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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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Analisis Email-Borne Malware: Teknik Infeksi melalui Lampiran Email dan Dampaknya terhadap Keamanan Siber 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.v31i2.10516

Abstract

Email masih menjadi salah satu vektor serangan siber paling dominan dalam penyebaran malware, baik terhadap organisasi maupun end-user. Berbagai laporan keamanan siber menunjukkan bahwa lebih dari 90% serangan malware diawali melalui email, dengan sekitar 30–40% email yang diterima organisasi dikategorikan sebagai spam atau berpotensi berbahaya, serta sebagian besar memanfaatkan lampiran berbahaya yang disamarkan sebagai dokumen sah. Hampir 95% organisasi dilaporkan pernah mengalami serangan phishing berbasis email setidaknya satu kali dalam satu tahun, dan lebih dari 50% insiden ransomware pada lingkungan korporasi bermula dari email yang mengandung lampiran malware. Pada tingkat end-user, ratusan juta email phishing dan malware dikirimkan setiap hari secara global, dengan sebagian berhasil mencapai kotak masuk pengguna dan menyebabkan pencurian data serta kerugian finansial. Penelitian ini bertujuan untuk menganalisis teknik infeksi email-borne malware melalui lampiran email serta dampaknya terhadap keamanan siber pada lingkungan korporasi dan end-user. Metode penelitian yang digunakan adalah analisis literatur dan studi kasus terhadap laporan insiden keamanan siber serta publikasi ilmiah terkini. Hasil analisis menunjukkan bahwa jenis malware yang paling sering disebarkan melalui lampiran email meliputi trojan, ransomware, dan spyware, dengan rekayasa sosial sebagai faktor utama keberhasilan serangan. Dampak yang ditimbulkan mencakup pencurian data sensitif, gangguan operasional sistem, kerugian finansial, serta penurunan tingkat kepercayaan pengguna dan reputasi organisasi. Penelitian ini menegaskan bahwa tingginya intensitas serangan email-borne malware menuntut penerapan strategi mitigasi yang komprehensif, seperti peningkatan kesadaran keamanan siber, pemanfaatan sistem penyaring email, dan penggunaan perangkat lunak keamanan yang andal untuk meminimalkan risiko infeksi malware melalui email.
ANALISA PENGENDALIAN PERSEDIAAN BAHAN BAKU DENGAN MENGGUNAKAN METODE ECONOMIC ORDER QUANTITY, SAFETY STOCK DAN REORDER POINT Febrianti Gresia Putri Handi; Dwiki Fransiska Sinaga; 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.v31i2.10522

Abstract

This research is driven by the high risk of production disruptions at PT. Central Proteina Prima Tbk. due to the uncertain availability of rice bran and wheat bran pollard raw materials. Inefficient inventory management potentially leads to inflated holding costs (overstock) or operational shutdowns caused by stockouts; therefore, optimized inventory control is essential. Utilizing a quantitative approach through the Economic Order Quantity (EOQ) method, this study determines the optimal order quantity while calculating Safety Stock (SS) and the Reorder Point (ROP)—with a 5-day lead time assumption—to maintain stock stability. Analysis of a 12-week period involving the consumption of 61.2 tons of rice bran and 40.8 tons of wheat bran pollard yielded specific technical parameters: for rice bran, a safety stock of 14.52 tons and an ROP of 18.16 tons; and for wheat bran pollard, a safety stock of 17.2 tons and an ROP of 19.59 tons. Overall, the integrated application of EOQ, SS, and ROP methods is proven effective in enhancing cost efficiency and ensuring the optimal continuity of the company's production processes.
Analisis Sentimen Publik terhadap Personal Branding Dedi Mulyadi di Tiktok Perbandingan Metode Manual dan Data Mining Cyintia Bella; Temi Ardiansyah
Dinamik Vol 31 No 2 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i2.10524

