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Dinamik
Published by Universitas Stikubank
ISSN : 08549524     EISSN : 26231786     DOI : 10.35315/dinamik.v28i1
Core Subject : Science,
Computer Science & IT
The Jurnal DINAMIK aims to: Promote a comprehensive approach to informatics engineering and management incorporating viewpoints of different applications (computer graphics, computer networks and security, computer vision, computational intelligence, databases, big data, IT project management, and other fields relevant to information technology. Encourage scientists, practicing engineers, and others to conduct research and similar activities.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 505 Documents
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Data mining M David; Diana Diana
Dinamik Vol 31 No 2 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i2.10418

Abstract

The purpose of this study is to provide recommendations to companies in determining stock levels based on previous sales data predictions using the K-Nearest Neighbor method. The data mining process carried out in this study follows the stages in Knowledge Discovery in Database (KDD) to produce information in accordance with a predetermined sequence consisting of data selection, preprocessing, transformation, data mining, and interpretation or evaluation. The tool used in the application of data mining is Python. The results of this study indicate the factors that influence product sales at PT. Alam Perkasa Lestari Palembang, including product price, sales (marketing), market competition, and others. The product sales prediction results based on the highest accuracy value are k=3168 with an accuracy of 97.00%. Thus, the KNN k=3168 algorithm method can be implemented to predict product sales at PT. Alam Perkasa Lestari Palembang.. Keywords : Data mining, K-Nearest Neighbor, Sales
Evaluasi Kualitas Rangkuman Teks Otomatis Menggunakan Algoritma ROUGE Studi Komparatif Model Google Gemini dan OpenAI ChatGPT Ridwan Mahenra; Reza Pajriansyah
Dinamik Vol 31 No 2 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i2.10485

Abstract

The rapid development of large language models has significantly improved the performance of automatic text summarization systems. However, each model may demonstrate different levels of effectiveness depending on its architectural characteristics and training approach. This study aims to evaluate the quality of Indonesian news text summarization generated by Google Gemini and OpenAI ChatGPT. A total of fifty news articles were used as the dataset, each accompanied by a manually written summary to serve as the ground truth. The research procedure involved web scraping, text preprocessing, automated summarization using both models, and performance evaluation through ROUGE metrics, including ROUGE-1, ROUGE-2, and ROUGE-L. The results show that ChatGPT consistently achieves higher ROUGE-1 and ROUGE-2 scores compared with Gemini, indicating a better ability to preserve key terms and lexical relations within the text. Meanwhile, ROUGE-L scores for both models are relatively close, suggesting that Gemini remains competitive in maintaining overall summary structure. Distribution analysis also reveals that ChatGPT demonstrates more stable performance across various text types. Overall, this study concludes that ChatGPT provides more accurate and consistent automatic summarization results than Gemini for Indonesian news texts.
ANALISIS PERAWATAN MESIN BOILER DENGAN PENDEKATAN PREDICTIVE MAINTENANCE MENGGUNAKAN METODE ARIMA (Studi Kasus di PT. Karya Serasi Jaya Abadi STA Resources). Antonius Caesar Tamba; Paris Johannes Ginting; Anita Christine Sembiring
Dinamik Vol 31 No 2 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i2.10486

