Vehicular Ad Hoc Network Mobility Model

Indonesia is one of developing countries with high land traffic density. This traffic density could cause traffic jam, traffic accidents and other disturbances. This research had developed a simulator that could calculate the traffic density of roads in urban areas. With the use of this simulator, the researcher could calculate the time needed if the source node transports the message to the destination node by using the ad hoc network communication facility. In this research, every vehicle utilizes multi-hop communication in a communication network. The vehicle sends the message through flooding message and passes on the received message to other vehicles. Based on the simulation done on map size 10 km x 10 km with a total of 20 vehicles on the road, it was calculated that the simulator could transmit the message to its destination on the 106th second from node 3 and with the total of 200 vehicles on the road, the simulator could transmit the message to its destination on the 22nd second from node 5.

Konstruksi Nilai-nilai Nasionalisme dalam Lirik Lagu (Analisis Semiotika Ferdinand De Saussure pada Lirik Lagu “Bendera”)

Penelitian ini bertujuan untuk mengetahui konstruksi nilai-nilai Nasionalisme dalam lirik lagu. Dimana dalam sebuah lirik lagu pasti memiliki sebuah pesan yang ingin disampaikan oleh penciptanya dan penyanyinya yang kemudian memiliki kesesuaian makna antara lirik lagu dengan dengan realitasnya.Penelitian ini menggunakan metode kualitatif dengan menggunakan analisis semiotik dari Ferdinand de Saussure dimana objek yang digunakan adalah sebuah lirik lagu yang dianalisis setiap baris dalam bait-baitnya. Dalam analisis ini tahapan yang dilakukan adalah (1) menentukan tanda (Sign) dari lirik lagu yang mewakili sebagai objek penelitian (2) menuliskan penandan ( signifier ) atau bentuk fisik yaitu lirik lagu “ Bendera” versi Peterpan (3) menuliskan pertanda (signified ) yaitu konsep dari penandanya (4) Tahap yang selanjutnya adalah dengan melihat antara tanda, bentuk tanda dan konsep tanda dengan realis sosial dalam bentuk refrent atau external reality.Penelitian ini menjelaskan bagaimana nilai-nilai nasionalisme dibentuk menjadi sebuah lirik lagu kemudian diunggah menjadi lagu yang bernada atau music sehingga menjadi sebuah karya yang dapat dinikmati. Selain itu juga, karya tersebut mengandung nilai-nilai, dimana nilai-nilai pada penelitian ini memfokuskan pada nasionalism

Penganalisa Jaringan Hotspot Berbasis Support Vector Machine

Penelitian ini membahas penganalisa jaringan menggunakan Support Vector Machine (SVM) setelah pada penelitian sebelumnya menggunakan Jaringan Syaraf Tiruan (JST). Ada banyak aplikasi yang tersedia di pasaran saat ini untuk memberikan grafik jaringan area hotspot kita. Grafik ini akan dianalisis oleh administrator jaringan. Karena area hotspot sering berjalan 24 jam, administrator mengalami kesulitan untuk memonitor lalu lintas setiap saat. Oleh karena itu kami merancang sistem otomatis untuk membantu administrator jaringan dalam memantau jaringan. Sistem ini akan menggantikan keterampilan manusia dalam menafsirkan grafik dengan SVM. Untuk meminimalkan jumlah vektor masukan kita menggunakan nilai rata-rata dari sumbu, sehingga misalnya mikro-komputer notebook, laptop, PDA, dan gadget lainnya dapat menangani sistem ini. Hasil pengujian menunjukkan sistem ini dapat mengklasifikasikan antara normal, lalu lintas tinggi dan un- normal grafik jaringan secara berkala. Kata Kunci: Penganalisa Jaringan, Support Vector Machine, Hotspot We present a Support Vector Machine-based network analyzer system for hotspot area as the extension of previous research that using Neural Networks (NNs). There are many applications that available in the market today for providing us the network graph of our hotspot areas. These graphs will be analyzed by a network administrator. Because a hotspot area often runs 24 hours, an administrator has a difficulty to monitor the traffic all the time. Therefore we proposed the automatic system to help a network administrator in monitoring the network. This system will replace human skill in interpreting the graph with a Support Vector Machine System. To minimize the number of input vector we use mean value of axis, so the micro-computer e.g. notebook, laptop, PDA, and other gadgets can handle this system. Testing result showed this system could classify between normal, high and un-normal traffic of network graph periodically.

