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Muhammad Khoiruddin Harahap
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Gedung Universitas Prima Indonesia Fakutas Teknologi dan Ilmu Komputer Program Studi Teknik Informatika Jl. Sekip Simp.Sikambing, Sei Putih Tim. I, Kec. Medan Petisah, Kota Medan, Sumatera Utara 20111
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JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP)
Published by Universitas Prima Indonesia
ISSN : 2621234X     EISSN : 2621234X     DOI : doi.org/10.34012
Core Subject : Science,
Computer Science & IT
JuTIKom PRIMA(Jurnal Teknologi dan Ilmu Komputer Prima) adalah jurnal yang di fokuskan Terhadap bidang ilmu Teknologi dan Ilmu Komputer. Tujuan jurnal adalah untuk mengakomodir para dosen dan mahasiswa baik dalam lingkungan kampus Universitas Prima Indonesia maupun dari kampus berbeda jurnal ini menerbitkan artikel berkualitas yang didedikasikan untuk semua perkembangan terbaru yang luar biasa di bidang teknik informatika. Ruang lingkupnya mencakup : Visi Komputer; Pembelajaran Mesin; Data Mining; Analisis Big Data; Pemrosesan Bahasa Alami, Sentimen Analisis, Sosial Media Analisis; Kecerdasan Robotik; Kecerdasan Buatan; Pemrosesan Gambar dan Pengenalan Pola; Keamanan Komputer; Interaksi Komputer Manusia; Intelijensi Bisnis
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 153 Documents
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Analisis Sentimen Kepuasan Pengguna Media Sosial Dengan Menggunakan Data Mining Dan Matlab Silalahi, Naomi Cristin Br; Ompusunggu, Elvis Sastra
JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Vol. 7 No. 2 (2024): Jurnal Teknologi dan Ilmu Komputer Prima (JUTIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jutikomp.v7i2.5713

Abstract

In today's digital era, social media has become an integral part of many people's daily lives, with platforms such as Twitter, Facebook, Instagram, and TikTok being used to share opinions, experiences, and satisfaction or dissatisfaction with various products and services. This research aims to analyze social media user satisfaction sentiment using data mining techniques and Matlab. With data collected from several social media platforms, this research identifies and categorizes user sentiment as positive, negative, or neutral. The methods used include data collection, pre-processing, feature extraction, model building, and model evaluation. The results show that data mining techniques and Matlab effectively classify user sentiment, providing valuable insights for companies to improve their quality and performance based on social media user feedback.
Analysis of Credit Card Usage Against Business Segmentation Using Agglomerative Hierarchical Clustering prabudi, shabrina; -, arnita; Siregar , Nugrah Anggara; Afdal, Reza Nur; Nasution, Raudha Izmainy
JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Vol. 7 No. 2 (2024): Jurnal Teknologi dan Ilmu Komputer Prima (JUTIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jutikomp.v7i2.5774

Abstract

This study analyzes credit card usage patterns and their impact on business segmentation using the Agglomerative Hierarchical Clustering (AHC) method. AHC was chosen because of its ability to group data in detail, especially for datasets with hierarchical relationships. The dataset includes balances, credit limits, monthly payments, and late history. The study aimed to identify high-risk credit card users in payments so that financial institutions can develop more effective risk management strategies. This study successfully identified customer groups with varying payment risks and offered solutions like debt consolidation and flexible payment programs. These findings contribute to the credit card industry in customer segmentation and credit risk management in the credit card industry.
Klasifikasi Tingkat Kematangan Buah Kersen Dengan Menggunakan Support Vector Machine Patricia, Artha; Arinal, Veri
JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Vol. 7 No. 2 (2024): Jurnal Teknologi dan Ilmu Komputer Prima (JUTIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jutikomp.v7i2.5876

Abstract

Kersen fruit, native to Southern Mexico and often found in Indonesia, has health benefits that attract people. It is round with a diameter of 1-1.5 cm, yellowish green in color when young, and turns red when ripe. Determining the maturity level of kersen, which has been done manually, is essential for people to consume good quality fruit. This study aims to simplify the identification of Kersen fruit maturity through image processing using the Support Vector Machine (SVM) method with a parameter value of C-25. The test results show that this method achieves the best accuracy level of 72% in identifying the ripeness of kersen fruit, so it can be an effective solution in making it easier for people to determine the level of fruit ripeness.
Sistem Pemantauan Pasang Surut Air Laut Berbasis Internet Of Things di Pantai Kurnia My Darling Wadly, Fachrid; Ramadhan, Zuhri; -, Muslim; Alfisyahri Sitompul, Dimas
JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Vol. 7 No. 2 (2024): Jurnal Teknologi dan Ilmu Komputer Prima (JUTIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jutikomp.v7i2.5923