Abstract

This study analyzes the boiler machine maintenance system at PT. Karya Serasi Jaya Abadi using a predictive maintenance approach based on time series analysis with the ARIMA method to forecast critical component failure times and optimize maintenance schedules. Historical failure data over three years (2022-2024) from three critical boiler components—Superheater Tubes, Economizer Tubes, and Feed Water Pump—were analyzed using Mean Time Between Failures (MTBF) calculations, yielding an average of 339-340 operating hours between failures. Stationarity testing using Box-Cox transformation and Augmented Dickey-Fuller (ADF) test confirms data stationarity at the 5% significance level. Model identification using Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plots yields ARIMA(1,0,1) as the best model for all three components with the lowest Mean Square Error (MSE). Model validation through Ljung-Box Q-test and Anderson-Darling test confirms that residuals are white noise and normally distributed. Forecasting over 12 months shows stable failure intervals at 340-341 hours with 95% confidence intervals of 297-384 hours. ARIMA(1,0,1) model proves effective for predictive maintenance systems, enabling more accurate and planned maintenance scheduling, reducing unplanned downtime by 30-40%, enhancing operational reliability of the palm oil processing plant, and optimizing machine maintenance cost allocation. Keywords: predictive maintenance, ARIMA, boiler machine, time series, MTBF, machine reliability, maintenance scheduling
Analisis Komparatif Performa Algoritma Random Forest dan Gradient Boosting dalam Klasifikasi Data Finansial Ridwan Mahenra; Hutriatmo Ilham Dito Armando
Dinamik Vol 31 No 2 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i2.10489

Abstract

This study aims to analyze and compare the performance of the Random Forest and Gradient Boosting algorithms in classifying credit card default risk using the Default of Credit Card Clients dataset. The dataset consists of 30,000 entries with 24 financial and demographic variables representing customers’ payment behavior over the past six months. The research procedure includes data acquisition and exploration, preprocessing through feature standardization, stratified data splitting, model construction, and performance evaluation using accuracy, precision, recall, and F1-score metrics. The experimental results indicate that both algorithms successfully capture complex financial patterns; however, Gradient Boosting demonstrates superior performance, particularly in recall and F1-score, highlighting its better sensitivity to default cases. The feature importance analysis confirms that payment history variables, especially PAY_0, play a major role in influencing model predictions. Overall, this study recommends the use of boosting-based models for credit risk prediction, particularly when dealing with imbalanced datasets, due to their ability to learn minority patterns and iteratively reduce prediction errors.
IMPLEMENTASI METODE ENTROPY DAN COPRAS UNTUK EVALUASI KINERJA PASOKAN SAYURAN PADA ROCKET CHICKEN AREA SEMARANG Dinda Okta Via; Saifur Rohman Cholil
Dinamik Vol 31 No 2 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i2.10490

Abstract

The development of the fast-food culinary business in Indonesia, particularly the Rocket Chicken network in Semarang, requires optimal management of fresh vegetable raw material supply to maintain quality consistency and operational continuity. This study aims to evaluate vegetable supply performance at ten Rocket Chicken branches in the Semarang area by integrating the Entropy method for objective weighting and Complex Proportional Assessment (COPRAS) for alternative ranking. Data were collected through questionnaires from 34 respondents covering assessments of five main criteria (Price, Quality, Timeliness, Supply Continuity, Service and Responsiveness) as well as actual supplier performance assessments. The Entropy weighting results show Quality as the most important criterion (62.1%), followed by Supply Continuity (24.2%), Price (8.1%), Timeliness (4.0%), and Service (1.5%). The application of COPRAS, treating Price as a cost criterion and other criteria as benefits, yields the highest performance ranking at the MENOREH branch with a utility value of 1.0690 (100.00%), followed by TLOGOSARI (96.75%), MRICAN (96.00%), NGAILYAN (94.03%), and SEKARAN (92.64%). Validation using Spearman rank correlation shows methodological consistency with a coefficient of ρ = -0.939, indicating excellent results. The differences between Entropy and COPRAS results indicate different evaluation perspectives, where Entropy emphasizes information variation in data, while COPRAS focuses on cost-benefit combination optimization. This study provides practical contributions in the form of supply performance improvement priority recommendations focusing on vegetable quality enhancement and methodological contributions through the integration of Entropy-COPRAS proven effective for evaluating fresh food supply chain performance in the context of the Indonesian culinary business.
Analisis Keamanan Sistem Informasi menggunakan Metode Penetration Testing berbasis Metasploit Framework Purwadi Purwadi; Herriyawan Herriyawan; Arief Wibowo
Dinamik Vol 31 No 2 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i2.10491

