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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 48 Documents
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Search results for , issue "vol 31 no 2 (2026)" : 48 Documents clear
Klasifikasi Tipe Pokémon Berdasarkan Statistik Tempur Menggunakan Algoritma Random Forest Nugroho Kumala Destianto; Yohanes Simarmata; Nurul Hidayanah; Icha Winadya Permadani; Heni Sulistiani
Dinamik Vol 31 No 2 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i2.10395

Abstract

This study aims to classify Pokémon types based on battle statistics using the Random Forest algorithm. The dataset used comes from the file pokemon_bw.csv, which contains information such as Pokédex number, name, type, abilities, and battle stat values (HP, Attack, Defense, Special Attack, Special Defense, Speed). Data preprocessing was carried out to clean and prepare the data, including primary type extraction, label encoding, feature selection, and feature standardization. After that, the dataset was split into training and testing data with an 80:20 ratio. The classification model was built using Random Forest with 100 decision trees and evaluated using accuracy, classification report, confusion matrix, and multiclass ROC Curve metrics. The results show that the model achieved an accuracy of 64.8%, with the best performance in the 'rock', 'steel', and 'dragon' classes, while the 'flying' and 'ghost' classes were still difficult to classify accurately. Confusion matrix. It shows that some types have quite significant misclassification errors, such as 'ground' which is often predicted as 'grass' and 'rock' which is often misclassified as 'steel'. ROC Curve evaluation also proves that most classes have an AUC above 0.80, indicating the model's ability to distinguish between classes. With this approach, this study provides an initial analysis of the potential for predicting Pokémon types based on battle statistics, which can be further developed through handling class imbalances or using other ensemble techniques.
Model Kolaborasi Chatbot dan Manusia dengan Mekanisme Handoff berbasis Trigger untuk Optimalisasi Layanan Pelanggan UMKM di Era Digital Dimas Adi Wicaksono; Heru Yulianto; Aji Priyambodo
Dinamik Vol 31 No 2 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i2.10398

Abstract

The development of artificial intelligence (AI) technology has changed the paradigm of customer service by introducing Chatbots as a solution to improve service efficiency and availability. However, in the context of micro, small, and medium enterprises (MSMEs), limited resources and the complexity of customer interactions require a hybrid approach that integrates Chatbots with human agents. This study designed a model for collaboration between Chatbots and humans with a trigger-based handoff mechanism that enables automatic transitions based on measurable indicators, such as negative sentiment, complex questions, and explicit customer requests. Using a simulation approach on 5,000 customer conversations, this study found that the hybrid model was able to increase the Customer Satisfaction Score (CSAT) from 3.6 to 4.2, reduce completion time from 6.2 minutes to 3.8 minutes, and increase customer loyalty from 52% to 68%. The implementation roadmap is structured in three levels (beginner, intermediate, advanced) to suit the varying digital capabilities of SMEs. This research contributes to the literature on human-AI collaboration and omnichannel customer experience, and offers an applicable framework for SMEs to accelerate the digital transformation of customer service.
IMPLEMENTASI SEGMENTASI WARNA HSV UNTUK IDENTIFIKASI KARAT PADA MATERIAL LOGAM Muhammad Bagus Bintang Timur; Royansyah Royansyah; Dewi Kusumaningsih
Dinamik Vol 31 No 2 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i2.10403

Abstract

Corrosion on metal surfaces poses significant risks to structural durability and operational safety in industrial environments. Manual detection is often time-consuming and prone to errors, especially under inconsistent lighting. This research addresses the challenge by implementing a color-based segmentation technique using the HSV (Hue, Saturation, Value) color space. The input images undergo preprocessing and conversion from RGB to HSV to enhance color differentiation. A defined range of HSV values, corresponding to typical rust tones, is used to generate binary masks that highlight corroded regions. The algorithm is built using the OpenCV library to support real-time analysis. A series of test images with varying illumination and corrosion patterns were analyzed to assess the method’s effectiveness. The results confirm that the HSV-based segmentation accurately separates rusted areas from unaffected surfaces, maintaining reliable performance under diverse conditions. The proposed approach offers a lightweight and efficient tool for early rust detection, supporting preventive maintenance strategies in industrial inspection workflows.
Analisis Sentimen Publik terhadap Kebijakan Pengenaan Komponen Dalam Negeri (TKDN) di Indonesia menggunakan Model Indobert Shalman Alfarisy; Adhitia Erfina; Cecep Warman
Dinamik Vol 31 No 2 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i2.10427

