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INDONESIA
Telematika : Jurnal Informatika dan Teknologi Informasi
Published by Universitas Pembangunan Nasional Veteran Yogyakarta
ISSN : 1829667X     EISSN : 24609021     DOI : 10.31315
Core Subject : Engineering,
Electrical & Electronics Engineering
Arjuna Subject : -
Articles 361 Documents
 33   34   35   36   37 
Implementation of Natural Language Processing with Deep Learning on Chatbot UKT (Uang Kuliah Tunggal) University Evitafany, Ridha; Wibowo, Rheza Ari; Nata, Imam Adi
Telematika Vol 22 No 2 (2025): Edisi Juni 2025
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v22i2.14637

Abstract

Tujuan: Penelitian yang dilakukan adalah membuat chatbot yang mampu mengklasifikasikan teks soal berdasarkan maksud/label secara lebih tepat, dan membantu memudahkan pelajar dalam memperoleh informasi tambahan terkait layanan UKT (Uang Kuliah Tunggal) dan biaya pendidikan. Chatbot ini mampu mengatasi keterbatasan yang dialami oleh Tata Usaha Fakultas Teknik (TU FT) Universitas Tidar, seperti human delay dalam merespon email atau live chat, keterbatasan jam layanan (office hour), dan keterbatasan informasi pada website kampus. Chatbot dikembangkan menggunakan pendekatan Natural Language Processing (NLP) dan algoritma Deep Learning BiDirectional Long Short-Term Memory (BiLSTM). Hasil sistem chatbot diintegrasikan ke dalam aplikasi Telegram untuk melihat tingkat kepuasan pengguna setelah berinteraksi.Desain/metodologi/pendekatan: Proses penelitian ini diawali dengan pengumpulan dataset berupa pertanyaan dan jawaban yang telah diberi tag atau label dalam format file JSON. Dataset tersebut dilakukan proses normalisasi teks atau preprocessing Natural Language Processing (NLP), dimana pada tahap ini dilakukan lower case atau case lipat, penghapusan tanda baca, penghapusan spasi berlebih, stopword dan stemming dengan perpustakaan Sastrawi, tokenisasi, dan padding. Selanjutnya dilakukan proses split dataset dengan Bagi 80% pelatihan 20% pengujian, sebelum diolah menjadi fitur ekstraksi, menggunakan FastText untuk penyematan kata. Selanjutnya dilakukan proses klasifikasi teks pertanyaan dengan model BiDirectional Long Short-Term Memory (BiLSTM) dan dilanjutkan evaluasi dengan matriks konvergensi. Tahap terakhir, yaitu integrasi chatbot ke dalam bot Telegram, kemudian dilakukan pengujian pengguna terhadap chatbot dan pengukuran tingkat kepuasan dengan metode Customer Satisfaction Score (CSAT).Temuan/Hasil: Model akurasi klasifikasi menghasilkan nilai sebesar 96,05% dan pengujian pengguna dengan penerapan metode Customer Satisfaction Score (CSAT) memberikan tingkat kepuasan rata-rata pada rentang 4 (Puas) dan 5 (Sangat Puas) dengan hasil sebesar 88,86% berdasarkan poin-poin berikut 'Kepuasan terhadap jawaban yang diberikan', 'Pemahaman pertanyaan dan jawaban mudah dipahami', dan 'Kinerja sesuai harapan'.Orisinalitas/nilai/keadaan terkini: Penelitian tentang klasifikasi teks pertanyaan pada chatbot dengan pendekatan Natural Language Processing (NLP) dan model BiDirectional Long Short-Term Memory (BiLSTM) untuk menangani permasalahan pertanyaan jawab layanan UKT (Uang Kuliah Tunggal) dan biaya pendidikan Fakultas Teknik Universitas Tidar belum pernah dilakukan sebelumnya.
Anomaly Detection of Automatic Rain Gauge Measurement Using Artificial Neural Network Long Short Term Memory Method Wahyudi, Niko
Telematika Vol 22 No 3 (2025): Edisi Oktober 2025
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v22i3.13858

