UIN (Universitas Islam Negeri) Sunan Kalijaga, Yogyakarta: E-Journal Fakultas Sains dan Teknologi
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Systematic Literature Review (SLR): Dampak Pemanfaatan Artificial Intelligence untuk Meningkatkan Cyber Security
No full textArtificial Intelligence (AI) adalah tambahan kecerdasan pada sistem yang dapat dikelola secara ilmiah dan berkembang di dunia teknologi untuk melayani berbagai aplikasi, termasuk keamanan siber. Kecerdasan buatan memainkan peran penting dalam keamanan siber, memungkinkan deteksi dini ancaman keamanan siber, analisis terperinci terhadap serangan yang muncul, dan respons yang cepat dan akurat. Penelitian ini menggunakan teknik tinjauan literatur sistematis (SLR) untuk menganalisis peran kecerdasan buatan dalam keamanan siber. Pengumpulan data dilakukan dengan mendokumentasikan semua makalah yang memuat temuan penelitian serupa dengan laporan penelitian ini. Makalah yang digunakan dalam penelitian ini adalah 20 makalah dari database ScienceDirect dan Google Scholar. Kecerdasan buatan telah menjadi elemen kunci dalam mendukung upaya untuk melindungi sistem informasi dan jaringan dari ancaman siber yang semakin kompleks. Dengan kemampuannya untuk belajar dari pola-pola data, AI memungkinkan untuk mendeteksi ancaman yang belum pernah terjadi sebelumnya dan memberikan respons secara real-time. Melalui tinjauan literatur sistematis ini, kami menyelidiki berbagai pendekatan dan teknik AI yang telah diterapkan dalam konteks keamanan siber, termasuk penggunaan jaringan syaraf tiruan, algoritma pembelajaran mesin, dan analisis teks. Hasil analisis kami menyoroti bahwa AI telah berhasil digunakan dalam mendeteksi serangan siber, menganalisis pola-pola perilaku yang mencurigakan, dan mengoptimalkan respons keamanan. Implikasi praktis dari penelitian ini adalah pentingnya terus mengembangkan dan mengadopsi solusi AI yang dapat memperkuat pertahanan siber dalam menghadapi ancaman yang terus berkembang.Kata Kunci: Artificial Intelligence, Cyber Security, Systematic Literature Review, Aplikasi Artificial Intelligence
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Artificial Intelligence (AI) is an augmentation of intelligence within systems that can be managed scientifically and is evolving in the world of technology to serve various applications, including cyber security. Artificial intelligence plays a crucial role in cyber security, enabling early detection of cyber security threats, detailed analysis of emerging attacks, and swift and accurate responses. This research utilizes the systematic literature review (SLR) technique to analyze the role of artificial intelligence in cyber security. Data collection was conducted by documenting all papers containing research findings similar to this research report. The papers used in this study comprise 20 papers from the ScienceDirect and Google Scholar databases.Artificial intelligence has become a key element in supporting efforts to protect information systems and networks from increasingly complex cyber threats. With its ability to learn from data patterns, AI enables the detection of previously unseen threats and provides real-time responses. Through this systematic literature review, we investigated various AI approaches and techniques that have been applied in the context of cyber security, including the use of artificial neural networks, machine learning algorithms, and text analysis. Our analysis highlights that AI has been successfully utilized in detecting cyber attacks, analyzing suspicious behavioral patterns, and optimizing security responses. The practical implications of this research underscore the importance of continually developing and adopting AI solutions that can strengthen cyber defense against evolving threats.
Keywords: Artificial Intelligence, Cyber Security, Systematic Literature Review, Application of Artificial Intelligen
Evaluasi Keamanan Sistem Informasi Dengan Indeks KAMI Dan COBIT 5 Di Pesantren
No full textPenelitian ini bertujuan untuk mengevaluasi keamanan sistem informasi di pondok pesantren se-Kabupaten Jombang dengan menggunakan Indeks KAMI dan kerangka kerja COBIT 5. Sampel penelitian mencakup 10 pesantren yang dipilih secara purposive sampling, mewakili dari total populasi sebanyak 210 pesantren. Evaluasi dilakukan dengan mengumpulkan data melalui kuesioner yang mengukur tingkat kapabilitas pada enam domain utama COBIT 5, yaitu DSS01 (Mengelola Operasi), DSS02 (Mengelola Permintaan Layanan dan Insiden), DSS03 (Mengelola Masalah), DSS04 (Mengelola Keberlanjutan), DSS05 (Mengelola Layanan Keamanan), dan DSS06 (Mengelola Kontrol Proses Bisnis). Analisis menunjukkan bahwa sebagian besar pesantren berada pada Level 2 kapabilitas, yang mencerminkan implementasi praktik dasar keamanan informasi, namun belum optimal. Analisis Indeks KAMI mengungkapkan bahwa hanya sedikit pesantren yang memenuhi validitas Tingkat III, dengan mayoritas pesantren berada pada Tingkat II, menunjukkan perlunya perbaikan dalam tata kelola keamanan informasi. Temuan penelitian ini menegaskan pentingnya penguatan manajemen keamanan informasi melalui pelatihan, pengembangan kebijakan yang komprehensif, dan penerapan teknologi yang lebih canggih untuk memastikan integritas, kerahasiaan, dan ketersediaan data dalam mendukung operasional pendidikan.
