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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JURNAL MEDIA INFORMATIKA BUDIDARMA JURTEKSI Jurnal Mantik IICS JME Amal Ilmiah: Jurnal Pengabdian Kepada Masyarakat Jurnal Abdimas Ilmiah Citra Bakti
Arie Satia Dharma
Institut Teknologi Del
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Social Sciences

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COMPARISON FEATURE EXTRACTION USING ARTIFICIAL NEURAL NETWORK ALGORITHM ON SMOKER PREDICTION Dharma, Arie Satia; Pardede, Cynthia Veronika; Sitorus, Jonggi Vegas
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 10 No. 4 (2024): September 2024
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v10i4.2933

Abstract

Abstract: The habit of smoking is dangerous because of the addictive substances that make cigarettes addictive. Its addictive nature poses a significant risk, affecting personality with stress, depression and nervous disorders. Body factors that indicate smoking include blood sugar levels, dental caries, and hemoglobin. To address this, research has been conducted with focused efforts to understand and address the risks associated with smoking and its impact on overall health. This research aims to choose the best method for predicting smokers by using feature selection techniques. The feature selection algorithms uses for that are Analysis of Variance (ANOVA), Recursive Feature Elimination (RFE), and Genetic Algorithm (GA) to select optimal attributes and uses the k-fold cross validation technique as the validation of the Artificial Neural Network algorithm. The data includes various parameters such as age, height, weight, vision, blood pressure, cholesterol, triglycerides, hemoglobin, AST, ALT, GTP, gender, dental caries and tartar. Hearing ability, urine protein content, and tartar were selected. The results showed that using the Analysis of Variance method showed higher accuracy (77.101%) compared to the Genetic Algorithm method (74.64%) and the Recursive Feature Elimination method (76.08%). Selection of relevant attributes increases the predictions and insights of the Artificial Neural Network model about the effects of smoking on health.            Keywords: artificial neural network; analysis of variance; genetic algorithm; recursive feature elimination; smoker prediction  Abstrak: Kebiasaan merokok berbahaya karena adanya zat adiktif yang membuat rokok menjadi ketagihan. Sifatnya yang membuat ketagihan menimbulkan risiko yang signifikan, mempengaruhi kepribadian dengan stres, depresi, dan gangguan saraf. Faktor tubuh yang mengindikasikan kebiasaan merokok antara lain kadar gula darah, karies gigi, dan hemoglobin. Untuk mengatasi hal ini, penelitian telah dilakukan dengan upaya terfokus untuk memahami dan mengatasi risiko yang terkait dengan merokok dan dampaknya terhadap kesehatan secara keseluruhan. Penelitian ini bertujuan untuk memilih metode terbaik dalam memprediksi perokok dengan menggunakan teknik seleksi fitur. Metode seleksi fitur yang digunakan adalah Analysis of Variance (ANOVA), Recursive Feature Elimination (RFE), dan Genetic Algorithm (GA) untuk memilih atribut yang optimal dan menggunakan teknik k-fold cross validation sebagai validasi algoritma Artificial Neural Network. Data tersebut mencakup berbagai parameter seperti umur, tinggi badan, berat badan, penglihatan, tekanan darah, kolesterol, trigliserida, hemoglobin, AST, ALT, GTP, jenis kelamin, karies gigi dan karang gigi. Kemampuan pendengaran, kandungan protein urin, dan karang gigi dipilih. Hasil penelitian menunjukkan bahwa penggunaan metode Analysis of Variance menunjukkan akurasi yang lebih tinggi (77,101%) dibandingkan dengan metode Genetic Algorithm (74,64%) dan metode Recursive Feature Elimination (76,08%). Pemilihan atribut yang relevan meningkatkan prediksi dan wawasan model Jaringan Syaraf Tiruan tentang dampak merokok terhadap kesehatan. Kata kunci: artificial neural network; analysis of variance; genetic algorithm; prediksi perokok; recursive feature elimination
Performance Trade-off of Anchor-Based and Anchor-Free Approaches of Faster R-CNN for Face Detection Dharma, Arie Satia; Herimanto, Herimanto; Simbolon, Niar Fujita; Nababan, Anton Roycar
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15804

