KARAKTERISTIK PASIEN DENGAN AMBLIOPIA DI POLI MATA RSUD DR.ZAINOEL ABIDIN BANDA ACEH

Amblyopia is a refractive error that is not fully corrected without any organic diseases. The purpose of this research was to investigate the characteristics of patients with amblyopia by age, sex, duration of complaints and type of the cause abnormality in Dr. Zainoel Abidin Hospital Banda Aceh. The design of this study were descriptive to determine the characteristics of amblyopia. The sample of this research were all of amblyopia patients who attended Ophthalmology polyclinic of Dr. Zainoel Abidin Hospital Banda Aceh from January 6th until March 31st 2012. The data were collected by interviews and recording, then analyzed by presented as percentage frequency distribution. Result of this study showed that patients with amblyopia were 34 people, with more common in children aged 6-11 years (47.1%), amblyopia occurs more in women (58.8%), patients with the duration of complain

OPTIMASI SEREAL PANGAN DARURAT SUBSITUSI TEPUNG BERAS, MAIZENA, DAN TAPIOKA METODE CRISP DAN FUZZY LINEAR PROGRAMMING

The purpose of this study was to determine the substitution of rice flour, maizena, and tapioca that used in making cereals product for emergency food. The experimental design used in this study were organoleptic responses which include attributes of color, aroma, taste and texture. The chemical responses used in this study were carbohydrate content, protein content, fat content, water content, ash content, and fiber content. The result showed that the ratio of rice flour, maizena, and tapioca were used for optimized cereal product used in Crisp Linear Programming to determine the highest total calory when the cost material stabil, and Fuzzy Linear Programming used to determine minimal the cost in case the cost of material fluctuated. The result showed that the ratio of rice flour, maizena, and tapioca had an effect on the organoleptic responses, the chemical responses, optimized the highest total calory, optimized the cost in case the cost of material fluctuated, and the determination of product shelf life.The purpose of this study was to determine the substitution of rice flour, maizena, and tapioca that used in making cereals product for emergency food. The experimental design used in this study were organoleptic responses which include attributes of color, aroma, taste and texture. The chemical responses used in this study were carbohydrate content, protein content, fat content, water content, ash content, and fiber content. The result showed that the ratio of rice flour, maizena, and tapioca were used for optimized cereal product used in Crisp Linear Programming to determine the highest total calory when the cost material stabil, and Fuzzy Linear Programming used to determine minimal the cost in case the cost of material fluctuated. The result showed that the ratio of rice flour, maizena, and tapioca had an effect on the organoleptic responses, the chemical responses, optimized the highest total calory, optimized the cost in case the cost of material fluctuated, and the determination of product shelf life.   &nbsp

OPTIMASI SEREAL PANGAN DARURAT SUBSITUSI TEPUNG BERAS, MAIZENA, DAN TAPIOKA METODE CRISP DAN FUZZY LINEAR PROGRAMMING

The purpose of this study was to determine the substitution of rice flour, maizena, and tapioca that used in making cereals product for emergency food. The experimental design used in this study were organoleptic responses which include attributes of color, aroma, taste and texture. The chemical responses used in this study were carbohydrate content, protein content, fat content, water content, ash content, and fiber content. The result showed that the ratio of rice flour, maizena, and tapioca were used for optimized cereal product used in Crisp Linear Programming to determine the highest total calory when the cost material stabil, and Fuzzy Linear Programming used to determine minimal the cost in case the cost of material fluctuated. The result showed that the ratio of rice flour, maizena, and tapioca had an effect on the organoleptic responses, the chemical responses, optimized the highest total calory, optimized the cost in case the cost of material fluctuated, and the determination of product shelf life.The purpose of this study was to determine the substitution of rice flour, maizena, and tapioca that used in making cereals product for emergency food. The experimental design used in this study were organoleptic responses which include attributes of color, aroma, taste and texture. The chemical responses used in this study were carbohydrate content, protein content, fat content, water content, ash content, and fiber content. The result showed that the ratio of rice flour, maizena, and tapioca were used for optimized cereal product used in Crisp Linear Programming to determine the highest total calory when the cost material stabil, and Fuzzy Linear Programming used to determine minimal the cost in case the cost of material fluctuated. The result showed that the ratio of rice flour, maizena, and tapioca had an effect on the organoleptic responses, the chemical responses, optimized the highest total calory, optimized the cost in case the cost of material fluctuated, and the determination of product shelf life.   &nbsp

