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All Journal JURNAL PENGABDIAN KEPADA MASYARAKAT JTT (Jurnal Teknologi Terpadu) Jurnal Informatika IJCIT (Indonesian Journal on Computer and Information Technology) Jurnal Mantik Penusa JIPN (Journal of Informatics Pelita Nusantara) Martabe : Jurnal Pengabdian Kepada Masyarakat JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Jurnal Sains dan Teknologi TRIDARMA: Pengabdian Kepada Masyarakat (PkM) JATI (Jurnal Mahasiswa Teknik Informatika) INFOKUM MEANS (Media Informasi Analisa dan Sistem) Journal of Soft Computing Exploration Bulletin of Information Technology (BIT) Archive: Jurnal Pengabdian Kepada Masyarakat PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer Journal of Artificial Intelligence and Engineering Applications (JAIEA) Sasambo: Jurnal Abdimas (Journal of Community Service)
Insan Taufik
Universitas Negeri Medan
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences

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E-umkm sebagai Upaya Peningkatan Ekonomi Usaha Bihun Ubi Kayu di Desa Melati II Kabupaten Serdang Bedagai Insan Taufik; Kana Saputra S; Dinda Kartika; Debi Yandra Niska; Fevi Rahmawati Suwanto
TRIDARMA: Pengabdian Kepada Masyarakat (PkM) Vol. 5 No. 2 (2022): Nopember: Pengabdian Kepada Masyarakat (PkM)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/abdimas.v5i2.3006

Abstract

Promosi sebagai satu dari empat unsur utama dalam menciptakan bauran pemasaran berperan penting agar hasil produksi dikenal bahkan dibeli oleh konsumen. Dengan adanya perkembangan teknologi, strategi pemasaranpun kini mulai mengalami pergeseran dari konvensional menjadi pemasaran melalui internet. Meskipun demikian, masih banyak ditemukan pelaku usaha khususnya usaha mikro yang belum mampu memasarkan produknya melalui internet. Salah satunya adalah mitra dalam kegiatan Program Kemitraan Masyarakat (PKM) ini, yaitu usaha bihun ubi kayu di Desa Melati II, Kecamatan Perbaungan, Kabupaten Serdang Bedagai. Pemasaran belum dilakukan secara langsung oleh pemilik usaha, melainkan hanya bergantung pada agen terbatas yang mendatangi dan membeli langsung bihun ke rumah produksi. Sebagai solusi bagi mitra sekaligus dukungan terhadap Usaha Mikro, Kecil, dan Menengah (UMKM) dalam rangka membangun perekonomian daerah, kegiatan ini telah menghasilkan media promosi berbasis web yang gratis yaitu e-umkm. Dengan adanya media tersebut, pemilik usaha bihun ubi kayu bahkan pemilik usaha lainnya yang datanya telah terdaftar dapat memasarkan produknya sendiri secara luas.
Penerapan Algoritma Convolutional Neural Network Untuk Menentukan Retinopati Hipertensi Melalui Citra Retina Fundus Kana Saputra S; Insan Taufik; Debi Yandra Niska; Raiyan Fairozi; Mhd Hidayat; Mohammed Hafizh Al-Areef
JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Vol. 6 No. 2 (2023): Jutikomp Volume 6 Nomor 2 Oktober 2023
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jutikomp.v6i2.4307

Abstract

Hypertension is a disease that spreads in the human body caused by increased blood pressure that exceeds normal limits. The increase occurs over a long period, causing complications in human organs that cannot be seen clearly, such as complications in the heart, kidneys, brain, and retina. One of the disorders or complications of high blood pressure is in the retina. The disorder in the retina can also be said as hypertensive retinopathy. Patients suffering from hypertensive retinopathy can only be diagnosed by an ophthalmologist; this is because hypertensive retinopathy cannot be seen with the naked eye. However, one of the earliest signs is the thinning of the arterioles, which can cause blindness. Therefore, computer-assisted processing and analysis of eye fundus images to identify hypertensive retinopathy is an important thing to do by applying the Convolutional Neural Network algorithm. There are nine Convolutional Neural Network architectures used, namely AlexNet, DenseNet, Inception-V3, InceptionResNetV2, Lenet-5, MobileNetV2, ResNet50, VGG16, and VGG19. Based on the experimental results, it was found that of the nine Convolutional Neural Network architectures, two of them, namely AlexNet and Lenet-5, obtained an F1 Measure value of 0.66 and the highest accuracy of 0.67.
Pelatihan dan Pembimbingan Media Pembelajaran Berbasis Aplikasi Canva Sebagai Media Pembelajaran di Yayasan Pedidikan Nurul Hasaniah Yulita Molliq Rangkuti; Said Iskandar Al Idrus; Izwita Dewi; Nurliani Manurung; Insan Taufik; Ahmad Landong; Muhammad Noer Fadlan
Archive: Jurnal Pengabdian Kepada Masyarakat Vol. 3 No. 2 (2024): Juni 2024
Publisher : Asosiasi Pengelola Publikasi Ilmiah Perguruan Tinggi PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55506/arch.v3i2.112