Abstract

Penelitian ini bertujuan untuk menganalisis sentimen publik terhadap kebijakan Tingkat Komponen Dalam Negeri (TKDN) di Indonesia dengan menggunakan model IndoBERT. Dataset terdiri dari 7.497 komentar yang dikumpulkan dari tiga platform media sosial utama, yaitu Instagram, TikTok, dan YouTube. Sentimen pada setiap komentar diklasifikasikan ke dalam tiga kategori utama, yaitu negatif, netral, dan positif. Hasil penelitian menunjukkan bahwa distribusi sentimen bervariasi antar platform, mencerminkan perbedaan karakteristik pengguna dan pola interaksi di masing-masing media sosial. Hasil penelitian menunjukkan bahwa distribusi sentimen bervariasi antarplatform. Instagram didominasi oleh sentimen positif (48%) dan netral (44%) , TikTok oleh sentimen netral (56%) dan negatif (28%) , sementara YouTube menunjukkan dominasi sentimen positif (56%). Model IndoBERT mencapai akurasi tertinggi pada platform YouTube (87,10%), diikuti oleh Instagram (85,93%) dan TikTok (82,63%). Recall tertinggi dicapai pada sentimen negatif dan positif (1,00), namun sangat rendah pada sentimen netral, terutama pada YouTube (0,38). Faktor utama yang mempengaruhi pembentukan sentimen meliputi kekhawatiran tentang pembatasan produk asing, pandangan terkait ketidakadilan ekonomi, serta dukungan terhadap semangat nasionalisme ekonomi yang semakin berkembang di kalangan masyarakat. Penelitian ini membuktikan bahwa model IndoBERT terbukti efektif dalam menganalisis dan memahami pandangan publik di media sosial secara mendalam, sekaligus mengidentifikasi berbagai tantangan yang muncul dalam proses klasifikasi sentimen netral yang sering kali memiliki konteks ambigu dan kompleks.
Implementasi Metode Backward Chaining pada Sistem Pakar Analisis Risiko Penularan Demam Berdarah Dengue (DBD) dan Penilaian Kesiapan Program Wolbachia Ing Kota (WINGKO) Semarang Vic Jeremy Prajogo; Siska Narulita; Aerrosa Murenda Mayadilanuari
Dinamik Vol 31 No 2 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i2.10430

Abstract

The high number of Demam Berdarah Dengue (DBD) cases poses a challenge to public health. The successful implementation of Wolbachia technology innovation depends on the analysis of transmission risks and community readiness. This research aims to develop an integrated expert system capable of performing both assessments using research and development methods. This expert system is designed to implement the backward chaining inference method as its primary reasoning mechanism in constructing a structured diagnostic flow. System testing was conducted using the blackbox testing method based on equivalence partitioning to test the system's functionality. The testing results involving users yielded a success rate of 99.28%. This indicates that all the test cases designed were successfully executed, but there was still a 0.72% discrepancy in features or functions.
Dinamika Spasio-Temporal Korelasi Pendorong Suhu Permukaan Lahan (LST) dan Polusi Nitrogen Dioksida (NO2) di Kota Semarang (2015–2024) menggunakan Google Earth Engine (GEE) Muhammad Andreanto; Agusta Praba Ristadi Pinem; Nurtriana Hidayati
Dinamik Vol 31 No 2 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i2.10431

Abstract

Penelitian ini mengeksplorasi dinamika spasial-temporal faktor lingkungan yang memengaruhi suhu permukaan lahan (LST) di Kota Semarang selama 2015–2024. Tujuan utama adalah mengukur peran vegetasi (NDVI) sebagai pendorong Urban Heat Island (UHI) dan keterkaitan LST dengan polutan NO₂. Metode analisis spasial berbasis Google Earth Engine (GEE) digunakan untuk mengolah data multi-sensor dari Landsat 8/9 (untuk LST dan NDVI) serta Sentinel-5P TROPOMI (untuk NO₂). Korelasi Pearson (r) dihitung dari 1500 titik sampel acak yang diekstrak di GEE, dengan visualisasi raster dan scatter plot untuk interpretasi. Hasil menunjukkan penguatan korelasi negatif antara LST dan NDVI, dari r = -0.322 (R² = 10.4%) pada 2015 menjadi r = -0.362 (R² = 13.1%) pada 2024, yang mengindikasikan sensitivitas termal kota semakin bergantung pada tutupan vegetasi akibat urbanisasi. Sebaliknya, korelasi LST-NO₂ pada 2024 sangat lemah (r = +0.177, R² = 3.1%), membuktikan bahwa polusi didorong emisi transportasi independen dari faktor termal. Studi ini berkontribusi pada pemahaman UHI di kota pesisir tropis, dengan rekomendasi kebijakan terpisah: konservasi Ruang Terbuka Hijau (RTH) untuk mitigasi UHI dan pengendalian emisi untuk NO₂, mendukung adaptasi iklim berkelanjutan.
VIKOR-Based Assessment of Indonesia’s Sharia-Compliant Peer-to-Peer Lending Platforms Itishom Al Khoiry; Ika Menarianti; Noventia Karina Putri
Dinamik Vol 31 No 2 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i2.10432