Kata kunci: Keamanan Informasi, Indeks KAMI, COBIT 5, Pondok Pesantren, Evaluasi Sistem.
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Abstract
This study aims to evaluate the information system security in pondok pesantren across Jombang Regency using the KAMI Index and the COBIT 5 framework. The study sample consisted of 10 pesantren selected through purposive sampling, representing 10% of the total population of 210 pesantren. The evaluation was conducted by collecting data through questionnaires that assessed capability levels across six main domains of COBIT 5, namely DSS01 (Manage Operations), DSS02 (Manage Service Requests and Incidents), DSS03 (Manage Problems), DSS04 (Manage Continuity), DSS05 (Manage Security Services), and DSS06 (Manage Business Process Controls). The analysis revealed that most pesantren were at Capability Level 2, indicating the implementation of basic information security practices, though not yet optimal. The KAMI Index analysis showed that only a few pesantren met the validity criteria for Level III, with the majority positioned at Level II, highlighting the need for improvements in information security governance. These findings underscore the importance of strengthening information security management through training, comprehensive policy development, and the adoption of advanced technologies to ensure data integrity, confidentiality, and availability in supporting educational operations.
Keywords: Information Security, KAMI Index, COBIT 5, Pondok Pesantren, System Evaluatio
Development of an Automated Temperature Control System for Optimized Chocolate Tempering: Pengembangan Sistem Kontrol Suhu Otomatis untuk Optimasi Tempering Cokelat
No full textThis study presents the design and development of a temperature control system for the chocolate tempering process using an RTD PT1000 sensor and an Arduino Uno microcontroller. Sensor characterization yielded a linear transfer function V = −0.0037T+3.4085V, with a strong correlation (r = −1.054), sensitivity of −0.0037 V/°C, and repeatability of 99.4%. The control system was programmed to maintain temperature within an optimal range by switching the heating element off above 53 °C and on below 40 °C. System testing across 10 cycles demonstrated a success rate of 97.7%, confirming the effectiveness and reliability of the system. The results suggest that the proposed solution can improve temperature stability and efficiency in small-scale chocolate processing applications
Modeling of Tsunami Inundation Maps Along the Pacitan Coast using ComMIT 1.8.1 and Quantum GIS 2.18.28 "Las Palmas" Software: Pemodelan Peta Genangan Tsunami Di Pantai Pacitan Dengan Menggunakan Software ComMIT 1.8.1 DAN Quantum GIS 2.18.28 "Las Palmas"
No full textIndonesia is a maritime country located on the equator with 3 main plates, namely the Eurasian plate, the Indo-Australian plate, and the Pacific plate which is known as the Ring of Fire (Pacific ring of fire). If these plates interact along the troughs and fractures of the earth\u27s crust which are the source of earthquakes at sea, it will trigger a tsunami. Geographically, Pacitan Regency is located between 110 55 - 111 25 East Longitude and 7 55 - 8 17 South Latitude bordering the Indian Ocean to the south and is located on the Java Megathrust which causes Pacitan to have the potential for earthquakes that cause tsunamis. That is why it needs to be studied in relation to wave height, tsunami arrival time and tsunami inundation map as measures of tsunami hazard warnings along the coast of Pacitan. The results show that the height of the tsunami waves along Pacitan Beach with the Mw 8.7 earthquake ranged from 6 meters to 13 meters with the fastest arrival time of 23 minutes. The tsunami inundation that occurs along Pacitan Beach ranges from 0.4 km to 0.7 km from the shoreline
Permutation Flowshop Scheduling in ED Aluminium Using Metaheuristic Approaches
No full textThis study proposes metaheuristics to solve the permutation flowshop scheduling problem in ED Aluminium which produces kitchen utensils. The aim is to find the processing sequence of products that results in the shortest total completion time, minimizing makespan and total flowtime. Three metaheuristics are developed, which are Simulated Annealing (SA), Large Neighborhood Search (LNS), and Ant Colony Optimization (ACO). Experiments are performed in this research to evaluate the three algorithms. The result using the simulated annealing algorithm is considered better because it has a shorter makespan. The contribution of this study is developing Simulated Annealing, Large Neighborhood Search, and Ant Colony Optimization to solve the problem
Design and Development of an Edugame Arabic for Learning Media
No full textLearning media provides significant advantages to students by improving their learning experience through the use of multimedia applications, resulting in a more engaging and fascinating learning environment while reducing the monotony associated with traditional manual learning techniques. Digital learning material, provides a platform for interesting learning activities, encouraging a delightful and cost-effective learning experience. The impact of learning media is especially noticeable in the subject of the Arabic language. Arabic is traditionally regarded as a difficult language, and many students dislike this language course. However, the Edugame Arabic was created to overcome this issue. Using the GDLC process, which includes phases of initialization, pre-production, production, testing, and publishing. This game-learning application was evaluated through a testing phase that included groups of school students who were actively involved in Arabic language lessons. Edugame Arabic has successfully been installed and runs smoothly on various Android smartphones. Moreover, the game\u27s offline capability allows users to continue their learning without an internet connection. The questionnaire responds, with users strongly agreeing that the app has an appealing design, an intriguing game premise, good material delivery, and considerable aid in learning Arabic. Furthermore, users generally acknowledged that the Edugame is simple to use and helps with vocabulary learning