Abstract

Proses pembelajaran sudah bertransformasi ke era digital 4.0. Materi pembelajaran digital diperlukan untuk meningkatkan kemampuan pemahaman dan aktivitas belajar siswa, ini merupakann urgensi sehingga kegiatan pengabdian masyarakat ini dilakukan. Tujuan adalah membuat meteri pembelajaran dengan aplikasi Canva dan pembuatan kuis interaktif mengunakan aplikasi Canva. Metode pelaksanaan dilakukan di Yayasan Pedidikan Nurul Hasaniah yang mempunyai level pendidikan dari SD hingga SMA. Kegiatan diawali dengan memberikan penjelasan pentingnya media pembelajaran, pengenalan media kuis interaktif, pengenalan canva dan implementasi pengelolaan media pembelajaran kuis interaktif. Hasil kegiatan ini menunjukkan bahwa: a) Hasil evaluasi proses melalui observasi selama kegiatan, pemahaman peserta sangat baik dan isi kegiatan dinilai baik, b) Evaluasi produk menunjukkan bahwa seluruh peserta terampil dalam membuat media Canva. c) Respon peserta pelatihan sangat positif, hal ini tercermin dari keaktifan peserta dalam menghadapi tantangan yang ada.
Pengembangan Fitur Rekapitulasi Pada Sistem Informasi Tridharma Perguruan Tinggi Program Studi Ilmu Komputer Universitas Negeri Medan Taufik, Insan; Saputra S., Kana; Al Idrus, Said Iskandar
Jurnal Sains dan Teknologi Vol. 4 No. 3 (2023): Jurnal Sains dan Teknologi
Publisher : CV. Utility Project Solution

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

Abstract

Abstrak−Tujuan dari penelitian ini adalah membantu dosen program studi Ilmu Komputer Unimed untuk mengurus/mengajukan kepangkatan/fungsional atau pemberkasan lain yang membutuhkan data tridharma perguruan tinggi. Sistem informasi Tridharma yang telah dibuat dapat menampung data tridharma dosen, seperti penelitian, pengabdian, pengajaran, penunjang dan semua luaran hasil dari kegiatan tridharma tersebut. Metode yang di laksanakan pada penelitian ini adalah metode SDLC (System Development Life Cycle) yang dimulai dari proses penyiapan data-data tridharma sampai proses implementasi fitur rekapitulasi. Hasil dari penelitian ini adalah fitur yang ditambahkan pada aplikasi tridharma perguruan tinggi dapat mencari semua data tridharma dosen program studi Ilmu Komputer Unimed yang sesuai dengan kriteria pencarian dan menyajikan data dalam satu halaman, yang selanjutnya dapat digunakan untuk kepentingan-kepentingan administrasi dosen program studi Ilmu Komputer Universitas Negeri Medan.
Implementation of You Only Look Once Version 8 Algorithm to Detect Multi-Face Drivers and Vehicle Plates Saputra S, Kana; Taufik, Insan; Ramadhani, Irham; Siregar, Angginy Akhirunnisa; Pinem, Josua; Lubis, Afiq Alghazali; Pane, Yeremia Yosefan; Putri, Rezkya Nadilla
Jurnal Informatika Vol 11, No 2 (2024): October
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v11i2.22026

Abstract

Checking the identity of motorcycle owners when leaving the college area is a mandatory activity for security officers to ensure that vehicles entering and exiting the college are the same driver. The conventional checking process often causes the impact of vehicle queues when the volume of vehicles increases. Therefore, an intelligent system is needed to detect multi-plate vehicles automatically. One approach in the world of image detection of an object is the use of the YOLO (You Only Look Once) algorithm. This algorithm predicts bounding boxes and possible classes in a single frame. This research divides objects into 3 classes, namely vehicles, driver's faces, and vehicle plates. The dataset used was 74 varied images consisting of 50 training data, 12 validation data and 12 testing data. The image was trained using 300 epochs and a batch size of 8 and resulted in an F1 score calculation for detecting objects reaching 92%.
Integrasi Algoritma Naïve Bayes Dan Website Untuk Deteksi Dini Penyakit DBD Di RSUD. DR. Pirngadi Tiopan Pandapotan Purba, Yeremia; Taufik, Insan
Bulletin of Information Technology (BIT) Vol 5 No 1: Maret 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v5i1.1152

