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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika METIK JURNAL Jurnal Mantik Jusikom: Jurnal Sistem Informasi Ilmu Komputer Community Engagement and Emergence Journal (CEEJ) Jurnal Teknik Informatika C.I.T. Medicom Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Pendidikan dan Teknologi Indonesia Jurnal Pengabdian kepada Masyarakat Politeknik Negeri Batam Data Sciences Indonesia (DSI) Jurnal Informatika: Jurnal Pengembangan IT
Yennimar, Yennimar
Unknown Affiliation
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Mechanical Engineering Physics

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Decision Support System for Determining Land Priority for Housing Development Using Fuzzy Analytical Process (Fuzzy-AHP) Method Simarmata, Allwin M; Yennimar, Yennimar
Sinkron : jurnal dan penelitian teknik informatika Vol. 4 No. 1 (2019): SinkrOn Volume 4 Number 1, October 2019
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (941.739 KB) | DOI: 10.33395/sinkron.v4i1.10243

Abstract

The National Housing Development Public Corporation (Perumnas) was established as a government solution in providing adequate housing for the middle to lower classes, to realize this, Perumnas set a target of building 100,000 houses / year. The aspect of development is to ensure that all communities occupy decent homes in a healthy environment, an increase in the number of residents in the city, especially the city of Medan, causes settlement problems because the area of land is a fixed factor, while the population is always growing, thus requiring a system that can help decision in determining the priority of land for housing development. In this study an analysis of patterns related to applicable criteria or rules is implemented by applying the Fuzzy Analytical Hierarchy Process (Fuzzy-AHP) method, to produce accurate and effective information for making priority decisions on the location of the best residential development land. The analysis framework uses a data collection approach sourced from Public Relations, then an analysis is carried out to determine the criteria, rules and standards used, then a system is built by implementing the Fuzzy-AHP method to produce optimal alternatives that can be used as information. Alternative results will be evaluated by quantitative and qualitative analysis compared to the existing system. The developed system is expected to be used as a tool in decision making in the Regional Sumatra Regional Public Corporation I company that is optimal according to the criteria, rules or standards used. Data collection using the method of literature study, observation, interviews, and sampling. This research is expected to be one of the references in the application of decision support systems in a real scope and contribute to building a constructive community culture based on logical and scientific values.
Comparison of Machine Learning Classification Algorithms in Sentiment Analysis Product Review of North Padang Lawas Regency Yennimar, Yennimar; Rizal, Reyhan Achmad
Sinkron : jurnal dan penelitian teknik informatika Vol. 4 No. 1 (2019): SinkrOn Volume 4 Number 1, October 2019
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (421.962 KB) | DOI: 10.33395/sinkron.v4i1.10416

Abstract

The growth of SMEs in Indonesia, which has increased by 6% every year, is driven by continued growth by many parties, including the government and private institutions that often conduct business coaching and assistance. Problems that are often encountered are the lack of willingness of MSME business practitioners to apply information technology and the internet, besides that most of them live in rural areas with very limited internet access and many are not yet digital-literate, adequate digital technology utilization capabilities and the will of business people For SMEs to understand customer needs, a service that is consistent with standard service procedures will give a good impression and pay attention to customer feedback. This research was conducted by collecting data on MSME products obtained from the North Padang Lawas District Trade Industry Office followed by the development of a Paluta Market website as a marketplace for media promotion and marketing of MSME products in North Padang Lawas by applying a sentiment analysis approach using machine learning classification algorithm to produce product rating values based on public opinion of MSME products contained on the website, in addition the system is able to classify consumer comment data on MSME products from various sources from the umkm web, so that it becomes useful information for MSME businesses especially in North Padang Lawas Regency and the community at large. The results of the application of sentiment analysis of a product on the Paluta Market website can be used as a reference in improving service and product quality, so as to create a variety of new opportunities that are profitable for MSME businesses.
Implementation Of Extreme Learning Machine Method With GVF Snake For Character Recognition: Implementation Of Extreme Learning Machine Method With GVF Snake For Character Recognition Yusna, Pradana; Yennimar, Yennimar
Jurnal Mantik Vol. 4 No. 1 (2020): May: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

