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COMPARISON BETWEEN APRIORI AND FP-GROWTH ALGORITHMS ON INVENTORY MODEL OF ITEM AVAILABILITY Radhiatul Husna; Yomei Hendra; Muhammad Imam Akbar
Jurnal Ipteks Terapan (Research Of Applied Science And Education ) Vol. 14 No. 3 (2020): Re Publish Issue
Publisher : Lembaga Layanan Pendidikan Tinggi Wilayah X

In this study, it will be discussed the comparison between apriori and fp-growth algorithms on aninventory model of item availability. The research about this topic becomes important and interestingto be studied because it illustrates the implementation of applied mathematics by constructing thematematical model, namely an inventory model of item availability. Then, the model will be solved byusing apriori and fp-growth algorthms related to the application of probability theory. In this case,apriori and fp-growth algorithms are used to specifythe pattern of dependency relationships between an item and other items so that the probability ofitem purchase based on other goods can be discovered. Moreover, the number of items which shouldbe provided by a seller in a shop or a supermarket can be calculated. In order to figure out the outputof this research which is the analysis of the comparison between apriori and fp-growth algorithms onthe inventory model of item availbility, then choosing and categorizing kinds of items based on thesales, using the apriori and fp-growth algorithms, constructing the model, solving and interpreting itare established

COMPARISON BETWEEN APRIORI AND FP-GROWTH ALGORITHMS ON INVENTORY MODEL OF ITEM AVAILABILITY Radhiatul Husna; Yomei Hendra; Muhammad Imam Akbar
Jurnal Ipteks Terapan Vol. 14 No. 3 (2020): Re Publish Issue
Publisher : Lembaga Layanan Pendidikan Tinggi Wilayah X

In this study, it will be discussed the comparison between apriori and fp-growth algorithms on aninventory model of item availability. The research about this topic becomes important and interestingto be studied because it illustrates the implementation of applied mathematics by constructing thematematical model, namely an inventory model of item availability. Then, the model will be solved byusing apriori and fp-growth algorthms related to the application of probability theory. In this case,apriori and fp-growth algorithms are used to specifythe pattern of dependency relationships between an item and other items so that the probability ofitem purchase based on other goods can be discovered. Moreover, the number of items which shouldbe provided by a seller in a shop or a supermarket can be calculated. In order to figure out the outputof this research which is the analysis of the comparison between apriori and fp-growth algorithms onthe inventory model of item availbility, then choosing and categorizing kinds of items based on thesales, using the apriori and fp-growth algorithms, constructing the model, solving and interpreting itare established

Penerapan Teknologi WebRTC pada Aplikasi E-Learning Sakinah, Putri; Thoriq, Muhammad; Hendra, Yomei; Hariani Manurung, Kiki; Hayati, Nova
Jurnal Informasi dan Teknologi 2023, Vol. 5, No. 4
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60083/jidt.v5i4.492

Perkembangan Teknologi Informasi pada era globalisasi telah meningkatkan produktifitas berbagai aktivitas manusia bagi setiap bidang dan pekerjaan salah satunya terhadap dunia pendidikan. Inovasi multimedia pembelajaran dihasilkan dari perkembangan teknologi yang dapat membantu proses belajar mengajar menjadi lebih fleksibel dengan adanya produk teknologi berbasis aplikasi E-Learning. Metode pembelajaran menggunakan sistem e-learning dapat membantu pendidik dan pelajar dalam distribusi materi pembelajaran dengan internet yang saling terhubung dimana saja dan kapan saja sehingga dapat memaksimalkan waktu pembelajaran di ruang kelas yang terbatas. Dengan adanya perkembangan teknologi, aplikasi e-learning dapat dikembangkan menjadi lebih komunikatif . WebRTC merupakan salah satu teknologi yang dapat membuat aplikasi e-learning memiliki fitur/menu yang dapat memiliki akses komunikasi secara real time dari penggunanya. Tujuan dari penelitian ini adalah untuk menerapkan penggunaan sebuah aplikasi E-Learning yang memiliki teknologi WebRTC untuk penunjang fasilitas belajar mengajar dari jarak jauh agar bisa tetap terlaksana.

Identification of Base Transceiver Station Device Health Using Fuzzy Multi Criteria Decision Making Method on Telkomsel Site Hamsar , Ali; Maulana , Fajar; Hendra , Yomei
Jurnal Multimedia dan Teknologi Informasi (Jatilima) Vol. 6 No. 02 (2024): Jatilima : Jurnal Multimedia Dan Teknologi Informasi
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jatilima.v6i02.661

The condition of the Base Transceiver Station (BTS) equipment plays an important role in supporting telecommunications services. One of the many complaints from the public about the quality of Telkomsel's network is that it is caused by an unhealthy condition of BTS equipment.In this issue, the aim is to provide information to the Engineer about the health conditions of Telkomsel's BTS devices.The number of BTS identified in this study was 20 BTS in Rokanhulu, Riau. In identifying the health of BTS devices using the Fuzzy Multi Criteria decision making method (FMCDM).The results of the identification of this BTS device in the form of a Dashboard display that becomes the Engineer's information source in determining their work priorities.It can be concluded in this study, the results of the study can help the Telkomsel Engineer to determine the health condition of Telkomsel BTS devices.

