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Taswanda Taryo
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Analisis Pengembangan Dalam Penerapan Recommender System Menggunakan Metode Algoritma Apriori Dan K-Means Clustering Pada Aplikasi E- Commerce. (Studi Kasus Di Big Sport Tangerang) Sukanda, Ahmad; Achmad Hindasyah; Taswanda Taryo
Jurnal Ilmu Komputer Vol 2 No 2 (2024): Jurnal Ilmu Komputer (Edisi Desember 2024)
Publisher : Universitas Pamulang

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The sales and marketing system in Big Sport is still carried out conventionally, causing the problem of sales transactions which causes a decrease in turnover. The solution to this problem is an e-commerce application for Big Sport and implementing a strategy recommendation system. By implementing the a priori algorithm method used to find out product recommendations on Big Sport to look for products that frequently appear (frequent itemset) with a minimum support calculation of 3 and a minimum Confidence of 50% from sales transaction data in June 2023 from 18 product data to determine the Association Rule for a combination of itemsets that gets an average lift ratio test value of 1.67 with a maximum Confidence value of 100% which forms 22 Association Rule results to provide good and accurate product recommendations for e-commerce applications based on sales transaction history data . The K-Means Clustering method was implemented using tolls rapidminer using transaction data for 6 months from 18 products. From the rapidminer run, the results from cluster 0 contain 8 items, cluster 1 has 7 items, and cluster 2 has 3 items with an average value. within a centroid distance of 2381.332, where cluster 0 has a value of 1975.234, cluster 1 has a value of 2995.918 and cluster 2 has a value of 2030.222. It can be concluded that items in cluster 0 are products with low sales levels, items in cluster 2 with medium sales levels, and items in cluster 1 with high sales levels. And the Davies Bouldin Index value is 0.462 which shows the fact that the centroid distance assessment results are almost close to 0 which can be concluded to have satisfactory results because the lower the DBI value, the better the cluster value so that it can be used as a reference in product procurement.
Analis Perancangan Dan Penerapan Keamanan Jaringan Menggunakan Metode Intrusion Detection System (IDS), Intrusion Prevention System (IPS) Dan Demilitarized Zone (DMZ) Pada PT. Maha Digital Indonesia (Mahapay) Trijanitra, Evan; Arya Adhyaksa Waskita; Taswanda Taryo
Jurnal Ilmu Komputer Vol 2 No 2 (2024): Jurnal Ilmu Komputer (Edisi Desember 2024)
Publisher : Universitas Pamulang

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Network security systems, in recent years have become the main focus in the world of securing other important data, this is due to the high number of suspicious threats (Suspicious Threats) and attacks from the Internet. Network security involves efforts to protect data and computer systems from detrimental threats, such as cyberattacks, malware, and data theft. The existence of increasingly complex and evolving threats has increased awareness of the need for strong network security. PT. Maha Digital Indonesia (Mahapay) is a company operating in the field of EDC Field Service where it is very important that client data is kept confidential. This requires good network security to maintain the confidentiality of the data. So the aim of this research is to implement network security using the Intrusion Detection System (IDS), Intrusion Prevention System (IPS) and Demilitarized Zone (DMZ) methods as network security at PT . Maha Digital Indonesia (Mahapay). The results of this research are the formation of connections between networks in the topology along with the successful functioning of the Intrusion Detection System (IDS) and Intrusion Prevention System (IPS) detecting and preventing suspicious activities carried out by attackers and the operation of rules for the DMZ area. Success in the application is tested again by carrying out several attack methods that will be analyzed such as Syn Flood Attack, Ping Of Death and Port Scanning which will be handled by the configuration that has been applied to the network and server.
Klasifikasi Berita Bahasa Indonesia Dengan Menggunakan Metode K-Nearest Neighbor Dan Naive Bayes Komariah Kukum Manieh Nuryasin; Taswanda Taryo; Sudarno
Jurnal Ilmu Komputer Vol 3 No 1 (2025): Jurnal Ilmu Komputer (Edisi Juli 2025)
Publisher : Universitas Pamulang

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In the era of rapid development of information technology, the need for a news classification system is crucial to manage the increasing volume of information. This study aims to develop a news classification system in Indonesian into five main categories: Politics, Economy, Health, Security, and Poverty. The methods used include the K-Nearest Neighbor (KNN) algorithm and Naïve Bayes. The dataset consists of 2,000 news items obtained from Kaggle, with preprocessing stages including cleaning, tokenizing, normalization, and TF-IDF weighting. The evaluation was carried out through three data sharing scenarios: 70%-30%, 80%-20%, and 90%-10%. The results showed that the KNN algorithm achieved the highest accuracy of 89% in the 80%-20% and 90%-10% scenarios, while Naïve Bayes produced the best accuracy of 78.66% in the 70%-30% scenario. KNN proved to be more reliable for data with balanced category distribution, while Naïve Bayes required further adjustment, especially for underrepresented data categories. This research provides significant contributions to the development of an automatic news classification system, which can be implemented to improve user experience in accessing information.
KLASIFIKASI PHISHING URL PADA WEBSITE BERBASIS METODE ENSEMBLE Bahrul Ulum; Taswanda Taryo; Sudarno
Jurnal Ilmu Komputer Vol 3 No 1 (2025): Jurnal Ilmu Komputer (Edisi Juli 2025)
Publisher : Universitas Pamulang