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

Abstract

In everyday life, sometimes it is necessary to change the contents of the printouts of certain documents, but the digital files from the printed documents have been lost. Retype the document manually will certainly spend a lot of time making it inefficient. To solve this problem, the printed document can be scanned into a digital image file and a character recognition system is implemented to recognize the characters contained therein. In this study, the Gradient Vector Flow Snake (GVF Snake) method is used to determine the boundaries of an object based on the computed vector gradient in the form of binary or gray-level values ??obtained from an image with several frameworks. After that, the Extreme Learning Machine (ELM) method will be used to predict the characters that have been broken up by the GVF Snake method. The results of this study are software that applies the GVF Snake and ELM methods to perform the character recognition process of an image printed by a document.
The Optimization of CNN Algorithm Using Transfer Learning for Marine Fauna Classification Fawwaz, Insidini; Yennimar, Yennimar; Dharsinni, N P; Wijaya, Bayu Angga
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.12893

Abstract

Marine fauna are all types of organisms that live in the marine environment. Marine fauna is also an important part of the marine ecosystem that has an important role in maintaining environmental balance. However, the survival of marine fauna is threatened due to activities carried out by humans, such as pollution, overfishing, industrial waste disposal into marine waters, plastic pollution and so on. Therefore, efforts are needed to monitor and protect marine fauna so that marine ecosystems can remain stable. One way to monitor marine fauna is by using classification technology. One of the technologies that can be used in marine fauna classification technology is Convolutional Neural Network (CNN). CNN is one of the classification methods that can be used to classify objects in images with a high level of accuracy. The CNN architecture models used are MobileNet, Xception, and VGG19. Furthermore, the method used to improve the performance of the CNN algorithm is the Transfer Learning method. The test results show that the MobileNet architecture model produces the highest accuracy value of 91.94% compared to Xception and VGG19 which only get an accuracy value of 87.64% and 88.42%. This shows that the MobileNet model has a more optimal performance in classifying marine fauna.
Implementasi Algoritma Naïve Bayes untuk Memprediksi Kemampuan Pemrograman Mahasiswa Teknik Informatika Menggunakan Dataset Kuesioner Zega, Ide Kristiani; Medina, Nabila; Aprillia S, Debora; Yennimar, Yennimar
Jurnal Pendidikan dan Teknologi Indonesia Vol 4 No 11 (2024): JPTI - November 2024
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.483

Abstract

Permintaan tenaga kerja di bidang pemrograman semakin meningkat, sementara banyak mahasiswa yang menghadapi kesulitan dalam menguasai keterampilan pemrograman. Penelitian ini bertujuan untuk memprediksi kemampuan pemrograman mahasiswa Teknik Informatika menggunakan algoritma Naïve Bayes, dengan mempertimbangkan pemahaman dasar algoritma pemrograman sebagai parameter. Model dikembangkan dan diuji menggunakan 210 data training dan 90 data testing. Hasil pengujian menunjukkan akurasi model sebesar 100%, dengan prediksi “Mampu” sesuai kenyataan sebanyak 82 data, sedangkan prediksi “Tidak Mampu” yang sesuai dengan kenyataan sebanyak 8 data. Tidak ditemukan kesalahan prediksi pada kategori “Mampu” dan “Tidak Mampu”. Precision dan recall masing-masing mencapai 100%, mengindikasikan bahwa model ini sangat efektif dalam mengklasifikasikan mahasiswa sebagai “Mampu” dan “Tidak Mampu”. Penelitian ini berkontribusi pada pengembangan metode berbasis data untuk mengevaluasi kemampuan pemrograman mahasiswa, memberikan wawasan penting bagi perbaikan kurikulum dan penilaian pendidikan di bidang Teknik Informatika.
IMPLEMENTATION OF SUPPORT VECTOR MACHINE ALGORITHM WITH HYPER-TUNING RANDOMIZED SEARCH IN STROKE PREDICTION Yennimar, Yennimar; Rasid, Alvin; Kenedy, Sun
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 6 No. 2 (2023): JURNAL SISTEM INFROMASI DAN ILMU KOMPUTER PRIMA (JUSIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v6i2.3479