Utilization Of New Media As Digital Fandom Among Korean Pop (K-POP) Fan Groups On The Social Media Platform Twitter

The popularity of Korean Pop (K-pop) has been continuously thriving in various parts of the world and has permeated different aspects of people's lives across diverse demographics. This phenomenon is also attributed to the advancements in technology, which have significantly eased the accessibility of K-pop content for the general public. This research employs a qualitative method with a virtual ethnography approach, utilizing data collection techniques such as in-depth interviews with seven informants, participant observation, literature review, and collecting digital documents in the form of screenshots. The results of this research depict fandom culture based on Lucy Bennett's perspective, which can be observed through four aspects: communication, creativity, knowledge, and organization and civil power, utilizing Twitter as a new media platform. Through the accounts created on Twitter, the researcher discovered that fan groups (fandoms) construct and produce their own culture in the digital realm. Twitter is used as a platform for interacting among fellow fans and with idols, serving as a source of information and a means of sharing information. Fans communicate through created virtual identities, fan speak, and fan jokes that can be observed on fan accounts, fanbases, and other platforms. Knowledge is obtained from official accounts as well as fanbases, where fans also produce works in various forms (audio, images, photos, and written works) that are shared through fan accounts. Fan groups also engage in fan projects through fanbase accounts, such as voting, voting, celebrating idols' birthdays, and making donations for their idols in various forms

Using Literature in the Social Studies Classroom and Cross Curricular Teaching at the High School Level

Educators in the current era acknowledge that high school level students are more sophisticated in their approaches to learning, and as such, this “multi-tasking” generation both requires different methods and desires practical application of the lessons taught. This thesis project asserts that as the American educational system ages it is important that new concepts be examined for potential inclusion of cross-curricular teaching. The research explores the benefits of collaboration between high school curriculums – history and language arts specifically. In the examination of current attitudes and feelings surrounding the practice of cross-curricular collaboration, this project suggests that trends affirm the advantages of this method of teaching history at the high school level. The idea that literature can help students practice reading for analysis, examine elements of language, style and more recently, history, is viewed as a means to strengthen critical thinking skills. Additionally, a brief look at the history behind the use of supplemental materials in the social studies classroom is also considered. Potential benefits to this type of learning practice include increasing imagination, providing differentiation and potential alternatives to textbooks. Concerns for the lack of inclusion considered are time constraints because of standardized test preparation, lack of resources, and lack of knowledge. The concluding sections catalog suggested means to incorporate and collaborate between the language arts and history staff and curriculum

Promotion Strategy Of Coffee Shop Through Instagram Social Media

In this study, researchers were interested in the activities of a coffee shop named Kopi Lain Hati wihich has the Instagram account of @kopilainhati. This study applied certain concepts and theories, namely New Media and also Promotion Theory. This was a qualitative descriptive study with case study method. Data were collected through interview and literature study. The results of the study revealed that Kopi Lian Hati used Instagram as a strategy to promote its newly launched brand in 2019 by creating interesting marketing content to be more effectively published on Instagram in order to expand the range of promotions carried out by Kopi Lain Hati

Perubahan Kerapatan Vegetasi dan Penutup Lahan Terhadap Urban Heat Island (UHI) di Kota Bekasi