EFEKTIVITAS TAMAN SRIWEDARI SEBAGAI RUANG PUBLIK DI KOTA SURAKARTA

Keberadaan ruang publik sebagai ruang kota merupakan bagian yang tidak terpisahkan dari suatu kota. Permasalahan ruang publik akan semakin rumit jika ruang publik berwujud warisan kota. Pada umumnya permasalahan ruang publik yang berwujud warisan kota banyak terjadi di kota – kota yang tergolong ke dalam Jaringan Kota Pusaka Indonesia (JKPI), seperti Kota Surakarta. Salah satu ruang publik di Kota Surakarta yang merupakan warisan kota adalah Taman Sriwedari. Taman Sriewedari adalah ruang publik yang memiliki nilai historis dan sudah berdiri sejak Tahun 1901 hingga saat ini. Kondisi Taman Sriwedari saat ini tidak mengalami kemajuan dan mulai kehilangan rohnya sebagai ruang publik yang memiliki nilai historis. Hal ini terlihat dari kondisi fisik Taman Sriwedari yang tidak terawat dengan baik serta aktivitas kebudayaan yang kalah dengan aktivitas modern. Perkembangan zaman dan kemajuan teknologi menyebabkan masyarakat modern saat ini cenderung kurang tertarik dengan aktivitas kebudayaan karena dianggap tidak menarik dan membosankan

Evaluation of Deep Learning Models in ITS Software-Defined Intrusion Detection Systems

Intelligent Transportation Systems (ITS), mainly Autonomous Vehicles (AV\u27s), are susceptible to security and safety problems that risk the users\u27 lives. Sophisticated threats can damage the security of AV\u27s communications and computational capabilities, slowing down their integration into our daily lives. Cyber-attacks are getting more complex, posing greater hurdles in identifying intrusions effectively. Failing to prevent the intrusions could tarnish the security services\u27 reliability, including data confidentiality, authenticity, and reliability. IDS is an overall prediction paradigm for detecting malicious network traffic in the ITS. This article studies the role of machine or deep learning in Software Defined-Intrusion Detection System (SD-IDS) in ITS; discusses the mathematical analysis of existing deep learning models and evaluates their performances on the basis of the various metrics (i.e., accuracy, precision, recall, f-measure) to observe which model gives the best results for the existing state of art. The results show that improved Recurrent Neural Networks (RNN) is best suited for the detection of SD-IDS attacks in the data plane and control plane

An Efficient CNN-Based Deep Learning Model to Detect Malware Attacks (CNN-DMA) in 5G-IoT Healthcare Applications

The role of 5G-IoT has become indispensable in smart applications and it plays a crucial part in e-health applications. E-health applications require intelligent schemes and architectures to overcome the security threats against the sensitive data of patients. The information in e-healthcare applications is stored in the cloud which is vulnerable to security attacks. However, with deep learning techniques, these attacks can be detected, which needs hybrid models. In this article, a new deep learning model (CNN-DMA) is proposed to detect malware attacks based on a classifier—Convolution Neural Network (CNN). The model uses three layers, i.e., Dense, Dropout, and Flatten. Batch sizes of 64, 20 epoch, and 25 classes are used to train the network. An input image of 32 × 32 × 1 is used for the initial convolutional layer. Results are retrieved on the Malimg dataset where 25 families of malware are fed as input and our model has detected is Alueron.gen!J malware. The proposed model CNN-DMA is 99% accurate and it is validated with state-of-the-art techniques

Smart Tourism Technology Dan Kepuasan Wisatawan Untuk Berkunjung Kembali Di Wisata Heritage Kota Surakarta

