeling techniques and feature engineering approaches. In conclusion, the study contributes to advancing wildlife classification efforts by leveraging state-of-the-art techniques and sheds light on opportunities to enhance classification accuracy in wildlife conservation.
Digital Green Education through Green Chemistry Supports the Green Economy by Improving Science Skills and Entrepreneurial Character Hamela Sari Sitompul; Intan Maulina; Doughlas Pardede
Jurnal Penelitian Pendidikan IPA Vol 11 No 10 (2025): October
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i10.12332

Abstract

Green education is integrated into chemistry learning to support the green economy, as it offers numerous applications in everyday life, including waste management and sustainable reforestation programs. Based on the Merdeka Curriculum, one aspect of chemistry learning in grade 10 is green chemistry, which explores global issues and problem-solving. Science skills are closely linked to green education in chemistry learning, as students gain scientific knowledge and attitudes from the theories they learn. All these efforts are directed toward achieving the Sustainable Development Goals (SDGs). The research used a quasi-experimental Design. The study had two groups, the experimental and the control groups. The sample was SMA Negeri 1 Gunung Meria, Deli Serdang Regency students. The research instruments used were essay tests and observations. The results of the posttest t-test analysis between the control and the experimental group at a significance level of 0.05, then 0.00 <0.05, then Ho is rejected and Ha is accepted. The results of the students' entrepreneurial character scores can be seen in Figure 1, where the experimental class has an average score of 77.70 and the control class 42.85. The results of the study can be concluded that education has a significant influence on students' science process skills. Students' entrepreneurial character shows a substantial difference, in that in the experimental class, there is good development.
RANCANG BANGUN WEBSITE MENGGUNAKAN CONTENT MANAGEMENT SYSTEM (CMS)/WORDPRESS UNTUK UMKM Aulia Ichsan, S.T., M.Kom.; Said Hambali Takhir; Doughlas Pardede; Sugeng Riyadi; Mhd Harry Azhari As’ad
Pengabdian Deli Sumatera Vol 4 No 1 (2025): Artikel Pengabdian Juli 2025
Publisher : LLPM Universitas Deli Sumatera

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

Abstract

Pelaku usaha mikro, kecil, dan menengah (UMKM) di Indonesia masih banyak yang belum memanfaatkan teknologi digital, khususnya website, sebagai sarana pemasaran dan pengembangan bisnis. Permasalahan utama yang dihadapi adalah keterbatasan pengetahuan teknis serta anggapan bahwa pembuatan website memerlukan keahlian pemrograman yang tinggi. Kegiatan pengabdian kepada masyarakat ini bertujuan memberikan pelatihan praktis kepada pelaku UMKM dalam membangun website secara mandiri menggunakan Content Management System (CMS) WordPress. Kegiatan dilaksanakan pada Sabtu, 27 September 2025, pukul 09.00 WIB di Aula Lantai I Gedung Universitas Deli Sumatera, dengan melibatkan lima dosen sebagai mentor/coach. Metode yang digunakan meliputi ceramah interaktif, demonstrasi langsung, dan praktik mandiri peserta. Hasil kegiatan menunjukkan peningkatan pemahaman dan kemampuan peserta dalam membuat serta mengelola website menggunakan WordPress. Peserta mampu menginstalasi WordPress, memilih tema, menambahkan konten, serta mengelola halaman bisnis secara mandiri. Kegiatan ini diharapkan mendorong digitalisasi UMKM sehingga meningkatkan jangkauan pasar dan daya saing usaha.
DIGITALISASI PAGUYUBAN WARGI SUNDA SUMATERA UTARA: IMPLEMENTASI WEBSITE DAN DATABASE Aulia Ichsan; Irwan Daniel; Doughlas Pardede; Sugeng Riyadi
Pengabdian Deli Sumatera Vol 4 No 1 (2025): Artikel Pengabdian Juli 2025
Publisher : LLPM Universitas Deli Sumatera