Abstract

This study applies the VIKOR method to evaluate six Sharia compliant peer to peer (P2P) lending platforms in Indonesia: Ethis, Qazwa, Ammana, Dana Syariah, Alami Sharia, and Duha Syariah. The evaluation focuses on five key criteria: default risk, platform reputation, regulatory compliance and legal protection, return on investment and transparency, and platform sustainability. Criteria weights were determined using the Rank Sum Weighting method, and platform scores were obtained through structured user evaluations. VIKOR, a multi criteria decision making method, ranks alternatives by measuring their closeness to an ideal solution, enabling fair comparison when trade offs exist between conflicting criteria. The method reveals clear distinctions among platforms, enabling balanced, compromise based evaluation aligned with Sharia principles. It offers a practical decision support tool for investors and contributes to the broader application of multi criteria methods in ethical fintech ecosystems across emerging markets.
Sistem Deteksi Dini Kebakaran berbasis IoT dengan Integrasi Database dan Aplikasi Android untuk Pengawasan Lingkungan pada Al Bumi It Store Muhammad Iyas Kustiantoro; Dendy Kurniawan; Bagus Sudirman; Edy Siswanto; Ahmad Ashifuddin Aqham
Dinamik Vol 31 No 2 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i2.10439

Abstract

The high risk of fire at the AL Bumi IT Store, an electronics shop, without an automatic detection system requires a proactive monitoring solution. This research aims to design and implement an Internet of Things (IoT)-Based Fire Early Detection System with Database Integration and an Android Application for fast and accurate environmental monitoring. The system uses an ESP32 as the main microcontroller, which processes data from Temperature (DHT11), Smoke (MQ-2), and Fire (Flame Sensor) sensors. Environmental status data are transmitted in real time to a MySQL database and visualized through an Android application to facilitate remote monitoring by users. In addition, the system is equipped with local alerts in the form of LEDs and a buzzer. The implementation of this system is expected to minimize material losses and secure company assets by ensuring an instant and centralized emergency response.
Prediksi Bed Occupancy Rate (BOR) Rumah Sakit Jiwa dengan Metode SARIMA: Perbandingan Seasonal Pattern Mingguan dan Bulanan Reynard Adelard; Siska Narulita
Dinamik Vol 31 No 2 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i2.10446

Abstract

The ideal bed occupancy rate (BOR) not only represents or reflects the efficiency of facility utilization, but also directly affects the quality of patient care, distribution of medical resources, and financial stability of healthcare institutions. The Abepura Specialized Hospital (RSK), which functions as a referral hospital in the field of mental health in the Papua region, faces its own challenges in managing bed capacity and the flow of inpatient facility usage. Unlike general hospitals, which have high patient turnover rates, RSK Abepura shows a pattern of much longer hospital stays with low basic occupancy rates and more planned patient visits. To address issues related to increasing hospital BOR values, researchers proposed a BOR prediction model using the time series forecasting method, the Seasonal Autoregressive Integrated Moving Average (SARIMA) method for predicting seasonal patterns tailored to RSK characteristics, comparing the performance of the model built with baseline methods, and validating the model's accuracy. This study successfully developed a SARIMA model for BOR prediction at RSK Abepura with unique RSJ characteristics. The SARIMA (0, 1, 2)(1, 1, 2)7 model with a weekly seasonal period proved to provide the best prediction performance with a WAPE of 52.26%, MAE of 5.54%, and RMSE of 6.81%, outperforming the monthly SARIMA model (s = 30).
SINTESIS KONTROL TSUKAMOTO-PID ADAPTIF PADA PENGERINGAN KUPANG Dwi Hadidjaja; Akhmad Ahfas; Agus Hayatal Falah
Dinamik Vol 31 No 2 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i2.10453

Abstract

Traditional clam drying often suffers from energy inefficiency and inconsistent product quality due to manual temperature control that is unable to respond to dynamic environmental changes. This study proposes an adaptive hybrid control system integrating Fuzzy Tsukamoto and PID to enhance thermal stability and drying performance. Temperature and humidity were monitored in real time using a DHT22 sensor, while fuzzy inference and a self-tuning PID mechanism automatically adjusted control parameters. Performance was evaluated through error metrics, temperature stability, drying rate, and statistical validation. The system improved thermal efficiency to 85.2%, reduced Integral Absolute Error by 41.3%, decreased drying time by 22 minutes per cycle, and lowered overshoot by 87.3%. The optimal operating window occurred between 11:00–12:00 with a minimal error of 0.5 °C and a fuzzy output of 64.3, indicating a medium–fast drying rate. Temperature stability of ±0.8 °C results in a more uniform distribution of water content, texture and color, thereby increasing the selling value of the product. These findings demonstrate the strong potential of applying adaptive Tsukamoto–PID control to the seafood-based drying industry to improve process efficiency and quality consistency.

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