Analisis Video Comments To Video Likes Ratio Instagram Pada 5 Brand Sandal Terhits Di Indonesia

Instagram, which is usually abbreviated as IG, is a photo and video sharing application that allows users to take photos, take videos, apply digital filters, and share them on various social networking services, including Instagram's own. One unique feature on Instagram is cropping photos into square shapes, so they look like the results of Kodak Instamatic and Polaroid cameras. This is different from the 4:3 or 16:9 aspect ratio that is commonly used by cameras on mobile devices. Instagram can be used on any iPhone, iPad or iPod Touch version with the iOS 7.0 operating system or later, any Android mobile phone with the operating system version 2.2 (Froyo) and above, and Windows Phone 8. This application can be downloaded via the Apple App Store and Google Play. On April 9, 2012, it was announced that Facebook had agreed to take over Instagram for approximately $1 billion. Instagram was first released on October 6, 2010, 11 years ago, the name Instagram Instagram comes from the understanding of the overall function of this application. The word "insta" comes from the word "instant", like the polaroid camera which at that time was better known as "instant photo". Instagram can also display photos instantly, like a polaroid in its display. As for the word "gram" comes from the word "telegram" which works to send information to others quickly. Similarly, Instagram can upload photos using the Internet, so the information you want to convey can be received quickly. That's why Instagram is an extension of the words instant and telegram. On April 9, 2012, it was announced that Instagram would be taken over by Facebook for nearly $1 billion in cash and stock. On May 11, 2016, Instagram introduced a new look as well as a new icon and new app design. Inspired by previous app icons, the new icon is a simple camera and a vivid rainbow in the form of a gradient. There are 5 sandal vendors in Indonesia that use Instagram accounts as a marketing platform, namely:

Understanding Users’ Behavior towards Applications Privacy Policies

Recently, smartphone usage has increased tremendously, and smartphones are being used as a requirement of daily life, equally by all age groups. Smartphone operating systems such as Android and iOS have made it possible for anyone with development skills to create apps for smartphones. This has enabled smartphone users to download and install applications from stores such as Google Play, App Store, and several other third-party sites. During installation, these applications request resource access permissions from users. The resources include hardware and software like contact, memory, location, managing phone calls, device state, messages, camera, etc. As per Google’s permission policy, it is the responsibility of the user to allow or deny any permissions requested by an app. This leads to serious privacy violation issues when an app gets illegal permission granted by a user (e.g., an app might request for granted map permission and there is no need for map permission in the app, and someone can thereby access your location by this app). This study investigates the behavior of the user when it comes to safeguarding their privacy while installing apps from Google Play. In this research, first, seven different applications with irrelevant permission requests were developed and uploaded to two different Play Store accounts. The apps were live for more than 12 months and data were collected through Play Store analytics as well as the apps’ policy page. The preliminary data analysis shows that only 20% of users showed concern regarding their privacy and security either through interaction with the development team through email exchange or through commenting on the platform and other means accordingly.

PROSES DIFUSI INOVASI DAN ADOPSI APLIKASI “PEDULI LINDUNGI” CALON PENUMPANG KRL DI STASIUN KLATEN - SOLO

Penelitian ini mengenai proses dufusi inovasi dan adopsi dalam penggunaan aplikasi PeduliLindungi bagi calon penumpang KRL di stasiun Klaten, Purwosari, dan Solo Balapan. Penerapan penunggunaan aplikasi PeduliLindungi diberlakukan sebagai syarat bagi masyarakat dalam melakukan aktivitas perjalanan darat, laut, dan udara. Kebijakan PPKM (Pemberlakuan Pembatasan Kegiatan Masyarakat) yang diberlakukan pemerintah kepada masyarakat merupakan upaya pemerintah dalam pencegahan penularan wabah pandemi Covid-19. Penggunaan aplikasi PeduliLindungi yang di-instal melalui Google Play Store dan App Store - termasuk syarat untuk memasuki area pusat perbelanjaan dan destinasi wisata – diterapkan secara ketat bagi para calon penumpang Kereta Rel Listrik (KRL). Aplikasi PedulLindungi merupakan sertifikat vaksinasi – baik yang sudah di vaksin dosis pertama maupun dosis kedua tetap diperkenankan - yang berguna sebagai syarat atau izin dalam melakukan perjalanan menggunakan KRL. Dalam penelitian ini, penulis menggunakan teori Difusi Inovasi yang digagas oleh Everett M. Rogers.

Transfer Learning with Social Media Content in the Ride-Hailing Domain by Using a Hybrid Machine Learning Architecture

The analysis of the content of posts written on social media has established an important line of research in recent years. The study of these texts, as well as their relationship with each other and their dependence on the platform on which they are written, enables the behavior analysis of users and their opinions with respect to different domains. In this work, a hybrid machine learning-based system has been developed to classify texts using topic modeling techniques and different word-vector representations, as well as traditional text representations. The system has been trained with ride-hailing posts extracted from Reddit, showing promising performance. Then, the generated models have been tested with data extracted from other sources such as Twitter and Google Play, classifying these texts without retraining any models and thus performing Transfer Learning. The obtained results show that our proposed architecture is effective when performing Transfer Learning from data-rich domains and applying them to other sources.

