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All Journal LINK Jurnal Informatika dan Teknik Elektro Terapan Jurnal Sisfokom (Sistem Informasi dan Komputer) JURIKOM (Jurnal Riset Komputer) Journal of Informatics and Computing Nusantara Journal of Artificial Intelligence and Information Systems
Khairul Huda
Universitas Karya Husada Semarang
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Health Professions

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RANCANG BANGUN SISTEM INFORMASI POINT OF SALES BERBASIS WEB PADA JDC RESTO Khairul Huda; Hargokendar Suhud; Youfih Herlina
Journal of Informatics and Computing (RANDOM) Vol. 2 No. 2 (2023): Journal of Informatics and Computing
Publisher : Politeknik Negeri Indramayu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31884/random.v2i2.24

Abstract

JDC restaurant is a business engaged in the catering sector which provides a form of service for ordering food and drinks both on a large and small scale. The sales system currently used is still manual, this makes the sales transaction process and reporting ineffective and inefficient. This manual process makes the data presented less accurate and has a high risk of errors, damage, and data loss. Based on these problems, it is necessary to have a web based point of sales system where all business processes are computerized so that it is expected to support performance, facilitate and optimize business processes at JDC restaurants. System testing is carried out using black box testing indicating that the system has succeeded in carrying out input, processing, and producing output as expected in the system design.
PENERAPAN PEMBOBOTAN TERM FREQUENCY-INVERSE DOCUMENT FREQUENCY DAN ALGORITMA K-NEAREST NEIGHBOR UNTUK ANALISIS ULASAN HOTEL DI SITUS TRIPADVISOR Huda, Khairul; Pohan, Sry Dhina; Herlina, Youfih
Jurnal Informatika dan Teknik Elektro Terapan Vol 12, No 3 (2024)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v12i3.4800

Abstract

Penelitian ini latarbelakangi oleh masalah evaluasi produk dan layanan menggunakan metode tradisional seperti survei, kuisioner dan wawancara yang sering menghasilkan analisis yang tidak konsisten dan tidak akurat. Salah satu pendekatan untuk mengatasi masalah tersebut adalah dengan menerapkan Teknik pembobotan Term Frequency-Inverse Document Frequency (TF-IDF) dan algoritma K-Nearest Neighbor untuk menganalisis ulasan pelanggan hotel dari situs TripAdvisor, yang dikategorikan menjadi 3 kelas sentimen yaitu netral, negatif dan positif menggunakan text mining. Algoritma K-Nearest Neighbor dipilih karena kemampuannya dalam komputasi yang efisien, mudah beradaptasi dengan berbagai data yang besar, serta relative rendah untuk kompleksitas algoritmanya. Hasil penelitian menunjukkan bahwa sistem ini mampu mengklasifikasikan ulasan hotel dengan tingkat akurasi yang optimal, mencapai 76% untuk data pelatihan dengan K=31, dan meningkatkan akurasi hingga 84% setelah melalui penerapan teknik random over-sampling untuk mengatasi imbalanced dataset
Aplikasi Integrative Empowerment Keluarga Berbasis E-Monitoring Roled Based Expert System Dalam Pencegahan Relapse Pada Skizofrenia Iswanti, Dwi Indah; Mendrofa, Fery Agusman Motuho; Huda, Khairul; Mujahidah, Sa'adah; Sawab, Sawab
Jurnal LINK Vol 20, No 2 (2024): NOVEMBER 2024
Publisher : Pusat Penelitian dan Pengabdian kepada Masyarakat, Poltekkes Kemenkes Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31983/link.v20i2.11851

Abstract

E-monitoring integrative empowerment berbasis roled based expert system yang dapat diakses kapan saja dan dimanapun berada menghadirkan Perawat jiwa belum terbentuk untuk membantu keluarga merawat dan mencegah relapse skizofrenia ketika dirumah. Pemberdayaan kemitraan masyarakat ini bertujuan untuk membentuk dan melihat efektifitas dari e-monitoring integrative empowerment berbasis roled based expert system. Evaluasi pemberdayaan kemitraan masyarakat dilakukan dengan desain pra-experimental pada 30 keluarga yang merawat skizofrenia. Kuesioner digunakan untuk mengukur kemampuan keluarga merawat dan mencegah relapse. Analisis data dengan uji Wilcoxon. Hasil: Ada efektifitas e-monitoring integrative empowerment berbasis roled based expert system terhadap kemampuan keluarga merawat dan mencegah relapse (p-value=0,0000,05). Perawat jiwa dan kader dilatih untuk dapat mensosialiasasikan dan memantau dari program aplikasi system ini sehingga dapat digunakan oleh keluarga yang merawat skizofrenia.
Penerapan Word2Vec dan SVM dengan Hyperparameter Tuning untuk Deteksi Phishing Wicaksana, Hilman Singgih; Huda, Khairul
JURIKOM (Jurnal Riset Komputer) Vol 12, No 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8729

