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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Dinamik Jurnal Daya Matematis Jurnal Pengabdian kepada Masyarakat Economic Reviews Journal Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Riset Rumpun Matematika dan Ilmu Pengetahuan Alam (JURRIMIPA)
Hermawan Syahputra
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Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Public Health Social Sciences Other

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Klasifikasi Varietas Tanaman Kelengkeng Berdasarkan Morfologi Daun Menggunakan Backpropagation Neural Network dan Probabilistic Neural Network Hermawan Syahputra; Agus Harjoko
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 5, No 3 (2011): November
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.5206

Abstract

Pengenalan daun memainkan peran penting dalam klasifikasi tanaman dan isu utamanya terletak pada apakah fitur yang dipilih stabil dan memiliki kemampuan yang baik untuk membedakan berbagai jenis daun. Pengenalan tanaman berbantuan komputer merupakan tugas yang masih sangat menantang dalam visi komputer karena kurangnya model atau skema representasi yang tepat. Fokus komputerisasi pengenalan tanaman hidup adalah untuk mengukur bentuk geometris berbasis morfologi daun. Informasi ini memainkan peran penting dalam mengidentifikasi berbagai kelas tanaman. Pada penelitian ini dilakukan pengenalan jenis tanaman berdasarkan fitur yang menonjol dari daun seperti fisiologis panjang (physiological length), lebar (physiological width), diameter,  keliling (leaf perimeter), luas (leaf area), faktor mulus (narrow factor), rasio aspek (aspect ratio), factor bentuk (form factor), rectangularity, rasio perimeter terhadap diameter, rasio perimeter panjang fisiologi dan lebar fisiologi yang dapat digunakan untuk membedakan satu sama lain. Berdasarkan hasil pengujian, ditunjukkan bahwa hasil pencocokkan daun kelengkeng dengan menggunakan neural network lebih baik dibandingkan dengan hasil pencocokkan daun kelengkeng dengan menggunakan probabilistic neural network. Akan tetapi ekstraksi fitur dengan menggunakan morfologi belum dapat memberikan informasi pembeda yang signifikan bagi pengenalan tanaman varitas kelengkeng berdasarkan daunnya.Keywords— klasifikasi, morfologi daun, neural network, probabilistic neural network
ANALYSIS OF STUDENTS' ABILITY TO UNDERSTAND THE MATHEMATICS CONCEPT IN THE APPLICATION OF MATLAB ASSISTED DISCOVERY LEARNING MODEL SANTI MARIA SIMARMATA; Bornok Sinaga; HERMAWAN SYAHPUTRA
Daya Matematis: Jurnal Inovasi Pendidikan Matematika Vol 10, No 1 (2022): Maret
Publisher : Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26858/jdm.v10i1.26745

Abstract

This study aims to describe: (1) The level of students' ability to understand mathematical concepts in the application of the Matlab-assisted Discovery Learning model; (2) The difficulty of students' understanding of mathematical concepts. in the application of the Matlab-assisted Discovery Learning model. This research is a qualitative research with a descriptive approach. Based on the research data, it was found that: (1) The level of ability to understand mathematical concepts of students in the application of the Matlab-assisted Discovery Learning model with good abilities had the highest percentage of 57% followed by excellent abilities students with a percentage of 24%, students with moderately capable with a percentage of 14 % and students with less ability with a percentage of 5%; (2) The difficulty in understanding students' mathematical concepts in the application of the Matlab-assisted Discovery Learning model is the difficulty of facts because it does not able to interpret the results obtained, unable to change the problem in a simpler model.
PENERAPAN ALGORITMA K-NEAREST NEIGHBOR (KNN) DALAM PENGENALAN POLA TULISAN TANGAN ANGKA 0-9 Hermawan Syahputra; R Givent A Simanjorang
Dinamik Vol 28 No 2 (2023)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v28i2.9360

Abstract

Penelitian ini bertujuan untuk menerapkan algoritma K-Nearest Neighbor (KNN) dalam pengenalan pola tulisan tangan angka 0-9. Penelitian ini menggunakan data sekunder berupa gambar angka 0-9 dalam bentuk bitmap yang diunduh dari internet. Setiap gambar angka diubah menjadi fitur numerik menggunakan metode ekstraksi fitur Zoning. Selanjutnya, data fitur numerik tersebut diuji menggunakan metode KNN untuk memprediksi angka yang ditulis.
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.
Implementation of MobileNet V3 In Classifying Butterfly Species with Android and Cloud Based Application Development Ihsan Zulfahmi; Said Iskandar Al Idrus; Hermawan Syahputra; Insan Taufik; Kana Saputra S
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.v4i2.797

