Aplikasi Klasifikasi SMS Berbasis Web Menggunakan Algoritma Logistic Regression

Jenis SMS spam adalah jenis pesan teks yang tidak diinginkan atau tidak diminta yang dikirim ke ponsel pengguna, seringkali untuk tujuan komersial. Untuk mengatasi masalah spam, diperlukan teknik untuk memilah kata atau kalimat termasuk spam atau bukan spam. Pada penelitian ini diusulkan menggunakan machine learning untuk mengklasifikasikan pesan mana yang spam dan mana yang tidak spam. Data yang digunakan pada penelitian ini terdiri dari 1140 pesan, dimana sudah diberi label 0 untuk pesan yang tidak spam dan 1 untuk pesan yang spam. Algoritma yang digunakan untuk kasus ini adalah Logistic Regression. Hasil penelitian menunjukkan model memiliki tingkat akurasi untuk mengklasifikasi pesan, sebesar 97%. Aplikasi yang dikembangkan untuk menerapkan hasil pemodelan machine learning menggunakan bentuk sebuah website sederhana dengan bantuan Flask framework dari Python. Hasil akhir dari aplikasi ini adalah model machine learning yang dapat dibuka melalui website

Analisis Amplifikasi Dan Indeks Kerentanan Seismik Di Kawasan Fmipa Ugm Menggunakan Metode HVSR

Daerah Istimewa Yogyakarta merupakan kawasan cekungan yang tersusun atas endapan material vulkanik tebal dan merupakan kawasan aktif seismik. Gelombang seismik yang terjebak pada lapisan sedimen tebal dapat mengakibatkan kerusakan parah pada bangunan apabila terjadi gempa. Pemetaan mengenai kerentanan seismik di kawasan FMIPA UGM perlu dilakukan melihat bertambahnya gedung-gedung baru yang tinggi di area ini. Analisis amplifikasi dan frekuensi natural diolah menggunakan metode HVSR (Horizontal to Vertical Spectral Ratio), sehingga dihasilkan nilai indeks kerentanan seismik di daerah penelitian. Berdasarkan hasil penelitian, didapatkan bahwa nilai fekuensi natural (fo) di area penelitian berkisar antara 0.636 – 0.943 Hz, Amplifikasi (Ao) berkisar antara 2.196 – 3.446 dan nilai kerentanan seismik  (Kg) sebesar 5,291 – 18,677. Berdasarkan hasil pengolahan data yang didapat, dapat disimpulkan bahwa subsurface kawasan FMIPA UGM tersusun atas lapisan sedimen tebal dengan ketebalan ≥30m. Hal ini berasosiasi terhadap area DIY yang tersusun di atas cekungan dengan material pengisi endapan vulkanik. Berdasarkan nilai fo, Ao, dan Kg, diketahui bahwa nilai kerentanan seismik yang paling tinggi terdapat di area gedung matematika FMIPA UGM

Kombinasi Single Linkage Dengan K-Means Clustering Untuk Pengelompokan Wilayah Desa Kabupaten Pemalang

K-Means is very dependent on determining the center cluster initial which has an impact on the quality  of clusters resulting, in addition to determining the center of cluster the number of k that will be used it can also affect the quality of the cluster from the method K-Means. Poverty is mostly experienced by rural communities, this can be seen from the lack of existing facilities to serve the interests of the community in various fields. To avoid the imbalance that occurs, a development plan is needed in accordance with the characteristics of the welfare of the people in the region. Therefore, we need an effort to group villages so that policy making is right on target. One of the algorithms clustering that is commonly used is the K-Means algorithm because it is quite simple, easy to implement, and has the ability to group large data groups very quickly. However, the K-Means algorithm has a weakness in determining the center cluster initial given. Initialization of centers cluster randomly may result in formation clusters changing (inconsistent). For this reason, the K-Means method needs to be combined with the hierarchical method in determining the center cluster initial. This combination method is called Hierarchical K-Means which is a combination of methods hierarchical and partitioning, where the process is hierarchical used to find the initial center initialization cluster and the process partitioning to get the cluster optimal. The hierarchical method used in this study is the method single linkage. Based on the method Elbow , the recommended amount of k for this study is k = 4.The combination of the single linkage and k-means algorithms with k = 4 in this study results in avalue silhouette coefficient of 0.685 which is a feasible or appropriate cluster category, while the evaluation measurement by Davies The Boulldin Index yielded a value of 0.577.

Measurement of Air Drag as Physics Experiment Enrichment at Senior High School Laboratory Using the Air Track Apparatus

Linear air track is often used in physics learning for linear motion experiments because it can reduce friction between objects with trajectories. However,  the use of air tracks for motion experiments in schools often does not care about aspects of air drag, so the purpose of this study is to calculate the air friction contained in the air track and as an offer of enrichment experiments at senior high school. The research method used is an experimental method that uses a set of air track experimental devices consisting of trajectors, carts, blower, and time counters with light sensors. Cart objects with a mass of 120.02 gram is given the initial velocity variation 12.272 cm/s, 16.286 cm/s and 24.599 cm/s. Then the time recorded when the cart crosses the distance of 10 cm to 110 cm at intervals of 10 cm. This experiment is conducted in the Integrated Science Laboratory, Faculty of Mathematics and Natural Sciences, Universitas Negeri Semarang. The second Newton law has been derived to obtain a special exponential function, so the relation between distance and time is obtained. The non-linear relation between distance and time shows the effect of air drag. Then, fitting the graph of the distance and time relation so that the air drag constants obtained are (10.6 ± 0.1) gram/s, (10.6 ± 0.2) gram/s, and (11.1 ± 0.2) gram/s. The results of the air drag constants obtained can be additional data as a factor affecting experiments using linear air track and can be enrichment experiments at senior high school laboratory