The replacement of vegetation by roads, buildings, and other structures leads to increased absorption and reflection of solar heat, resulting in elevated surface temperatures in urban areas. This leads to the formation of more hotspots, triggering changes in weather and climate, which are key indicators of the Urban Heat Island (UHI) phenomenon. UHI refers to the phenomenon where urban areas experience higher temperatures compared to their surrounding areas. The primary factor influencing UHI is the conversion of vegetated land cover into developed areas due to urban growth. This causes an increase in surface temperatures due to a reduction in vegetation density and an increase in building density. Changes in land cover within the study area can be identified using unsupervised classification analysis, followed by the analysis of the Normalized Difference Vegetation Index (NDVI) to assess the vegetation index's impact on Land Surface Temperature (LST) and determine the surface temperature of Bekasi city. Accordingly, the objective of this research is to analyze the Urban Heat Island in Bekasi city using a quantitative approach that utilizes Landsat satellite imagery. The results indicate that the temperature in Bekasi city ranges from 25 to 31 degrees Celsius.  Keywords: Landsat-8, Land Surface Temperature, Land Use, NDVI, USGS   Abstrak Pergantian vegetasi oleh jalan, bangunan, dan struktur lainnya menyebabkan peningkatan penyerapan dan pantulan panas matahari, yang mengakibatkan kenaikan suhu permukaan di kota. Akibatnya, terbentuk lebih banyak titik panas yang memicu perubahan cuaca dan iklim, yang menjadi pemicu terjadinya Urban Heat Island (UHI). UHI adalah fenomena di mana wilayah perkotaan mengalami suhu yang lebih tinggi dibandingkan dengan wilayah sekitarnya. Faktor utama yang mempengaruhi terjadinya UHI adalah konversi lahan vegetasi menjadi area perkotaan akibat pembangunan kota. Hal ini menyebabkan peningkatan suhu permukaan karena berkurangnya kerapatan vegetasi dan peningkatan kerapatan bangunan. Perubahan tutupan lahan di dalam area penelitian dapat diidentifikasi melalui analisis klasifikasi tak terbimbing, diikuti oleh analisis Indeks Vegetasi Perbedaan Ternormalisasi (NDVI) untuk mengetahui pengaruh indeks vegetasi terhadap Suhu Permukaan Tanah (LST) dan menentukan suhu permukaan kota Bekasi. Dengan demikian, tujuan penelitian adalah untuk menganalisis Urban Heat Island di kota Bekasi dengan pendekatan kuantitatif yang menggunakan citra satelit Landsat. Hasil penelitian menunjukkan bahwa suhu kota Bekasi berkisar antara 25 hingga 31 derajat Celsius. Kata kunci: Landsat-8, Land Surface Temperatur, NDVI, Tata Guna Lahan, USG

Penggunaan Matlab dan Python dalam Klasterisasi Data

Abstract   Organizations need to dig through the data clustering process, both past data and data from the internet. Sometimes the data has to be re-clustered to match the actual conditions. Therefore, it is necessary to prepare clustering support equipment. In this study the K-Means method was chosen for comparing two technical computational languages, i.e. Matlab and Python which are currently in great demand by researchers and can be used by organizations for a clustering process. This study showed both Matlab and Python have enough libraries (libraries) and toolboxes to help users in data clastering as well as graphics presentation. The test results show that the two programming languages are capable of carrying out the clustering process with two clusters; cluster 1 with a center point at coordinates (1.24, 1.34) and cluster 2 with a center point at coordinates (3.1, 3.07) and are presented by a cluster distribution plot.   Keywords: Clusterization, K-Means, Matlab, Python.   Abstrak   Organisasi perlu menggali data lewat proses klasterisasi data, baik data lampau maupun data dari internet. Terkadang data harus dilakukan klasterisasi ulang untuk mencocokan dengan kondisi yang sebenarnya. Oleh karena itu perlu dipersiapkan peralatan pendukung klasterisasi. Dalam penelitian ini metode K-Means dipilih untuk membandingkan dua bahasa komputasi teknis yaitu Matlab dan Python yang sekarang ini banyak diminati para peneliti yang dan dapat digunakan oleh organisasi yang membutuhkan proses klasterisasi. Hasil dari penelitian ini menunjukan baik Matlab maupun Python memiliki cukup pustaka (library) dan toolbox dalam membantu pengguna mengklasterisasi data, mempresentasikan grafik. Hasil pengujian menunjukan kedua Bahasa pemrograman mampu menjalankan proses klasterisasi berupa klaster 1 yang memiliki titik pusat yang berada pada koordinat (1.24, 1.34) dan klaster 2 dengan titik pusat yang berada pada koordinat (3.1, 3.07) disertai dengan plot sebaran klasternya.   Kata kunci: Klasterisasi, K-Means, Matlab, Python

