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Teknomatika: Jurnal Informatika dan Komputer
Published by Universitas Jenderal Achmad Yani Yogyakarta
ISSN : 19797656     EISSN : 30310865     DOI : 10.30989
Core Subject : Science,
Computer Science & IT
Teknomatika: Jurnal Informatika dan Komputer ISSN: 3031-0865 (Online), 1979-7656 (Print) is a free and open-access journal published by Fakultas Teknik dan Teknologi Informasi Universitas Jenderal Achmad Yani Yogyakarta, Indonesia. Teknomatika publishes scientific articles from scholars and experts worldwide related to the computer science, informatics, computer systems and information systems. This journal accommodates articles covering: Mathematics and Statistics Algorithms and Programming Intelligent System Artificial Intelligence Software Engineering Computer Architecture Distributed System Cyber Security Electronics and Embedded Systems Data and Information Management Information Systems Enterprise System All published articles will have a Digital Object Identifier (DOI). The Journal publication frequency is twice a year (sixth monthly: Maret and September).
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 6 Documents
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Search results for , issue "Vol 14 No 2 (2021): TEKNOMATIKA" : 6 Documents clear
Rekomendasi Posting Promosi pada Sosial Media Berdasarkan Pengelompokan Hasil Penjualan Produk (Studi Kasus: Maula Hijab) Taufaldisatya Wijatama Diwangkara; Ulfi Saidata Aesyi; Netania Indi Kusumaningtyas
Jurnal Teknomatika Vol 14 No 2 (2021): TEKNOMATIKA
Publisher : Fakultas Teknik dan Teknologi Informasi, Universitas Jenderal Achmad Yani Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30989/teknomatika.v14i2.1096

Abstract

Maula Hijab is an MSME (Small and Micro Medium Enterprises) located in Sidomoyo, Godean District, Sleman Regency, Yogyakarta Special Region Province that sells Muslim clothing products. Maula Hijab sells its products directly and through marketplace platforms such as Shopee, Lazada, and Tokopedia. In addition, Maula Hijab promotes its products through social media, one of which is Instagram. Social media is used to promote Maula Hijab products, but there is a decrease in the number of viewers reached by the Maula Hijab Instagram account. In addition, a decline in sales of Maula Hijab was found. Therefore, it is necessary to analyze the level of product promotion performance on Instagram on product sales. To analyze the two data, the Data Mining technique used in this study is K-Means Clustering. The K-Means Clustering algorithm is used to group, classify, or group a set of objects based on their attributes or features into a number of similar groups called clusters. This study aims to provide recommendations for promotion of Maula Hijab products using the K-Means Clustering algorithm. This study uses the K-Means Clustering method. The final result of this research is that 3 product clusters are produced, namely product clusters that are recommended to be promoted more often, product clusters that can be re-promoted, and product clusters that have good promotions. The recommendation system built can run to retrieve Instagram data and process the data to produce output in the form of product promotion recommendations.
ANALISIS SENTIMEN DI MEDIA SOSIAL TWITTER DENGAN STUDI KASUS KARTU PRAKERJA Iqbal Hadi Subekti; Muhammad Habibi; Aris Wahyudi Murdiyanto; Alfun Roehatul Jannah
Jurnal Teknomatika Vol 14 No 2 (2021): TEKNOMATIKA
Publisher : Fakultas Teknik dan Teknologi Informasi, Universitas Jenderal Achmad Yani Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30989/teknomatika.v14i2.1101

Abstract

Kartu Prakerja is one of the government's flagship programs in providing training to the workforce. In its implementation there is a lot of information scattered, especially on social media Twitter both in the pros and cons of Kartu Prakerja program. Based on information in the form of tweets that have not been analyzed in depth, it is necessary to analyze sentiment on the Kartu Prakerja in order to obtain appropriate information based on the opinions of netizen s on Twitter. This study discusses sentiment analysis of tweet data with the keyword “Kartu Prakerja” which uses data as many as 6658 tweet data taken in the period May 27 - August 5, 2021. This research uses the Naive Bayes Classification method which has several stages, namely data retrieval, data preprocessing, manual labeling, data training and testing. The solution offered in this study is to create an analysis model that can be used to perform sentiment analysis about Kartu Prakerja on Twitter. Based on the results of this study obtained that the calculation of accuracy obtained a value of 86% for training data and 87% for data testing. This study concluded that the Kartu Prakerja has a positive sentiment by Twitter netizens based on the results of Classification that discusses many positive sentiments such as the benefits, effectiveness and addition of the Kartu Prakerja budget.
Analisis Kata Kunci untuk Mendapatkan Konversi Tertinggi dari Platform Google dan Tokopedia Dimas Pratama Jati; Aris Wahyu Murdiyanto; Kharisma; Nurul Fatimah
Jurnal Teknomatika Vol 14 No 2 (2021): TEKNOMATIKA
Publisher : Fakultas Teknik dan Teknologi Informasi, Universitas Jenderal Achmad Yani Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30989/teknomatika.v14i2.1107