Developing a smart city in Surakarta is to attract domestic and foreign tourists, as well as attract investors to develop infrastructure and open new businesses. The acquisition of foreign exchange is an effort in national development in supporting the national economy, one of which is supported by the tourism sector. This study aims to determine Smart tourism technology on the intention of returning tourists through tourist satisfaction and experiences—direct survey to respondents with quantitative research as the research method. Tourists who visit heritage sites in Surakarta City are the research population. The number of sampling is 200 respondents with a convenience sampling technique. The analytical method used is Structural Equation Modeling using SmartPLS 3.0. The results show that smart tourism technology affects the tourist experience, tourism experience affects tourist satisfaction, tourist satisfaction affects the intention to revisit, and smart tourism technology affects the intention to revisit through tourist experiences and tourist satisfaction in heritage in Surakarta City.   Keywords: Smart tourism technology, tourist experience, tourist satisfaction, revisit intention, heritage.   Abstrak Pengembangan smart city di Kota Surakarta salah satunya bertujuan untuk menarik wisatawan domestik dan wisatawan mancanegara, serta menarik minat investor dalam pembangunan infrastruktur dan pembukaan bisnis baru. Perolehan devisa negara sebagai upaya dalam pembangunan nasional dalam menyokong perekonomian nasional salah satunya didukung oleh sektor pariwisata. Tujuan penelitian ini untuk mengetahui Smart tourism technology terhadap niat berkunjung kembali wisatawan melalui kepuasaan wisatawan dan pengalaman wisata. survey langsung kepada responden dengan jenis penelitian kuantitatif sebagai metode penelitian ini. Wisatawan yang berkunjung pada wisata heritage di Kota Surakarta adalah populasi penelitian. Jumlah sampel penelitian 200 responden dengan teknik convenience sampling. Metode analisis yang digunakan adalah Structural Equation Modelling menggunakan Smart PLS 3.0. Hasil diperoleh bahwa (1) smart tourism technology berpegaruh terhadap pengalaman wisata, (2) pengalaman wisata berpengaruh terhadap kepuasan wisatawan, (3) kepuasan wisatawan berpengaruh terhadap niat berkunjung kembali dan (4) smart tourism technology berpengaruh terhadap niat berkunjung kembali melalui pengalaman wisata dan kepuasan wisatan di wisata heritage di Kota Surakarta.   Kata kunci: Smart tourism technology, Pengalaman Wisata, Kepuasan Wisatawan, Heritage, Niat Berkunjung Kembal

An Efficient and Lightweight Deep Learning Model for Human Activity Recognition Using Smartphones

Traditional pattern recognition approaches have gained a lot of popularity. However, these are largely dependent upon manual feature extraction, which makes the generalized model obscure. The sequences of accelerometer data recorded can be classified by specialized smartphones into well known movements that can be done with human activity recognition. With the high success and wide adaptation of deep learning approaches for the recognition of human activities, these techniques are widely used in wearable devices and smartphones to recognize the human activities. In this paper, convolutional layers are combined with long short-term memory (LSTM), along with the deep learning neural network for human activities recognition (HAR). The proposed model extracts the features in an automated way and categorizes them with some model attributes. In general, LSTM is alternative form of recurrent neural network (RNN) which is famous for temporal sequences’ processing. In the proposed architecture, a dataset of UCI-HAR for Samsung Galaxy S2 is used for various human activities. The CNN classifier, which should be taken single, and LSTM models should be taken in series and take the feed data. For each input, the CNN model is applied, and each input image’s output is transferred to the LSTM classifier as a time step. The number of filter maps for mapping of the various portions of image is the most important hyperparameter used. Transformation on the basis of observations takes place by using Gaussian standardization. CNN-LSTM, a proposed model, is an efficient and lightweight model that has shown high robustness and better activity detection capability than traditional algorithms by providing the accuracy of 97.89%

Detection of Android Malware in the Internet of Things through the K-Nearest Neighbor Algorithm

Predicting attacks in Android malware devices using machine learning for recommender systems-based IoT can be a challenging task. However, it is possible to use various machine-learning techniques to achieve this goal. An internet-based framework is used to predict and recommend Android malware on IoT devices. As the prevalence of Android devices grows, the malware creates new viruses on a regular basis, posing a threat to the central system’s security and the privacy of the users. The suggested system uses static analysis to predict the malware in Android apps used by consumer devices. The training of the presented system is used to predict and recommend malicious devices to block them from transmitting the data to the cloud server. By taking into account various machine-learning methods, feature selection is performed and the K-Nearest Neighbor (KNN) machine-learning model is proposed. Testing was carried out on more than 10,000 Android applications to check malicious nodes and recommend that the cloud server block them. The developed model contemplated all four machine-learning algorithms in parallel, i.e., naive Bayes, decision tree, support vector machine, and the K-Nearest Neighbor approach and static analysis as a feature subset selection algorithm, and it achieved the highest prediction rate of 93% to predict the malware in real-world applications of consumer devices to minimize the utilization of energy. The experimental results show that KNN achieves 93%, 95%, 90%, and 92% accuracy, precision, recall and f1 measures, respectively