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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) JUITA : Jurnal Informatika Scientific Journal of Informatics POSITIF JOIN (Jurnal Online Informatika) JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Komputer Terapan Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Sisfokom (Sistem Informasi dan Komputer) JSiI (Jurnal Sistem Informasi) JURTEKSI MIND (Multimedia Artificial Intelligent Networking Database) Journal PEDULI: Jurnal Imiah Pengabdian Pada Masyarakat Jurnal Tekno Kompak Building of Informatics, Technology and Science Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Algoritma Jurnal Repositor
S, Vinna Rahmayanti
Universitas Muhammadiyah Malang
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Public Health Social Sciences

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Prediksi Pendapatan Kargo Menggunakan Arsitektur Long Short Term Memory Bagas Aji Aprian; Yufis Azhar; Vinna Rahmayanti Setya Nastiti
Jurnal Komputer Terapan  Vol. 6 No. 2 (2020): Jurnal Komputer Terapan
Publisher : Politeknik Caltex Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (230.827 KB) | DOI: 10.35143/jkt.v6i2.3621

Abstract

Indonesian air cargo transportation is currently experiencing quite significant development. One of the cargo services in Indonesia is Garuda Indonesia Cargo and has several branch offices. The existence of a revenue forecast model is expected to provide insight into a branch office. This research proposes an income prediction using the Deep Learning algorithm, Long Short Term Memory (LSTM). LSTM is used because the data to be processed is time series data. The results of testing accuracy are measured using Root Mean Squared Error (RMSE). The data in this study are income from one branch office, the Cargo Service Center (CSC) Tangerang City. Data contains collections of goods delivery transactions every day. The data goes through 4 preprocessing processes, namely subtotal, outlier detection, difference, and scaling. The results of this study show the best prediction results, namely the composition of the 90% train data and 10% test data with RMSE values of train data 641375.70 and test data 594197.70.
Accessibility Analysis of Websites of Provincial Governments in Indonesia Vinna Rahmayanti Setyaning Nastiti; Amelia Deastu; Gita Indah Marthasari
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 7, No. 1, February 2022
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v7i1.1406

Abstract

Web accessibility means that people with disabilities can use, navigate, and interact with the website. The World Wide Web Consortium (W3C) has provided important guidelines on web accessibility known as the Web Content Accessible Guidelines (WCAG). The Indonesian government encourages the use of new media, namely website, via Presidential Instruction number 3 of 2003 concerning the National Policy and Strategy for e-government development, which mandates every government agency to build a website. In the previous study, the tools used had limitations and were unable to complete the websites evaluation. Therefore, in this study, WCAG 2.0 standard was applied to analyze the websites of provincial governments in Indonesia. Two accessibility evaluation tools were employed, namely TAW and aXe. In addition, for data analysis and interpretation, descriptive statistics and normality tests were applied. The results showed that the most common violation was found in perceivable principle. It was expected that the findings of this study could provide insight and recommendation for web developers working on provincial government website in Indonesia.
PREDIKSI MASA SUBUR PADA WANITA MENGGUNAKAN PEMODELAN STATE SPACE BERBASIS SUHU BASAL TUBUH Vinna Rahmayanti Nastiti; Diah Risqiwati
Jurnal Tekno Kompak Vol 14, No 1 (2020): Februari
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jtk.v14i1.475

Abstract

Mengamati perubahan yang dialami tubuh selama menstruasi akan memberikan petunjuk penting tentang status kesuburan. Salah satu metode yang dianggap paling efektif dan mudah untuk diimplementasikan adalah dengan pemantauan suhu basal tubuh. Pada dasarnya, suhu basal tubuh (BBT) adalah kondisi dimana suhu berada pada level terendah. Masuknya masa subur ditandai oleh sedikit penurunan suhu, diikuti oleh kenaikan secara tiba-tiba setidaknya 0,2 °C (0,4 °F) selama 48 jam berikutnya. Tentunya siklus masa subur tesebut termasuk dalam data deret waktu dikarenakan datanya saling berhubungan satu sama lain yang berurutan, disusun dari satu objek yang terdiri dari beberapa waktu periode. Salah satu metode prediksi data deret waktu adalah state-space. Teknik yang digunakan di dalam state-space adalah memisah waktu masa lalu dengan masa sekarang. Oleh karena itu pada penelitian di prediksi masa subur wanita berbasis suhu basal tubuh menggunakan pemodelan state-space, hasilnya sangat baik dengan akurasi rata-rata kesalahan prediksi dibawah 5%
Classification of Face Mask Detection Using Transfer Learning Model DenseNet169 Lidya Fankky Oktavia Putri; Ahmad Junjung Sudrajad; Vinna Rahmayanti Setyaning Nastiti
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 5 (2022): Oktober 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i5.4336

