Articles
<![CDATA[Comparative Study of VGG16 and MobileNetV2 for Masked Face Recognition ]]>
<![CDATA[Faisal Dharma Adhinata]]>;
<![CDATA[Nia Annisa Ferani Tanjung]]>;
<![CDATA[Widi Widayat]]>;
<![CDATA[Gracia Rizka Pasfica]]>;
<![CDATA[Fadlan Raka Satura]]>
<![CDATA[ Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 7, No 2 (2021): August ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.26555/jiteki.v7i2.20758
<![CDATA[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.]]>
<![CDATA[ANALISIS PEAK GROUND ACCELERATION (PGA) KOTA TEGAL MENGGUNAKAN METODE HVSR (HORIZONTAL TO VERTICAL SPECTRA RATIO) ]]>
<![CDATA[Nia Annisa Ferani Tanjung]]>;
<![CDATA[Indah Permatasari]]>;
<![CDATA[Abdul Hakim Prima Yuniarto]]>
<![CDATA[ Jurnal Geosaintek Vol 7, No 1 (2021) ]]>
Publisher : <![CDATA[Institut Teknologi Sepuluh Nopember ]]>
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DOI: 10.12962/j25023659.v7i1.8126
<![CDATA[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.]]>
<![CDATA[Analisis Amplifikasi Dan Indeks Kerentanan Seismik Di Kawasan Fmipa Ugm Menggunakan Metode HVSR ]]>
<![CDATA[Nia Annisa Ferani Tanjung]]>;
<![CDATA[Hakim Prima Yuniarto]]>;
<![CDATA[Danang Widyawarman]]>
<![CDATA[ Jurnal Geosaintek Vol 5, No 2 (2019) ]]>
Publisher : <![CDATA[Institut Teknologi Sepuluh Nopember ]]>
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DOI: 10.12962/j25023659.v5i2.5726
<![CDATA[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.]]>
<![CDATA[Implementation of LSTM-RNN for Bitcoin Prediction ]]>
<![CDATA[Nur Ghaniaviyanto Ramadhan]]>;
<![CDATA[Nia Annisa Ferani Tanjung]]>;
<![CDATA[Faisal Dharma Adhinata]]>
<![CDATA[ Indonesia Journal on Computing (Indo-JC) Vol. 6 No. 3 (2021): December, 2021 ]]>
Publisher : <![CDATA[School of Computing, Telkom University ]]>
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DOI: 10.34818/INDOJC.2021.6.3.592
<![CDATA[Bitcoin is a cryptocurrency that is used worldwide for digital payments or simply for investment purposes. Bitcoin is a new technology so there are currently very few prices prediction models available. Problems arise when someone uses bitcoin without understanding strong fundamentals. This can result in a lot of loss for the person. These problems certainly need to be overcome by predicting bitcoin prices using a machine learning approach. The purpose of this research is to predict the bitcoin USD price using the Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) model. The LSTM-RNN model was chosen because it is better than the traditional neural network model. Measurement of the results in this study using the Root Mean Square Error (RMSE). The RMSE results obtained on the application of the LSTM-RNN model 6461.14.]]>
<![CDATA[Perancangan Sistem Monitoring Konduktivitas dan Padatan Terlarut PDAM Banyumas Berbasis IoT ]]>
<![CDATA[Indah Permatasari]]>;
<![CDATA[Nia Annisa Ferani Tanjung]]>;
<![CDATA[Nur Afifah Zen]]>
<![CDATA[ Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 10 No 1: Februari 2021 ]]>
Publisher : <![CDATA[Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada ]]>
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DOI: 10.22146/jnteti.v10i1.1023
<![CDATA[PDAM is a company engaged in the distribution of clean water for the community. Some Indonesian people have become PDAM water customers to meet their water needs for their daily activities. Water quality is an important issue because it is closely related to health. In this paper, the design of the water quality monitoring system in PDAM Banyumas based on IoT is carried out by reviewing the parameters of electrical conductivity (EC) and dissolved solids (TDS). The result of measurement data can be accessed via the Android App on smartphone. The application is designed using HTTP and MQTT protocols. HTTP protocol is used on the user interface to retrieve the last measurement data. Meanwhile, the MQTT protocol is used to update measurement data so that the data transmission process is faster. The system will send notification via telegram if the water quality is below quality standard. The measurement accuracy test is done by comparing the monitoring device with the certified measuring instrument on samples of bottled drinking water and PDAM water. The results show that the performance of the designed monitoring device was 97.31% and the quality of the PDAM Banyumas water is very stable and safe for consumption.]]>
<![CDATA[Aplikasi Klasifikasi SMS Berbasis Web Menggunakan Algoritma Logistic Regression ]]>
