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All Journal Elkom: Jurnal Elektronika dan Komputer Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) JURNAL ILMIAH PENDIDIKAN FISIKA AL BIRUNI Jurnal Geosaintek Indonesian Journal on Computing (Indo-JC) JURNAL MEDIA INFORMATIKA BUDIDARMA Journal of Innovation Information Technology and Application (JINITA) Teknika Journal of Dinda : Data Science, Information Technology, and Data Analytics Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer (JURITEK) Jurnal Nasional Teknik Elektro dan Teknologi Informasi IJCOSIN : Indonesian Journal of Community Service and Innovation

Ferani, Nia Annisa

Institut Teknologi Telkom Purwokerto

Author-ID : 666172

Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Physics Social Sciences Other

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Measurement of air drag as physics experiment enrichment at senior high school laboratory using the air track apparatus Prasetyo Listiaji; Muhamad Taufiq; Ni Luh Tirtasari; Anisia Kholidah; Nia Annisa Ferani Tanjung
Jurnal Ilmiah Pendidikan Fisika Al-Biruni Vol 9, No 1 (2020): Jurnal Ilmiah Pendidikan Fisika Al-Biruni
Publisher : Universitas Islam Negeri Raden Intan Lampung, Indonesia

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 Faisal Dharma Adhinata; Nia Annisa Ferani Tanjung; Widi Widayat; Gracia Rizka Pasfica; Fadlan Raka Satura
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 7, No 2 (2021): August
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v7i2.20758

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.

ANALISIS PEAK GROUND ACCELERATION (PGA) KOTA TEGAL MENGGUNAKAN METODE HVSR (HORIZONTAL TO VERTICAL SPECTRA RATIO) Nia Annisa Ferani Tanjung; Indah Permatasari; Abdul Hakim Prima Yuniarto
Jurnal Geosaintek Vol 7, No 1 (2021)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j25023659.v7i1.8126

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.

Analisis Amplifikasi Dan Indeks Kerentanan Seismik Di Kawasan Fmipa Ugm Menggunakan Metode HVSR Nia Annisa Ferani Tanjung; Hakim Prima Yuniarto; Danang Widyawarman
Jurnal Geosaintek Vol 5, No 2 (2019)
Publisher : Institut Teknologi Sepuluh Nopember

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.

Implementation of LSTM-RNN for Bitcoin Prediction Nur Ghaniaviyanto Ramadhan; Nia Annisa Ferani Tanjung; Faisal Dharma Adhinata
Indonesia Journal on Computing (Indo-JC) Vol. 6 No. 3 (2021): December, 2021
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2021.6.3.592

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.

Perancangan Sistem Monitoring Konduktivitas dan Padatan Terlarut PDAM Banyumas Berbasis IoT Indah Permatasari; Nia Annisa Ferani Tanjung; Nur Afifah Zen
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 10 No 1: Februari 2021
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

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.

Aplikasi Klasifikasi SMS Berbasis Web Menggunakan Algoritma Logistic Regression Fitran Dwi Pramakrisna; Faisal Dharma Adhinata; Nia Annisa Ferani Tanjung
Teknika Vol 11 No 2 (2022): Juli 2022
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v11i2.466

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 Sentimen Masyarakat Terhadap COVID-19 Pada Media Sosial Twitter Ardianne Luthfika Fairuz; Rima Dias Ramadhani; Nia Annisa Ferani Tanjung
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 1 No 1 (2021): February
Publisher : Research Group of Data Engineering, Faculty of Informatics

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.

A Hybrid DenseNet201-SVM for Robust Weed and Potato Plant Classification Muhammad Dzulfikar Fauzi; Faisal Dharma Adhinata; Nur Ghaniaviyanto Ramadhan; Nia Annisa Ferani Tanjung
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 8, No 2 (2022): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v8i2.23886

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.

Sistem Penilaian Inovasi Karyawan Digital Amoeba Menggunakan Desain Arsitektur Microservice Pada Aplikasi Mobile Fitran Dwi Pramakrisna; Faisal Dharma Adhinata; Nia Annisa Ferani Tanjung
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 3 (2022): Juli 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i3.4187

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

Co-Authors Abdul Hakim Prima Yuniarto Adinda Hashina Anisia Kholidah Ardianne Luthfika Fairuz Condro Kartiko Fadlan Raka Satura Faisal Dharma Adhinata Fauzi, Muhammad Dzulfikar Fitran Dwi Pramakrisna Fitran Dwi Pramakrisna Gracia Rizka Pasfica Indah Permatasari Listiaji, Prasetyo Muhamad Taufiq Nila Aulia Musa Nur Ghaniaviyanto Ramadhan Ramadhani, Rima Dias Sehat Simatupang Sintiya Tirtasari, Ni Luh Tri Ginanjar Laksana Widi Afandi Widi Widayat Widyawarman, Danang Zen, Nur Afifah

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