Abstract

Purpose: The purpose of this research is to accurately detect anomalies in the results of automatic rain gauge measurements using the Long Short Term Memory (LSTM) method, so that measurement errors can be immediately identified and the equipment can be repaired immediately.  Design/methodology/approach: Detection of anomalies from rain gauge measurements is carried out using quality control (QC) methods based on range and step check, spatial check and error check which produce anomaly labels which are totaled to become Total Anomaly QC. Total Anomaly QC is transformed via one-hot encoding and then the results of the Total QC data transformation are used to build an anomaly detection classification model using the LSTM algorithm.Findings/result: The model performance was tested with a confusion matrix. LSTM is able to classify data anomalies in the western, eastern and coastal clusters quite well. The accuracy value of these clusters is more than 0.9, so that >90% of the anomalies are classified correctly. The results of this research can improve BMKG's ability to detect rainfall measurement anomalies from automatic rain gauges and assist in maintaining the validity of rainfall data so that equipment maintenance is carried out on time.Originality/value/state of the art: This research uses different methods and parameters from previous research. The results obtained are quite satisfactory as shown by an accuracy above 0.9.
Pemodelan Spasial Kelembaban Tanah Berbasis Indeks Spektral dengan Integrasi Citra Satelit Multi Sensor Kholdani, Al Fath Riza; Dharmawati, Adani -; Puspitasari, Desy Ika; Qur'ana, Tri Wahyu; Y. Anwar, Rezky Izzatul
Telematika Vol 22 No 3 (2025): Edisi Oktober 2025
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v22i3.13866

Abstract

Tujuan: Keakuratan estimasi kelembaban tanah merupakan faktor penting dalam memonitor dan manajemen sumber daya air serta mitigasi dampak lingkungan. Pengukuran kelembaban tanah secara in-situ terbatas oleh biaya dan cakupan spasial yang rendah. Karenanya, integrasi data iklim dan citra satelit menjadi alternatif yang menarik untuk meningkatkan keakuratan estimasi kelembaban tanah. Penelitian ini mengembangkan model spasial kelembaban tanah dengan menggabungkan data iklim (suhu permukaan tanah dan curah hujan) dan indeks spektral dari citra satelit multi-sensor, termasuk Landsat 8 dan Sentinel-2, serta menggunakan algoritma Random Forest untuk klasifikasi kelembaban tanah. Hasil penelitian menunjukkan bahwa pendekatan ini menghasilkan peta kelembaban tanah dengan akurasi Overall Accuracy (OA) sebesar 0.8 dan kappa 0.75 untuk Random Forest, dan akurasi OA sebesar 0.93 dan kappa 0.92 untuk Gradient Boosting. Penelitian ini menyimpulkan bahwa integrasi data iklim dan citra satelit multi-sensor secara signifikan meningkatkan akurasi prediksi kelembaban tanah, memberikan manfaat signifikan bagi perencanaan dan pengelolaan lahan.
Performance Analysis and Accuracy of Machine Learning Algorithms for Heart Disease Prediction Yuliasari, Silpani; Rahmatulloh, Alam
Telematika Vol 22 No 3 (2025): Edisi Oktober 2025
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v22i3.14022

Abstract

Purpose: This research aims to analyze the performance and accuracy of machine learning algorithms in predicting heart disease, which is a cause of death throughout the world.Design/methodology/approach: The algorithms analyzed include Logistic Regression, Naive Bayes, Support Vector Machine, K-Nearest Neighbors, Decision Tree, Random Forest, XGBoost, and Neural Network. A publicly available dataset containing patients' medical records was utilized, with the methodology encompassing data collection, Exploratory Data Analysis (EDA), model training, and performance evaluation.Findings/result: The results indicate that the Random Forest algorithm achieved the highest accuracy with an accuracy of 90.16%, followed by Logistic Regression and Naive Bayes with accuracies of 85.25%. The K-Nearest Neighbors algorithm exhibits the lowest accuracy at 67.21%.Originality/value/state of the art: This research highlights the advantages of certain machine learning algorithms in predicting heart disease and contributes knowledge to early detection technology in the health sector.
Analysis of the Effectiveness of Data Warehousing in Management Information Systems Using the Neural Networks Method Ginting, Muliani; Rahmatulloh, Alam
Telematika Vol 22 No 3 (2025): Edisi Oktober 2025
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v22i3.14027