Abstract

COVID-19 has become a threat to the world because it has spread throughout the world. The fight against this pandemic is becoming an unavoidable reality for many countries. The government has set policies on various transmission prevention efforts. One of these efforts is for everyone to wear masks to break the transmission chain. With such conditions, the government must continue to monitor so that people can apply the appeal in their daily lives when participating in outdoor activities. The present time involves new problems in so many fields of information technology research, especially those related to artificial intelligence. The purpose of this study is to discuss the classification of face image detection in people who wear masks and do not wear masks. designed using the Convolutional Neural Network (CNN) model and built using the transfer learning method with the DenseNet169 model. The model used is also combined with the DenseNet169 transfer learning method and the fully connected layer model architecture, to optimize the performance test in the evaluation. These models were trained under similar conditions and evaluated on benchmarks with the same training and validation images. The result of this research is to get an accuracy value of 96% by combining the two datasets. This dataset is the same as previous research; the number of datasets is 8929 images
APLIKASI KASIR SEBAGAI INCOME GENERATOR PADA HOME INDUSTRY DIMSUM NGALAM Vinna Rahmayanti Setyaning Nastiti; Evi Dwi Wahyuni; Denar Regata Akbi
Community Development Journal : Jurnal Pengabdian Masyarakat Vol. 4 No. 1 (2023): Volume 4 Nomor 1 Tahun 2023
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/cdj.v4i2.9258

Abstract

Upaya konkrit yang dapat dilakukan untuk meningkatan ekonomi masyarakat salah satunya adalah pemberdayaan Usaha Mikro dan Kecil (UMK). Ditinjau dari segi jumlah usaha (establishment) maupun dari segi penciptaan lapangan kerja, UMK merupakan unit usaha yang memiliki peran sangat vital dalam pembangunan ekonomi daerah maupun nasional. Namun demikian, meskipun potensi yang dimiliki UMK cukup besar sebagai pondasi perekonomian nasional yang cukup tangguh dan tidak mudah terpengaruh oleh guncangan eksternal, masih terdapat berbagai masalah yang menghambat perkembangan UMK diantaranya adalah kualitas SDM pelaku usaha yang masih cukup rendah, keterbatasan modal yang dimiliki, serta kesulitan UMK dalam mengakses permodalan eksternal. Dimsum Ngalam merupakan salah satu pelaku usaha yang bergerak di bidang food and beverage yang berdiri tahun 2018 dengan menawarkan produk dimsum. Kendala yang dihadapi oleh Dimsum Ngalam salah satunya adalah kesulitan dalam mengelola pesanan dan mencatat transaksi penjualan yang terjadi. Pesanan yang masuk masih dicatat secara manual dan rawan sekali terjadinya kesalahan pencatatan sehingga secara tidak langsung dapat mempengaruhi proses produksi. Berdasarkan penjelasan tersebut, maka solusi yang ditawarkan untuk menyelesaikan permasalahan yang dihadapi mitra adalah dengan memberikan sebuah aplikasi kasir dan melakukan pelatihan serta pendampingan terhadap mitra dalam mengoperasikan aplikasi tersebut dengan harapan dapat membantu mitra untuk mengelola keuangan dari setiap transaksi yang terjadi. Proses pelatihan dan pendampingan yang dilakukan meliputi pengajaran tentang bagaimana mengoperasikan aplikasi, bagaimana melakukan pencatatan transaksi dan manajemen keuangan sehingga tercapai pengelolaan keuangan Dimsum Ngalam yang lebih baik. Luaran pada pengabdian ini adalah jurnal terakreditasi nasional sehingga diharapkan permasalahan, solusi, dan kontribusi pada pihak UMK dapat dipublikasikan secara nasional agar dapat memajukan ekonomi UMKM di Indonesia. 
Implementation of Convolutional Neural Network Method in Identifying Fashion Image Christian Sri Kusuma Aditya; Vinna Rahmayanti Setyaning Nastiti; Qori Raditya Damayanti; Gian Bagus Sadewa
JUITA: Jurnal Informatika JUITA Vol. 11 No. 2, November 2023
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v11i2.17372