Abstract

The advancement of information technology in today’s digital age takes place very rapidly from one time to another. This phenomenon is accompanied by increasing cybersecurity threats like phishing. Phishing links are often designed with uniform resource locator (URL) structures that appear convincing and are difficult to distinguish from genuine links. This research proposes a word-to-vector (Word2Vec) and Support Vector Machine (SVM) approach with hyperparameter tuning where Word2Vec is a word embedding technique used to create a word vector representation of a particular URL, SVM is used as a machine learning (ML) approach used in this research, and hyperparameter tuning is used as a technique to find the best combination of parameters to produce an optimal SVM model in detecting phishing. The purpose of this research is to compare the performance between SVM and XGBoost models that have been optimized and deploy ML models into a prediction system using the Streamlit framework to detect phishing based on input made by users in the form of certain URLs. The findings of this study indicated that the SVM model performed very well compared to the XGBoost model, with precision, recall, f1-score, and accuracy values of about 99.84% for SVM. On the other hand, the XGBoost model recorded precision, recall, f1-score, and accuracy values of about 99.70% each. Thus, the SVM model is the optimal model to detect phishing precisely and accurately.
Website Quality Evaluation of PCNU Kota Depok Based on User Satisfaction using WebQual 4.0 Huda, Khairul; Juwono, Musaid Purnomo; Nabawi, Fezan
Nusantara Journal of Artificial Intelligence and Information Systems Vol. 1 No. 1 (2025): June
Publisher : Faculty of Engineering and Computer Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47776/nuai.v1i1.1606

Abstract

This study aims to analyze the quality of the PCNU Kota Depok online news website in relation to user satisfaction. The method used is WebQual 4.0, one of the techniques for measuring website quality based on user perceptions across four dimensions: usability, information quality, interaction quality, and overall impression. The results of the study, based on Crosstab and Chi-Square tests, indicate that differences in perception across attributes in the four dimensions are not entirely influenced by respondent characteristics such as education, gender, age, and occupation. According to user perceptions, the service quality of the PCNU Kota Depok website can be measured by the highest levels of satisfaction in the following order: Overall Impression (4.09), Information Quality (3.92), Usability (3.76), and Interaction Quality (3.61). These results provide valuable insights for improving user experience and guiding future development of the PCNU Kota Depok website.
Optimized Machine Learning Approach for Malware Detection Wicaksana, Hilman Singgih; Huda, Khairul
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 01 (2026): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i01.2547

Abstract

The rapid evolution of information technology has created vast opportunities in multiple domains, yet it also brings critical challenges in the realm of cybersecurity, particularly with the growing frequency of malware attacks. Modern malware utilizes advanced evasion and spreading techniques, such as polymorphic and metamorphic transformations, which undermine the performance of conventional detection systems. This research aims to evaluate and compare the effectiveness of several machine learning algorithms optimized through hyperparameter tuning to determine the most accurate and reliable model for malware detection. The study applies a supervised learning approach using labeled data and examines five algorithms: Multilayer Perceptron, Random Forest, Support Vector Machine, Extreme Gradient Boosting, and Hist Gradient Boosting. Each model was fine-tuned to identify its optimal configuration, and performance was measured using accuracy, precision, recall, and F1-score. The experiments were conducted on a dataset comprising 58,596 records that had been thoroughly cleaned and preprocessed. The findings indicate that the Multilayer Perceptron achieved superior results, obtaining 99.97% across all evaluation metrics. These outcomes demonstrate the model’s strong potential for reliable malware detection and its suitability for integration into cybersecurity frameworks that demand fast response, high precision, and adaptability to evolving attack patterns.
Co-Authors Dwi Indah Iswanti Fery Agusman Motuho Mendrofa Hargokendar Suhud Herlina, Youfih Juwono, Musaid Purnomo Mujahidah, Sa'adah Nabawi, Fezan Pohan, Sry Dhina Sawab Sawab Wicaksana, Hilman Singgih Youfih Herlina