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INDONESIA
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 Simple Additive Weighting Dalam Pemilihan Guru Terbaik Pada SDN Duri Kepa 07 Muhammad Kurnia Affandi; Hasugian, Humisar; Wulandari Wulandari; Nofiyani Nofiyani
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 8 No 2 (2025): Jurnal SKANIKA Juli 2025
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v8i2.3392

Abstract

Fair and objective teacher performance evaluation is essential for improving the quality of education. However, the process of selecting outstanding teachers at the elementary school level is often conducted subjectively without a structured approach.. This study aims to develop a web-based decision support system using the Simple Additive Weighting (SAW) method to enhance teacher performance evaluation based on measurable criteria. The system utilizes seven primary criteria, Service-Oriented, Accountable, Competent, Harmonious, Loyal, Adaptive, and Collaborative. Each criterion is assigned a specific weight used to calculate the final preference value for each teacher as an alternative. System validation was carried out through Black Box Testing, White Box Testing, and User Acceptance Testing (UAT) involving end users. The test results indicate that all system features function properly and are capable of accurately displaying the teacher ranking order. One teacher achieved the highest preference score, demonstrating the effectiveness of the SAW method in supporting objective and efficient decision-making. The system has the potential to be widely implemented to improve clarity and accountability in teacher performance evaluation.
Implementasi Algoritma Clustering DBSCAN terhadap Pola Navigasi Pengguna di Perpustakaan Digital untuk Mengungkap Zona Buta Akses Informasi dan Optimalisasi Antarmuka Sistem Sherly Rosa Anggraeni
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 8 No 2 (2025): Jurnal SKANIKA Juli 2025
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v8i2.3524

Abstract

Advances in information technology have encouraged the transformation of libraries to digital form, increasing accessibility, but not all collections can be reached equally by users. This study aims to identify user navigation patterns and information access blind zones in the INLISLite digital library system using the DBSCAN clustering algorithm. Simulation log data representing common user exploration sessions were analyzed through the stages of one-hot representation, density-based clustering, and two-dimensional visualization with PCA. The results showed the formation of six main clusters with different navigation behavior characteristics and 15% of the sessions were classified as outliers. Pages such as “Advanced Search” and “Favorites” were detected as blind zones because they were not reached in most sessions. These findings indicate a failure of the interface to bridge users to the entire spectrum of information. Recommendations of navigation redesign, contextual pop-up of hidden content, and adaptive interface approaches were proposed as solutions. The DBSCAN-based approach proved effective for evaluating the effectiveness of digital information systems in terms of user behavior, and has the potential to be applied in the development of more responsive and inclusive digital libraries.
ANALISIS SENTIMEN OPINI PUBLIK TERHADAP KASUS KORUPSI BAHAN BAKAR MINYAK OPLOSAN PT PERTAMINA DENGAN HYBRID MODEL DEEP LEARNING Muhammad Ramdhan Awali; Sawali Wahyu; Anik Hanifatul Azizah
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 8 No 2 (2025): Jurnal SKANIKA Juli 2025
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v8i2.3525

Abstract

The corruption case related to oplosan fuel oil involving PT Pertamina has become a national issue that has drawn diverse responses from the public. Sentiment analysis of public opinion on social media can provide important insights for the government and stakeholders in understanding public perceptions of the case. This study aims to analyze public opinion sentiment regarding the alleged fuel adulteration corruption case involving PT Pertamina, using a hybrid deep learning model approach. Data were collected from the social media platform Twitter (X) between February 24 and March 19, 2025, resulting in 12,365 tweets after preprocessing. The study implements four model architectures: IndoBERT, CNN, LSTM, and a hybrid IndoBERT-CNN-LSTM model. Evaluation results show that IndoBERT achieved the highest accuracy at 90%, followed by CNN (86%), hybrid (84%), and LSTM with the lowest accuracy (69%). In addition, the K-Fold cross-validation scheme produced more stable model evaluation results than the Hold-Out method. Based on sentiment distribution analysis, public opinion was dominated by negative sentiment at 72%, while positive and neutral sentiments each accounted for 16%. These findings indicate that the public tends to respond negatively to the Pertamina fuel corruption issue. This study contributes to the understanding of public opinion on social media through a deep learning-based sentiment analysis approach and highlights the importance of selecting appropriate model architectures and validation strategies in the task of classifying Indonesian-language text.
Penerapan Algoritma Naive Bayes dan SVM untuk Analisis Sentimen terhadap Penggunaan True Wireless Stereo (TWS) Risca Lusiana Pratiwi; Zulia Imami Alfianti; Ahmad Fauzi; Ginabila Ginabila
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 8 No 2 (2025): Jurnal SKANIKA Juli 2025
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v8i2.3535