Abstract

Stroke is a severe health problem and can significantly impact a person's quality of life. Therefore, it is crucial to predict stroke early so that preventive measures can be taken before it is too late. This study demonstrates the importance of hyper tuning and hyperparameters in a stroke prediction model. Literature studies show that many studies on stroke prediction need to explain this, even though this is very important for developing the performance of stroke prediction models. In this study, we use the Support Vector Machine (SVM) algorithm to predict stroke and evaluate the algorithm's performance without hyper tuning and with hyper tuning Randomized Search CV. We also divide the data into training and test data by 75% and 25%. The results of this study indicate that hyper-tuning can improve the accuracy of the stroke prediction algorithm. The algorithm's accuracy is 77% without hyper-tuning, whereas, with hyper-tuning, the accuracy increases to 96%. Hypertuning with the Randomized Search CV method can improve the performance of the stroke prediction algorithm and is very important to do in developing predictive models.
COMPARISON OF SUPPORT VECTOR REGRESSION AND RANDOM FOREST REGRESSION ALGORITHMS ON GOLD PRICE PREDICTIONS Hutagalung, Samuel Valentino; Yennimar, Yennimar; Rumapea, Erikson Roni; Hia, Michael Justin Gesitera; Sembiring, Terkelin; Manday, Dhanny Rukmana
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 7 No. 1 (2023): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v7i1.4125

Abstract

This research was conducted to test how the Support Vector Regression and Random Forest Regression algorithms predict gold futures prices. The data used in this research was taken from the Investing.com website which will later be processed into a prediction model by comparing the SVR and RVR algorithms. The Support Vector Regression and Random Forest Regression algorithms will be tested to see the performance of each prediction model. The test results show that the Support Vector Regression model is superior in terms of accuracy with a value of 83%. However, the Random Forest Regression algorithm is superior with a smaller error rate, namely with an MSE value of 270.85 and an MAE value of 12.53. Keyword: Comparison, Prediction, Support Vector Regression, Random Forest Regression.
Implementation of the Dual Channel Convolution Neural Network Method for Detecting Rice Plant Diseases Jauhary, Wilson; Yaphentus, Albert Julius; Yennimar, Yennimar
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14654

Abstract

Rice is a strategic and important food crop for the economy in Indonesia. Rice can be infected with diseases caused by fungi, bacteria and viruses. The disease that attacks rice plants goes unnoticed by farmers and farmers often do not understand the diseases that attack rice plants so that it is too late in treating them to diagnose the symptoms, causing rice production to decrease. To solve this problem, it is necessary to carry out a disease detection process in rice plants. In this research, the Dual-Channel Convolutional Neural Network (DCCNN) method will be used. This DCCNN method consists of two channels, namely deep channel and shallow channel. The process of detecting grape plant diseases using the DCCNN method will start from the process of extracting leaf parts from the input image using the Gabor Filter method. After that, the Segmentation Based Fractal Co-Occurrence Texture Analysis method will be used to carry out the process of extracting characteristics, color and texture from the extracted leaf parts. Finally, the DCCNN method will be applied to carry out the process of classifying and detecting types of grape plant diseases. The results of this research are that the DCCNN method can be used to detect types of leaf diseases in rice plants. The accuracy of disease detection results using the DCCNN method depends on the number of datasets contained in the system with an accuracy level of up to 85%. However, more datasets will cause the execution process to take longer.
Analisis Wawasan Penjualan Supermarket dengan Data Science Harahap, Mawaddah; Rozi, Fachrul; Yennimar, Yennimar; Siregar, Saut Dohot
Data Sciences Indonesia (DSI) Vol. 1 No. 1 (2021): Article Research Volume 1 Issue 1, June 2021
Publisher : Yayasan Cita Cendikiawan Al Kharizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/dsi.v1i1.1173