Abstract

Abstract Information system security has become a crucial aspect as organizations' reliance on digital technology increases. Increasingly complex cyber threats demand systematic and ongoing security evaluations. This study aims to analyze the security level of information systems by applying a penetration testing method based on the Metasploit Framework. The research methodology refers to the Penetration Testing Execution Standard (PTES), which includes pre-engagement, intelligence gathering, vulnerability analysis, exploitation, post-exploitation, and reporting stages. The test results showed that of the 10 vulnerabilities identified, four were categorized as high risk, three as medium risk, and three as low risk. The exploitation phase demonstrated a 70% success rate, allowing researchers to gain initial access to the target system. In the post-exploitation phase, the access gained allowed attackers to access system files, read configurations, and escalate limited privileges. These findings confirm that implementing regular penetration testing can help organizations improve their information system security posture and minimize the risk of data leaks and service disruptions.
ANALISIS PREDIKSI TINGKAT KECEMASAN PADA PASIEN IGD DENGAN CATBOOST CLASSIFIER BERDASARKAN PARAMETER MEDIS Muhammad Ayub Al Farizi; Dedi Kaldo Malau; Steven Lee; David Ronaldo Sibarani; Donni Nasution
Dinamik Vol 31 No 2 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i2.10498

Abstract

The anxiety levels of patients in the Emergency Department (ED) have a significant impact on medical management and care, making accurate early detection crucial. This study aims to develop and evaluate a machine learning-based classification model to predict patient anxiety levels into four categories: Normal (0), Mild (1), Moderate (2), and Severe (3), using the CatBoost Classifier algorithm. This approach utilizes physiological parameters such as systolic and diastolic blood pressure, heart rate, respiratory rate, as well as demographic variables and hypertension history. The data were trained and validated through appropriate dataset splitting, with comprehensive evaluation using accuracy, precision, recall, F1-score metrics, and learning curve analysis to assess model generalization. The evaluation results showed very high performance, with an overall accuracy reaching 99%. The model provides remarkable consistency across all classes: for the Normal class (0), precision, recall, and F1-score reached 1.00; the Mild class (1) achieved 0.98, 0.99, and 0.99; the Moderate class (2) each reached 0.98; and the Severe class (3) reached 1.00, 0.99, and 0.99. The learning curve indicates no overfitting and the model's ability to learn effectively as the amount of training data increases. Feature importance analysis confirms that both systolic and diastolic blood pressure are dominant predictors of anxiety levels, in line with observed patient physiological responses. Overall, the CatBoost model is proven to be highly reliable and holds great potential as a clinical decision support system that can assist healthcare professionals in the early detection and management of patient anxiety in a dynamic ER environment.
Analisis Perbandingan Algoritma Machine Learning untuk Prediksi Risiko Kanker Paru dengan Teknik Smote: Implementasi Random Forest, SVM, XGBoost, dan Logistic Regression Erliyan Redy Susanto; Gita Rahmawati; Neneng Neneng
Dinamik Vol 31 No 2 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i2.10506

Abstract

Lung cancer is the leading cause of cancer-related deaths worldwide, often detected at advanced stages. This study aims to develop an accurate and efficient lung cancer risk prediction system by comparing the performance of four machine learning algorithms: Random Forest, Support Vector Machine (SVM), XGBoost, and Logistic Regression. To address the class imbalance problem in the dataset, this study implements the Synthetic Minority Over-sampling Technique (SMOTE). The dataset used comes from Kaggle and consists of 5000 patient records with 29 predictive features. The research process includes data collection, pre-processing, data splitting, application of SMOTE, data scaling, model definition, model training, model evaluation, and performance comparison of the models. The results of the study show that the Logistic Regression and SVM models demonstrate the best performance with accuracies of 97.20% and 97.40%, respectively, and ROC-AUC scores of 99.80% and 99.66%, respectively. The implementation of the model in a web-based system allows both the general public and health professionals to use the model for predicting lung cancer risk based on identified factors. These results contribute to the development of a lung cancer risk prediction model that can assist in making better medical decisions.
Analisis Kelayakan Implementasi Titik Akses Virtual (VAP) sebagai Solusi Efisiensi Infrastruktur Jaringan untuk Usaha Mikro, Kecil, dan Menengah (UMKM) dari Perspektif Tekno-Ekonomi Indah Ratu Aulia; Nyoman Bogi Aditya Karna; Sofia Naning Hertiana
Dinamik Vol 31 No 2 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i2.10508