Abstract

Social media, particularly TikTok, has become a strategic platform for politicians to build personal branding and shape public opinion. This study aims to analyze public sentiment toward Dedi Mulyadi’s personal branding on TikTok and to compare sentiment analysis results obtained through manual labeling and data mining methods using the Naïve Bayes algorithm. This research employs a quantitative descriptive–comparative approach. The dataset consists of 800 manually labeled comments and 3,226 comments collected through web scraping and automatically classified. The results show that sentiment analysis using the manual method produces positive sentiment as the dominant category, accounting for 55.4%, followed by neutral sentiment at 30.6% and negative sentiment at 14.0%. In contrast, the data mining–based sentiment analysis indicates neutral sentiment as the most dominant category at 45.66%, followed by negative sentiment at 28.51% and positive sentiment at 25.83%. These differences are influenced by data volume, labeling techniques, and the limitations of algorithms in interpreting informal language and implicit expressions. This study concludes that both manual and data mining approaches have distinct strengths and limitations; therefore, their combined use can provide a more comprehensive understanding of public sentiment toward political personal branding on social media.
Analisis Perbandingan Kinerja Model Transfer Learning VGG16 Dan CNN Baseline Dalam Deteksi Penyakit Tanaman Tomat Berdasarkan Citra Daun Budi Riansyah; Styawati Styawati
Dinamik Vol 31 No 2 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i2.10533

Abstract

Tomatoes (Solanum lycopersicum) are a vital horticultural commodity that is highly susceptible to pathogen attacks, making visual symptoms on leaves the main indicator for early detection. However, these automatic detection efforts face significant challenges related to the limited variety and number of image datasets, which often hinder the performance of Deep Learning models. This study aims to compare the performance of Baseline CNN (training from scratch) with VGG16 (fixed feature extraction) on 10 classes of tomato leaf diseases. The evaluation results show that Baseline CNN achieved an accuracy of 87.30% and VGG16 achieved 85.30%. The advantage of the Baseline model lies in its flexibility in learning visual features from scratch, making it more adept at capturing specific details such as ring patterns in Early Blight. In contrast, VGG16 provides computational efficiency with 40% less parameter training load and proves superior in recognizing texture patterns, such as in Bacterial Spot (Recall 97%). In conclusion, although Transfer Learning is efficient and robust, this model has limitations in understanding the unique characteristics of plants (semantic gap).
Penerapan Algoritma Random Forest Classifier untuk Klasifikasi Kualitas Buah Pisang Berdasarkan Fitur Fisik dan Karakteristik Organoleptik Anisha Yuliantari; Debby Alita
Dinamik Vol 31 No 2 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i2.10534

Abstract

Objectively determining the quality of bananas (Musa spp.) is an important challenge in post-harvest management to ensure product standardization and minimize losses. This study aims to implement and evaluate the performance of the Random Forest Classifier (RFC) algorithm in predicting banana quality (Good or Bad) based on a combination of morphological features and organoleptic characteristics. A secondary dataset consisting of 8,000 tabular data samples was used, covering features such as Size, Weight, Sweetness, Softness, Ripeness, Acidity, and HarvestTime. The data was processed through Z-Score standardization and divided into a training:testing ratio of 80:20. Testing results showed that the RFC model achieved an exceptionally high classification accuracy of 96.62%, with balanced Precision and Recall values (0.97). Feature importance analysis revealed that Sweetness, Weight, and Size were the most dominant features and contributed significantly to quality decisions. This study proves that a data-based Machine Learning approach can provide an efficient, accurate, and non-destructive method of assessing banana quality, making it a prospective solution for automatic sorting systems in the agricultural industry.
Analisis Metode Eoq untuk Mengoptimalkan Persediaan pada Sistem Rantai Pasok (Studi Kasus: Kaulamuda Coffee Space) Selly Tri Amanda; William Ramdhan; Chitra Latiffani
Dinamik Vol 31 No 2 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i2.10536

