MODEL TEKNIK KEAMANAN VOIP DENGAN VIRTUAL PRIVATE NETWORKING KRIPTOGRAFI PADA JARINGAN WIRELESS LAN 802.11B DAN KORELASI TERHADAP INTELLIGIBILITY SUARA DAN BANDWIDTH VOIP

Abstrak \ud \ud Voice Over Internet Protocol (VoIP) merupakan suatu terobosan dalam komunikasi data. Penggunaan Teknologi VoIP pada jaringan nir-kabel (Wireless LAN) 802.11b memberikan kemudahan pergerakan pengguna (mobility). Namun demikian faktor keamanan data pada VoIP masih rentan terhadap kemungkinan penyalahgunaan\ud (abuse), hacking, data-sniffing dan berbagai macam ancaman lainnya.\ud Pada paper ini telah dicapai suatu system yang dapat digunakan untuk mengamankan komunikasi VoIP pada jaringan nir-kabel. Dengan menggunakan teknologi Virtual Private Networking (VPN) dapat digunakan untuk mengamankan jalur yang digunakan, serta metode kriptografi pada aplikasi VoIP dapat mengacak suara yang akan\ud dikirimkan sehingga tidak dapat disadap. Dengan eksperimen yang telah dilakukan, penggunaan sistem VPN dan kriptografi pada VoIP Sistem Operasi Linux\ud dengan jaringan nir-kabel 802.11b terdapat korelasi terhadap bandwidth dan intelligibility suara.\ud \ud Kata kunci: VoIP Sistem Operasi Linux, VPN, Enkripsi, Keamanan Jaringan, Bandwidth, Intelligibility suar

Zakah Management System Using Approach Classification

The often problematic faced by Muslims are lack of understanding in calculating Zakah and determining the feasibility of compliant recipients based on Islamic Shari'a. This study aimed to establish Zakah management system to support calculation process based on Al Qaradhawi method, helping Board of Zakah in distributing Zakah funds to mustahik. The algorithm used for the classification of Zakah recipients is Naive Bayes. The classification was combination of discrete and continuous data which is conducted by experiments using feasible and unfeasible data as a novelty approach. The results have shown that the Naive Bayes method could solve the problem with 85% of average

Early Detection and Education of Potential Obesity for Prospective Brides Using an Android-Based Botting Macca Application

Non-communicable diseases related to being overweight or obese have become a worldwide problem. There are various ways to help the weight loss program. This research aims to develop an android-based application for early detection and education of potential obesity for prospective brides, to test validation of media and material experts on products, small and large sample trials with a pre-post test on prospective brides. This study is categorized as research and development (R&D) based on Borg and Gall development model.  Quasi-experimental design with a pre-post test was used in the research. Purposive sampling was used with 20 respondents of the prospective bride who were given an android-based application and 20 respondents were given print media. The research was conducted in Makassar, South Sulawesi in January - July 2020. The final results were carried out by the Man-Whitney statistical test to see the effect of giving applications to the prospective bride. The results of the research obtained the Botting Macca application was feasible to be developed based on the assessment of the media and material expert validation tests, and the results of small sample trials. The results of the large sample trial obtained p-value 0.001 <p value 0.05 meaning that the Botting Macca application affects early detection and education of potential obesity in the prospective bride.  The research implies an Android-based of Botting Macca application program is applicable and suitable for future use. Keywords: Applications, Android, Media, Obesity, Print Out DOI: 10.7176/ALST/82-03 Publication date:October 31st 2020

Four Elements Array of Lungs Shape Patch Antenna for Nanosatellite Telemetry

Abstract???The paper discusses some technical issues on the construction of antenna system incorporated with a designed nano satellite. The preliminary LEO satellite model has been developed for the environmental telemetry application. The designed four elements lungs patch structure was initially installed outside the prism body of nano satellite. The lungs antenna array was constructed in such away for electronic\ud rotateable along 90 degree rotation, vertical or horizontal orientation. Both the numerical and experimental evaluations of 2.4 GHz lungs array confirmed a sufficient operation bandwidth suitable for long distance telemetry application, abruptly, 50 MHz could\ud be achieved. The elliptical polarization property was verified via the numerical computation where the axial ratio was slightly greater than 5 dB

