STRATEGI PENCEGAHAN, PEMBERANTASAN DAN REHABILITASI PENYALAHGUNA NARKOBA PADA KALANGAN PELAJAR DAN MAHASISWA DI KOTA SEMARANG OLEH BNNP JATENG

This research is motivated by the researchers' concerns about drug abuse among students and students in Semarang City. The high case of narcotics abuse becomes a special attention for the Government which is also very attention to the public in the city of Semarang. Even considering its importance, the Government is calling for an emergency state of narcotics. Therefore, the Government's strategy to overcome drug abuse in accordance with the provisions of Law no. 35 Year 2009 on Narcotics is expected to be able to overcome the problem of Narcotics that occurred. The method used in this research is qualitative research method with descriptive-analysis approach. The purpose of this research is to obtain data on the Government's strategy in overcoming drug abuse among students and college students in the city of Semarang. Assessment and analysis based on interviews and literature studies from reliable sources. The results of this study indicate the strategy of prevention, eradication and rehabilitation of drug abusers among the Students and Students in Semarang City designed by BNNP Central Java is not fully running as expected, so can not reduce the case of drug abuse significantly Based on SWOT analysis it can be known explanation in more detail. And therefore, the Government's role in protecting its citizens from the dangers of drugs should be further enhanced through the optimization of the BNN function, the refinement of the P4GN strategy, and the empowerment of the community, especially for the young generation of the nation's successors, so as to create noble ideals of Indonesia free of narcotics

Distant Location Selection Using Genetic Algorithm for Live Migration Method in OpenFlow Networks

In the last decade, the massive undersea earthquake and Tsunami occurred in Taiwan on Tuesday December 26, 2006 and Japan on Friday March 11, 2011. Those natural disasters had affected the telecommunication in the worldwide. They disrupted the infrastructures including not only Internet services but also business and financial transactions. Thus, keeping the system and functions alive are particularly crucial to many organizations relying on them. Migration is one of the solutions to keep the systems alive. This paper introduces a migration technique to migrate network systems from origin sites to other remote sites. We propose a Genetic Algorithm (GA) approach to solve shortest path problems for selecting the best possible remote site prior to initiate a migration. Network virtualization in OpenFlow technology is particularly valuable in the implementation to relocate the systems by using network segmentation technique

EXPLORING FIRST YEAR UNIVERSITY STUDENTS’ STATISTICAL LITERACY: A CASE ON DESCRIBING AND VISUALIZING DATA

Statistical literacy, which is the ability to use statistics in daily life, is an essential skill for facing society 5.0. This study aims to explore first-year university students’ ability to properly use simple descriptive statistics and data visualization. Qualitative data were collected using a set of questions from 39 undergraduate students. Many students were able to calculate various descriptive statistics, but some of them were still unable to determine suitable statistics to describe the data clearly. Related to data visualization, many students failed to provide a meaningful chart that effectively shows the difference between two groups of data. Students with higher statistical literacy tend to use comparison or variability reasoning to determine the usage of descriptive statistics, and use data-based reason in visualizing the data. Improvement in statistical teaching – both in the university and the secondary school – is needed so that the students can use descriptive statistics and data visualization correctly

Sentiment Mining of Community Development Program Evaluation Based on Social Media

It is crucial to support community-oriented services for youth awareness in the social media with knowledge extraction, which would be useful for both government agencies and community group of interest for program evaluation. This work provided to formulate effective evaluation on community development program and addressing them to a correct action. By using classification based SVM, evaluation of the achievement level conducted in both quantitative and qualitative analysis, particularly to conclude which activities has high success rate. By using social media based activities, this study searched the sentiment analysis from every activities comments based on their tweet. First, we kicked off preprocessing stage, reducing feature space by using principle of component analysis and estimate parameters for classification purposes. Second, we modeled activity classification by using support vector machine. At last, set term score by calculating term frequency, which combined with term sentiment scores based on lexicon.The result shows that models provided sentiment summarization that point out the success level of positive sentiment

Bloom Filter Implementation in Cache with Low Level of False Positive

Searching techniques significantly determine the speed of getting the information or objects. Finding an object in a set is related to membership checking. In the case of massive data, it needs an appropriate technique to search an object accurately and faster. This research implements searching methods, namely Bloom Filter and Sequential Search algorithms, to find objects in a set of data. It aims to improve our system getting a proper item. Due to the possibility of False-Positive existence as a result of Bloom filter technique, there is a potentially inaccurate representation to object sought. Some parameters are influencing False-Positive, namely the number of objects, available bits, and the number of mapped-bit. A Combination of those parameters could decrease the level of False-Positive and improve their accuracy and faster accessibility. In this research, we use three data object variations with the biggest object size of  2000000. Cached objects used in our experiments is between 2 – 20% of variation from the generated objects. The best results with the lowest False-Positive is a combination of bit = 8, mapped bit = 7, and 6% of cache size from 2000000 generated objects

Weighted Round Robin Load Balancer to Enhance Web Server Cluster in OpenFlow Networks