Abstract

Kaulamuda Coffee Space merupakan usaha kuliner yang menghadapi tantangan dalam pengelolaan persediaan bahan baku, di mana sering terjadi ketidakstabilan stok berupa kekurangan bahan (stockout) yang menghambat penjualan maupun penumpukan bahan (overstock) yang berisiko rusak. Penelitian ini bertujuan untuk mengatasi permasalahan tersebut dengan merancang sistem informasi Supply Chain Management (SCM) yang menerapkan metode Economic Order Quantity (EOQ). Metode EOQ digunakan untuk menganalisis dan menentukan jumlah pemesanan bahan baku yang paling ekonomis, titik pemesanan kembali (Reorder Point), dan persediaan pengaman (Safety Stock). Sistem ini dikembangkan berbasis web menggunakan bahasa pemrograman PHP dan database MySQL. Hasil dari penelitian ini adalah sebuah aplikasi manajemen persediaan yang mampu memberikan rekomendasi pengadaan bahan baku secara tepat jumlah dan tepat waktu. Penerapan sistem ini diharapkan dapat meminimalkan total biaya persediaan, meningkatkan efisiensi operasional, serta menjamin ketersediaan stok demi kelancaran proses produksi dan kepuasan pelanggan di Kaulamuda Coffee Space
Optimasi Model Prediktif untuk Deteksi Dini Penyakit Hati Kronis melalui Seleksi Fitur dan Teknik Oversampling berbasis Machine Learning Agvina Maharani; Erliyan Redy Susanto
Dinamik Vol 31 No 2 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i2.10537

Abstract

Chronic liver disease is one of the leading causes of global morbidity and mortality, including in Indonesia. Early detection is essential to prevent disease progression to cirrhosis and hepatocellular carcinoma; however, clinical diagnosis is often delayed due to non-specific early symptoms and limited access to invasive diagnostic procedures. This study aims to develop and optimize a machine learning–based predictive model for early detection of chronic liver disease using clinical patient data. The Liver Cirrhosis dataset obtained from Kaggle was utilized, with preprocessing steps including missing value imputation, categorical variable encoding, feature selection using SelectKBest, and class imbalance handling through the Synthetic Minority Oversampling Technique (SMOTE). Three classification algorithms—Support Vector Machine (SVM), Random Forest, and XGBoost—were evaluated under a binary classification scheme of early-stage and advanced-stage disease. Model performance was assessed using accuracy, precision, recall, F1-score, and AUC-ROC metrics. The results indicate that the integration of feature selection and SMOTE improves model performance, particularly in enhancing sensitivity toward advanced-stage cases. XGBoost achieved the best overall performance based on AUC-ROC values, while Random Forest demonstrated a favorable balance between predictive performance and computational efficiency. This approach shows strong potential as a clinical decision support tool for early screening of chronic liver disease.
Pemanfaatan E-CRM dalam meningkatkan Loyalitas Pelanggan pada Intan Kosmetik Store Sri Yanun Nanda Harahap; Fauriatun Helmiah; Sumantri Sumantri
Dinamik Vol 31 No 2 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i2.10542

Abstract

Intan Cosmetic Store adalah usaha yang bergerak di bidang penjualan produk kecantikan yang saat ini masih menghadapi kendala operasional, seperti pencatatan transaksi yang belum terintegrasi, jangkauan pasar yang terbatas, dan kurangnya pengelolaan hubungan pelanggan yang efektif. Penelitian ini bertujuan untuk merancang dan membangun sistem penjualan online berbasis website yang menerapkan strategi Electronic Customer Relationship Management (E-CRM) guna meningkatkan kepuasan dan loyalitas pelanggan. Metode pengembangan sistem yang digunakan dalam penelitian ini memanfaatkan bahasa pemrograman PHP dengan framework CodeIgniter 3 dan database MySQL. Hasil dari penelitian ini adalah sebuah sistem informasi berbasis web yang tidak hanya berfungsi sebagai media transaksi penjualan online, tetapi juga menyediakan fitur pelayanan pelanggan, informasi promosi, dan pengelolaan data konsumen yang lebih efisien. Penerapan sistem E-CRM ini diharapkan dapat membantu Intan Cosmetic Store dalam memperluas jangkauan pasar, meningkatkan kualitas pelayanan, serta membangun hubungan jangka panjang yang kuat dengan pelanggan.
Pengelolaan Distribusi Produk Ritel untuk Mengatur Stok pada Winmart dengan SCM Method Haliza Tasfa Nasution; Fauriatun Helmiah; Sahren Sahren
Dinamik Vol 31 No 2 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i2.10543