The Use of Partograph Bugis Midwives Application as a Learning Media for Normal Labor Care by Lecturers and Land Preceptor

This article aims to discuss the design of a web-based Bugis Midwife application as a learning medium for normal childbirth care for lecturers in terms of improving the skills of providing normal delivery care and filling out student partographs and SOAP before going to the field. The type of research used in this research is Research and Development (RnD). The test analysis uses the experimental design model One Group pretest - posttest Design to see whether the application of web-based Bugis Midwives can be better in improving the provision of normal delivery care for students, with a sample of 40 students divided into two groups, a small sample of 10 people and a sample 30 people then analyzed the data using the Wilcoxon Test on SPSS. The results of the posttest after using the application for 3 weeks showed that there were 28 students who scored above 75 with the pass category (93.33%) and there were 2 students who scored below 75 with the category of still needing guidance (6.66%). Furthermore, based on the Wilcoxon test, the results obtained were P value 0.00 <0.05, which means that there is a significant difference between the pretest and posttest scores with the pretest average value of 74 with a sufficiently increased category in the posttest score to 82 in the good category. Based on the analysis of the test results, there is an increase in student learning outcomes on the post-test scores, so the web-based learning media application of the Bugis Midwife is more effective in increasing the knowledge and skills of students

Survivability with Adaptive Routing and Reactive Defragmentation in IP-over-EON after A Router Outage

The occurrence of a router outage in the IP layer can lead to network survivability issues in IP-over-elastic-optical networks with consequent effects on the existing connections used in transiting the router. This usually leads to the application of a path to recover any affected traffic by utilizing the spare capacity of the unaffected lightpath on each link. However, the spare capacity in some links is sometimes insufficient and thus needs to be spectrally expanded. A new lightpath is also sometimes required when it is impossible to implement the first process. It is important to note that both processes normally lead to a large number of lightpath reconfigurations when applied to different unaffected lightpaths. Therefore, this study proposes an adaptive routing strategy to generate the best path with the ability to optimize the use of unaffected lightpaths to perform reconfiguration and minimize the addition of free spectrum during the expansion process. The reactive defragmentation strategy is also applied when it is impossible to apply spectrum expansion because of the obstruction of the neighboring spectrum. This proposed strategy is called lightpath reconfiguration and spectrum expansion with reactive defragmentation (LRSE+RD), and its performance was compared to the first Shortest Path (1SP) as the benchmark without a reactive defragmentation strategy. The simulation conducted for the two topologies with two traffic conditions showed that LRSE+RD succeeded in reducing the lightpath reconfigurations, new lightpath number, and additional power consumption, including the additional operational expense compared to 1SP

Score Detection and Anemia Education Prospective Bridals Using Android Based Macca Botting Application

Anemia is the highest cause of maternal death in Indonesia. Various methods were used to assist in preventing and overcoming anemia. The method used was Research and Development with the Borg and Gall development model which was simplified by the Research Center for Policy and Education Innovation Team of the National Education Research and Development Agency (Pultijaknov) and quantitative research methods with a quasi-experimental research design. The research was conducted in January-July 2020 at religious affairs office Biringkanaya Makassar. The subject of this research was the bride and groom at the Biringkanaya Religious Affairs Office in Makassar. Data analyzed used the Mann Whitney test. The results showed that the application of score detection and anemia education for the bride and groom was assessed by material experts with an average score of 3.30 (very good), the media expert's assessment was 3.25 (good) and the assessment of the prospective bride and groom in the small sample trial got a score of 3.63 (very good). The results of the large sample trial obtained p value 0.000 <p value 0.05 that the botting macca application has an effect on the detection of scores and anemia education of the prospective bride. The development of an Android-based Botting Macca application program can be developed and it was suitable for future used. Keywords: android, anemia, score detection application, education. DOI: 10.7176/ALST/82-05 Publication date:October 31st 202

