Enhancement of K-Parameter Using Hybrid Stratified Sampling and Genetic Algorithm

Clustering is a technique used to classify data into clusters based on their similarities. K-means is a clustering algorithm method that classifies the objects based on their closest distance to the cluster center to the groups that have most similarities among the members. In addition, K-means is also the most widely used clustering algorithm due to its ease of implementation. However, the process of selecting the centroid on K-means still randomly. This results K-means is often trapped in local minimum conditions. Genetic algorithm is used in this research as a metaheuristic method where the algorithm can support K-means in reaching global optimum function. Besides, the stratified sampling is also used in this research, where the sampling functions by dividing the population into homogeneous areas using stratification variables. The validation value of the proposed method with iris dataset is 0.417, while the K-means is only 0.662.Clustering is a technique used to classify data into clusters based on their similarities. K-means is a clustering algorithm method that classifies the objects based on their closest distance to the cluster center to the groups that have most similarities among the members. In addition, K-means is also the most widely used clustering algorithm due to its ease of implementation. However, the process of selecting the centroid on K-means still randomly. This results K-means is often trapped in local minimum conditions. Genetic algorithm is used in this research as a metaheuristic method where the algorithm can support K-means in reaching global optimum function. Besides, the stratified sampling is also used in this research, where the sampling functions by dividing the population into homogeneous areas using stratification variables. The validation value of the proposed method with iris dataset is 0.417, while the K-means is only 0.662

Sistem Keamanan Ruangan Berbasis Internet of Things Menggunakan Single Board Computer

Closed Circuit Television (CCTV) is a security system to monitoring a room. In recent years, the use of CCTV is becoming less effective. CCTV usually have expensive rental fees and expensive device. Surveillance system using CCTV still need security officer to monitoring room condition through TV Screen. In this research purposed to build surveillance system using artificial intelligence method. The system features are detect object and send notification through Short Message Service (SMS). Single Board Computer (SBC) is used to processing video data. Technique for detecting objects is Structural Similarity (SSIM). Thought this technique, system have more accuration because it can't read shadow as object. Based on testing result obtained that system can detect object and send notification to user through SMS. System can't read object if low light intensity, but if high intensity of light the system can detect objects that have far position. Maximum frame rate that used to capture video is 60 fps, because limitation of SBC that used

PENGUATAN KAPASITAS PERAN AKTIF PEREMPUAN MELALUI PROGRAM WANITA MELEK PERENCANAAN DESA (WANI LEMPER) BERBASIS TEKNOLOGI INFORMASI DI DESA LOGEDE, KABUPATEN KEBUMEN

WANI LEmPER (Wanita Melek Perencanaan) merupakan upaya pemberdayaan masyarakat untuk meningkatkan partisipasi perempuan dalam pembangunan desa Logede Kabupaten Kebumen melalui pemahaman dan pembekalan dalam perencanaan desa. Program ini bertujuan untuk mendorong wanita agar aktif dan berpengaruh dalam seluruh tahapan pembangunan desa, mulai dari perencanaan hingga evaluasi. Pengenalan proses perencanaan desa dianggap efektif dalam menginspirasi partisipasi yang berarti dari wanita dalam penganggaran, pelaksanaan, pemantauan, dan evaluasi pembangunan desa. Program ini merupakan bentuk pelayanan inklusif yang bertujuan membekali wanita dengan pengetahuan dan keterampilan yang diperlukan untuk berperan aktif dalam perencanaan desa, dengan harapan mereka dapat memengaruhi kebijakan pembangunan desa. Dalam rangka mendukung semangat WANI LEmPER, dibuatlah aplikasi PERMATA (Perbincangan Emak-Emak untuk Membangun Desa). Aplikasi ini berfungsi sebagai platform diskusi khususnya perempuan, untuk mengaspirasikan usulan-usulan terkait pembangunan desa. Aplikasi PERMATA telah dapat diunduh dan sudah didaftarkan di Play Store, dan mendukung perempuan desa dalam menyampaikan aspirasi mereka. Program WANI LEmPER dan aplikasi PERMATA menjadi embrio inovasi baru dalam memperkuat pemberdayaan perempuan dalam pembangunan desa, sejalan dengan Sustainable Development Goals (SDGs) terkait Keterlibatan Perempuan Desa yang dicanangkan oleh Kementerian Desa dan PDTT. Keseluruhan upaya ini mendukung penguatan partisipasi wanita dalam merumuskan dan mengawal pembangunan desa secara efisien, efektif, dan real-time

