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All Journal Jurnal Teknovasi : Jurnal Teknik dan Inovasi Mesin Otomotif, Komputer, Industri dan Elektronika Jurnal Teknik Informatika UNIKA Santo Thomas MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Mantik KAKIFIKOM : Kumpulan Artikel Karya Ilmiah Fakultas Ilmu Komputer Jatilima : Jurnal Multimedia Dan Teknologi Informasi INFOKUM Jurnal Sistem Komputer dan Informatika (JSON) MEANS (Media Informasi Analisa dan Sistem) Journal of Computer Networks, Architecture and High Performance Computing Jurnal Info Sains : Informatika dan Sains Jurnal Pengabdian Masyarakat IPTEK Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Jurnal Armada Informatika Jurnal Masyarakat Indonesia Journal of Blockchain, Nfts and Metaverse Technology "Journal of Data Science
Simangunsong, Pandi Barita Nauli
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Other

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Peningkatan Kualitas Citra Pada Studio Photography Dengan Menggunakan Metode Gaussian Filter Simangunsong, Pandi Barita Nauli
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 3 No 1: Tahun 2018
Publisher : LPPM UNIKA Santo Thomas

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (365.4 KB) | DOI: 10.17605/jti.v3i1.247

Abstract

Photography sangatlah sering didengar dalam kehidupan sehari-hari dan pemanfaatan pada citra juga berkembang oleh sebab itu maka diperlukan suatu teknik untuk menghasilkan citra photography yang baik. Kualitas citra di studio photography sering diabaikan sehingga konsumen kurang merasa puas dengan hasil citra yang dihasilkan oleh sebab itu maka sangatlah dibutuhkan sebuah cara untuk memperbaiki citra tersebut dengan cara meningkatkan kualitas pada citra tersebut. Metode gaussian filter dapat meningkatkan kualitas citra photography dengan cara memfilter bagian citra yang memiliki gangguan terhadap kualitas.
Perancangan Aplikasi Pengamanan Citra Berwarna dengan Algoritma RSA Simangunsong, Pandi Barita Nauli; Fitri, Komariah
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 3 No 2: Tahun 2018
Publisher : LPPM UNIKA Santo Thomas

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (531.489 KB) | DOI: 10.17605/jti.v3i2.295

Abstract

Kriptografi adalah teknik yang digunakan untuk pengamanan sebuah pesan (message) yang bersifat rahasia. Implementasi Kriptografi banyak berkembang dalam pemanfaatannya dalam bidang komputer. Prisnsip kerja dari kriptografi adalah mengubah pesan yang bersifat asli yang disebut dengan (plainteks) menjadi pesan yang tidak dapat sama sekali dimengerti oleh pihak lain yang disebut dengan (cipherteks ) yang artinya pesan cipherteks adalah pesan yang tidak dapat dipahami oleh pihak yang tidak berkepentingan . Proses kerja tersebut merupakan proses enkripsi dan proses deskripsi. Algoritma asimetri adalah teknik yang digunakan oleh algoritma RSA, proses enkripsi dan deskripsi memiliki kunci yang tidak sama, kunci untuk enkripsi tidak akan pernah sama dengan kunci yang digunakan untuk mendeskripsi. Kunci puplik akan berguna saat dilakukan enkripsi sedangkan untuk proses deskripsi yang digunakan adalah kunci privat. Algoritma RSA yang dikenal dengan model kunci public. Teknik pengerjaan algoritma RSA sering dikenal dengan teknik pembangkit kunci acak, enkripsi dan deskripsi.
Analisis Komputasi dan Efisiensi Pelatihan Model Deep Learning pada Skala Besar Bangunan Simangunsong, Pandi Barita Nauli; Tuti Andriani; Muhammad Amin; Pristiwanto; Sirait, Matias Julyus Fika
Jurnal Multimedia dan Teknologi Informasi (Jatilima) Vol. 7 No. 02 (2025): Jatilima : Jurnal Multimedia Dan Teknologi Informasi
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jatilima.v7i02.1499

