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All Journal Jurnal Pekommas Telematika : Jurnal Informatika dan Teknologi Informasi Jurnal Teknologi dan Sistem Komputer Compiler IJID (International Journal on Informatics for Development) Jurnal Penelitian Pers dan Komunikasi Pembangunan JISKa (Jurnal Informatika Sunan Kalijaga) Indonesian Journal on Data Science Teknomatika: Jurnal Informatika dan Komputer International Journal of Artificial Intelligence in Medical Issues
Aris Wahyu Murdiyanto
Universitas Jenderal Achmad Yani Yogyakarta
Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Electrical & Electronics Engineering Health Professions Languange, Linguistic, Communication & Media Library & Information Science Medicine & Pharmacology Public Health

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Identification Of Keywords That Impact Of Increasing The Click Through Rate Of Online Advertising On Search Engines Aris Wahyu Murdiyanto; Aris Wahyu Murdiyanto; Arif Himawan
Telematika Vol 19, No 1 (2022): Edisi Februari 2022
Publisher : Jurusan Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v19i1.6450

Abstract

Purpose: To identify keywords that can be chosen to increase CTR on the website so that the potential revenue of targeted prospects through search engines is higher.Design/methodology/approach: This study applies the weighted product method based on the criteria that will be determined to find the best keyword list.Findings/result: The results of identification by ranking using the weighted product method based on the criteria C1, C2, and C3 resulted in an average increase in CTR of 16.18% to 22.92%. With this increase, business owners can be more efficient in the online advertising process.Originality/value/state of the art: The identification of keywords that can be chosen to increase CTR on a website by ranking using the weighted product method has never been done by previous researchers. 
Analisis Sentimen Transfer Pemain Klub La Liga Spanyol Pada Bursa Transfer Musim Dingin Eropa Di Twitter Ahmad Adita Shiddiq; Aris Wahyu Murdiyanto; Arif Himawan
INDONESIAN JOURNAL ON DATA SCIENCE Vol 1 No 1 (2023): Indonesian Journal on Data Science
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat Universitas Achmad Yani Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30989/ijds.v1i1.859

Abstract

Dari beberapa kompetisi Sepak Bola yang ada, Liga Champions UEFA yang paling digemari oleh masyarakat. Pada tahun 2022 bursa transfer pemain Eropa dibuka, bursa transfer yang dilakukan merupakan cara jangka pendek untuk memperbaiki tim dalam mengejar prestasi sepak bola Dengan media sosial sebagai wadah komunitas, para penggemar sepak bola dapat juga menyalurkan opini, informasi dan berita tentang klub kesayangan kepada masyarakat. Opini masyarakat terhadap transfer pemain Liga Spanyol memiliki peranan penting. Dengan dilakukannya analisis sentimen terhadap opini, dapat dijadikan suatu pola prediksi penilaian masyarakat terhadap transfer pemain serta dapat memberikan saran kepada tim sepak bola terkait bursa transfer pemain pada periode musim selanjutnya. Membuat analisis sentiment penggemar sepak bola terhadap transfer pemain Liga Spanyol apakah bersifat positif dan negatif. Metode Naïve Bayes Classifer (NBC) dalam penelitian ini dipilih dikarenakan pada algoritma NBC dapat melakukan proses pengolahan data diskrit dan data kuantitatif dengan menggunakan sampel yang relative sedikit dan juga perhitungan pada algoritma NBC lebih cepat. Pengambilan data berupa topik mengena keyword “Transfer La Liga”, “Transfer Real Madrid”, “Transfer Barcelona”, “Transfer Liga Spanyol” dan “Transfer Copa Del Ray”. Data tweet di ambil dari periode 1 Januari 2020 sampai dengan 31 Mei 2022, dengan jumlah data total 11.282. Pada penelitian telah berhasil mendapatkan akurasi dengan nilai 81,67 % pada data training dan 85 % untuk data testing. Pada penelitian ini berhasil membuat model analisis sentimen berupa file.pickle yang dimana untuk melakukan klasifikasi dan prediksi pada data tweet untuk mendapatkan sebuah hasil sentimen positif dan negative. Penelitian ini telah berhasil mendapatkan akurasi dengan nilai 81,67 % pada data training dan 85 % untuk data testing.Hasil analisis sentimen akhir dalam klasifikasi penelitian ini bernilai “Sentimen Negatif”
Analisis Sentimen Di Media Sosial Twitter Dengan Studi Kasus Vaksinasi Covid-19 Nufia Alfi Rohyana; Aris Wahyu Murdiyanto; Kharisma
INDONESIAN JOURNAL ON DATA SCIENCE Vol 1 No 1 (2023): Indonesian Journal on Data Science
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat Universitas Achmad Yani Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30989/ijds.v1i1.861