Penerapan Metode Convolutional Neural Network pada Face Recognition untuk Smart Loker

Today, security systems are an important issue for the general public, with theft being the most common crime. The weak security system implemented is no doubt behind the many cases of theft. Biometrics is an opportunity to create a strong security system, because each person has their own unique characteristics, such as fingerprints, voice, irises and facial features. One of the biometrics that is considered strong when building a security system is facial recognition. This research uses a Convolutional Neural Network (CNN) derived from Deep Learning as a facial recognition method to create a facial recognition system for cabinets or warehouses. 8820 data was used in system design, which was divided into training data (80%) and test data (20%), the results of the training process obtained validation accuracy reaching 99.81% and validation loss reaching 0.004 after going through 12 epochs. Then the data training process was carried out using the deep learning method using the CNN (Convolutional Neural Network) model. Then a test analysis is carried out to get the accuracy percentage of the entire system. The tests performed in this study gave the system 87.5% accuracy for identifying one individual in the data set and 100% accuracy for individuals not in the data set (unknown). Testing was also carried out with one person in the material who was not in the material in front of the camera in one frame. As a result, the system can recognize all faces and differentiate between people who are in the dataset and those who are not in the dataset (unknown). For testing with two similar people's faces, it was found that the model created was unable to differentiate between the two faces, where the faces detected by the camera showed the same label, namely aqifa with confidence values ​​of 100% and 99.99% respectively.  Saat ini, sistem keamanan merupakan isu penting bagi masyarakat umum, dengan pencurian menjadi kejahatan yang paling umum. Lemahnya sistem keamanan yang diterapkan tidak diragukan lagi dibalik banyaknya kasus pencurian. Biometrik merupakan peluang untuk menciptakan sistem keamanan yang kuat, karena setiap orang memiliki karakteristik uniknya masing-masing, seperti sidik jari, suara, iris mata, dan fitur wajah. Salah satu biometrik yang dianggap kuat saat membangun sistem keamanan adalah pengenalan wajah atau facial recognition. Penelitian ini menggunakan Convolutional Neural Network (CNN) turunan dari Deep Learning sebagai metode pengenalan wajah untuk membuat sistem pengenalan wajah lemari atau gudang. 8820 data digunakan dalam perancangan sistem, yang terbagi menjadi data latih (80%) dan data uji (20%), hasil dari proses training diperoleh validation accuracy mencapai 99.81% serta  validation loss mencapai 0.004 setelah melalui 12 epochs. Kemudian dilakukan proses pelatihan data dengan metode deep learning menggunakan model CNN (Convolutional Neural Network). Kemudian dilakukan analisis pengujian untuk mendapatkan persentase akurasi dari keseluruhan sistem. Tes yang dilakukan dalam penelitian ini memberikan sistem akurasi 87,5% untuk mengidentifikasi satu individu dalam kumpulan data dan akurasi 100% untuk individu yang tidak ada dalam kumpulan data (tidak diketahui). Pengujian juga dilakukan dengan satu orang dalam materi yang tidak dalam materi yang berada di depan kamera dalam satu frame. Hasilnya, sistem dapat mengenali seluruh wajah dan membedakan antara orang yang ada di dataset dan yang tidak ada di dataset (unknown). Untuk pengujian dengan dua wajah orang yang mirip diperoleh bahwa model yang dibuat tidak mampu untuk membedakan kedua wajah tersebut dimana wajah yang terdeteksi oleh kamera menujukkan label yang sama yaitu aqifa dengan nilai confidance masing-masing 100% dan 99,99%