DATA MINING MENGGUNAKAN ALGORITMA K-MEANS CLUSTERING UNTUK MENENTUKAN STRATEGI PROMOSI UNIVERSITAS DIAN NUSWANTORO

Proses penerimaan mahasiswa baru Universitas Dian Nuswantoro menghasilkan data mahasiswa yang sangat berlimpah berupa data profil mahasiswa dan data kegiatan belajar mengajar. Hal tersebut terjadi secara berulang dan menimbulkan penumpukan terhadap data mahasiswa, sehingga mempengaruhi pencarian informasi terhadap data tersebut. Penelitian ini bertujuan untuk melakukan pengelompokan terhadap data mahasiswa Universitas Dian Nuswantoro dengan memanfaatkan proses data mining dengan menggunakan teknik Clustering. Metode yang digunakan adalah CRISP-DM dengan melalui proses business understanding, data understanding, data preparation, modeling, evaluation dan deployment. Algoritma yang digunakan untuk pembentukan cluster adalah algoritma K-Means. K-Means merupakan salah satu metode data non-hierarchical clustering yang dapat mengelompokkan data mahasiswa ke dalam beberapa cluster berdasarkan kemiripan dari data tersebut, sehingga data mahasiswa yang memiliki karakteristik yang sama dikelompokkan dalam satu cluster dan yang memiliki karakteristik yang berbeda dikelompokkan dalam cluster yang lain. Implementasi menggunakan RapidMiner 5.3 digunakan untuk membantu menemukan nilai yang akurat. Atribut yang digunakan adalah kota asal, program studi dan IPK mahasiswa. Cluster mahasiswa yang terbentuk adalah tiga cluster, dengan cluster pertama 804 mahasiswa, cluster kedua 2792 mahasiswa dan cluster ketiga sejumlah 223 mahasiswa. Hasil dari penelitian ini digunakan sebagai salah satu dasar pengambilan keputusan untuk menentukan strategi promosi berdasarkan cluster yang terbentuk oleh pihak admisi UDINUS

Perancangan Contingency Planning Disaster Recovery Unit Teknologi Informasi menggunakan NIST SP800-34

Pembangunan institusi pendidikan selama ini telah bertumbuh pesat sesuai dengan kebutuhan masyarakat dan menjadikan sebuah insitusi yang semakin komplek dengan kebutuhan fungsi operasional sistem layanan informasinya. Untuk menjalankan fungsinya, institusi pendidikan didukung oleh infrastruktur sistem layanan teknologi informasi yang sangat kompleks. Dalam penyelenggaraan fungsi operasional layanan tersebut, perguruan tinggi membutuhkan peran sistem teknologi informasi yang handal dalam keberlangsungan kegiatan kerjanya. Semua komponen teknologi informasi merupakan komponen yang rentan terhadap gangguan baik itu dari internal maupun eksternal, untuk itu dalam penyelenggaraan institusi pendidikan, perguruan tinggi dalam hal ini wajib memiliki rencana untuk menanggulangi segala gangguan maupun bencana.  Dalam hal ini penanganan penanggulangan ganguan dan bencana memuat beberapa prosedur dan mekanisme tersendiri dalam pengamanan datanya. Disaster Recovery Plan (DRP) merupakan langkah tepat dalam membangun penanganan gangguan dan bencana terhadap infrastruktur sistem layanan teknologi informasi yang ada di perguruan tinggi. Penerapan untuk membangun penanganan bencana ini mengacu pada NIST SP 800-34 Rev.1 yang didalamnya terdapat beberapa tahapan penilaian resiko, menganalisa dampak bisnis, mengidentifikasi pencegahannya dan pengembangan strategi mitigasi.  Hasil akhir dari penelitian ini adalah rancangan dokumen DRP berdasarkan NIST SP 800-34 Rev.1 yang disesuaikan dengan kondisi di perguruan tingg

The Accuracy Comparison Between Word2Vec and FastText On Sentiment Analysis of Hotel Reviews

Word embedding vectorization is more efficient than Bag-of-Word in word vector size. Word embedding also overcomes the loss of information related to sentence context, word order, and semantic relationships between words in sentences. Several kinds of Word Embedding are often considered for sentiment analysis, such as Word2Vec and FastText. Fast Text works on N-Gram, while Word2Vec is based on the word. This research aims to compare the accuracy of the sentiment analysis model using Word2Vec and FastText. Both models are tested in the sentiment analysis of Indonesian hotel reviews using the dataset from TripAdvisor.Word2Vec and FastText use the Skip-gram model. Both methods use the same parameters: number of features, minimum word count, number of parallel threads, and the context window size. Those vectorizers are combined by ensemble learning: Random Forest, Extra Tree, and AdaBoost. The Decision Tree is used as a baseline for measuring the performance of both models. The results showed that both FastText and Word2Vec well-to-do increase accuracy on Random Forest and Extra Tree. FastText reached higher accuracy than Word2Vec when using Extra Tree and Random Forest as classifiers. FastText leverage accuracy 8% (baseline: Decision Tree 85%), it is proofed by the accuracy of 93%, with 100 estimators.  Word embedding vectorization is more efficient than Bag-of-Word in word vector size. Word embedding also overcomes the loss of information related to sentence context, word order, and semantic relationships between words in sentences. Several kinds of Word Embedding are often considered for sentiment analysis, such as Word2Vec and FastText. Fast Text works on N-Gram, while Word2Vec is based on the word. This research aims to compare the accuracy of the sentiment analysis model using Word2Vec and FastText. Both models are tested in the sentiment analysis of Indonesian hotel reviews using the dataset from TripAdvisor.Word2Vec and FastText use the Skip-gram model. Both methods use the same parameters: number of features, minimum word count, number of parallel threads, and the context window size. Those vectorizers are combined by ensemble learning: Random Forest, Extra Tree, and AdaBoost. The Decision Tree is used as a baseline for measuring the performance of both models. The results showed that both FastText and Word2Vec well-to-do increase accuracy on Random Forest and Extra Tree. FastText reached higher accuracy than Word2Vec when using Extra Tree and Random Forest as classifiers. FastText leverage accuracy 8% (baseline: Decision Tree 85%), it is proofed by the accuracy of 93%, with 100 estimators