Abstract

The face is a unique biometric feature that plays a crucial role in individual identification as it holds essential information for identity recognition. Face detection technology has been experiencing significant advancements in the field of computer vision. However, face detection technology continues to face challenges in balancing high detection accuracy with computational efficiency. While deep learning has advanced this field, there remains a lack of comparative studies that compare the performance trade-offs between anchor-based and anchor-free region proposal mechanisms within a Faster R-CNN framework. This research objective is comparing the performance of face detection using two approaches: anchor-based and anchor-free. The anchor-based approach use anchor boxes to predict bounding boxes, while the anchor-free approach predicts bounding boxes directly from pixel positions oriented around a point. The anchor-based approach is implemented use base line region proposed network method, whereas the anchor-free approach use a centerpoint method. The study utilizes a custom dataset comprising 1,000 formal images of students from Del Institute of Technology, split into 900 training images and 100 testing images. Performance evaluation is conducted based on metrics such as intersection over union, precision, recall, and latency. The results demonstrate that the anchor-based approach achieves superior accuracy with an average IoU of 0.98 but requires a longer detection time of approximately 2.33 seconds per image. Conversely, the anchor-free approach offers significantly faster processing at 0.14 seconds per detection, though with a lower average IoU of 0.78. This study concludes that while anchor-based methods excel in precision, anchor-free architectures provide alternative for time-critical applications, offering a clear reference for optimizing future face detection systems.
Pengembangan Sistem Informasi Desa Berbasis Web untuk Monitoring Kependudukan dan Pertanian Desa Jaya Santoso; Herimanto Herimanto; Ranty Deviana Siahaan; Arlinta Christy Barus; Arie Satia Dharma; Johannes Harungguan Sianipar
Amal Ilmiah: Jurnal Pengabdian Kepada Masyarakat Vol. 7 No. 2 (2026): Edisi Juli 2026
Publisher : FKIP Universitas Halu Oleo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36709/amalilmiah.v7i2.538

Abstract

Pengembangan sistem informasi desa berbasis web menjadi kebutuhan penting dalam mendukung tata kelola data di tingkat desa, khususnya terkait kependudukan dan pertanian. Kegiatan pengabdian kepada masyarakat ini dilaksanakan di Desa Kuta Dame, Kabupaten Pakpak Bharat, dengan tujuan merancang dan mengimplementasikan sistem informasi desa yang mampu memfasilitasi monitoring  kependudukan, penguasaan lahan, dan aktivitas pertanian. Metode kegiatan meliputi analisis kebutuhan mitra untuk mengetahui permasalahan utama, perancangan sistem menggunakan pendekatan terstruktur, implementasi berbasis teknologi web dengan desain antarmuka yang sederhana dan mudah digunakan, serta diseminasi hasil melalui pelatihan dan pendampingan aparatur desa. Hasil kegiatan menunjukkan bahwa sistem berhasil mengintegrasikan 2.563 data penduduk ke dalam platform digital serta digunakan oleh 40 aparatur desa dalam kegiatan pelatihan dan uji coba operasional. Sistem mampu menyajikan informasi kependudukan dan pertanian secara sistematis, terintegrasi, dan dapat diakses secara real-time, sehingga meningkatkan efisiensi pengelolaan data serta mempermudah proses monitoring  dan pengambilan keputusan di tingkat desa. Dengan demikian, pengembangan sistem informasi desa berbasis web terbukti mendukung peningkatan kapasitas digital aparatur desa dan memperkuat tata kelola pemerintahan desa yang lebih efektif dan transparan.
Recognition Image Text Using Faster Region-Based Convolutional Neural Network with Optical Character Recognition Arie Satia Dharma; Arlinta Christy Barus; Samuel Herlinton Sibuea; Nanchy Monika Siadari
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 10 No. 1 (2026): Issues July 2026
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v10i1.17700