Abstract

Internet of Things technology provides an effective solution for monitoring changes in sea water levels in real-time from a distance. This research aims to design and create a sea level monitoring device based on NodeMCU ESP32 and HC-SR04 sensor that can preserve the mangrove ecosystem at Pantai Kurnia My Darling. The research results show that the IoT-based sea level monitoring instrumentation tool has a high level of accuracy, making it suitable for field testing. The tool has also successfully conducted real-time testing using ThingSpeak and can send notification alerts via WhatsApp. In the future, the data from this research will serve as a standard for creating better tools to monitor mangrove seedlings, ensuring the preservation of mangrove forests.
Application of Support Vector Machine in Measuring Stress Levels Based on EEG Signals Wijaya, Bryan; Sitanggang, Delima; Lee, Brandon; Angie, Vicky; Siahaan, Eric Simon Giovanni
JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Vol. 8 No. 1 (2025): Jurnal Teknologi dan Ilmu Komputer Prima (JUTIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jutikomp.v8i1.6584

Abstract

This study aims to classify stress levels based on electroencephalography (EEG) signals using the Support Vector Machine (SVM) algorithm. The data used in this study came from 21 subjects with a total of 379 datasets, which included the main variables of Subject, Electrode Channel (E), Theta, Beta 1, and Beta 2. Preprocessing was done to ensure data quality, including blank data elimination, normalization, and feature engineering. One of the main features developed was the Beta Average, which was obtained by calculating the average between Beta 1 and Beta 2, and stress level classification, which was determined based on the comparison between the Beta Average and Theta. The SVM algorithm was applied to build a stress classification model with an initial stage of manual calculation to understand the basic concepts, followed by the Python programming language implementation. The evaluation results show that the developed model has an accuracy of 92.76%, with the highest precision, recall, and f1-score values reaching 100% and the lowest value of 85%. The confusion matrix analysis showed that the model could classify low stress with 100% accuracy, while it reached 87.8% for high stress. The findings of this study prove that the SVM algorithm effectively classifies EEG signal-based stress levels. This model can be the basis for further development of stress detection methods, especially in mental health and neuroinformatics applications.
Implementation of Grid Search Optimization Algorithm and Adaptive Response Rate Exponential Smoothing for Hyperparameter Tuning in Production Activity Determination Sanjaya, Federico; Alvina, Jesslyn; Putra, Muhammad Amsar; Sitanggang, Delima
JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Vol. 8 No. 1 (2025): Jurnal Teknologi dan Ilmu Komputer Prima (JUTIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jutikomp.v8i1.6593

Abstract

This research aims to improve the accuracy of production planning at PT Bilah Baja Makmur Abadi by combining the Adaptive Response Rate Exponential Smoothing (ARRES) algorithm and Grid Search optimization. The main problems faced are unpredictable demand fluctuations, dead stock risks, and high operational costs due to imbalances between production and demand. The ARRES algorithm is used for demand forecasting with adaptive exponential weighting, while Grid Search optimizes the alpha and initial year parameters to improve prediction accuracy. This study uses a 5-year sales dataset (2017-2021) with model evaluation using Mean Absolute Percentage Error (MAPE). The results showed that the combination of Grid Search and ARRES optimization algorithms proved effective in helping predict production needs. This can be seen from the significant decrease in the average MAPE value, which is 7.07% using this combination method, compared to 8.18% in the ARRSES method. The lower MAPE value indicates that the Grid Search method is effective in optimizing the ARRSES model parameters. With relatively high prediction accuracy (MAPE < 10%), this method is able to cope with unexpected demand fluctuations.
Recent Trends and Innovations in Elementary School Educational Game Development: A Literature Review Atika, Syarifah
JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Vol. 8 No. 1 (2025): Jurnal Teknologi dan Ilmu Komputer Prima (JUTIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jutikomp.v8i1.6660