Abstract

Micro, Small, and Medium Enterprises (MSMEs) often face network infrastructure inefficiencies due to fragmented management and budget constraints. This study aims to analyze the feasibility of implementing Virtual Access Point (VAP) technology as a solution for network infrastructure efficiency in MSME shop clusters. Using a hybrid simulation approach, this study compares the performance between conventional infrastructure (Scenario A) and VAP-based integrated infrastructure (Scenario B). Technical evaluations were conducted using UniFi Design Center for signal propagation analysis and GNS3 for logical topology and network security validation. Economic analysis was performed using the Total Cost of Ownership (TCO) and Return on Investment (ROI) methods. The results showed that Scenario B was able to eliminate Co-Channel Interference, maintain signal stability in the range of -60 dBm to -65 dBm, and ensure data isolation between tenants through the IEEE 802.1Q (VLAN) protocol. From an economic perspective, the implementation of VAP reduces initial investment costs (CAPEX) by 32% and operational costs (OPEX) by 21.6% per year. With an ROI value of 102.36% and a payback period of 5.9 months, VAP technology has proven to be highly feasible as an efficient, secure, and profitable standard solution for MSME network infrastructure.
Analisis Perilaku Pelanggan dan Segmentasi Pasar pada Driving Range menggunakan Teknik Data Mining Purwadi Purwadi; Herriyawan Herriyawan; Arief Wibowo
Dinamik Vol 31 No 2 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i2.10513

Abstract

Penelitian ini bertujuan untuk menganalisis penerapan teknik data mining dalam mengidentifikasi pola perilaku pelanggan dan melakukan segmentasi pasar pada fasilitas driving range. Seiring meningkatnya persaingan di industri olahraga rekreasi, khususnya golf, pemahaman berbasis data terhadap perilaku pelanggan menjadi krusial untuk mendukung strategi pemasaran dan peningkatan layanan. Metode yang digunakan dalam penelitian ini meliputi eksplorasi data menggunakan Kernel Density Estimation (KDE) untuk menganalisis distribusi usia pelanggan, serta K-Means Clustering untuk melakukan segmentasi pelanggan berdasarkan usia, lama bermain golf, dan frekuensi kunjungan per minggu. Penentuan jumlah klaster optimal dilakukan menggunakan Elbow Method, yang menunjukkan nilai K optimal sebesar tiga klaster. Data penelitian diperoleh dari transaksi harian pelanggan, data keanggotaan, dan survei kepuasan pelanggan. Hasil analisis menunjukkan bahwa mayoritas pelanggan berada pada rentang usia 20–40 tahun dengan puncak distribusi pada usia sekitar 30–32 tahun. Hasil clustering mengidentifikasi tiga segmen utama, yaitu pelanggan berpengalaman dengan usia rata-rata 43 tahun dan lama bermain sekitar 116 jam, pelanggan senior atau profesional dengan usia rata-rata 53 tahun dan pengalaman bermain tertinggi sekitar 310 jam, serta pelanggan pemula dengan usia rata-rata 32 tahun dan pengalaman bermain sekitar 17 jam namun memiliki frekuensi kunjungan relatif tinggi, yaitu sekitar 1,9 kali per minggu. Temuan ini menunjukkan bahwa penerapan data mining efektif dalam mendukung segmentasi pelanggan dan pengambilan keputusan strategis dalam pengelolaan driving range.

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