Abstract

This research aimed to develop an Android application capable of classifying butterfly species using cloud computing and deep learning technologies. MobileNetV3-Large, a Convolutional Neural Network (CNN) architecture, was employed to process and classify six butterfly species. The dataset was divided into two ratios, 70:30 and 80:20, for training and testing. Evaluation results indicated that the optimal model was achieved with an 80:20 ratio, yielding an accuracy of 94% and precision, recall, and F1-Score values exceeding 90% for each species class. Google Cloud Platform (GCP) was utilized to manage and run the model using the Cloud Run service, enabling the application to function efficiently even with limited resources on Android devices. The application incorporates an encyclopedia of species and a camera scanning feature, making it a valuable educational tool
PENINGKATAN KEMAMPUAN LITERASI NUMERASI GURU MATEMATIKA SMP SEKABUPATEN LANGKAT MELALUI MEDIA PEMBELAJARAN KREATIF Syawal Gultom; Martina Restuati; Ani Sutiani; Hermawan Syahputra; Imelda Wardani Br Rambe
J-ABDI: Jurnal Pengabdian kepada Masyarakat Vol. 5 No. 3: Agustus 2025
Publisher : Bajang Institute

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

Abstract

Kemampuan literasi numerasi merupakan salah satu kompetensi penting abad ke-21 yang perlu dimiliki peserta didik, khususnya dalam konteks pembelajaran matematika. Rendahnya capaian literasi numerasi siswa masih menjadi tantangan, salah satunya disebabkan keterbatasan kemampuan guru merancang pembelajaran kontekstual dan menarik. Kegiatan Pengabdian Masyarakat yang dilaksanakan oleh tim dosen FMIPA Universitas Negeri Medan ini bertujuan untuk meningkatkan kemampuan literasi numerasi guru matematika melalui pelatihan penggunaan media pembelajaran kreatif. Sasaran kegiatan ini adalah 32 guru matematika SMP se-Kabupaten Langkat. Kegiatan dilaksanakan melalui tiga tahapan utama, yaitu: (1) sosialisasi konsep literasi numerasi; (2) pelatihan dan pendampingan penggunaan media pembelajaran berbasis AI, gamifikasi, dan GeoGebra; serta (3) evaluasi melalui angket respons peserta. Hasil evaluasi menunjukkan bahwa kegiatan ini berhasil meningkatkan pemahaman guru terhadap literasi numerasi dan memotivasi mereka untuk mengintegrasikan media pembelajaran kreatif dalam proses pembelajaran matematika. Kegiatan ini diharapkan dapat memberikan kontribusi positif terhadap peningkatan kualitas pembelajaran matematika di tingkat SMP.
Penerapan Data Mining Untuk Menganalisis Penjualan Produk Menggunakan Algoritma Apriori Berbasis WEB Syarief Afifi Sumantri; Hermawan Syahputra
JURNAL RISET RUMPUN MATEMATIKA DAN ILMU PENGETAHUAN ALAM Vol. 2 No. 2 (2023): Oktober : Jurnal Riset Rumpun Matematika dan Ilmu Pengetahuan Alam
Publisher : Pusat riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jurrimipa.v2i2.1532

Abstract

This study aims to determine the best selling food and beverage products at Caffe Kopi Kito. Data mining is the process of extracting useful information and patterns from very large data. Data mining includes data collection, data extraction, data analysis, and data statistics. The Apriori algorithm is a classic algorithm in data mining. This algorithm is used to see the intensity of occurrence of the relevant itemset or frequent items or association rules. This study uses consumer transaction data for 30 days in January 2023. Transaction data will be collected first based on the day and number of transactions, then the transaction data that has been collected will be grouped according to each item, the data that has been grouped will be carried out a priori algorithm process to determine the most dominant product. Then a system design will be carried out whose result will be a website. The results showed that using the website-based a priori algorithm could determine the most dominant product at Caffe Kopi Kito and make it easier for users to determine the most dominant product. Based on the results of product sales analysis at Cafee Kopi Kito, it can be concluded that working on the a priori algorithm on Caffe Kopi Kito using a website can be said to have the result of a product combination and in the future it can be used to create the best-selling menu packages at Cafee Kopi Kito.
Optimalisasi Dashboard Pemesanan Makanan Online Menggunakan Looker dan JavaScript Angginy Akhirunisa Siregar; Citra; Khairun Nadiah; Hermawan Syahputra; Fanny Rahmadani
Economic Reviews Journal Vol. 3 No. 3 (2024): Economic Reviews Journal
Publisher : Masyarakat Ekonomi Syariah Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56709/mrj.v3i3.322

Abstract

Advances in information technology have enabled rapid development in internet-based services, including online food ordering applications. This growth demands efficient data management and analysis systems to improve user experience and operational performance. This research focuses on developing an optimal online food ordering dashboard using Looker and JavaScript. Research methods include needs identification, literature study, needs analysis, design, and implementation. The research results show that the dashboard developed is able to manage and analyze order data effectively, identify trends, predict customer needs, and increase operational efficiency. This dashboard visualizes important information such as number of orders based on age, gender, income, as well as customer behavior analysis. In doing so, service providers can gain better insight into consumer behavior and ordering trends, supporting more informed and strategic decision making. The results of this study contribute to the literature on the use of data visualization technologies in the online food service sector.
Co-Authors Agus Harjoko Alfattah Atalarais Angginy Akhirunisa Siregar Ani Sutiani BORNOK SINAGA citra Fanny Rahmadani Ihsan Zulfahmi Insan Taufik Kana Saputra S Khairun Nadiah Martina Restuati R Givent A Simanjorang Rambe, Imelda Wardani Said . Iskandar SANTI MARIA SIMARMATA Syarief Afifi Sumantri Syawal Gultom