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SKANIKA: Sistem Komputer dan Teknik Informatika
Published by Universitas Budi Luhur Jakarta
ISSN : -     EISSN : 27214788     DOI : 10.36080
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Engineering
SKANIKA: Sistem Komputer dan Teknik Informatika adalah media publikasi online hasil penelitian yang diterbitkan oleh Program Studi Sistem komputer dan Teknik Informatika, Fakultas Teknologi Informasi, Universitas Budi Luhur. Scope atau Topik Jurnal: Kriptografi, Steganografi, Sistem Pakar / Artificial Intelligence , Sistem Penunjang Keputusan, Bioinformatika, Kecerdasan Komputasional, Semantics Web dan Ontologies, Data Mining,Text Mining,Natural Language Processing, Pengelolaan Citra Digital, Otomasi Berbasis Sensor, Wireless Sensor Network, Network Management dan Maintenance, Sistem Operasi, Sosial Network Analysis, Security, Augmented Reality, Game Development, Virtual Reality, Webservice / API, Internet of Things (IoT)
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 340 Documents
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PENERAPAN METODE K-MEANS ++ UNTUK PENGELOMPOKAN WILAYAH RAWAN KEKERASAN ANAK DAN PEREMPUAN DI KABUPATEN NAGEKEO Poa, Marshella Angela Merici; Setiawan, Ahmad Fahrudi; Irawan, Joseph Dedy
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 9 No 1 (2026): Jurnal SKANIKA Januari 2026
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v9i1.3633

Abstract

Violence against women and children in Nagekeo Regency is a crucial social issue requiring targeted intervention. The Department of PMD-P3A faces challenges in analyzing regional vulnerability, which has historically been manual and subjective. This research aims to develop a web-based vulnerability grouping system implementing the K-Means++ Clustering method. This method was strategically selected for its ability to optimize initial centroid selection through distance probability calculations, resulting in more stable and accurate clustering compared to the standard K-Means algorithm. The system was developed using the Laravel framework and MySQL database, utilizing historical data from 2020 to 2025. The clustering process is based on two key parameters: Type of Violence and Place of Occurrence, mapping regions into three levels: Highly Vulnerable, Vulnerable, and Non-Vulnerable. The results demonstrate excellent system performance with a Silhouette Score of 0.6633 and a Davies-Bouldin Index (DBI) of 0.4520, indicating a solid and optimally separated cluster structure. Beyond statistical data, the system provides interactive digital mapping visualizations. This implementation is expected to serve as a decision-support tool for the local government in formulating more effective and efficient social protection policies in Nagekeo Regency.
ANALISIS PENGARUH RANDOM SEARCH PADA LOGISTIC REGRESSION DALAM KLASIFIKASI SENTIMEN PENGGUNA APLIKASI PDAM INFO Ramadani, Suci Awalia; Heliawaty Hamrul; Nurhikma Arifin
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 9 No 1 (2026): Jurnal SKANIKA Januari 2026
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v9i1.3646

Abstract

Digital transformation increases the demand for fast and responsive technology-based public services through mobile applications, including the PDAM Info application. User reviews provide important insights for improving service quality, but their large volume makes manual analysis inefficient, requiring text-based sentiment analysis using machine learning. Default machine learning parameters are often suboptimal; therefore, Random Search is applied to improve classification performance. This study analyzes user sentiment and examines the effect of Random Search on sentiment classification of the PDAM Info application. A total of 2,400 Google Play Store reviews were collected, resulting in 1,677 data after preprocessing, labeled using a lexicon-based approach, and represented using TF-IDF. Logistic Regression and Support Vector Machine were used for classification with Random Search for hyperparameter tuning. The results indicate that negative sentiment dominates user reviews, mainly related to service coverage and payment methods. Random Search improves classification performance, achieving 88% accuracy and 83% F1-score, particularly in predicting positive and neutral classes on imbalanced data. The contribution of this study provides insights into user perceptions for PDAM Info application developers and demonstrates that Random Search.
PREDIKSI KELULUSAN MAHASISWA MENGGUNAKAN ALGORITMA XGBOOST Imron, Syaiful; Faizah, Arbiati; Sugianto, Sugianto
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 9 No 1 (2026): Jurnal SKANIKA Januari 2026
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v9i1.3647