Implementation of a Forward Chaining Expert System in Diagnosing Laptop Damage Sakinah, Putri; Hendra, Yomei; Satria, Budy; Rahman, Zumardi; Maulana, Fajar; Syaputra, Aldo Eko
Internet of Things and Artificial Intelligence Journal Vol. 4 No. 3 (2024): Volume 4 Issue 3, 2024 [August]
Publisher : Association for Scientific Computing, Electronics, and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/iota.v4i3.791

Laptops have become a primary need for almost everyone, but the damage rate is also high. Manual diagnosis of laptop damage requires special expertise and is prone to errors that can exacerbate damage. The purpose of this study was to develop an expert system based on the forward chaining method to diagnose laptop damage. Data obtained through expert interviews, literature study, and the internet comprised 13 symptoms and five main types of laptop damage. Relate data in tables to form IF-THEN rules of the forward chaining method. The test results on six symptoms indicate that the system can diagnose IC Power damage with 100% accuracy, which is the highest diagnosis. In conclusion, the forward chaining method can diagnose laptop damage based on emerging symptoms.

Integrasi Model Pembelajaran Mesin dalam Game Menggunakan Gerakan Tangan Hendra, Yomei; Sakinah, Putri; Maulana, Fajar; Manurung, Kiki Hariani
Jurnal Informatika Vol 12, No 3: INFORMATIKA
Publisher : Fakultas Sains & Teknologi, Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/informatika.v12i3.6826

This study develops a Tetris game controlled through hand gestures using a machine learning model. The primary objective of this research is to create an interactive and responsive gaming experience by utilizing hand gesture detection as the main control mechanism. A hand gesture dataset was collected from videos segmented into individual frames, which were then analyzed using MediaPipe to detect and label gestures. The machine learning model employs a Convolutional Neural Network (CNN) trained to recognize hand gesture patterns and translate them into commands within the game. After implementation, an evaluation was conducted by distributing questionnaires to 18 Informatics students at Adzkia University to assess the system's comfort and responsiveness. The questionnaire results showed a high satisfaction level, with an average score of 84.56, covering evaluations of control ease, gesture detection accuracy, and system responsiveness. The average score for ease of use reached 85, indicating that the majority of users found the gesture-based controls comfortable. This study demonstrates that applying machine learning models in gesture-based control games can provide a more interactive and responsive experience, with potential applications in other interactive technologies.

Optimasi Penjualan Oleh-oleh Sumbar Menggunakan Analisa Diferensial dan Strategi E-Business D-CRM Syaputra, Aldo Eko; Hendra, Yomei; Mardiah, Ainil
JURNAL FASILKOM Vol. 14 No. 3 (2024): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v14i3.7938

UMKM Sanjai Tek Gadih, merupakan salah satu pelaku usaha mikro, kecil, dan menengah di Indonesia, menghadapi dan mempertahankan keberlangsungan bisnisnya merupakan tantangan besar dalam perkembangan pasar. Dinamika purchase interest konsumen dan kebutuhan dalam mempertahankan loyalitas pelanggan menjadi faktor kunci yang mempengaruhi kesuksesan jangka panjang UMKM ini. Dalasm menghadapi tantangan tersebut, inovasi dan adopsi teknologi menjadi sangat penting. Aplikasi analisis diferensial dan D-CRM dirancang untuk memberikan solusi komprehensif dalam mengatasi dinamika ini. Adopsi teknologi ini juga dapat membantu UMKM mengoptimalkan strategi pemasaran, meningkatkan kualitas layanan, dan membuat keputusan bisnis yang lebih tepat berdasarkan data yang akurat. Penelitian ini bertujuan untuk meningkatkan performa UMKM Sanjai Tek Gadih melalui, pengidentifikasian faktor-faktor yang mempengaruhi dinamika minat beli konsumen terhadap produk UMKM tersebut. Merancang dan Mengembangkan sebuah aplikasi dengan mengadopsi dua pendekatan yakni analisa diferensial dan DCRM, dimana analisa diferensial dimaksudkan untuk memberikan keputusan, sedangkan DCRM dimaksudkan untuk memudahkan pelanggan dalam berinteraksi dengan UMKM ini. Metode pengumpulan data yang digunakan berupa observasi, wawancara, analisis dokumen yang ada, serta studi literatur sebagai landasan teori. Serta menggunakan metode pengembangan sistem Waterfall meliputi analysis, design, coding, dan testing. Penelitian ini memiliki urgensi yang tinggi mengingat pentingnya pemahaman mendalam tentang dinamika purchase interest dan hubungan pelanggan untuk mempertahankan keberlanjutan UMKM di tengah persaingan pasar yang ketat. Dengan solusi teknologi yang tepat, UMKM Sanjai Tek Gadih dapat lebih adaptif terhadap perubahan pasar dan mampu mencapai pertumbuhan yang berkelanjutan di masa depan.