Anomaly-Based Intrusion Detection System for the Internet of Medical Things
No full textThe use of the Internet of Things (IoT) in the health sector, known as the Internet of Medical Things (IoMT), allows for personalized and convenient (e)-health services for patients. However, there are concerns about security and privacy as unethical hackers can compromise these network systems with malware. We proposed using hyperparameter-optimized Machine and Deep Learning models to address these concerns to build more robust security solutions. We used a representative Anomaly Intrusion Detection System (AIDS) dataset to train six state-of-the-art Machine Learning (ML) and Deep Learning (DL) architectures, with the Synthetic Minority Oversampling Technique (SMOTE) algorithm used to handle class imbalance in the training dataset. Our hyperparameter optimization using the Random search algorithm accurately classified normal cases for all six models, with Random Forest (RF) and K-Nearest Neighbors (KNN) performing the best in accuracy. The attention-based Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) model was the second-best performer, while the hybrid CNN-LSTM model performed the worst. However, there was no single best model in classifying all attack labels, as each model performed differently in terms of different metrics
Faktor Penyebab Artefak pada Hasil Radiograf (Soft File) Digital Radiografi di RSUD Dr. Moewardi Surakarta
No full textArtifact is a structure or appearance that deviates from what should normally be seen on a radiograph (Walz-Flannigan, 2018). Artifacts can originate from various sources such as the Imaging Plate (IP), X-Ray machine, user errors, and foreign objects. All of these factors mentioned can be sources of artifacts. The aim of this research is to identify the factors and the most significant factor causing artifacts in radiographic results. This study employs a qualitative research approach utilizing a survey method. The research was conducted from June to July 2023 at RSUD Dr. Moewardi Surakarta. Data collection methods included direct observation, documentation, and interviews. The study involved 4 radiographers and 2 radiology specialist doctors as respondents. Subsequently, the collected data were processed, tabulated, and presented in the form of pie charts to draw conclusions. The research results indicate that the primary causes of artifacts in the Radiology Department at RSUD Dr. Moewardi Surakarta are: 1. Foreign objects, 2. Imaging Plate (IP), and 3. X-Ray machine. This is supported by the respective percentages of artifacts found: foreign objects 77%, Imaging Plate (IP) 19%, and X-Ray machine 4%
Segmentasi Pelanggan Penjualan Online Menggunakan Metode K-means Clustering
No full textCustomer segmentation is an essential strategy in the online selling industry to understand customer preferences and behavior. This article proposes applying the K-means clustering method in online sales customer segmentation. The method used is the descriptive method. The steps of the research method include literature studies and data processing to be analyzed using the K-means clustering method. The K-means clustering method is then applied to customer data to group it based on relevant attributes. The segmentation results are evaluated and scored using the clustering evaluation metric. The main objective is to explain the use of the K-means clustering method in online sales customer segmentation, focusing on obtaining more profound insights into customer behavior. Efficient customer segmentation allows companies to target customer groups more precisely and efficiently. This article provides practical insights and guidance for e-commerce companies in implementing customer segmentation using K-means clustering to increase efficiency in targeting segmented customers
Identifikasi Kematangan Buah Pisang Berdasarkan Variasi Jarak Menggunakan Metode K-Nearest Neighbor
No full textThis research aims to identify the level of ripeness of kepok bananas based on the color of their skin using the K-Nearest Neighbor (K-NN) method. Bananas are an important commodity in Indonesia, and various ripeness levels need to be identified. The current process of identifying banana ripeness is still done manually, which requires a lot of labor and tends to be subjective. The K-NN method is used to classify bananas based on their skin color. This research involves the collection of banana images with three ripeness levels (raw, ripe, and overripe) and the extraction of RGB color features from these images. Three distance methods, namely Euclidean, Minkowski, and Manhattan, are also employed to compare accuracy results. The evaluation results of this research show that the accuracy value for the Euclidean distance method is 84%, the Minkowski distance method is 82%, and the Manhattan distance method is 80%. Thus, the findings indicate that the K-NN method and the Euclidean distance method provide good results in identifying the ripeness level of bananas. By implementing the K-NN algorithm, this research attempts to address the weaknesses of the time-consuming and subjective manual identification process, with the hope of providing a more accurate and efficient solution for the banana industry. The results of this research can be used to automate the identification process of banana ripeness levels and improve efficiency in banana sorting. It is expected that this research can provide practical benefits to the community and serve as a basis for further research in this field