<![CDATA[Fitran Dwi Pramakrisna]]>;
<![CDATA[Faisal Dharma Adhinata]]>;
<![CDATA[Nia Annisa Ferani Tanjung]]>
<![CDATA[ Teknika Vol 11 No 2 (2022): Juli 2022 ]]>
Publisher : <![CDATA[Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya ]]>
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DOI: 10.34148/teknika.v11i2.466
<![CDATA[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.]]>
<![CDATA[Analisis Sentimen Masyarakat Terhadap COVID-19 Pada Media Sosial Twitter ]]>
<![CDATA[Ardianne Luthfika Fairuz]]>;
<![CDATA[Rima Dias Ramadhani]]>;
<![CDATA[Nia Annisa Ferani Tanjung]]>
<![CDATA[ Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 1 No 1 (2021): February ]]>
Publisher : <![CDATA[Research Group of Data Engineering, Faculty of Informatics ]]>
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DOI: 10.20895/dinda.v1i1.180
<![CDATA[Akhir tahun 2019 lalu dunia digemparkan oleh munculnya suatu penyakit yang disebabkan oleh virus SARS-CoV-2 yang merupakan jenis virus terbaru dari coronavirus. Penyakit ini dikenal dengan nama COVID-19. Penyebaran penyakit ini terbilang cukup luas dan cepat. Dalam waktu singkat penyakit ini mulai menyebar ke segala penjuru dunia tak terkecuali Indonesia. Dengan tingkat penyebaran yang begitu tinggi dan belum ditemukannya vaksin untuk COVID-19, menyebabkan kekacauan di tengah masyarakat. Hal ini mempengaruhi banyak sektor kehidupan masyarakat. Tak sedikit masyarakat yang aktif bersosial media dan menuliskan pendapat, opini serta pemikirannya di platform media sosial seperti Twitter. Terjadinya pandemi ini mendorong masyarakat untuk menuliskan opini, pemikiran serta pendapatnya terhadap COVID-19 pada media sosial Twitter. Dibutuhkan suatu model sentiment analysis untuk mengklasifikasi tweet masyarakat di Twitter menjadi positif dan negatif. Sentiment analysis merupakan bagian dari Natural Language Processing yang membuat sebuah sistem guna mengenali serta mengekstraksi opini dalam bentuk teks. Pada penelitian ini digunakan algoritma Naive Bayes dan K-Nearest Neighbor untuk digunakan dalam membangun model sentiment analysis terhadap tweet pengguna Twitter terhadap COVID-19. Didapatkan akurasi sebesar 85% untuk algoritma Naïve Bayes dan 82% untuk algoritma K-Nearest Neighbor pada nilai k=6, 8, dan 14.]]>
<![CDATA[A Hybrid DenseNet201-SVM for Robust Weed and Potato Plant Classification ]]>
<![CDATA[Muhammad Dzulfikar Fauzi]]>;
<![CDATA[Faisal Dharma Adhinata]]>;
<![CDATA[Nur Ghaniaviyanto Ramadhan]]>;
<![CDATA[Nia Annisa Ferani Tanjung]]>
<![CDATA[ Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 8, No 2 (2022): June ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.26555/jiteki.v8i2.23886
<![CDATA[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.]]>
<![CDATA[Sistem Penilaian Inovasi Karyawan Digital Amoeba Menggunakan Desain Arsitektur Microservice Pada Aplikasi Mobile ]]>
<![CDATA[Fitran Dwi Pramakrisna]]>;
<![CDATA[Faisal Dharma Adhinata]]>;
<![CDATA[Nia Annisa Ferani Tanjung]]>
<![CDATA[ JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 3 (2022): Juli 2022 ]]>
Publisher : <![CDATA[STMIK Budi Darma ]]>
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DOI: 10.30865/mib.v6i3.4187
<![CDATA[Digital Amoeba employees want an application system that can be integrated directly into the Ideabox application to be assessed the innoavtions directly. Therefore, innovations do not need to be assessed using third parties such as Google Sheets SurveyMonkey. Therefore, an application system called Scoring was created. This application system is designed using a microservice architecture design, where each service has its own database. The services used in the Scoring application system are User Service, Ideas Service, Event Service, and Scoring Service. The application system is built using the PHP programming language and the CodeIgniter version 4 framework. This system is implemented both on web and mobile platform]]>
<![CDATA[Kombinasi Single Linkage Dengan K-Means Clustering Untuk Pengelompokan Wilayah Desa Kabupaten Pemalang ]]>
<![CDATA[Sintiya]]>;
<![CDATA[Tri Ginanjar Laksana]]>;
<![CDATA[Nia Annisa Ferani Tanjung]]>
<![CDATA[ Journal of Innovation Information Technology and Application (JINITA) Vol 3 No 1 (2021): JINITA, June 2021 ]]>
Publisher : <![CDATA[Politeknik Negeri Cilacap ]]>
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DOI: 10.35970/jinita.v3i1.589
<![CDATA[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.]]>