Evaluasi Kinerja Algoritma Apriori Dalam Pengelompokan Data Transaksi Penjualan Untuk Analisis Pola Pembelian Hendra, Yomei; Sakinah, Putri; Thoriq, Muhammad
Journal of Student Development Information System (JoSDIS) Vol 3, No 2: JoSDIS | Juli 2023
Publisher : Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/josdis.v3i2.4728

The increasing volume and complexity of sales transaction data in the digital era have prompted companies and organizations to capitalize on the valuable information it holds. Understanding purchase patterns in sales transaction data is critical for discerning product associations and consumer behavior, thus optimizing marketing strategies and data-driven decision-making. This study concentrates on assessing the performance of the Apriori algorithm, a popular association analysis technique, in clustering sales transaction data to uncover purchase patterns. Using sales transaction data from retail stores, which includes customer identities and purchased products, the Apriori algorithm identifies frequent itemsets that represent common purchase patterns. The results of the purchase pattern analysis and product associations offer valuable insights for companies to fine-tune marketing strategies and enhance the overall customer experience. The research demonstrates that the Apriori algorithm effectively identifies frequent purchase patterns and product associations in sales transaction data. The algorithm's efficiency makes it suitable for analyzing retail sales data effectively. This research contributes to understanding the Apriori algorithm's performance in analyzing sales transaction data for purchase pattern analysis, empowering businesses to make informed decisions based on product associations and customer preferences.

Efektivitas dan Kelemahan Autentikasi Berbasis Web Menggunakan One-Time Password (OTP) dalam Mencegah Akses Tidak Sah Maulana, Fajar; Hendra, Yomei; Sakinah, Putri; Eirlangga, Yofhanda Septi; Ayun, Aisyah Qurrata
Jurnal Informatika Vol 12, No 3: INFORMATIKA
Publisher : Fakultas Sains & Teknologi, Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/informatika.v12i3.7482

Authentication based on one-Time Password (OTP) is one method that is widely used in securing access to web systems. This study aims to analyze in depth the effectiveness and weaknesses of the OTP authentication system in preventing unauthorized access. Through a qualitative approach based on literature studies, as well as comparisons between other authentication methods, it was found that OTP is able to increase a significant additional layer of security, especially when combined with other authentication methods such as passwords or biometrics. However, this system still has various weaknesses, such as the risk of phishing attacks, man-in-the-middle (MITM) attacks, and vulnerabilities to SIM swapping attacks, especially in the implementation of OTP via SMS. Dependence on user devices and communication networks is also a limiting factor in the effectiveness of OTP. This study provides recommendations for the implementation of strengthening measures such as Multi-Factor Authentication (MFA), the use of authenticator applications, and the implementation of end-to-end encryption to reduce security risks. The results of this study are expected to be a reference for system developers and organizations in choosing and implementing authentication methods that are more secure and in accordance with current cybersecurity needs.

Frequent Pattern Mining for Cyberattack Detection Using FP-Growth on Network Traffic Logs Hamsar, Ali; Maulana, Fajar; Hendra, Yomei; Nasyuha, Asyahri Hadi; Aly, Moustafa H
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15221

Cybersecurity threats have become increasingly complex, coordinated, and adaptive, creating significant challenges for traditional intrusion detection systems (IDS) that rely on static, signature-based mechanisms. These systems often fail to recognize novel, evolving, or multi-vector attacks that do not match predefined patterns. To overcome these limitations, this study proposes a data-driven framework that applies the Frequent Pattern Growth (FP-Growth) algorithm to analyze co-occurring events within network traffic logs. Using the CIC-IDS2017 benchmark dataset, which includes a wide range of real-world attack scenarios, network events were preprocessed and transformed into transactional data. This transformation enabled the efficient extraction of frequent itemsets and association rules without the computational burden of candidate generation. The experimental results show that the proposed method effectively uncovers meaningful attack correlations, such as brute force attempts preceding privilege escalation or malware infections leading to large-scale DDoS attacks. The model achieved a precision of 77.27%, recall of 70.83%, and F1-score of 73.91%, confirming its reliability in detecting sophisticated attack chains. A heatmap visualization was also generated to improve interpretability, allowing security analysts to quickly identify critical attack relationships. In conclusion, this research demonstrates that FP-Growth provides a scalable, interpretable, and computationally efficient approach to cyberattack detection, with potential integration into real-time IDS environments. Future work will focus on temporal sequence mining and hybrid models combining FP-Growth with machine learning to enhance adaptive, context-aware threat detection.

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