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

Abstract

Paguyuban Wargi Sunda Sumatera Utara (PWS Sumut) merupakan organisasi kultural yang menjadi wadah bagi masyarakat Sunda di Sumatera Utara untuk melestarikan identitas budaya, mempererat ikatan sosial, serta menguatkan peran aktif komunitas dalam kehidupan berbangsa dan bernegara. Namun dalam era digitalisasi informasi yang terus berkembang pesat, organisasi ini menghadapi tantangan serius berupa keterbatasan sistem dokumentasi, pengelolaan data anggota yang belum terstruktur secara digital, minimnya kehadiran online yang dapat menjangkau komunitas lebih luas, serta rendahnya efisiensi komunikasi dan koordinasi antar pengurus dan anggota yang tersebar di berbagai wilayah Sumatera Utara. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk melaksanakan digitalisasi menyeluruh bagi PWS Sumut melalui pembangunan website organisasi berbasis Content Management System (CMS) dan implementasi sistem database keanggotaan yang terintegrasi. Kegiatan dilaksanakan pada Sabtu, 18 Januari 2025 di Convention Hall Gedung Perpustakaan Universitas Medan Area (UMA), dalam rangkaian Seminar Nasional Kebangsaan bertema Penerapan Falsafah Sunda Dalam Ketahanan Sosial Budaya Nasional Di Era Digitalisasi Informasi. Metode pelaksanaan meliputi pelatihan teknis, demonstrasi langsung, dan pendampingan implementasi sistem. Hasil kegiatan menunjukkan bahwa PWS Sumut berhasil memiliki website resmi yang memuat profil organisasi, agenda kegiatan, berita, galeri, serta database keanggotaan yang dapat dikelola secara digital oleh pengurus. Lebih dari 87,5% peserta pelatihan menyatakan mampu mengoperasikan sistem secara mandiri, dan lebih dari 91,7% menyatakan puas terhadap keseluruhan kegiatan. Digitalisasi ini diharapkan meningkatkan efektivitas komunikasi organisasi, memperluas jangkauan komunitas Sunda di Sumatera Utara, dan mendukung pelestarian nilai budaya Sunda di era digital.
Penerapan Algoritma Boyer-Moore pada Aplikasi Glosarium Kesehatan Shelya Amanda; Doughlas Pardede; Aulia Ichsan
InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Vol 10, No 1 (2025): InfoTekJar September
Publisher : Universitas Islam Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30743/infotekjar.v10i1.12886

Abstract

Pencarian istilah medis dalam glosarium kesehatan merupakan kebutuhan penting bagi mahasiswa, tenaga medis, dan masyarakat umum untuk memahami terminologi kesehatan. Namun, proses pencarian yang kurang efisien dapat memperlambat akses informasi. Penelitian ini membahas penerapan algoritma Boyer-Moore dalam aplikasi glosarium kesehatan guna meningkatkan efisiensi pencarian istilah medis. Metode penelitian meliputi studi literatur, analisis kebutuhan, perancangan sistem, implementasi, serta pengujian performa pencarian. Hasil implementasi menunjukkan bahwa algoritma Boyer-Moore lebih cepat dibandingkan metode pencarian sederhana (naïve search), dengan pengurangan jumlah perbandingan karakter dan waktu eksekusi hingga 50% pada dataset uji berisi 1000 istilah medis. Kesimpulan dari penelitian ini adalah bahwa algoritma Boyer-Moore efektif digunakan dalam aplikasi glosarium kesehatan karena mampu mempercepat proses pencarian istilah medis dan meningkatkan pengalaman pengguna.
Prediksi Viralitas Hoaks Menggunakan Explainable Machine Learning Doughlas Pardede; Muhamad Sayid Amir Ali Lubis; Agus Fahmi Limas Ptr
InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Vol 10, No 1 (2025): InfoTekJar September
Publisher : Universitas Islam Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30743/infotekjar.v10i1.13089

Abstract

The spread of hoaxes on social media has become a systemic threat, potentially triggering opinion polarization, mass panic, and disruption of social stability. Previous research has primarily focused on hoax detection through classification, while predictive efforts to anticipate the extent of their spread remain limited. This study aims to develop a machine learning model to predict the propagation level of hoax content on social media (low, medium, high) and identify the most influential factors contributing to its virality. The dataset was collected from TurnBackHoaks and MAFINDO repositories, comprising 2,500 Indonesian-language hoax contents published throughout 2022-2023. Feature extraction included TF-IDF-based text features and sentiment analysis, temporal features (upload time), and early engagement features (number of likes, shares, comments within the first hour). Three algorithms were compared: Logistic Regression, Random Forest, and XGBoost, with class imbalance handled using SMOTE. The results showed that XGBoost achieved the best performance with a macro average F1-score of 0.82, outperforming Random Forest (0.79) and Logistic Regression (0.70). SHAP analysis revealed that early engagement (shares and likes within the first hour) was the most dominant predictor, followed by content emotionality and nighttime uploads. The model demonstrated high sensitivity to the high-spread class (recall 0.85), indicating its potential for integration into early warning systems by social media platforms and fact-checking organizations. This research contributes to the development of predictive approaches in disinformation mitigation and the strengthening of digital literacy in Indonesia.
Evaluasi Model Machine Learning Pada Deteksi Kematangan Buah Tomat Berdasarkan Warna dia adillia; Sugeng Riyadi; Doughlas Pardede
Jurnal Nasional Teknologi Komputer Vol 6 No 3 (2026): Juli 2026
Publisher : CV. Hawari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61306/jnastek.v6i3.496