Abstract

Purpose: The purpose of this research is to investigate the effectiveness of data warehousing and the application of Neural Networks methods in analyzing bicycle travel app user data, with a focus on enhancing the annual membership of app users in North America.Design/methodology/approach: This study utilizes a dataset that includes membership and usage data from relevant bicycle travel apps. It involves comparing the performance of different Neural Networks architectures, such as Feedforward Neural Networks, Convolutional Neural Networks (CNN), and other suitable models, to evaluate their effectiveness in predicting user membership.Findings/result: The analysis results demonstrate that the implementation of Neural Networks can improve prediction accuracy, with the most effective model achieving 76.03% accuracy. The research also highlights the importance of preprocessing steps, such as data normalization and transformation, in contributing significantly to model performance. However, challenges such as overfitting were identified, suggesting the need for further testing with model and parameter variations.Originality/value/state of the art: This research provides valuable insights for application developers and policy makers, helping them create data-driven strategies to improve the bicycle travel management information system. It also supports efforts to sustainably grow user membership. The study contributes to the field by exploring the practical application of Neural Networks for data analysis in the context of bicycle travel management, filling a gap in current research on effective predictive models for user membership growth.
Performance Analysis of SVM Kernels in Sentiment Classification on Indonesian Local Skincare Dataset Merdiriyani, Sindy; Rahmatulloh, Alam
Telematika Vol 22 No 3 (2025): Edisi Oktober 2025
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v22i3.14033

Abstract

Purpose: Sentiment analysis is an important aspect of understanding consumers' views on products, especially in the growing skincare industry. This study aims to compare the accuracy and effectiveness of various kernels in the Support Vector Machine (SVM) algorithm, including linear, polynomial (poly), and radial basis function (RBF) kernels, in predicting three types of sentiment: positive, neutral, and negative based on reviews of local Indonesian skincare products.Design/methodology/approach: The dataset used includes consumer reviews classified by rating, which are then processed using Term Frequency-Inverse Document Frequency (TF-IDF) technique for feature extraction.Findings/result: The evaluation results show that the RBF kernel achieves the highest accuracy of 74.78%, followed by the linear kernel with 74.51% accuracy, and the polynomial kernel with 74.10% accuracy. Although the difference between the three kernels is not significant, the RBF kernel excels in positive sentiment classification, while all three kernels struggle in predicting neutral and negative classes.Originality/value/state of the art: These findings make an important contribution to the development of effective sentiment analysis methods, especially in the context of datasets with high class imbalance. To handle class imbalance, techniques such as oversampling smaller classes or using cost-sensitive learning techniques to give more weight to negative and neutral classes can be used. 
Systematic Literature Review: Application of Interactive Educational Games ‘Climate Change and Mitigation Effort’ Hadi, Muhammad Aulia Syamsul; Kurniawan, Fachrul
Telematika Vol 22 No 3 (2025): Edisi Oktober 2025
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v22i3.14092

Abstract

The purpose of this Systematic Literature Review (SLR) is to explore the role of interactive educational games in increasing public awareness about climate change and the mitigation strategies that can be adopted. The review examines how educational games can enhance understanding of climate change by integrating narratives, simulations, and gamification. A systematic approach was used to collect and analyze 28 relevant academic papers, focusing on interactive games used to teach climate change. The methodology involved identifying studies from various databases, applying specific inclusion and exclusion criteria, and synthesizing findings from studies that explore the effectiveness of games in environmental education.The review found that interactive educational games, especially those utilizing augmented reality (AR), simulation, and narrative-based approaches, are effective tools for raising awareness about climate change. These games engage players by simulating real-world environmental challenges and offering mitigation solutions. However, the effectiveness varies depending on the audience's age, background, and technical skills. Challenges such as limited access to technology and differing levels of engagement across age groups were identified, but these can be addressed by using more accessible mobile platforms and gamified learning experiences. This SLR contributes to the understanding of how interactive games can be a valuable tool in climate change education. It highlights the potential of combining emerging technologies like AR and machine learning with traditional educational methods to create engaging and effective learning experiences. The paper provides insights into the current state of research on game-based climate change education. 
Analysis of Information System Security Using OWASP ZAP on a Web-Based Electronic Archiving System Putri, Virda Ramadhani; Sobandi, Ade; Santoso, Budi
Telematika Vol 22 No 3 (2025): Edisi Oktober 2025
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v22i3.14241