Abstract

The use of wireless audio devices such as True Wireless Stereo (TWS) has become increasingly popular among Indonesian society as a solution to the limitations of wired earphones. As TWS usage continues to grow, understanding public sentiment toward these devices becomes essential to support product development and assist consumers in making informed purchasing decisions. This study aims to analyze user sentiment toward TWS on the social media platform X using the Naive Bayes and Support Vector Machine (SVM) algorithms. To improve classification performance, the Synthetic Minority Oversampling Technique (SMOTE) is applied to handle imbalanced data, while Particle Swarm Optimization (PSO) is used to optimize the model. The results show that the SVM algorithm outperforms Naive Bayes, achieving an accuracy of 80.46% and an AUC score of 0.854, with more balanced precision and recall values across both classes. Meanwhile, Naive Bayes demonstrated strength in detecting negative sentiment but with a lower accuracy of 78.00% and an AUC of 0.780
Rancang Bangun Sistem Monitoring Arduino yang Terintegrasi SCADA dan Basis Data Menggunakan Metode Komunikasi Serial Berza H. Sanjaya; Ardi Pujiyanta; Riky Dwi Puriyanto
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 8 No 2 (2025): Jurnal SKANIKA Juli 2025
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v8i2.3553

Abstract

Monitoring systems are very important in daily life for storing data permanently, not just temporarily. Many previous studies did not save data into databases, resulting in data loss after some time. With technological advancements, innovations have emerged that allow for large-capacity data storage and retrieval even after long periods. The Arduino microcontroller, especially the Arduino UNO R3, has limited storage capacity and cannot display processed variable values. Therefore, an alternative solution is needed to store and display this data. This research develops a monitoring system for analog input on the Arduino UNO R3 with a 0-5 VDC signal that sends data serially to AVEVA Edge SCADA. SCADA functions as an interface to display the data and transfer it to a MySQL database. Testing results show that the system performs well, can display data via SCADA, and saves it comprehensively in MySQL with configurable sampling times. Ten tests yielded 100% accuracy and 0% error, proving that this system is reliable and effective for monitoring analog data using Arduino and SCADA
KLASIFIKASI KELAYAKAN PENERIMA BANTUAN LANGSUNG TUNAI DANA DESA (BLT DD) MENGGUNAKAN ALGORITMA NAÏVE BAYES DI DESA TARAJU: Bahasa Indonesia Neng Sri Lathifah Zulfa; Iffah Athifah
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 8 No 2 (2025): Jurnal SKANIKA Juli 2025
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v8i2.3560

Abstract

The Direct Cash Assistance from Village Funds (BLT-DD) program is designed to provide support to rural communities with limited economic means. To ensure that the assistance is properly targeted, the selection process for beneficiaries must be carried out carefully. This study applies the Naïve Bayes algorithm to classify the eligibility of BLT-DD recipients in Taraju Village. Three variants of the Naïve Bayes algorithm were tested, namely Bernoulli Naïve Bayes, Gaussian Naïve Bayes, and Complement Naïve Bayes, using 10-fold cross-validation for evaluation. The results showed that Bernoulli Naïve Bayes achieved the highest accuracy at 91%, followed by Gaussian Naïve Bayes with 90%, and Complement Naïve Bayes with 64%. These findings indicate that Bernoulli Naïve Bayes is more effective in classifying the eligibility of BLT-DD recipients compared to the other two variants.
Komparasi Algoritma Klasifikasi Machine Learning Dengan Penerapan Metode Ensemble Stacking untuk Menganalisa Sentimen terhadap Kesehatan Mental Annisa Maulana Majid; Karina Imelda; Ismasari Nawangsih
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 8 No 2 (2025): Jurnal SKANIKA Juli 2025
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v8i2.3561

Abstract

Mental health often goes undetected due to the absence of physical symptoms, which hinders timely and appropriate intervention. Many individuals choose to express their emotions on social media rather than access professional services. However, the use of social media can potentially worsen mental health conditions and even impact physical well-being. Therefore, early detection through the analysis of digital data, particularly social media posts, using machine learning approaches is essential. Previous research on mental health sentiment analysis has utilized classification algorithms, but accuracy improvement remains necessary. This study compares single classification algorithms and applies an ensemble stacking method that combines multiple classifiers as base learners and a meta-learner. The results show that the stacking method achieves a higher accuracy of 88.13%.
Identifikasi Penyakit Tanaman Berdasarkan Citra Daun Berbasis Web dengan Pendekatan Algoritma Convolutional Neural Network Sri Mulyana; Mansur AS; Angga Warjaya; Inna Muthmainnah; Said Iskandar Al Idrus; Zulfahmi Indra
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 8 No 2 (2025): Jurnal SKANIKA Juli 2025
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v8i2.3573