Abstract

Educational games have emerged as interactive learning media that enhance elementary students’ motivation, engagement, and understanding. This study analyzes recent trends in academic game development over the past five years by reviewing 15 peer-reviewed articles published between 2021 and 2025, sourced from Google Scholar. The analysis reveals that 60% of the studies focused on mobile-based games, particularly Android applications developed using Unity and Construct 2, due to their high accessibility and engaging interactive features. Additionally, web-based games such as Wordwall and desktop-based visual novel games developed with TyranoBuilder were found to improve students’ concept mastery by up to 30%, especially in language and mathematics learning. However, key challenges remain, including limited platform compatibility, the absence of adaptive learning features, and weak integration with formal curriculum standards. To enhance their effectiveness, future educational games should prioritize cross-platform accessibility, implement adaptive learning mechanisms, and ensure strong alignment with academic curricullum.
Design and Development of an Android-Based Application for Hydroponic Introduction and Learning Media Fitriyah, Nur Qodariyah; Rosyidah, Ulya Anisatur; Oktavianto, Hardian
JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Vol. 8 No. 1 (2025): Jurnal Teknologi dan Ilmu Komputer Prima (JUTIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jutikomp.v8i1.6668

Abstract

Learning media will make it easier for people to learn hydroponic vegetable planting material compared to books because there are visualizations of images and videos so that it will be understood more quickly than just reading. Android is one of the open-source programming languages that allows makers to modify and distribute the results of making applications freely and freely. Developing learning media for hydroponic vegetable planting using Android will make it easier for readers because this application can be used anywhere without carrying more weight than books. In this learning media to be built, the advantages of this application are the visualization of attractive graphics, and simple decision support features related to the implementation of hydroponic farming, which includes capital and available land, as well as suitable plant species. The application has been successfully made, and based on the test results, it can be concluded that it can be appropriately used and produce results according to the design. The suggestions from this research are related to the application's appearance, which can still be developed to be more attractive.  
Supply Chain Analysis in the Health Sector Using Gradient Boosting Regression Algorithm Wijaya, Bayu Angga; Halawa, Nestina
JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Vol. 8 No. 1 (2025): Jurnal Teknologi dan Ilmu Komputer Prima (JUTIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jutikomp.v8i1.6822

Abstract

Supply chain analysis in healthcare is a crucial aspect in ensuring efficient and optimized resource distribution. This study uses the Gradient Boosting Regression algorithm to predict demand in healthcare supply chains to improve the accuracy of stock planning and management trained using supply datasets from hospitals. The model evaluation results show that most of the predictions are close to the actual values, as seen from the points clustered around the reference line. Despite the slight deviations, the Mean Absolute Error (MAE) value of 157.16 indicates that the average prediction error is relatively small compared to the demand scale which ranges from 0 to 14,000. This indicates that the Gradient Boosting Regression model performs reasonably well in estimating supply chain demand in the healthcare sector. Thus, this approach has the potential to be used in more accurate decision-making, in order to improve the efficiency of distribution and availability of health resources
Cardiac Abnormality Detection Using Adaptive Neuro-Fuzzy Inference System Naibaho, Ono Iyan
JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Vol. 8 No. 1 (2025): Jurnal Teknologi dan Ilmu Komputer Prima (JUTIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jutikomp.v8i1.6999

Abstract

Heart defects are one of the leading causes of death worldwide, making early detection crucial to prevent more serious complications. Electrocardiogram signals are an important diagnostic tool that can be used to detect heart abnormalities in real-time. In this study, an Adaptive Neuro-Fuzzy Inference System artificial intelligence model is used to analyze ECG signal data and detect heart abnormalities early. The ECG signal data used was taken from 30 research subjects, then processed to reduce distracting noise. The combination of artificial neural networks and fuzzy systems aims to overcome the problem of uncertainty in ECG signal data. Thus, this method can be used as a solution that helps in the early diagnosis of heart disorders. The performance evaluation of the proposed Adaptive Neuro-Fuzzy Inference System revealed a perfect True Positive Rate of 1.0 on the Receiver Operating Characteristic (ROC) curve, demonstrating its exceptional ability to correctly identify all instances of cardiac abnormality within the dataset.

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