Abstract

Pengelolaan distribusi dan pengaturan stok merupakan faktor krusial dalam menjaga kelancaran operasional usaha ritel, khususnya pada skala usaha kecil dan menengah. Winmart sebagai usaha ritel yang berlokasi di Jalan Madong Lubis, Kabupaten Asahan, masih menghadapi berbagai permasalahan dalam pengelolaan distribusi produk, seperti pencatatan stok yang dilakukan secara manual, ketidakseimbangan antara ketersediaan stok dan permintaan konsumen, serta keterlambatan proses restock dari supplier. Kondisi tersebut berdampak pada rendahnya efisiensi operasional dan berpotensi menurunkan tingkat kepuasan pelanggan.Penelitian ini bertujuan untuk menerapkan metode E-Supply Chain Management (E-SCM) dalam mengelola distribusi produk ritel guna mengatur stok secara lebih efektif dan efisien pada Winmart. Metode penelitian yang digunakan adalah metode kualitatif dengan teknik pengumpulan data berupa observasi, wawancara, dokumentasi, dan studi pustaka. Proses pengembangan sistem dilakukan melalui tahapan analisis kebutuhan, perancangan sistem menggunakan Unified Modeling Language (UML), pembangunan sistem berbasis web dengan bahasa pemrograman PHP dan database MySQL, serta tahap implementasi dan pengujian sistem.Hasil penelitian menunjukkan bahwa penerapan E-Supply Chain Management berbasis web mampu meningkatkan akurasi pencatatan stok, mempercepat proses distribusi produk dari supplier ke toko, serta membantu pemilik usaha dalam memantau ketersediaan produk secara real-time. Sistem yang dibangun juga menghasilkan laporan penjualan dan stok yang terstruktur sehingga mendukung pengambilan keputusan manajerial yang lebih tepat. Dengan demikian, penerapan E-SCM pada Winmart terbukti dapat meningkatkan efisiensi operasional, mengoptimalkan pengelolaan distribusi produk, dan menjaga keseimbangan stok sesuai dengan kebutuhan pasar
A Context-Aware Ensemble Learning Framework for Regression on Heterogeneous Tabular Data Purwadi Purwadi; Muhammad Bagus Bintang Timur; 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.v31i2.10544

Abstract

Regression on heterogeneous tabular data remains a challenging problem in machine learning due to mixed numerical and categorical features, non-linear relationships, and context-dependent feature relevance. In many real-world datasets, feature contributions vary across spatial, temporal, and categorical contexts, reducing the effectiveness of conventional regression and ensemble methods that treat all features uniformly. This paper proposes a context-aware ensemble learning framework for regression on heterogeneous tabular data, where contextual information is explicitly modeled through structured feature grouping. Contextual attributes are organized into predefined context groups and integrated into the learning pipeline to capture context-dependent feature interactions. The framework evaluates multiple ensemble models, including Random Forest, XGBoost, and LightGBM, under consistent preprocessing and evaluation settings. Model performance is assessed using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) with k-fold cross-validation, while interpretability is enhanced through Explainable Artificial Intelligence (XAI) techniques using feature importance analysis and SHAP values. Experimental results demonstrate that explicit context modeling consistently improves regression performance across all evaluated ensemble methods compared to baseline approaches. The proposed framework contributes a systematic and generalizable approach to context-aware regression and ensemble interpretability, supported by experimental results showing consistent reductions in MAE and RMSE.

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