Abstract

This research aims to develop a mustard plant disease classification system using the Convolutional Neural Network (CNN) method integrated into a web-based platform. Classification is carried out on three classes, namely Spotted Mustard Leaves, Rotten Mustard Leaves, Healthy Mustard Leaves, with the addition of the Not Mustard Leaf class as a distractor class to test the robustness of the model against images that are not included in the main classification category. The dataset used consists of 800 images, 200 images each per class. The CNN model was built with a sequential architecture consisting of several convolutions, pooling, dropout, and dense layers, and using ReLU and SoftMax activation functions in the output layer. The training process is carried out up to 100 epochs, but with the use of Early Stopping callback, the training stops at the 60th epoch, with the best performance (best epoch) achieved at the 32nd epoch. Evaluation of the model on test data showed an accuracy of 93.75%, with high precision, recall, and F1-score values in each class. The model was then implemented into a web interface so that users could upload leaf images and obtain classification results automatically. The results of this study show that CNN is effective in detecting mustard leaf disease and has the potential to be applied as a digital image-based diagnostic tool in agriculture.
ANALISIS SENTIMEN WISATA AIR TERJUN DI KABUPATEN LOMBOK TENGAH MENGGUNAKAN METODE SUPPORT VECTOR MACHINE (SVM) M. Syamsul Hadi; Jihadul Akbar; Muhammad Fauzi Zulkarnain
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 8 No 2 (2025): Jurnal SKANIKA Juli 2025
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v8i2.3578

Abstract

The tourism potential of waterfalls in Central Lombok Regency is quite prominent; however, it has not been fully optimized. With the advancement of digital technology, user reviews on online platforms can serve as valid indicators to assess the quality of a tourist destination. This study aims to analyze the sentiment of visitor reviews regarding waterfall tourist attractions using the Support Vector Machine (SVM) algorithm. Data were collected through web scraping from Google Maps, then processed through several preprocessing stages, automatically labeled using IndoBERT, and features were extracted using the TF-IDF method. A total of 1,250 reviews were analyzed and classified into three sentiment categories: positive, neutral, and negative. Three types of SVM algorithms were tested: LinearSVC, RBF kernel, and Polynomial kernel. Based on the results, LinearSVC achieved the best performance with an accuracy of 84% and an F1-score of 86%. These findings indicate that a machine learning-based approach, particularly SVM, is highly effective in automatically and systematically identifying visitor perceptions. The resulting data may also serve as a reference in developing tourism policies grounded in empirical evidence.
Prediksi Kunjungan Wisatawan Di Kabupaten Bantul Menggunakan Metode Seasonal Autoregressive Integrated Moving Average (SARIMA) Anisa Tri Banowati; Dhina Puspasari Wijaya; Dita Danianti; Deden Hardan Gutama
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 8 No 2 (2025): Jurnal SKANIKA Juli 2025
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v8i2.3583

Abstract

The Bantul Regency Tourism Office faces challenges in collecting tourist attraction data because it is still done manually. This study aims to develop a web-based prediction system using the Seasonal Autoregressive Integrated Moving Average (SARIMA) method. This system is designed to facilitate data collection on tourist attractions while generating predictions of visitor numbers. Based on the results of accuracy testing using MAE (Mean Absolute Error) and MAPE (Mean Absolute Percentage Error), the Parangtritis and Depok Beach tourist areas have MAE values of 35,157.41 and MAPE 22.75%, indicating a fairly large absolute prediction error but still reasonable considering the high volume of visits. Meanwhile, Samas Beach recorded the highest MAPE value of 165.22%, due to data fluctuations that make predictions inaccurate. Conversely, predictions for Goa Cemara Beach, Kwaru Beach, Goa Selarong Area, and Goa Cerme Area have MAPE values below 15%, indicating the model is able to provide fairly good prediction results with a small average error. However, at Pandansimo Beach, the MAPE value reached 46.47%, indicating the model was not yet adequate for this location due to unstable data trends. The results showed that the SARIMA model can be applied to a system to predict tourist visits, but with varying levels of accuracy at each tourist destination, depending on the stability of each tourist destination's data. Keywords: Tourist visit prediction, SARIMA, time series forecasting, web-based system, Bantul Regency Tourism Office

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