Abstract

Dengue Hemorrhagic Fever (DHF) poses a serious threat to public health, caused by the Dengue virus transmitted through Aedes aegypti or Aedes albopictus mosquitoes. DHF can affect individuals of all age groups, especially children, with a mortality rate reaching 25% among children, as noted by the World Health Organization (WHO). Despite a decrease in DHF cases in 2020, the numbers remain high, presenting a significant issue in Indonesia, particularly in Kota Medan. RSUD Dr. PIRNGADI in Kota Medan is one of the hospitals addressing DHF cases. The primary challenge faced is the substantial increase in DHF patients in 2020, leading to a decrease in the effectiveness of patient care and an accumulation of administrative registration issues. Currently, there is no specific predictive system or research on DHF at RSUD Dr. PIRNGADI. This research aims to integrate the Naïve Bayes Algorithm and a website for early detection of DHF at RSUD Dr. PIRNGADI. Data on DHF symptoms are collected through patient medical records, and the Naïve Bayes Algorithm is employed to predict the likelihood of DHF. The detection system will be linked to an API and integrated into the RSUD Dr. PIRNGADI website, enabling users to conduct online DHF detection by entering patient symptoms. With this research, the goal is to contribute to enhancing the management of DHF cases at RSUD Dr. PIRNGADI, improving the efficiency of patient care, and providing a solution for the increasing number of DHF cases. The findings of this research can also serve as a foundation for developing similar systems in other hospitals and contribute to more effective efforts in preventing and controlling DHF
MENENTUKAN WARNA MAKE UP YANG COCOK BERDASARKAN JENIS SKINTONE PADA CITRA WAJAH MENGGUNAKAN NAIVE BAYES CLASSIFIER Syifa Cendikia, Yolanda; Taufik, Insan; Arnita, Arnita; Indra, Zulfahmi; Chairunisah, Chairunisah
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 1 (2025): JATI Vol. 9 No. 1
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i1.12494

Abstract

Make up adalah produk kosmetik yang digunakan untuk mempercantik atau memperbaiki penampilan wajah dan kulit. Pemilihan warna make up yang sesuai dengan skintone seseorang merupakan aspek penting dalam dunia kecantikan. Skintone yang tidak sesuai dengan warna make up dapat mengurangi penampilan alami seseorang. Penelitian ini membahas pengembangan sistem berbasis citra yang mampu menentukan warna make up yang cocok berdasarkan jenis skintone pada citra wajah menggunakan algoritma Naive Bayes Classifier. Sistem ini dirancang untuk menganalisis citra wajah, mengidentifikasi skintone, dan merekomendasikan warna make up yang sesuai. Data yang digunakan berasal dari citra wajah yang telah diklasifikasikan berdasarkan jenis skintone. Selanjutnya, algoritma Naive Bayes digunakan untuk melakukan klasifikasi dan prediksi warna make up yang paling cocok. Hasil penelitian sistem klasifikasi jenis skintone menggunakan algoritma Naive Bayes Classifier dengan hasil pengujian dari 80 data training dan 20 data testing mendapat akurasi sebesar 85%. Hasil dari penelitian ini dapat membantu pengguna dalam memilih warna make up yang tepat secara otomatis, sehingga dapat menghemat waktu dan memberikan hasil yang lebih presisi. Pengujian dilakukan untuk mengevaluasi keakuratan sistem dan hasil menunjukkan bahwa pendekatan Naive Bayes memberikan hasil yang cukup akurat dalam menentukan rekomendasi warna make up berdasarkan skintone.
Comparison of supervised machine learning methods in predicting the prevalence of stunting in north sumatra province Saragih, Vinny Ramayani; Arnita, Arnita; Indra, Zulfahmi; Taufik, Insan; Sinaga, Marlina Setia
Journal of Soft Computing Exploration Vol. 5 No. 4 (2024): December 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i4.498

Abstract

Stunting is a growth and development disorder in children caused by chronic malnutrition and repeated infections. Stunting has significant short- and long-term impacts and is one of the major health issues currently faced by Indonesia. The prevalence of stunting in North Sumatra Province is 18.9%, and the provincial government aims to reduce this prevalence to 14% by 2024. This study aims to compare the performance of several supervised machine learning methods in predicting stunting prevalence in North Sumatra Province. The data used is secondary data from 2021 to 2023, covering 33 districts/cities in the province. This study evaluates three machine learning models: Support Vector Regression (SVR), Decision Tree, and Random Forest, using evaluation metrics such as Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). The analysis results show that Random Forest provides the most accurate and consistent predictions, with lower MSE, MAE, RMSE, and MAPE values compared to the other models in most areas. Decision Tree yields good results in some regions but tends to produce higher errors in certain cases. SVR exhibits a more varied performance, with some regions showing higher prediction errors. Overall, Random Forest is the superior model for predicting district/city-level data, although model selection should be tailored to the data characteristics and application needs
Website based classification of karo uis types in north sumatra using convolutional neural network (CNN) algorithm Purba, Boy Hendrawan; Syahputra, Hermawan; Idrus, Said Iskandar Al; Taufik, Insan
Journal of Soft Computing Exploration Vol. 5 No. 4 (2024): December 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i4.500