Abstract

Penerapan deep learning pada sistem bangunan pintar telah menunjukkan potensi besar dalam meningkatkan efisiensi energi melalui prediksi beban, deteksi okupansi, dan pengendalian sistem HVAC. Namun, pelatihan model dalam skala besar menghadapi tantangan serius terkait kebutuhan data besar dan beban komputasi tinggi. Penelitian ini bertujuan menganalisis efektivitas dan efisiensi pelatihan model deep learning pada skala besar bangunan, serta mengevaluasi peran transfer learning sebagai strategi pengurangan beban pelatihan. Penelitian menggunakan arsitektur MLP, LSTM, dan CNN, serta pendekatan feature extraction dan fine-tuning dalam transfer learning. Evaluasi dilakukan terhadap akurasi, F1-score, RMSE, serta metrik efisiensi seperti waktu pelatihan, penggunaan memori, dan jumlah parameter. Hasil menunjukkan bahwa MLP memberikan performa terbaik untuk klasifikasi okupansi, sementara LSTM unggul dalam prediksi energi. Transfer learning terbukti efektif dalam mempertahankan akurasi dengan efisiensi pelatihan yang lebih tinggi. Model ringan seperti MLP + Transfer Learning (FE) mengurangi konsumsi memori hingga 35% dibanding pelatihan penuh, tanpa penurunan akurasi signifikan. Penelitian ini menyimpulkan bahwa kombinasi pemilihan arsitektur yang tepat dan strategi pelatihan efisien sangat penting dalam implementasi praktis model deep learning pada bangunan pintar berskala besar.
Comparative Evaluation of Data Clustering Accuracy through Integration of Dimensionality Reduction and Distance Metric Hasugian, Paska Marto; Mathelinea, Devy; Simamora, Siska; Simangunsong, Pandi Barita Nauli
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 3 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i3.5057

Abstract

The primary issue in clustering analysis of multivariate data is the low accuracy resulting from a mismatch between the Distance Metric used and the characteristics of the data. This study aims to comprehensively evaluate the effect of eight Distance Metric in the KMeans algorithm integrated with the Principal Component Analysis (PCA)dimension reduction technique. The analysis process was conducted by transforming the data into two principal components using PCA, then applying K-Means to each Distance Metric. Performance evaluation was conducted based on five internal metrics: Silhouette Score, Davies-Bouldin Index, Sum of Squared Errors, Calinski-Harabasz Index, and Dunn Index. The results show that the Bray-Curtis formula provides the best performance, with a Silhouette Score of 0.4291 and SSE of 30.3673. This is followed by Euclidean and Minkowski, which yield the highest Calinski-Harabasz Index value of 2239.85 and Dunn Index of 0.0108, respectively. In contrast, Hamming’s formula yielded the lowest performance across all metrics, with a Silhouette Score of 0.0000 and an SSE of 1996.00. The ANOVA test revealed significant differences between the Distance Metric, with a p-value of ¡0.000 for all metrics, which was further supported by the Tukey HSD follow-up test results. The implications of these findings confirm the importance of selecting an appropriate Distance Metric in the clustering process to ensure the validity, efficiency, and interpretability of multivariate data analysis results.
Penggunaan Mendeley dalam Penulisan Karya Tulis Ilmiah bagi Komunitas Akademik Simangunsong, Pandi Barita Nauli; Siagian, Novriadi Antonius; Sirait, Matias Julyus Fika; Maulidina; Pristiwanto
Jurnal Masyarakat Indonesia (Jumas) Vol. 4 No. 02 (2025): Jurnal Masyarakat Indonesia (Jumas)
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jumas.v4i02.265

Abstract

Pelatihan Penggunaan Mendeley dalam Penulisan Karya Tulis Ilmiah bagi Komunitas Akademik dilaksanakan di Sekolah Tinggi Ilmu Kesehatan Darmo dengan tujuan untuk meningkatkan kompetensi dosen dalam mengelola referensi secara efisien dan sesuai standar akademik. Mendeley sebagai aplikasi manajemen referensi dipilih karena kemampuannya dalam menyusun sitasi dan daftar pustaka secara otomatis serta integrasinya dengan berbagai platform penulisan ilmiah. Pelatihan ini mencakup materi instalasi, pembuatan akun, penggunaan Mendeley secara manual, dan pemanfaatan fitur Web Importer. Metode pelatihan bersifat partisipatif dan berbasis praktik langsung. Hasil evaluasi menunjukkan bahwa 92% peserta merasa pelatihan ini sangat bermanfaat dan 89% tertarik untuk mengikuti pelatihan lanjutan. Selain itu, peserta mengusulkan agar kegiatan ini dijadikan program rutin untuk mendukung peningkatan mutu publikasi ilmiah dosen. Pelatihan ini terbukti efektif dalam membekali dosen dengan keterampilan teknis yang relevan dan mendorong terbentuknya budaya penulisan ilmiah yang profesional dan terstandar di lingkungan perguruan tinggi.
Selection of Digital Investment Instruments Applying the Multi-Objective Optimization by Ratio Analysis Method Patria, Lintang; Marbun, Nasib; Sitompul, Bernad J. D.; Simangunsong, Pandi Barita Nauli
SaNa: Journal of Blockchain, NFTs and Metaverse Technology Vol. 1 No. 1 (2023): August 2023
Publisher : CV. Media Digital Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58905/sana.v1i1.187