Abstract

Penentuan tingkat kematangan buah tomat sering kali dilakukan secara visual, sehingga cenderung subjektif dan menghasilkan evaluasi yang kurang stabil. Penelitian ini bertujuan untuk menilai dan membandingkan kinerja algoritma K-Nearest Neighbors (KNN), Decision Tree, dan Random Forest dalam mengklasifikasikan tingkat kematangan buah tomat berdasarkan fitur warna RGB guna mendapatkan model dengan performa terbaik. Kumpulan data yang digunakan mencakup 300 gambar buah tomat yang dibagi menjadi tiga kategori, yaitu mentah, setengah matang, dan matang. Seluruh gambar melewati tahap Pre-pocessing yang mencakup penghapusan latar belakang, pengubahan ukuran dan ekstraksi fitur dengan menggunakan metode Mean RGB Color Feature Extraction. Dataset selanjutnya dipisahkan menjadi 80% data pelatihan dan 20% data pengujian dengan menggunakan Random Sampling yang diulang sebanyak 10 kali melalui Stratified Sampling. Evaluasi model dilakukan menggunakan Area Under Curve (AUC), Accuracy, F1-Score, Precision, Recall, Confusion Matrix, dan ROC Analysis. Hasil penelitian menunjukkan bahwa KNN memperoleh nilai AUC tertinggi sebesar 100%, sedangkan Random Forest memperoleh nilai Accuracy, F1-Score, Precision, dan Recall tertinggi, yaitu masing-masing sebesar 96,7%. Hasil Confusion Matrix menunjukkan bahwa Random Forest memiliki performa yang lebih seimbang pada ketiga kelas, sementara kesalahan klasifikasi terutama terjadi pada kelas setengah matang yang memiliki karakteristik warna yang berdekatan dengan kelas lainnya. Berdasarkan keseluruhan metrik evaluasi, Confusion Matrix, dan ROC Analysis, Random Forest ditetapkan sebagai model terbaik. Hasil penelitian ini menunjukkan bahwa fitur warna RGB dan algoritma Random Forest berpotensi digunakan untuk mengidentifikasi tingkat kematangan buah tomat secara lebih objektif, konsisten, dan efisien serta dapat dikembangkan untuk mendukung sistem sortasi berbasis citra digital.
Analisis dan perancangan sistem informasi e catreing berbasis wordpress Irvan Maulana; Doughlas Pardede; Aulia Ichsan
Jurnal Nasional Teknologi Komputer Vol 6 No 3 (2026): Juli 2026
Publisher : CV. Hawari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61306/jnastek.v6i3.505

Abstract

Digitalisasi layanan pada usaha catering diperlukan untuk mempermudah penyampaian informasi, promosi, dan proses pemesanan yang sebelumnya dilakukan secara manual. Penelitian ini bertujuan merancang dan mengimplementasikan sistem informasi e-catering berbasis WordPress pada Bakso Rizky sebagai solusi terapan terhadap kebutuhan tersebut. Jenis penelitian yang digunakan adalah penelitian terapan dengan pendekatan pengembangan sistem yang meliputi pengumpulan data, analisis kebutuhan, perancangan, implementasi, pengujian fungsional, dan evaluasi. Data kebutuhan sistem diperoleh melalui observasi, wawancara dengan pengelola usaha, dan dokumentasi. Sistem dikembangkan menggunakan CMS WordPress dengan dukungan PHP, HTML, CSS, dan MySQL. Hasil penelitian berupa website e-catering yang menyediakan informasi usaha, katalog menu dan paket catering, detail layanan, konten promosi, informasi kontak, fasilitas pemesanan daring, serta halaman administrator untuk pengelolaan konten dan pesanan. Pengujian fungsional menunjukkan bahwa fungsi utama sistem dapat dijalankan sesuai kebutuhan pengguna. Penelitian ini menghasilkan solusi teknologi terapan yang mengintegrasikan informasi, promosi, dan pemesanan catering dalam satu sistem berbasis web. Kata Kunci: e-catering, WordPress, sistem informasi, penelitian terapan, UMKM
Co-Authors Agus Fahmi Limas Ptr Aulia Ichsan Aulia Ichsan, S.T., M.Kom. dia adillia Hamela Sari Sitompul Intan Maulina Irvan Maulana Irwan Daniel Mhd Harry Azhari As’ad Muhamad Sayid Amir Ali Lubis Said Hambali Takhir Shelya Amanda Sugeng Riyadi Sugeng Riyadi