Abstract

Indonesia is one of the largest archipelagic countries in the world. It has abundant cultural diversity including nature, tribes. One of the tribes in Indonesia is the Batak Karo tribe. Batak Karo is a tribe that inhabits the Karo plateau area, North Sumatra, Indonesia. Batak Karo has various cultures, one of which is a traditional cloth known as uis. Unfortunately, the Karo Batak community, especially the younger generation, has insufficient knowledge of the types of uis. Thus, a solution that is easily accessible both in terms of time, cost and experts in recognizing Uis is needed. This research aims to build a website-based application that can classify the types of Karo Uis. This research uses Convolution neural network (CNN) using Alex Net architecture, to get the best model this research compares several hyper parameters, namely learning rate of 10-1 to 10-4, and data division with a ratio of 70:30 and 80:20. The best model falls on a ratio of 70:30 and a learning rate of 10-4 with an accuracy of 98%, and a validation accuracy of 99%, then the model is stored in h5 format in this study successfully builds and implements the model into a web-based application.
Automatic Waste Type Detection Using YOLO for Waste Management Efficiency Alfattah Atalarais; Kana Saputra S; Hermawan Syahputra; Said Iskandar Al Idrus; Insan Taufik
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.770

Abstract

The management of waste in Indonesia is currently suboptimal, with only 66.24% being effectively managed, leaving 33.76% unmanaged. This highlights a significant challenge in waste management, primarily due to a lack of understanding in selecting appropriate waste types. Advances in deep learning and computer vision offer promising solutions to this issue. This study employs the YOLOv8l model, a well-regarded deep learning model for object detection, to develop an automated waste type detection system integrated with trash bins. The dataset comprises 2800 images across four classes, each containing 700 images, and is split with an 80:10:5 ratio for training, validation, and testing. Evaluation on test data yields a mean Average Precision (mAP) of 96.8%, indicating robust model performance in object detection. The model's accuracy is further validated with a score of 89.98%. Real-time testing conducted at Merdeka Park, Binjai, demonstrates the system's capability to detect waste with varying confidence levels, consistently above the 0.5 threshold. The highest confidence was observed in bottle detection at 0.94, and the lowest in cans at 0.64, underscoring the system's reliability across different detection scenarios within a 30cm range.
Co-Authors Abdul Ghofur Abdul Ghofur Ahmad Landong Alfattah Atalarais Amran Sitohang Andriyani S, Rahayu Antonius Purba Arjon Samuel Sitio Arnita Arnita Arnita Arnita Chairunisah Chairunisah, Chairunisah Dede Yusuf, Dede Dinda Farahdilla Dharma Dinda Kartika Farhan Ramadhan, Haikal Fevi Rahmawati Suwanto Fitra, Awaludin Fristy Riandari Hafiz Harahap, Fauzan Hasugian , Paska Marto Hermawan Syahputra Hermawan Syahputra Ihsan Zulfahmi Irham Ramadhani Isda Pramuniati Izwita Dewi Kana Saputra S Kuraini, Atifa Nuzulul Latifah Hasibuan, Najwa Lubis, Afiq Alghazali MANSUR AS Mhd Hidayat Mhd Hidayat Mohammed Hafizh Al-Areef Muhammad Noer Fadlan Muhammad, Fachrezzy Muthmainnah, Inna Nadilla Putri, Rezkya Niska, Debi Yandra Nurliani Manurung Pane, M Iqbal Anata Pane, Yeremia Yosefan Pinem, Josua Purba, Boy Hendrawan Putri, Rezkya Nadilla Raiyan Fairozi Said . Iskandar Saragih, Vinny Ramayani Sasalia S, Putri Simamora, Elmanani Sinaga, Marlina Setia Siregar, Angginy Akhirunnisa Sri Mulyana Syawali, Yusfi Syifa Cendikia, Yolanda Tengku Ratna Soraya Tiopan Pandapotan Purba, Yeremia TONNI LIMBONG Wahabi Hasibuan, Rahman Warjaya, Angga Wete Polili, Andi Wete Polili, Andi Yulita Molliq Rangkuti Zulfahmi Indra, Zulfahmi