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This study analyzes the performance of ensemble learning algorithms in detecting phishing URLs using the PhiUSIIL Phishing URL dataset. The three algorithms compared are CatBoost, XGBoost, and LightGBM. The research stages include data preprocessing, data division into an 80:20 train-test split, and performance evaluation based on accuracy, precision, recall, and F1-score metrics. The results show that XGBoost has the best performance with an accuracy of 97.54% and an ROC AUC of 93.05%, followed by CatBoost with an accuracy of 97.46% and an ROC AUC of 92.94%. LightGBM, although it has lower performance, still shows good results with an accuracy of 96.99% and an ROC AUC of 91.85%. The data cleaning process successfully improves efficiency by eliminating irrelevant attribute analysis. This study confirms that ensemble algorithms can be implemented for the development of more effective and accurate phishing detection systems. XGBoost is recommended as the primary algorithm in detecting phishing threats in cybersecurity applications, thanks to its ability to handle large and complex data.
Analisis Prediksi Penerima Bantuan Bea Study Menggunakan Algoritma Id3, Naïve Bayes Dan K-Nearest Neighbor (Studi Kasus Pada Lembaga Amil Zakat Rydha) Muhamad Sibli; Taswanda Taryo; Murni Handayani
Jurnal Ilmu Komputer Vol 3 No 1 (2025): Jurnal Ilmu Komputer (Edisi Juli 2025)
Publisher : Universitas Pamulang

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The RYDHA Amil Zakat Institution has not yet implemented a data-driven predictive system to objectively determine B-Best scholarship recipients, leaving the selection process manual and prone to bias. This study aims to compare the performance of ID3, Naïve Bayes, and K-Nearest Neighbors (KNN) algorithms in classifying scholarship eligibility. Primary data were obtained from the 2024 B-Best applicants’ records, including demographic, socio-economic, academic, and supporting documents, while secondary data consisted of selection guidelines and internal reports, collected through interviews, documentation, and observation. Data analysis employed the three algorithms with evaluation using the Confusion Matrix and ROC Curve. The results show that KNN achieved the best performance with 96.3% accuracy, 0.958 AUC, 0.944 F1-score, 0.944 precision, and 0.944 recall, thus recommended as the predictive model to support a more objective and accurate scholarship selection system.
Analisis Tipe Kecerdasan Majemuk Siswa Sekolah Dasar Berbasis Catatan Perilaku Menggunakan Algoritma Naive Bayes, K-Nearest Neighbors, dan Support Vector Machine Nursalam, Asep Herman; Agung Budi Susanto; Taswanda Taryo
Jurnal Ilmu Komputer Vol 3 No 1 (2025): Jurnal Ilmu Komputer (Edisi Juli 2025)
Publisher : Universitas Pamulang

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This study aims to identify the types of multiple intelligences of elementary school students based on Howard Gardner's theory by utilizing machine learning algorithms, namely Naive Bayes, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The data used comes from student behavior records and intelligence type questionnaires obtained from students or parents. The SEMMA method (Sample, Explore, Modify, Model, Assess) is used, including text preprocessing and TF-IDF feature extraction. The classification process is carried out using Orange Data Mining software and evaluated using accuracy, precision, recall, F1-score, and AUC metrics. The evaluation results show that the SVM algorithm provides the best performance with an accuracy of 93.30% and AUC of 0.997. Naive Bayes follows with 90.50% accuracy and 0.994 AUC, while KNN reaches 89.50% accuracy and 0.941 AUC. The study also results in a web-based application prototype that classifies students' intelligence types and provides personalized learning recommendations. This confirms the effectiveness of machine learning in supporting personalized learning and student potential development.
Co-Authors Achmad Hindasyah AGUNG BUDI SUSANTO Agus Sofwan Ahmad Musyafa Ajeng Permata Suri Arya Adhyaksa Waskita BAHRUL ULUM Baskoro Abhi P Dahlan Supriatna Desmayadi Desmayadi Entis Sutisna Farkhan Mubarok Gregorius Eduard D. P. H. E. Nurzaman Komariah Kukum Manieh Nuryasin Kun Wardana Masbah R.T. Siregar Muhamad Sibli Mukhlis Catio Mukhlishoh Syaukati Robbi Murni Handayani Nahampun, Rajin Nining Suharwati Nursalam, Asep Herman R. Suharyadi Reno Saeprani Riani, Ambar Sarwani Sarwani, Sarwani Sewaka Sri Vidawati Sudarno Suharno Suharno Sukanda, Ahmad Suriansjah Suriansjah Trijanitra, Evan Uliyatunisa Yustini Yustini