Abstract

The fashion industry has changed a lot over the years, which makes it hard for people to compare different kinds of fashion. To make it easier, different styles of clothing are tried out to find the exact and precise look desired. So, we opted to employ the Convolutional Neural Network (CNN) method for fashion classification. This approach represents one of the methodologies employed to utilize computers for the purpose of recognizing and categorizing items. The goal of this research is to see how well the Convolutional Neural Network method classifies the Fashion-MNIST dataset compared to other methods, models, and classification processes used in previous research. The information in this dataset is about different types of clothes and accessories. These items are divided into 10 categories, which include ankle boots, bags, coats, dresses, pullovers, sandals, shirts, sneakers, t-shirts, and trousers. The new classification method worked better than before on the test dataset. It had an accuracy value of 95. 92%, which is higher than in previous research. This research also uses a method called image data generator to make the Fashion MNIST image better. This method helps prevent too much focus on certain details and makes the results more accurate.
PERBANDINGAN KINERJA PRE-TRAINED INDOBERT-BASE DAN INDOBERT-LITE PADA KLASIFIKASI SENTIMEN ULASAN TIKTOK TOKOPEDIA SELLER CENTER DENGAN MODEL INDOBERT Wildan Amru Hidayat; Nastiti, Vinna Rahmayanti Setyaning
Jurnal Sistem Informasi Vol 11 No 2 (2024)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jsii.v11i2.9168

Abstract

Era digital telah membawa revolusi dalam dunia e-commerce dengan mengintegrasikan platform media sosial dan platform e-commerce, yang menghasilkan inovasi seperti aplikasi TikTok Tokopedia Seller Center. Aplikasi ini menggabungkan platform e-commerce dengan fitur media sosial, memungkinkan pengguna untuk mengelola penjualan sekaligus memperluas jangkauan pasar dan mempromosikan produk melalui video pendek yang interaktif pada platform media sosial TikTok. Dengan adanya inovasi fitur baru dalam aplikasi ini, penelitian ini melakukan analisis sentimen untuk memahami persepsi dan ulasan berbahasa Indonesia dari para pengguna aplikasi TikTok Tokopedia Seller Center menggunakan model deep learning IndoBERT. Data ulasan dikumpulkan menggunakan teknik scraping pada Google Play Store sebanyak 3.145 ulasan yang dilabeli secara manual menjadi 1.755 klasifikasi sentimen negatif dan 1390 klasifikasi sentimen positif. Tahapan preprocessing seperti teks cleaning, case folding, normalisasi teks, dan stopword removal dilakukan untuk memberihkan data teks sebelum digunakan untuk pelatihan model. Data yang sudah dibersihkan terbagi menjadi 64% data training sebesar 2.012 data, 16% data validation sebesar 504 data, dan 20% data testing sebesar 629 data. Dua varian pre-trained model IndoBERT, yaitu Indobert-base-p2 versi besar dan Indobert-lite-base-p2 versi lebih ringan digunakan dalam penelitian ini untuk pemrosesan bahasa alami khusus bahasa Indonesia. Hasil penelitian menunjukkan bahwa komparasi model IndoBERT dengan kedua pre-trained menunjukkan bahwa pre-trained Indobert-base-p2 mendapatkan hasil akurasi yang lebih unggul dibandingkan Indobert-lite-base-p2, dengan akurasi sebesar 97%, presisi sebesar 97%, recall sebesar 97%, dan f1-score sebesar 97%, sedangkan pre-trained Indobert-lite-base-p2 dengan akurasi sebesar 94%, presisi sebesar 94%, recall sebesar 94%, dan f1-score sebesar 94%.
Implementasi Extra Trees Classifier dengan Optimasi Grid Search CV pada Prediksi Tingkat Adaptasi AINA, LISTYA NUR; NASTITI, VINNA RAHMAYANTI SETYANING; ADITYA, CHRISTIAN SRI KUSUMA
MIND (Multimedia Artificial Intelligent Networking Database) Journal Vol 9, No 1 (2024): MIND Journal
Publisher : Institut Teknologi Nasional Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/mindjournal.v9i1.78-88