Abstract

Student graduation times are often difficult to predict early, a major challenge facing institutions. Manual evaluations often fail to identify problematic students, leading to inaccurate graduation times that are detrimental to both students and institutions. This is crucial because study duration and timely graduation are important criteria in assessing institutional accreditation and quality. As an innovative solution, this study developed a graduation prediction model using the XGBoost and Random Forest algorithm, applying hyperparameter optimization techniques through Grid Search Cross Validation. The results showed that with default parameters, Random forest was superior to XGBoost. However, after hyperparameter tuning, XGBoost achieved better accuracy than Random Forest with a significant increase in accuracy, from 88.15% to 92.66% (precision 91.87%, recall 91.67%, and F1-score 91.38%). This confirms that appropriate hyperparameter tuning is a strategic key to maximizing the effectiveness of classification models. Thus, this model can be a tool for institutions to monitor and intervene early on in potential student delays.
EVALUASI KINERJA KOMPUTER MIKRO RASPBERRY PI DENGAN PEMBELAJARAN MESIN UNTUK PENGENALAN WAJAH Prayogi, Denis
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 9 No 1 (2026): Jurnal SKANIKA Januari 2026
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v9i1.3649

Abstract

Limited computing resources and high hardware costs often limit the implementation of facial recognition systems on embedded devices. This study aims to evaluate and compare the performance of Support Vector Machine (SVM) and Convolutional Neural Network (CNN) algorithms on a Raspberry Pi 4B microcomputer. The research method involves testing the CNN architecture and SVM kernel on a Kaggle dataset consisting of 2,000 facial images from five identity classes. The evaluation parameters used include accuracy, precision, recall, F-Score, resource usage (CPU/RAM), and inference speed. The test results show that the CNN algorithm achieves 93% accuracy but takes longer inference time, averaging 268.52 ms per image. On the other hand, SVM achieves 87% accuracy with much faster inference time, averaging 8.02 ms per image. Based on the test results, this study concludes that although CNN is superior in accuracy, SVM is more recommended for real-time biometric system applications on microcomputers due to its computational time efficiency and lower resource usage.
SISTEM KONTROL KUALITAS UDARA DALAM RUANGAN BERBASIS ARDUINO Derik Dwi Heavyanto; Yudi Kristyawan; Budi Santoso
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 9 No 1 (2026): Jurnal SKANIKA Januari 2026
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v9i1.3650

Abstract

Indoor air quality issues are increasing due to high exposure to pollutants such as carbon monoxide (CO) and PM2.5 particulates, especially in buildings located near highways. Most air control devices are still operated manually or continuously without considering actual conditions, making them inefficient and unresponsive to changes in air quality. This study designed an Arduino-based air quality control system capable of real-time monitoring and automatic control using MQ-7, PMS5003, and PIR sensors. The method employed was a prototype approach that included hardware design, microcontroller programming, and system function testing using black-box testing. The testing scenarios included conditions where CO and PM2.5 levels exceeded thresholds, human presence detection, and a combination of all parameters in two test rooms. The test results showed that the system was able to respond well to all scenarios, with the exhaust fan activating according to the predetermined control logic. Based on the analysis of the test results, the system achieved a 100% success rate in all scenarios tested and was able to display real-time air quality information via LCD. This study proves that the Arduino-based control system can work effectively in automatically maintaining indoor air quality and is an easy-to-implement solution.
MENJELAJAHI PENGALAMAN PEMAIN DALAM GAME HOROR AMBIENT SUREALISME DENGAN METODE GDLC DAN EKSPERIMEN Wibowo, Tony; Joyce, Joyce; Andrew, Willson; Arispratama, Jefrry; Rianti, Angelina; Pratama, Jimmy
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 9 No 1 (2026): Jurnal SKANIKA Januari 2026
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v9i1.3653

Abstract

As the popularity of horror games continues to grow, understanding the role of atmospheric elements such as ambience and surrealism in shaping fear and players’ emotional engagement has become an important yet underexplored area of study. This research aims to examine the relationship between surrealism, ambience, and fear in horror games through an experimental case study set in an environment inspired by Southeast Asia. Using a qualitative approach, the study involved 128 participants who played a game developed by the researcher and participated in in-depth interviews to explore their emotional experiences. Findings from the 128 participants indicate that audio elements are the most dominant factor in eliciting fear, particularly sounds such as creaking doors, environmental noises, silence, and dripping water. Visual elements, including dim lighting, narrow corridors, and decaying environments, serve as supporting components that enhance the surreal and immersive atmosphere. These findings suggest that effective horror is more strongly constructed through subtle ambience and psychological cues rather than explicit visual threats, demonstrating that audio- and psychologically driven design approaches effectively enhance player immersion and emotional engagement.
SEGMENTASI CITRA ECHOCARDIOGRAPHY MENGGUNAKAN DENSE-AIDAN Nugraha, Made Prastha; Rachmadan Amri, Muhammad Febrian; Sunarmodo, Wismu
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 9 No 1 (2026): Jurnal SKANIKA Januari 2026
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v9i1.3654

