Article Per Year (5 Year)
p-Index From 2021 - 2026
4.405
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal Rekayasa Material, Manufaktur & Energi Digital Zone: Jurnal Teknologi Informasi dan Komunikasi JOURNAL OF SCIENCE AND SOCIAL RESEARCH Indonesian Journal of Electrical Engineering and Computer Science INFOKUM Indonesian Journal of Education, Social Sciences and Research (IJESSR) Jurnal Ilmiah MADIYA: Masyarakat Mandiri Berkarya International Journal of Research in Vocational Studies (IJRVOCAS) Majalah Iptek Politeknik Negeri Medan Polimedia J-COSCIS : Journal of Computer Science Community Service Jurnal Nasional Teknologi Komputer Proceeding Applied Business and Engineering Conference International Journal of Applied Research and Sustainable Sciences (IJARSS) Journal Global Technology Computer Jurnal Pengabdian Barelang Komposit : Jurnal Pengabdian Dan Pemberdayaan Masyarakat
Faza, Sharfina
Politeknik Negeri Medan
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

Published : 28 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search
Journal : JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING

 1 
Applications For Detecting The Rate Of Fruit In Mangrove Plants Sharfina Faza; Meyatul Husna; Ajulio Padly Sembiring; Rina Anugrahwaty; Silmi; Romi Fadillah Rahmat; Rhama Permadi Ahmad
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 6 No. 2 (2023): Issues January 2023
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v6i2.8379

Abstract

Mangrove plants are one of the plants that really help aquatic ecosystems between the sea, coast, and land. Mangrove plants provide many ecological, social, and economic benefits. In Indonesia, mangrove plants have 202 species with the same anatomy as other plants in general, consisting of roots, fruits, stems and leaves. Nowadays, the location of mangrove plants in Indonesia has experienced the fastest damage in the world due to conversion to ponds, settlements, industry and plantations. One of the efforts to restore aesthetic value and restore the ecological function of mangrove forest areas is rehabilitation using mangrove fruit. In the rehabilitation process, farmers generally use the manual method with the naked eye to determine fruit ripeness on mangrove plants, so the resulting level of accuracy is not optimal. To overcome this problem, an application is needed that can facilitate farmers in determining fruit maturity in mangrove plants so that it can help determine the maturity level of mangrove fruit. The development of this application utilizes the Deep Learning method as well as the utilization of digital image processing techniques with Grayscaling, Adaptive Threshold, Sharpening and Smoothing techniques. The results of this study are an application that can detect the level of fruit maturity in mangrove plants with an accuracy of 99.11%. With this application, determining the maturity level of fruit on mangrove plants can be easily done.
Analyzing Food Prices in North Sumatra Province in 2022 – 2024 Using the Linear Regression Method Faza, Sharfina; Firjatullah, Muhammad; Azhar, Muhammad Fauzan; Hidayatullah, Rafly Artha; Lubis, Arif Ridho
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 8 No. 1 (2024): Issues July 2024
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v8i1.11788

Abstract

The food price data used was obtained from literature studies which include main food ingredients such as rice, beef, chicken, eggs, red onions, red chili pepper and cooking oil. The linear regression method is used to model the relationship between the independent variable (year) and the dependent variable (food prices), with the aim of predicting future food prices based on historical data. Although linear regression can provide fairly accurate food price estimates, improvements in prediction models can be achieved by incorporating other analysis methods such as time series analysis or machine learning. The implications of this research highlight the importance of effective policy planning to maintain food price stability and ensure sufficient food availability for the community. Future research could involve more in-depth analysis of the factors influencing food prices, as well as the development of more sophisticated prediction models to support decision-making in agriculture and food. This research aims to analyze food prices in the Northern region of Sumatra Province during the period 2022 to 2024 using the linear regression method. The results show that rice, meat, chicken meat, chicken eggs, shallots, red chilies and edible oils all increased, but only chicken meat, shallots and edible oils also experienced a decrease.
Multi-Detection System Using Faster R-CNN for Fish Species Classification and Quality Assessment on Android Faza, Sharfina; Lubis, Arif Ridho; Meryatul Husna; Rina Anugrahwaty; Muhammad Rafif Rasyidi; Romi Fadillah Rahmat
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 9 No. 2 (2026): Issues January 2026
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v9i2.16374

Abstract

Species identification and quality assessment of fish in trade still rely on manual visual observation, which is subjective and requires specialized expertise. This method's limitations make it difficult for consumers to distinguish species with similar morphology and accurately assess fish quality, which can lead to inappropriate purchasing decisions. This research develops a multi-detection system based on Faster R-CNN with VGG16 backbone for fish species classification and quality assessment simultaneously on Android platform. The system uses convolutional layers to extract visual features from input images, Region Proposal Network for fish object detection and localization, and fully connected layers for simultaneous classification of species and quality levels. The research dataset consists of 3,000 images of five fish species (gourami, tilapia, nile tilapia, snapper, and pomfret) with four quality levels, divided into 2,400 training images and 600 testing images. The trained model is converted to TensorFlow Lite format for implementation on Android devices. Test results show the multi-detection system achieves 92% accuracy in fish species classification and quality assessment, demonstrating the effectiveness of the Faster R-CNN approach for multi-detection applications in the Android-based fisheries sector.
Co-Authors ., Cholish Abdul Azis Abdulah Abdullah Ajulio Padly Sembiring Ajulio Padly Sembiring Ajulio Padly Sembiring Al Khowarizmi Andam Lukcyhasnita Andam Lukcyhasnita Andi Supriadi Chan, Andi Supriadi Aprilza Aswani Azhar, Muhammad Fauzan Banjarnahor, Wiwin Cholish Dani Gunawan F. Friendly Faisal Irsan P Fawwaz, Insidini Firjatullah, Muhammad Friendly, Friendly Gunawan Gunawan Hidayatullah, Rafly Artha Husna, Meryatul Ibnu Hajar Ibnu Hajar Janferson Panggabean Joko Kurnianto Julham Julham Lubis, Arif Ridho Luckyhasnita, Andam Lukcyhasnita, Andam Mardianto, Willy Meyatul Husna Mhd Faris Pratama Muhammad Fermi Pasha Muhammad Rafif Rasyidi Muharman Lubis Nadi, Farhad Poppy Andriyani Purnamawati, Sarah Rhama Permadi Ahmad Rina Anugrahwaty Rina Anugrahwaty Rina Anugrahwaty, Rina Riza Sulaiman Rizka, Ade Romi Fadillah Rahmat Sarah Purnamawati Sembiring, Ajulio Padly Silmi Silmi Silmi Silmi Simarmata, Anggi C Simarmata Siregar, Mhd. Ikhsan P. Sri Novida Sari Sulistianingsih, Indri Tumanggor, Orli Binta Virdyra Tasril Winda Syafitri Winda Syafitri Wiwin Sry Adinda Banjarnahor Yusnida, Yuyun Yusuf, Kadri Zumhari