Abstract

The business world is very closely related to advertising, advertisements in print, electronic and digital media, In advertising on digital media we need keywords as a reference for search engines to find what we want, Targeting the right keywords in articles is very important to help websites easy to find in search engines. However, in choosing these keywords it is often not appropriate or not in accordance with what is desired, where the inaccuracy of the keywords will make the ad not suitable for the site or product being marketed, so that it is not optimal. The first step is to determine the items to be advertised and then look for the right keywords by looking at the Click Through Rate (CTR), which is the ratio of the number of clicks to the number of impressions, then running ads based on the keywords that have been obtained for an item, then analyze the results of running ads. The results of the two platforms between Google Ads and Tokopedia get an increase of visits after running ads, running ads using keywords with high CTR is very influential on visits and sales. It was recorded that during the ads run there were 2 sales that entered Tokopedia with a total of 3 items sold. If the purpose of the ad is for Brand Awareness, it is better to use Google ads to run ads, because the number of impressions of Google ads is better, but if the purpose of the ad is to sell then it is better to use Tokopedia because the number of conversions is more than Google ads.
Analisis Sentimen dan Klasifikasi Terhadap Tren “UU ITE” di Media Sosial Twitter Risky Setyadi Putra; Muhammad Habibi; Aris Wahyu Murdiyanto; Nafisa Alfi Sa'diya
Jurnal Teknomatika Vol 14 No 2 (2021): TEKNOMATIKA
Publisher : Fakultas Teknik dan Teknologi Informasi, Universitas Jenderal Achmad Yani Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30989/teknomatika.v14i2.1116

Abstract

Undang-undang Informasi and Transaksi Elektronik abbreviated UU ITE is a law that regulates information and electronic transactions, or information technology in general. This study discusses sentiment analysis from tweet data with keywords “UU ITE” Who uses as much data 7.407 tweet data and re-tweets taken in the period July 21 - August 16, 2021, with details 914 data that has been manually labeled and 6,493 data labeled using Predicting that the data was taken using authentication on the Twitter API and executed using the Python library. This research uses methods Support Vector Machine because it has several advantages including It is capable of handling the classification of two classes, and its implementation is relatively easy. For the support vector machine stage, namely data retrieval, preprocessing data, manual labeling, data training and testing. As for the solution offered in this research is to create an analysis model that can be used to conduct sentiment analysis about the ITE Law on social media Twitter. This research was successful using the Support Vector Machine method to create a sentiment analysis model with an accuracy of 81.20% for data Training and 87% for data testing. This study provides results that UU ITE have negative sentiments by netizens on social media Twitter based on on the results of classification and calculations on the model and tweet data and the number of Negative discussions.
PENERAPAN METODE SURF DAN FLANN UNTUK MENDETEKSI TERBITAN SPAM PADA INSTAGRAM Dwi Sandi Yulianto; Adri Priadana; Andika Bayu Saputra; Fajar Syahruddin
Jurnal Teknomatika Vol 14 No 2 (2021): TEKNOMATIKA
Publisher : Fakultas Teknik dan Teknologi Informasi, Universitas Jenderal Achmad Yani Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30989/teknomatika.v14i2.1125

Abstract

Social media is a new media that utilizes the internet to share information, interact, participate and others, and to be used with each other. Currently there are many social media circulating, one of which is Instagram. At first Instagram was only used to share photos, then along with the development of technology and media, Instagram also developed into being able to share videos and shop on Instagram. Instagram is also one of the social media specifically used to upload images and videos. The growing use of Instagram in supporting promotion makes Instagram faced with various problems, one of which is the emergence of spam issues. For example, the publication of spam on Instagram is published by several sellers of products or the like continuously. It's good to promote a product. But on the other hand, it will interfere with other users if the spam often appears. This is exacerbated by the mass use of popular hashtags, done with the aim of getting more views. Popular hashtags are hashtags that are followed by many Instagram users. Based on these problems, it takes a computer program to detect spam issues based on certain hashtags on Instagram. In this final task, the Speeded-Up Robust Features (SURF) and Fast Library for Approximate Nearest Neighbor (FLANN) methods will be applied to detect spam publications on Instagram. The results of experiments that have been conducted on 12 images that produce 66 comparisons, the application of SURF and FLANN methods can be said to be very good in detecting the similarity of images between Instagram publications that indicate that the same image is a spam issue, which is with a maximum accuracy value of 100%.
Analisis Sentimen Pergerakan Harga Saham Sebuah Perusahaan di Media Sosial Twitter Agung Purwanto Soedarbe; Muhammad Rifqi Ma'arif; Aris Wahyu Murdiyanto; M. Abu Amar Al Badawi
Jurnal Teknomatika Vol 14 No 2 (2021): TEKNOMATIKA
Publisher : Fakultas Teknik dan Teknologi Informasi, Universitas Jenderal Achmad Yani Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30989/teknomatika.v14i2.1129

Abstract

Twitter has become an essential platform for traders and stock investors worldwide, including major countries like America. Traders rely on Twitter to gather information, similar to how they use Bloomberg terminals. While Twitter provides valuable insights, it also contains negative elements such as false information. The sentiment surrounding stocks on Twitter has been growing, and this study aims to analyze the sentiment of Telkom Indonesia's stock price based on tweets. The research involved several stages. First, data was collected from Twitter and labeled manually into positive, neutral, and negative sentiments. The data then underwent pre-processing, including cleaning and dividing it into training and testing datasets using K-Fold Cross Validation. The data was further weighted using the TF-IDF method, and a training process was conducted to develop a model. The final stage involved testing the accuracy of the model. The study successfully implemented the Multinomial Naïve Bayes (MNB) method, achieving an accuracy of 89.0%. The tweet classification results revealed that out of 1000 tweets, 76.5% were classified as positive, 14.3% as negative, and 9.2% as neutral.

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