Abstract

Business cards serve as a form of identification that facilitates communication, but managing large amounts of contact information on business cards can often be challenging. To address this issue, this study developed an end-to-end architecture model to automatically extract information from business cards image. This model utilizes Optical Character Recognition and the Faster Region-Based Convolutional Neural Network method. This model allows users to extract contact information from business cards. Using a dataset of 450 business card images, we conducted experiments to evaluate their impact on the task of detecting text in images. We used an image batch size of 500 with 50, 100, and 500 epochs as hyper experiment parameters. The highest accuracy achieved was 0.8342 with mAP was 0.8513. For the character recognition task, Optical Character Recognition produced results with a Character Error Rate (CER) less than 0.08. These findings suggest that the integration of Faster R-CNN and OCR is effective in detecting and extracting textual content from diverse business card layouts. In conclusion, the proposed approach provides a reliable and efficient solution for automated business card digitization and shows strong potential for practical applications in contact information management systems.
RE-ENGINEERING DAN PELATIHAN PENGGUNAAN WEBSITE SEKOLAH SMA NEGERI 1 BALIGE Ranty Siahaan; Arie Satia Dharma; Arlinta Christy Barus; Johannes Harungguan Sianipar; Iustisia Natalia Simbolon; Herimanto; Prans Daniel Simarmata; Charlos Pardomuan Purba; Okdini Nigita Hutagalung; Dian Grecia Natalie Gulo
Jurnal Abdimas Ilmiah Citra Bakti Vol. 7 No. 2 (2026)
Publisher : STKIP Citra Bakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38048/jailcb.v7i2.6876

Abstract

Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan pemanfaatan website SMA Negeri 1 Balige sebagai media informasi, komunikasi, dan promosi sekolah yang lebih efektif dan berkelanjutan. Permasalahan yang dihadapi mitra meliputi belum optimalnya beberapa fitur website serta keterbatasan kemampuan guru dan siswa dalam mengelola website sekolah secara mandiri. Metode pelaksanaan meliputi observasi kebutuhan, re-engineering website, sosialisasi, pelatihan teknis, dan evaluasi melalui survei kepuasan mitra. Kegiatan dilaksanakan pada 16 November 2024 dengan melibatkan 30 guru dan staf serta 70 siswa SMA Negeri 1 Balige. Hasil kegiatan menunjukkan bahwa website sekolah berhasil diperbarui melalui perbaikan fitur Data Alumni, Agenda, dan Berita sesuai kebutuhan mitra. Selain itu, pelatihan yang diberikan meningkatkan pemahaman peserta dalam pengelolaan dan pemanfaatan website sekolah. Evaluasi terhadap 21 responden menunjukkan bahwa seluruh peserta memberikan respons positif terhadap program, dengan mayoritas penilaian berada pada kategori sangat baik untuk aspek kesesuaian materi, metode pelaksanaan, manfaat program, kompetensi pelaksana, dan keberlanjutan kerja sama. Kegiatan ini berhasil memperkuat kapasitas sekolah dalam mengelola website secara mandiri serta mengoptimalkan fungsi website sebagai sarana komunikasi, informasi, dan promosi sekolah di era digital.
Co-Authors Arlinta Christy Barus Arlinta Christy Barus Charlos Pardomuan Purba Dian Grecia Natalie Gulo Evi Rosalina Silaban H. Justin Manurung Hana Maria Siahaan Hatigoran, Andreas Herimanto Herimanto Herimanto Humasak T. A. Simanjuntak Iustisia Natalia Simbolon Jaya Santoso Johannes Harungguan Sianipar Nababan, Anton Roycar Nanchy Monika Siadari Okdini Nigita Hutagalung Oktavi Yanty Silitonga Pardede, Cynthia Veronika Pardosi, Chantika Nadya Serebella Prans Daniel Simarmata Purba, Daniel Somanta Ranty Deviana Siahaan Ranty Siahaan Samuel Herlinton Sibuea Samuel Indra Gunawan Situmeang Saragih , Yosua Giat Raja Silaen, Zan Peter Simbolon, Niar Fujita Sitorus, Jonggi Vegas Sitorus, Judah Michael Parluhutan Tambunan, Veronika Tampubolon, Lily Andayani