Comparative Study of VGG16 and MobileNetV2 for Masked Face Recognition

Indonesia is one of the countries affected by the coronavirus pandemic, which has taken too many lives. The coronavirus pandemic forces us to continue to wear masks daily, especially when working to break the chain of the spread of the coronavirus. Before the pandemic, face recognition for attendance used the entire face as input data, so the results were accurate. However, during this pandemic, all employees use masks, including attendance, which can reduce the level of accuracy when using masks. In this research, we use a deep learning technique to recognize masked faces. We propose using transfer learning pre-trained models to perform feature extraction and classification of masked face image data. The use of transfer learning techniques is due to the small amount of data used. We analyzed two transfer learning models, namely VGG16 and MobileNetV2. The parameters of batch size and number of epochs were used to evaluate each model. The best model is obtained with a batch size value of 32 and the number of epochs 50 in each model. The results showed that using the MobileNetV2 model was more accurate than VGG16, with an accuracy value of 95.42%. The results of this study can provide an overview of the use of transfer learning techniques for masked face recognition

Comparative Study of VGG16 and MobileNetV2 for Masked Face Recognition

Indonesia is one of the countries affected by the coronavirus pandemic, which has taken too many lives. The coronavirus pandemic forces us to continue to wear masks daily, especially when working to break the chain of the spread of the coronavirus. Before the pandemic, face recognition for attendance used the entire face as input data, so the results were accurate. However, during this pandemic, all employees use masks, including attendance, which can reduce the level of accuracy when using masks. In this research, we use a deep learning technique to recognize masked faces. We propose using transfer learning pre-trained models to perform feature extraction and classification of masked face image data. The use of transfer learning techniques is due to the small amount of data used. We analyzed two transfer learning models, namely VGG16 and MobileNetV2. The parameters of batch size and number of epochs were used to evaluate each model. The best model is obtained with a batch size value of 32 and the number of epochs 50 in each model. The results showed that using the MobileNetV2 model was more accurate than VGG16, with an accuracy value of 95.42%. The results of this study can provide an overview of the use of transfer learning techniques for masked face recognition

A Combination of Transfer Learning and Support Vector Machine for Robust Classification on Small Weed and Potato Datasets

Agriculture is the primary sector in Indonesia for meeting people's daily food demands. One of the agricultural commodities that replace rice is potatoes. Potato growth needs to be protected from weeds that compete for nutrients. Spraying using pesticides can cause environmental pollution, affecting cultivated plants. Currently, agricultural technology is being developed using an Artificial Intelligence (AI) approach to classifying crops. The classification process using AI depends on the number of datasets obtained. The number of datasets obtained in this research is not too large, so it requires a particular approach regarding the AI method used. This research aims to use a combination of feature extraction methods with local and deep feature approaches with supervised machine learning to classify of small datasets. The local feature method used in this research is Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG), while the deep feature method used is MobileNet and MobileNetV2. The famous Support Vector Machine (SVM) uses the classification method to separate two data classes. The experimental results showed that the local feature HOG method was the fastest in the training process. However, the most accurate result was using the MobileNetV2 deep feature method with an accuracy of 98%. Deep features produced the best accuracy because the feature extraction process went through many neural network layers. This research can provide insight on how to analyze a small number of datasets by combining several strategie

ANALISIS PEAK GROUND ACCELERATION (PGA) KOTA TEGAL MENGGUNAKAN METODE HVSR (HORIZONTAL TO VERTICAL SPECTRA RATIO)

Seismisitas kota Tegal dipengaruhi oleh keberadaan segmen sesar baribis-kendeng yang melewati kota Tegal dengan kecepatan rata-rata 4,5 mm/tahun. Kota Tegal merupakan kota yang sedang berkembang, sehingga mikrozonasi kegempaan perlu dilakukan untuk mendukung tata letak pembangunan di Kota Tegal. Mikrozonasi dilakukan dengan menganalisis nilai dari PGA (Peak Ground Acceleration) data mikrotremor di 37 titik di Kota Tegal. Data diolah dengan menggunakan metode HVSR (Horizontal to Vertical Spectral Ratio) untuk mendapatkan nilai frekuensi dominan (fo) dan amplifikasi (A) daerah penelitian. Analisis PGA (Peak Ground Acceleration) dilakukan dengan menggunakan metode Kannai dan didapatkan nilai PGA di Kota Tegal mulai dari 5.88 – 27.59 gal

A Hybrid DenseNet201-SVM for Robust Weed and Potato Plant Classification

Potato plant growth needs to be protected from weeds that grow around it. Currently, the manual spraying of pesticides by farmers is not only precise on weeds but also on cultivated plants. Therefore, we need an intelligent system that can appropriately classify potato plants and weeds. The research contribution combines feature extraction and appropriate classification methods to obtain optimal accuracy. In addition, the small amount of data also contributes to this research. In this research, it is proposed to use a combination of feature extraction using deep learning techniques and classification using machine learning. We use the feature extraction method with the DenseNet201 model because this study's data is not too much. Complex vectors from DenseNet201 were reduced using Principal Component Analysis (PCA). Then we classified it with the Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) classification methods. The experimental results show that the PCA method can reduce the complexity of high-dimensional features into 2 and 3 dimensions. The average of the best classification results using SVM was obtained with a 3-dimensional PCA configuration, but on the contrary, using KNN obtained the best results in a 2-dimensional PCA configuration. The results showed 100% accuracy on the DenseNet201-SVM hybrid. The SVM kernel configuration used is a linear kernel. The results of this study can be an insight into an accurate classification method for separating weeds and potatoes so that agricultural technology can apply this method for classification