Big Data Analytics to Analyze Sentiment, Emotions, and Perceptions of Travelers (Case Study: Tourism Destination in Purwokerto Indonesia)

Big data analytics can extract travelers' sentiment, emotions, and experiences from their internet opinions. This study analyzes sentiment, emotion, and traveler experiences at eight tourism destinations in Purwokerto Central Java, Indonesia. The methods are lexicon using NCR vocabulary(EmoLex) and word cloud analysis. The results show visitors generally have a positive sentiment.  The five destinations with high positive sentiment are the Village (91%), Lokawisata Baturaden(81%), Baturaden Forest (79%), Limpa Kuwus (78%), and Taman Andang(.77%). In comparison, other destinations achieve positive sentiment under 70%. Only a few visitors give negative sentiment to all tourism destinations. The emotion of visitors stands out in Joy and Trust. NRC revealed sadness dan anger emotion but only about 20%. Cloud analysis does not reveal a distinguish keyword because the word feature still contained noise such as conjunction, adverb, and the name of the sites. Further research must consider other text preprocessing to handle noises

A New Integral Function Algorithm for Global Optimization and Its Application to the Data Clustering Problem

The filled function method is an approach to finding global minimum points of multidimensional unconstrained global optimization problems. The conventional parametric filled functions have computational weaknesses when they are employed in some benchmark optimization functions. This paper proposes a new integral function algorithm based on the auxiliary function approach. The proposed method can successfully be used to find the global minimum point of a function of several variables. Some testing global optimization problems have been used to show the ability of this recommended method. The integral function algorithm is then implemented to solve the center-based data clustering problem. The results show that the proposed algorithm can solve the problem successfully

Optimasi Akurasi Metode Convolutional Neural Network untuk Identifikasi Jenis Sampah

Waste is goods / materials that have no value in the scope of production, where in some cases the waste is disposed of carelessly and can damage the environment. The Indonesian government in 2019 recorded waste reaching 66-67 million tons, which is higher than the previous year, which was 64 million tons. Waste is differentiated based on its type, namely organic and anorganic waste. In the field of computer science, the process of sensing the type waste can be done using a camera and the Convolutional Neural Networks (CNN) method, which is a type of neural network that works by receiving input in the form of images. The input will be trained using CNN architecture so that it will produce output that can recognize the object being inputted. This study optimizes the use of the CNN method to obtain accurate results in identifying types of waste. Optimization is done by adding several hyperparameters to the CNN architecture. By adding hyperparameters, the accuracy value is 91.2%. Meanwhile, if the hyperparameter is not used, the accuracy value is only 67.6%. There are three hyperparameters used to increase the accuracy value of the model. They are dropout, padding, and stride. 20% increase in dropout to increase training overfit. Whereas padding and stride are used to speed up the model training process.Sampah merupakan barang/bahan yang tidak memiliki nilai dalam lingkup produksi, dimana dalam beberapa kasus sampah dibuang sembarangan dan dapat merusak lingkungan. Pemerintah Indonesia tahun 2019 mencatat sampah mencapai 66-67 juta ton, dimana lebih tinggi dibandingkan tahun sebelumnya yaitu 64 juta ton. Sampah dibedakan berdasarkan jenisnya yaitu sampah organik dan anorganik. Pada bidang ilmu komputer, proses penginderaan jenis dan bentuk sampah dapat dilakukan menggunakan kamera dan metode Convolutional Neural Networks (CNN) yang merupakan jenis neural network yang bekerja dengan cara menerima masukan berupa citra. Masukan tersebut akan di training menggunakan arsitekur CNN sehingga akan menghasilkan output yang dapat mengenali objek yang diinputkan. Pada penelitian ini dilakukan optimasi penggunaan metode CNN untuk mendapatkan hasil yang akurat dalam mengidentifikasi jenis sampah. Optimasi dilakukan dengan menambah beberapa hyperparameter pada arsitektur CNN. Dengan menambahkan hyperparameter diperoleh nilai akurasi yang tinggi yaitu 91,2%. Sedangkan apabila tidak menggunakan hyperparameter nilai akurasi hanya sebesar 67,6%. Terdapat tiga hyperparameter yang digunakan untuk menaikan nilai akurasi model yaitu dropout, padding, dan stride. Penambahan dropout sebesar 20% untuk meningkatkatkan overfitting saat pelatihan. Sedangkan padding dan stride digunakan untuk mempercepat proses pelatihan model