Abstract

Choosing a digital investment instrument to make digital investments is not easy because behind the expected profits are also accompanied by balanced risks. Therefore, not a few novice investors are confused to determine the choice of digital investment instruments that are most appropriate for use in the long term. In this study the authors applied the decision support system method (Multi-Objective Optimization by Ratio Analysis) to facilitate the decision-making process in choosing digital investments for novice investors. The results of this study indicate that Alternative A4 (Trading) with a value of 0.17097 has the highest value compared to other alternatives, so Alternative A4 (Trading) is the most recommended digital investment instrument for use by novice investors.
Gradient Magnitude Based Image Classification and Edge Detection for Pattern Recognition in Grayscale Images Simangunsong, Pandi Barita Nauli; Andriani, Tuti; Sirait, Matias Julyus Fika
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 4 (2024): Articles Research October 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i4.4611

Abstract

Image classification is a crucial technique in digital image processing, used in various applications such as object recognition, surveillance systems, and medical image analysis. This research explores the use of gradient magnitude-based edge detection and Robert's Cross methods in improving the classification accuracy of grayscale images. Edge detection is used to identify object boundaries, while gradient magnitude amplifies intensity differences, thus clarifying existing patterns. Through experiments conducted on grayscale images, the results show that this method is able to detect edges with significant accuracy. The gradient values obtained from the combination of Rx and Ry matrices give a strong indication of the presence of edges, which plays an important role in image classification. With an accuracy of 75%, the method proved to be effective, although there are still challenges in dealing with images with high noise or low contrast. The conclusion of this study shows that the combination of edge detection and gradient magnitude is a promising approach for image classification, providing results that can be applied in various domains, including medical and surveillance. Further research is recommended to optimize this approach and extend its application to more complex datasets.
Testing the K-Means Clustering Algorithm in Processing Student Assignment Grades Using the RapidMiner Application Simangunsong, Pandi Barita Nauli; Manalu, Mamed Rofendi
Journal Of Data Science Vol. 1 No. 02 (2023): Journal Of Data Science, September 2023
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/jds.v1i02.2838

Abstract

In the current era of computers and the internet, educational data analysis has become very important to optimize the teaching and learning process. This study focuses on the use of the K-Means clustering algorithm used by the RapidMiner application to group assignment grades given to students during one academic semester. The goal is to discover patterns of achievement and areas that require intervention. The results show that the algorithm is very effective in identifying groups of students based on their performance and dividing them into middle, high, and those who need help. In short, the use of K-Means Clustering with RapidMiner offers a useful analytical approach for education. This allows for a more customized learning approach that is based on analysis of student achievement.
Co-Authors Alex Rikki Arisman Ayuningsih, Ekatri Barus, Apriwati Devri Suherdi Fithrie Soufitri Fitri, Komariah Furqan Ginting, Erika Fahmi Hasugian , Paska Marto Indra Sidabutar Irwan Jani Tarigan kamil erwansyah Lintang Patria M.Rajagukguk, Denni Makmur Tarigan Mamed Rofendy Manalu Manalu, Mamed Rofendi Mathelinea, Devy Matias Julyus Fika Sirait Maulidina Monang Juanda Tua Sihombing Muhammad Amin Muhammad Amin Nasib Marbun Panjaitan, Muhammad Iqbal Pristiwanto Rita Hamdani Siagian, Novriadi Antonius Simamora, Siska Simatupang, Baharis Setia Adisahputra Simbolon, Hasanal Fachri Satia Sitompul, Bernad J. D. Sitorus, Syahrial Sony Bahagia Sinaga Sridewi, Nurmala Syarifah Fadillah Rezky Tuti Andriani Tuti Andriani Yati, Nurhayati