Abstract

AbstrakTeknologi terus maju, terutama dalam komunikasi, pendidikan, dan informasi. Pendidikan online semakin diminati di banyak lembaga pendidikan, mendorong perlunya pemahaman sejauh mana peserta didik dapat beradaptasi dengan lingkungan online. Memprediksi tingkat adaptasi peserta didik menjadi penting untuk meningkatkan efektivitas dan kualitas pengalaman belajar. Dalam penelitian ini, menggunakan dataset dari Kaggle, metode Extra Trees Classifier dioptimalkan dengan Hyperparameter Tuning Grid Search CV. Sebelum optimalsi, akurasi mencapai 95.85%, setelahnya meningkat menjadi 96.26%, menunjukkan peningkatan sebesar 0.41%. Implementasi metode Extra Trees Classifier dengan optimasi Hyperparameter Tuning Grid Search CV lebih unggul dibandingkan penggunaan algoritma tanpa optimasi.Kata kunci: Prediksi, Extra Trees, Classifier, Hyperparameter, CVAbstractTechnology continues to advance, especially in communication, education and information. Online education is increasingly in demand in many educational institutions, prompting the need to understand the extent to which learners can adapt to the online environment. Predicting learners' adaptation level is important to improve the effectiveness and quality of the learning experience. In this study, using a dataset from Kaggle, the Extra Trees Classifier method was optimized with Hyperparameter Tuning Grid Search CV. Before optimization, the accuracy reached 95.85%, after which it increased to 96.26%, showing an improvement of 0.41%. The implementation of the Extra Trees Classifier method with Hyperparameter Tuning Grid Search CV optimization is superior to the use of the algorithm without optimization.Keywords: Prediction, Extra Trees, Classifier, Hyperparameter, CV
Perbandingan Kinerja Pre-Trained Word Embedding Terhadap Performa Klasifikasi Sentimen Ulasan Produk Tokopedia Dengan Long Short-Term Memory(LSTM) Dirfas, Naufal Angling; Nastiti, Vinna Rahmayanti Setyaning
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5634

Abstract

The product review dataset is a rapidly growing and interesting source of data to explore. The increase in the number of internet users and customer shopping habits through online stores has a significant impact on the growth of product review data, especially for online stores in Indonesia, such as Tokopedia. The sample data used amounted to 1079. This research aims to evaluate the performance of three types of pre-trained word embeddings, namely FastText, GloVe, and Word2Vec, in the Long Short-Term Memory (LSTM) model for sentiment classification of product reviews on Tokopedia. An automated sentiment classification system is needed to process many product reviews, making it easier for sellers to know what consumers think of their products. This research contributes by evaluating the impact of various pre-trained word embeddings on the performance of LSTM models in sentiment classification tasks. In addition, this research also aims to measure the effectiveness of LSTM models combined with multiple pre-trained word embeddings. By implementing a deep learning architecture, computers can learn and recognize contextual data stored in review sentences. The research was conducted in three stages: model selection, layer setup, and hyperparameter optimization, to feature in-depth testing of the deep learning architecture used and the appropriate combination of layers and parameters to obtain high sentiment classification performance. The experimental results show that FastText with LSTM provides the best performance with 85.08% accuracy, Word2Vec with 84.62% accuracy, and GloVe with 83.04% accuracy. The main contribution of this research is to present an in-depth test of the product review dataset and provide a deep learning architecture along with a combination of layers and parameters that has the best performance in recognizing sentiment on the product review dataset. This architecture achieves higher performance than the BERT method with CNN and BiLSTM layers.
Klasifikasi Tingkat Kemampuan Adaptasi Siswa dalam Pembelajaran Online Menggunakan Decision Tree Lailiyah, Asmaul; Nastiti, Vinna Rahmayanti Setyaning; Wahyuni, Evi Dwi; Aditya, Christian Sri Kusuma
Techno.Com Vol. 23 No. 1 (2024): Februari 2024
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v23i1.9739