TAHLIL AL MUFRODATI AL 'ARABIYYATI AL DAKHILATI ILA AL LUGHATI AL URDIYYATI

This study discusses the analysis of Arabic vocabulary into Urdu. Urdu is one of the languages in India and is the official language in Pakistan and Bangladesh. Urdu language with its development is heavily influenced by Arabic because during the Islamic era, its expansion was very rapid in the Indian Subcontinent.The purpose of this study is to: ) Knowing what Arabic vocabulary is used by Urdu in the Google Play Store application ) Knowing the changes in writing Arabic vocabulary into Urdu. ) Knowing the sound changes in Arabic vocabulary into Urdu. ) Knowing the changes in the meaning of Arabic vocabulary into Urdu. The type of research used is qualitative research. It can be concluded from the results of this study: ) There are Arabic vocabulary that goes into Urdu on the Google Play Store application ) There is a change in writing Arabic vocabulary that goes into Urdu ) There is a change in the sound of Arabic vocabulary that goes into Urdu. in urdu ) There is a change in the meaning of arabic vocabulary into urdu.

Analisis Sentimen Kepuasan Pengguna Aplikasi Whatsapp Menggunakan Algoritma Naïve Bayes Dan Support Vector Machine

Teknologi informasi dan komunikasi saat ini sangat berkembang pesat, salah satunya Aplikasi Chat atau pesan instan seperti WhatsApp, Line dan Telegram. Pada bulan Oktober 2020, mayoritas pengguna aplikasi pesan instan adalah pengguna aplikasi WhatsApp, dengan total 2 miliar pengguna. Sekalipun aplikasi whatsapp tersebut masuk dalam peringkat teratas dan mendapat skor tertinggi, akan tetapi hal tersebut tidak dapat dijadikan tolak ukur kepuasan karena masih terdapat pandangan yang negatif terhadap aplikasi whatsapp, sebagian pengguna menganggap bahwa whatsapp seringkali eror pada saat digunakan, kemudian masalah lain yang muncul seperti jaringan yang digunakan pengguna tidak stabil. Untuk melakukan analisis mengenai hal tersebut diperlukan pendekatan analisis sentimen guna mengkategorikan komentar pengguna menjadi positif atau negatif. Penelitian ini menggunakan algoritma Naïve Bayes dengan Support Vector Machine dalam menganalisa komentar positif dan negatif terhadap kepuasan pengguna aplikasi Whatsapp di Google Play Store. Dari hasil pengujian yang dilakukan terhadap 1500 data komentar pengguna, evaluasi model menggunakan 10 Fold Cross Validation menunjukan bahwa tingkat keakurasian untuk kepuasan pengguna aplikasi whatsapp berdasarkan algoritma Naïve Bayes adalah sebesar 70,40% dan Support Vector Machine sebesar 77,00%, sedangkan nilai AUC Naïve Bayes sebesar 0,585 dan Support Vector Machine adalah 0,876. Dari hasil tersebut algoritma Support Vector Machine dapat digunakan untuk penelitian dengan karakteristik data yang sama.

ANALISIS SENTIMEN ULASAN APLIKASI TIKTOK DI GOOGLE PLAY MENGGUNAKAN METODE SUPPORT VECTOR MACHINE (SVM) DAN ASOSIASI

Corona virus pandemic requires people to do activities from home so the number of internet usage in Indonesia has increased because information is carried out through social media. One of the popular social media in Indonesia is TikTok. However, the Tiktok’s popularity cannot be separated from the footsteps of TikTok in Indonesia which was blocked by government for committing many violations. Each application allows users to provide a review about the application. To find out the users TikTok’s sentiment, sentiment analysis was carried out to classify reviews into positive and negative sentiments. Classification is carried out using the Support Vector Machine (SVM) with kernel Radial Basis Function (RBF) method which is more effective classification algorithm and kernel function, seen from previous studies. The parameters used in the SVM gamma default 0.0004255 and the Cost (C) parameter experiment used is 0,01; 0,1; 1; 10; 100; 1000. The results can provide information that can be retrieved using the association method. The steps are scrapping data, data preprocessing, sentiment scoring, TF-IDF weighting, classifying using the SVM RBF kernel method and text association. Evaluation of the model using a confusion matrix with the value of accuracy and kappa. The greater the value of accuracy and kappa, the better the performance of the classification model. The review classification resulted in the best accuracy rate of 90.62% and the best kappa of 81.24% which means that it includes an almost perfect classification result. Based on the data association, positive reviews are given because users like and are comfortable with the current version of TikTok which contains funny videos on fyp. Meanwhile, negative reviews were given because the user failed to register and his account was blocked, so the user asked TikTok to continue to make improvements.

QUERY EXPANSION RANKING PADA ANALISIS SENTIMEN MENGGUNAKAN KLASIFIKASI MULTINOMIAL NAÏVE BAYES (Studi Kasus : Ulasan Aplikasi Shopee pada Hari Belanja Online Nasional 2020)

Shopee is one of the e-commerce sites that has many users in Indonesia. Shopee provides various attractive promos on special days such as National Online Shopping Day on December 12. Shopee site was a complete error on December 12, 2020. Complaints and opinions of Shopee users were also shared through various media, one of them was Google Play Store. Sentiment analysis was used to see the user's response to the Shopee’s incident. Sentiment analysis results can be extracted to obtain information regarding positive or negative reviews from Shopee users. Sentiment analysis was performed using the Multinomial Naïve Bayes classification. the simplest method of probability classification, but it is sensitive to feature selection so that the amount of data is determined by the results of feature selection Query Expansion Ranking. The algorithm that has the highest accuracy and kappa statistic is the best algorithm in classifying Shopee’s users sentiment. The results showed that the classification performance using Multinomial Naïve Bayes with 80% of the features (terms) which have the highest Query Expansion Ranking value was obtained at the accuracy and kappa statistics values are 89% and 77.62%. This means that Multinomial Nave Bayes has a good performance in classifying reviews and the number of features used affects the performance results obtained.