Abstract

Data science atau ilmu data adalah suatu disiplin ilmu yang khusus mempelajari data, khususnya data kuantitatif (data numerik), baik yang terstruktur maupun tidak terstruktur. Pemanfaatkan siklus dalam pengembangan analisis untuk membuat keputusan bisnis yang praktis dan berbasis data, dan menerapkan perubahan berdasarkan keputusan tersebut. Makalah ini menyajikan analisis wawasan yang berguna pada kumpulan transaksi penjualan supermarket selama 3 bulan dari 3 cabang yang berbeda. Berdasarkan hasil analisis nilai rating terting adalah 10, terendah 4 dengan rata-rata rating produk 6.9 dan wanita lebih dominan membeli produk Aksesoris Fashion dan pria Kesehatan & Kecantikan
Analisis Sentimen Publik Terkait Danantara Menggunakan Algoritma IndoBERT pada Platform Media Sosial Pratama, Ahlul Yoga; Sanjaya, Gauri Ananda; Lubis , Nadya Khairunisa; Rangga Aditya , Muhammad; Yennimar, Yennimar
METIK JURNAL (AKREDITASI SINTA 3) Vol. 9 No. 1 (2025): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/metik.v9i1.1055

Abstract

This study aims to analyze public sentiment toward the Indonesia Investment Authority (Badan Pengelola Investasi – BPI) Danantara using artificial intelligence technology. Data was collected through crawling using an X API token, resulting in 4,269 tweets stored in CSV format, consisting of 15 columns including tweet text and user metadata. The data underwent a pre-processing stage, including text cleaning, case folding, and tokenization, to prepare it for analysis. Manual labeling was conducted to classify sentiment into three categories: positive (32%), negative (45%), and neutral (23%). Due to class imbalance, a data augmentation technique was applied, increasing the total number of records to 23,623. The IndoBERT-base model was employed using a transfer learning approach for three-class sentiment classification. After five training epochs, the model achieved an accuracy of 97.71%. Evaluation results demonstrate high computational efficiency, with the model capable of processing data quickly. This study highlights the importance of applying artificial intelligence technologies, particularly BERT-based language models, in sentiment analysis in the digital era.
Co-Authors Achmad Ridwan Achmad Ridwan, Achmad Agung Prabowo Ali Ramadhan, Mhd Aprillia S, Debora Bayu Angga Wijaya Cantona, Devin Chandra, Irvin Delima Sitanggang, Delima Dharsinni, N P Ebert, Steven Fawwaz, Insidini Felim, Raynaldi Ginting, Riski Titian Hia, Michael Justin Gesitera HS, Christnatalis Hutagalung, Gani Mores Hutagalung, Samuel Valentino Jauhary, Wilson Kamti, Elvies Kenedy, Sun Lenny, Lenny Leonardi, William Limbong, Endson Danielgar Lubis , Nadya Khairunisa Manday, Dhanny Rukmana Mawaddah Harahap, Mawaddah Medina, Nabila Pandiangan, Very Andreas Perangin Angin, Despaleri Pratama, Ahlul Yoga Rangga Aditya , Muhammad Rasid, Alvin Rizal, Reyhan Achmad Robert, Michael Rozi, Fachrul Rumapea, Erikson Roni Sanjaya, Gauri Ananda Sembiring, Terkelin siagian, Rivaldo calvin Simarmata, Allwin M Siregar, Saut Dohot Situngkir, Boy Betrand Togar Timoteus Gultom Violyn, Violyn Weide, Harris wyjaya, Andy Yaphentus, Albert Julius Yusna, Pradana Zega, Ide Kristiani