Smart Home: Controlling and Monitoring Households Appliances using GSM Network

This study discussed about using the smart home automation systems for household appliances such as lights and fans, by utilizing the GSM network as a communication medium to control and monitor the household appliances. In this study, the simulations were performed by the two following modes 1). Self-automated and 2). Manually-automated. In controlling the lamp, the light intensity that values less than 280 lux will give response of turned ON the lamp, while if the value of light intensity is more than 280 lux, it will give a response of turned OFF the lamp. To control the fan, a room temperature value of more than or equal to 30 ° C will turn ON the fan, while the room temperature value which is less than 30 ° C will turn OFF the fan. Simulations were performed by placing the devices in the room and operating it for three days. The test and simulation results were recorded in the form of log files or history files. The self-automated operations mode controlled the system using a sensor (Light Dependent Resistor and LM35 Sensor) unit to detect the environmental condition and take actions according to detected environmental condition. In the manually-automated operation mode, user could control the household appliances according to the user’s intention. The light intensity values obtained in the morning were equal to 280 lux, so it will turn off the lights. The room temperature obtained was in the range of 28oC - 33oC; the fan will turn on if the temperature value is equal to or more than 30oC

Prediksi Kelas Jamak dengan Deep Learning Berbasis Graphics Processing Units

For the first time, machine learning did the classical classification process using two classes (bi-class) such as class -1 and class +1, 0 and 1, or the form of categories such as true and false. Famous methods used are Artificial Neural Networks (ANN) and Support Vector Machine (SVM). The current development was a problem with more than two classes, known as multi-class classes. For SVM sometimes the plural classes are overcome by doing a gradual process like a decision tree (DT) method. Meanwhile, ANN has experienced rapid development and is currently being developed with a large number of layers with the new activation functions, i.e. the rectified linear units (ReLu), and the probabilistic-based activation, i.e. softmax, including its optimizer methods (adam, sgd, and others). Then the term changed to Deep Learning (DL). This study aimed to compare two well-known methods (DL and SVM) in classifying multiple classes. The number of DL layers was six with the neuron composition are 128, 64, 32, 8, 4, and 3, while SVM uses a radial kernel base function with gamma and c respectively 0.7 and 5. Besides, this study intends to compare the use of the Graphics Processing Unit (GPU) available on Google Interactive Notebook (Google Colab), an online Python language programming application. The results showed that DL accuracy outperformed SVM but required large computational resources, with the accuracy for DL and SVM are 99% and 98%, respectively. However, the use of the GPU can overcome these problems and is proven to increase the speed of the process as much as 47 times. Keywords: Artificial Neural Networks, Graphics Processing Unit, Google Interactive Notebook, Rectified Linear units, Support Vector Machine. Abstrak Di awal perkembangannya mesin pembelajaran melakukan proses klasikfikasi menggunakan dua kelas (bi-class) misalnya kelas -1 dan kelas +1, 0 dan 1, atau bentuk kategori seperti benar dan salah. Metode terkenal yang digunakan adalah Jaringan Syaraf Tiruan (JST) dan Support Vector Machine (SVM). Perkembangan selanjutnya adalah problem dengan kelas yang lebih dari dua kelas, dikenal dengan istilah kelas jamak (multi-class). Untuk SVM terkadang kelas jamak diatasi dengan melakukan proses berjenjang mirip pohon keputusan (decision tree). Sementara itu JST telah mengalami perkembangan yang pesat dan saat ini sudah dikembangkan dengan jumlah layer yang banyak disertai dengan fungsi-fungsi aktivasi terkini seperti rectified linear unit (ReLu), dan softmax yang berbasis probabilistik, termasuk juga metode-metode optimizernya (adam, sgd, dan lain-lain). Kemudian istilahnya berubah menjadi Deep Learning (DL). Penelitian ini mencoba membandingkan dua metode terkenal (DL dan SVM) dalam melakukan klasifikasi kelas jamak. Jumlah layer DL sebanyak enam dengan masing-masing neuron sebesar 128, 64, 32, 8, 4, dan 3, sementara SVM menggunakan kernel radial basis function dengan gamma dan c berturut-turut 0.7 dan 5. Selain itu penelitian ini bermaksud membandingkan penggunaan Graphics Processing Unit (GPU) yang tersedia di Google Interactive Notebook (Google Colab), sebuah aplikasi online pemrograman bahasa Python. Hasil penelitian menunjukan akurasi DL unggul tipis dibanding SVM namun memerlukan sumber daya komputasi yang besar masing-masing dengan akurasi 99% dan 98%. Namun penggunaan GPU mampu mengatasi permasalahan tersebut dan terbukti meningkatkan kecepatan proses sebanyak 47 kali. Kata kunci: Jaringan Syaraf Tiruan, Graphics Processing Unit, Google Interactive Notebook, Rectified Linear units, Support Vector Machine