Abstract

Purpose: Web-based information systems have become an essential bridge for facilitating accessibility and the use of information. However, with the convenience of access and usage, serious threats related to data security in web systems have also emerged. These threats may arise due to vulnerabilities in the web system, which can be exploited by irresponsible parties to carry out cyberattacks aimed at stealing, damaging, or altering the available information. Therefore, this research is conducted as a preventive measure against these threats through preventive actions by analyzing security vulnerabilities on websites using penetration testing, one of which utilizes the Open Web Application Security Project (OWASP).Design/methodology/approach: Security analysis of information systems using OWASP ZAP with a penetration testing method.Findings/result: The testing results and analys conducted on the target website of the web-based electronic archiving system, http://silancarbedas.bandungkab.go.id/, revealed 13 security vulnerabilities categorized under several OWASP ZAP 10:2021 frameworks. Based on these findings, several suggestions or recommendations have been provided to address the website vulnerabilities, which can be used by the website developers to enhance the site's securityOriginality/value/state of the art: Vulnerability testing on the web-based electronic archiving information system at http://silancarbedas.bandungkab.go.id/ has not been conducted previously.
Strategic Planning of IT/IS Using Ward and Peppard (PT Wahid Bangun Semesta Yogyakarta) Pratiwi, Inge Dwi
Telematika Vol 22 No 3 (2025): Edisi Oktober 2025
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v22i3.14302

Abstract

Purpose: This research aims to design an IS/IT strategy for PT Wahid Bangun Semesta to propose prioritized information systems that align with the company's existing challenges and to provide guidelines for application development. Design/methodology/approach: This study focuses on designing an IS/IT strategy using the Ward and Peppard methodology. Findings/result: Based on the findings, PT Wahid Bangun Semesta Yogyakarta is advised to implement an integrated information system to support operations and enhance competitiveness. Fingerprint systems are recommended to reduce attendance misuse. The application portfolio is designed to strengthen business processes according to identified needs. The IT strategy includes LAN and wireless networks at the head office, as well as USB modems and web-based applications at branch offices for flexible information access. Establishing an IT division is also recommended for more focused management, enabling IS/IT investments to positively contribute to company growth. Originality/value/state of the art: This study designs an IS/IT strategy for PT Wahid Bangun Semesta Yogyakarta to enhance operations and competitiveness through integrated information solutions. Proposed solutions include implementing an Integrated Information System, a fingerprint machine to prevent attendance misuse, LAN and wireless networks at the head office, broadband access at branch offices, and web-based applications.Establishing an IT division is recommended  to manage IS/IT resources and ensure successful investments that support the company’s growth.    
Expert System for Coffee Leaf Disease Classification With Convolutional Neural Networks Asriyani, Wa Ode
Telematika Vol 22 No 3 (2025): Edisi Oktober 2025
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v22i3.14323

Abstract

Tujuan: Untuk mengembangkan sistem pakar menggunakan Convolutional Neural Network (CNN) untuk mengklasifikasikan Penyakit Daun Kopi ke dalam empat kategori (penambang, phoma, karat, dan tanpa penyakit), memberikan diagnosis yang cepat dan akurat bagi petani.Desain/metodologi/pendekatan: Penelitian ini mengimplementasikan sistem pakar berbasis CNN menggunakan kumpulan data 1.664 gambar daun kopi. Metodologi ini mencakup praproses data dengan standarisasi dan penambahan gambar, pengembangan model CNN dengan enam blok konvolusional, pelatihan model dengan pengoptimal Adam, dan evaluasi komprehensif menggunakan metrik validasi.Temuan/hasil: Sistem mencapai akurasi validasi 97,66% dengan waktu pemrosesan yang efisien (49-161 ms per prediksi). Model menunjukkan keandalan yang tinggi di semua kategori penyakit, dengan tingkat keyakinan secara konsisten di atas 80% dan mencapai hingga 100% untuk kondisi tertentu.Orisinalitas/nilai/keadaan terkini: Penelitian ini memperkenalkan integrasi baru sistem pakar dengan teknologi CNN untuk klasifikasi Penyakit Daun Kopi, yang menawarkan akurasi yang lebih unggul dibandingkan dengan pendekatan probabilistik tradisional. Sistem ini menyediakan visualisasi waktu nyata dan tingkat keyakinan untuk setiap prediksi, menjadikannya alat praktis bagi petani.

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