Web server clusters require a reliable network management for increasing the quality of service (QoS). A load balancer system installed in a software-defined network (SDN) is one method that can improve the performance and availability of web server services. SDN is a dynamic and a programmable network management approach, and one protocol that supports it is OpenFlow. This research aims to design and analyse a model of a load balancer on OpenFlow networks, implementing a Weighted Round Robin (WRR) algorithm. The analysis process is conducted by measuring the value of a QoS web server performance parameters, such as response time, throughput, HTTP success, and loss connection. The results showed the WRR algorithm can be implemented for balancing a network system with dynamic resource allocation. The weight workload of each service can be obtained from the needs and existing network resources. The performance of a load balancer on an OpenFlow network is 57% better than in a traditional one for testing of response time conducted in a high connection. However, the throughput and HTTP success connection decreased by 2% and 10%, respectively, while HTTP loss connection increased by 49%

Sistem Kelayakan Borang Akreditasi Program Studi Menggunakan Fuzzy Inference System

The Board of National Accreditation Body for Higher Education) evaluates the performance of a study program. This compels it to improve and maintain its performance. The process of accreditation involves several steps, one of which is the completion of accreditation documents by the study program. The completion requires data from various sources, both from the study program and the institution. However, the data required are often not recorded properly and kept by different sources. As a result, it takes longer time to complete the arrangement. Moreover, inconsistent data format becomes another factor which holds back this process. The application of fuzzy inference system, as used by Kesatuan Business and Informatics Institute, as in the above case can result in a better scoring in which each component is assessed and it will yield the prediction of the expected accreditation status before it is submitted to BAN-PT. This research aims to develop a web based accreditation system with fuzzy inference system and construct a prediction of score ad accreditation status in IBI Kesatuan, using 4 variables of input assessment criteria, namely: external condition, institutional profile, criteria and analysis, and development program decision. The resulting output is the status of non- accredited, good accredited, excellent accredited, and superior accredited. There are 4 phases in the methods: problem identification, needs and system analysis, system design, system implementation and testing system. The result of blackbox test show that 8 features can operate well. The features consist of criteria menu, indicator menu, date user, LKPS data, LED data, LKPS and LED assessment input, recapitulation and conclusion. Based on the results of the study, it can be concluded that accreditation system can be applied to predict the scores and accreditation status of information system study program of IBI Kesatuan.   Keywords: accreditation, fuzzy inference syste

PENGARUH MATHEMATICS SELF-EFFICACY (MSE) TERHADAP HASIL DARI UJIAN NASIONAL MATEMATIKA SISWA

Penelitian ini bertujuan untuk menjelaskan pengaruh Mathematics self-efficacy (MSE) terhadap hasil UN Matematika siswa tahun 2015. Penelitian ini merupakan penelitian ex-postfacto dengan desain kausal komparatif yang melibatkan 1 grup eksperimen dan 1 grup kontrol. Total sampel adalah 110 siswa, yaitu 55 siswa jurusan IPA dan 55 siswa jurusan IPS. Instrumen yang digunakan dalam penelitian ini adalah skala MSE. Data penelitian dianalisis menggunakan regresi sederhana dan Analysis of Covariance (ANCOVA). Hasil penelitian ini menunjukkan besarnya pengaruh MSE terhadap hasil UN Matematika adalah sebesar  untuk jurusan IPA. Sedangkan untuk jurusan IPS, besarnya pengaruh MSE terhadap hasil UN Matematika adalah sebesar . Hasil pengujian lebih lanjut, ANCOVA, menunjukkan terdapat perbedaan hasil UN Matematika siswa antar kategori MSE dengan melakukan kontrol terhadap hasil TO UN Matematika. Dengan kata lain, dapat disimpulkan bahwa Mathematics self-efficacy (MSE) berpengaruh terhadap hasil UN Matematika siswa tahun 2015

Service High Availability Pada Native Server dan Virtual Server Menggunakan Proxmox VE

Teknologi virtualisasi dapat meningkatkan kemampuan layanan menjadi dua mesin server atau lebih secara virtual. Sistem failover dan failback merupakan teknik high availability dalam mengatasi terjadinya kegagalan layanan pada master server. Penelitian ini bertujuan untuk menganalisis kemampuan service high availability dengan sistem failover dan failback pada dua arsitektur server yang berbeda yaitu native server dan virtual server menggunakan sistem virtualisasi Proxmox VE. Metodologi penelitian menggunakan tahapan analisa permasalahan dan kebutuhan sistem, disain arsitektur dan implementasi, pengujian dengan pengukuran kemampuan sistem, dan analisa hasil. Berdasarkan proses pengujian, high availability pada sistem virtual server memiliki tingkat kinerja lebih rendah rata-rata 4.15% dengan sistem native server. Kesimpulannya sistem virtualisasi memiliki keunggulan karena dapat memberikan dua layanan server virtual yang berbeda pada satu mesin native server sehingga lebih efisien dari sisi anggaran dan lebih efektif dalam pengelolaan sistem administrasi server

Streamed Sampling on Dynamic data as Support for Classification Model

Data mining process on dynamically changing data have several problems, such as unknown data size and changing of class distribution. Random sampling method commonly applied for extracting general synopsis from very large database. In this research, Vitter’s reservoir algorithm is used to retrieve k records of data from the database and put into the sample. Sample is used as input for classification task in data mining. Sample type is backing sample and it saved as table contains value of id, priority and timestamp. Priority indicates the probability of how long data retained in the sample. Kullback-Leibler divergence applied to measure the similarity between database and sample distribution. Result of this research is showed that continuously taken samples randomly is possible when transaction occurs. Kullback-Leibler divergence with interval from 0 to 0.0001, is a very good measure to maintain similar class distribution between database and sample. Sample results are always up to date on new transactions with similar class distribution. Classifier built from balance class distribution showed to have better performance than from imbalance one