Abstract

Congenital heart disease is a structural abnormality of the heart present from birth, affecting about 1% of all newborns, which make early detection of abnormal heart conditions is essential. Detection can be performed by calculating the traced area of end-systole and end-diastole segmentation in cardiac echocardiography videos. This study aims to perform segmentation on echocardiography images using the Dense-AIDAN method. The research workflow conducted in this study includes data collection and preparation, model development, and evaluation. The dataset used in this study is the public EchoNet-Dynamic echocardiography video dataset showing the four-chamber view of the heart. The echocardiography videos from the dataset are first converted into image frames. The image frames are generated based on the two tracings mentioned above. These images are then divided into training, validation, and test sets. The training images are used as input to train the Dense-AIDAN model. The trained model is then used to segment the left ventricle of the heart from the input test images. The implementation of the Dense-AIDAN method yields a Dice Similarity Coefficient (DSC) of 0.81 and an Intersection over Union (IoU) of 0.68. The study concludes that using DenseNet201 provides better segmentation results compared to ResNet50 on medical images, especially echocardiography images.
RANCANG BANGUN SISTEM MONITORING KONSUMSI ENERGI LISTRIK PADA PLTS BERBASIS IOT Risnauli N, Renta; Umi Kalsum, Toibah; Mardiana, Yessi
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 9 No 1 (2026): Jurnal SKANIKA Januari 2026
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v9i1.3657

Abstract

Technological developments and the intensity of community activities have driven an increase in demand for electrical energy, thereby necessitating the exploration of sustainable alternative energy sources. One relevant solution is the utilization of Solar Power Plants (SPPs). This research focuses on the design and development of an energy consumption monitoring system for PLTS by adopting the Internet of Things (IoT) concept. The system development integrates PZEM-004T sensors to measure AC voltage, current, power, and energy parameters, ACS712 sensors for accurate current measurement, and special voltage sensors to monitor DC battery conditions. The entire control and data acquisition process is managed by the NodeMCU ESP32 module as the main processing unit. The data obtained is then displayed in real-time through the Blynk application interface. The research method applied is experimental, covering the stages of hardware design, software development, and overall IoT system integration. The evaluation results show that the system operates well, is capable of providing accurate electrical parameter readings, displays data with very low latency (around 0.1 seconds), and effectively supports remote ON/OFF control of electrical loads. Thus, this system is expected to contribute to improving
PERBANDINGAN KINERJA ALGORITMA KNN DAN SVM DALAM KLASIFIKASI KEMATANGAN BUAH JERUK MEDAN BERDASARKAN CITRA DIGITAL Putri, Fadilla Julianifa; Nurjannah, Siti Laila; Wati, Dwi Febrina; Daulay, Silvia Ariani; Sistamarien, Indira; Giri, Endang Purnama; Mindara, Gema Parasti
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 9 No 1 (2026): Jurnal SKANIKA Januari 2026
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v9i1.3661

Abstract

As a regional flagship commodity with a promising selling value, the process of grouping the maturity level of Medan Orange is still dominated by manual visual techniques. This often triggers data inconsistency and requires a long duration of processing due to personnel subjectivity factors. This research aims to compare the performance of two machine learning algorithms, namely KNN and SVM, in classifying the maturity level of Medan Orange fruit based on digital images. The dataset used is a primary dataset collected directly from Medan Orange farmers in field conditions. The research stages include image acquisition, pre-processing, extraction of HSV-based color features and GLCM-based textures, as well as classification of maturity levels into three classes, namely raw, semi-cooked, and mature. The performance of both algorithms is evaluated using accuracy, precision, and recall metrics. The research results show that the KNN algorithm has a superior performance compared to SVM, with an accuracy rate of 96,25%, while SVM produces an accuracy of 91,25%. This result shows that KNN is effective and more suitable to be applied to the automation system of classification of the maturity of Medan Orange fruit based on digital images.
ANT NESTING OPTIMIZATION UNTUK PENINGKATAN AKURASI CNN DALAM DIAGNOSTIK BRAIN TUMOR Arini, Florentina Yuni; Oktavian, Aloysius; Hidayaturrohmah, Nia Nur; Aryaputra, Daffa Pramata; Syanjalih, Alul Hidja; Aldevis, Mohammad Farrel; Aisar, Muhammad Zidan
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 9 No 1 (2026): Jurnal SKANIKA Januari 2026
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v9i1.3669

Abstract

This study discusses the application of a new optimization algorithm, namely Ant Nesting Optimization (ANO), to improve the performance of Convolutional Neural Networks (CNN) in brain tumor classification based on MRI images. ANO is inspired by the behavior of Leptothorax ants in selecting optimal nest locations, which is applied in the model's exploration and exploitation processes. The optimized CNN model shows an increase in classification accuracy of up to 97%, with superior performance in detecting various types of brain tumors. The evaluation results show that the proposed model has faster and more stable loss convergence compared to the standard model. This optimization method not only improves classification precision but also accelerates model stabilization during the training process. With these results, the research proves the effectiveness of ANO as an optimization method in deep learning networks and opens up wider application opportunities in the field of artificial intelligence-based diagnostics.

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