Abstract

Kemajuan dalam ilmu pengetahuan dan teknologi mendorong adaptasi terhadap pemanfaatan teknologi di berbagai sektor, seperti komunikasi, pendidikan, dan informasi. Terutama dalam konteks teknologi pendidikan, dapat diamati bahwa pembelajaran online sedang mendapatkan popularitas yang signifikan di berbagai lembaga pendidikan. Oleh karena itu, penting untuk mengeksplorasi seberapa baik peserta didik dapat beradaptasi dengan lingkungan pembelajaran online. Memprediksi tingkat adaptasi peserta didik memiliki signifikansi yang besar bagi pendidik dan pengembang platform pembelajaran online, dengan tujuan meningkatkan efisiensi dan kualitas pengalaman belajar. Penelitian ini menggunakan dataset “Students Adaptability Level in Online Education” dengan menerapkan pendekatan Algoritma Decision Tree. Hasil penelitian memperoleh akurasi sebesar 95%, meningkat 7,44% dari penelitian sebelumnya yang hanya memperoleh akurasi sebesar 87,56% dengan menggunakan algoritma yang sama tanpa Feature Engineering. Hal ini menunjukkan bahwa Feature Engineering memegang peranan penting dalam klasifikasi tingkat kemampuan adapatasi siswa untuk mendapatkan hasil yang baik dengan akurasi yang tinggi.
Co-Authors Abdy Yoga Syantara Achmad Rizal Yogaswara Adnan Burhan Hidayat Kiat Ahmad Junjung Sudrajad AINA, LISTYA NUR Akbi, Denar Regata Amelia Deastu Amelia Dwi Deastu Amelia Dwi Deastu Andriani Eka Pramudita Andriani Eka Pramudita Anhar Anhar Anhar Aria Maulana Asty Try Yuliandari Bagas Aji Aprian Bagus Wicaksono Basuki, Setio Bella Chintia Eka Merita Bella Chintia Merita Chintia Eka Merita, Bella Christian Sri Kusuma Aditya Christian Sri Kusuma Aditya Christian Sri Kusuma Aditya Christian Sri kusuma Aditya, Christian Sri kusuma Dede Nor Alfiansyah Dede Nor Alfiansyah Denar Regata Akbi Dewi Aprilitasari Diah Risqiwati Dignifo Nauval Muhammady Dini Tri Purwaningsih Dirfas, Naufal Angling Evi Dwi Wahyuni Farriel Arrianta Akbar Pratama Ferdiansyah, M.Firdaus Ibadi Fitriyani, Annisaa Salsabila Shafiyyah Gian Bagus Sadewa Giffari Zakawaly Gita Indah Marthasari Haidar Aldy Eka Nugraha Haidar Zakki Jumali Hilman Hilman Hilman Hilman Imsa Nur Dwijayanti Kenanga, Larynt Sawfa Lailiyah, Asmaul Lidya Fankky Oktavia Putri Maryaeni Maryaeni Muhammad Eka Nur Arief Muhammad Gofur Muhammad Rivaldi Asyhari Muhammad Robby Fathuda Muhammad Robby Fathuda Nadhira Ulya Nisa Nadya, Fhara Elvina Pingky Naila Hidayah, Tia Cahyani Naila, Tia Cahyani Naufal Adiftya Falah Novita Daian Novita Daian Marlissa Nur Hayatin Nuryasin, Ilyas Oktaviani, Chintya Tria Diana Pratama, Ramadan Qori Raditya Damayanti Ramadan Pratama Reikisyifa, Clarissa Sanindita Rifky Ahmad Saputra Riska Septiana Putri Saputri, Indah Sari, Zamah Setiawan, Muhamad Jodi Sirril Asrory Titin Eka Puspitawati Wahyu Andhyka Kusuma Wildan Amru Hidayat Wildan Suharso Wiyono, Briansyah Setio Yufis Azhar Zamah Sari