Abstract

With the COVID-19 pandemic, the World Health Organization or WHO conducted research and research trials on the COVID-19 vaccine. The Indonesian government has made several policies, one of which is the "Mass Vaccination Program". However, the COVID-19 vaccination program in the field received mixed responses in the community, there were those who supported the vaccine program and some who rejected the vaccine program. In this study, researchers conducted research on sentiment analysis on the opinion of vaccination programs against anti-vaccine community groups based on Twitter social media data using the Naïve Bayes Classifier algorithm to provide information on opinion assessments that lead to positive and negative sentiments. Objective: The purpose of this study is to find out the public perception of AntiVaccine against the COVID-19 Vaccination Program in Indonesia. This study uses the Naïve Bayes Classification. The use of the Naïve Bayes Classifier (NBC). This research uses tweets obtained from Twitter with the keywords/hashtags “Anti Covid-19 Vaccines” or by collecting data based on accounts related to news about vaccination programs such as @ The Ministry of Health of the Republic of Indonesia. Data collection was carried out in the period August 2021-December 2021, with a total of 889 data. This study has succeeded in obtaining an accuracy of 72 % for testing. The result of the final sentiment analysis in the classification of the Anti-Vaccine group in this study is "Negative Sentimen".
SISTEM REKOMENDASI EVALUASI AWAL SITUS WEB SECARA OTOMATIS TERHADAP MESIN PENCARI Aris Wahyu Murdiyanto
Jurnal Teknomatika Vol 10 No 2 (2018): TEKNOMATIKA
Publisher : Fakultas Teknik dan Teknologi Informasi, Universitas Jenderal Achmad Yani Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Evaluasi awal pada situs web (onpage) sangat diperlukan agar situs web search engine friendly, dan diharapkan dapat menempatkan situs web tersebut pada peringkat yang lebih baik pada mesin pencari menggunakan kueri tertentu. Tujuan dari penelitian ini adalah untuk mengembangkan sebuah sistem yang dapat memberikan rekomendasi perbaikan atau evaluasi website kepada pemilik situs web otomatis terhadap mesin pencari berdasarkan panduan Google SEO Starter Guide. Hasil rekomendasi sistem selanjutnya digunakan sebagai pedoman dasar bagi pemilik situs web untuk melakukan evaluasi awal situs web untuk meningkatkan probabilitas kemunculan situs web pada mesin pencari (search engine result page atau SERPs) menggunakan kueri tertentu, dengan tujuan akhir yaitu meningkatnya jumlah pengunjung situs web secara natural dan organik melalui mesin pencari. Pengujian sistem rekomendasi dilakukan pada sepuluh halaman web dengan dimana hasil kesesuaiannya sebesar 100%, serta hasil pengujian sistem rekomendasi evaluasi awal situs web secara otomatis terhadap mesin pencari memberikan hasil dengan tingkat kesesuaian 90%.
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.
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.
Desain User Interface Dan User Experience Prototype Mobile Learning Menggunakan Metode Design Thinking Metode Design Thinking Muhamad Arabi Rizki Angkotasan; Aris Wahyu Murdiyanto; Arif Himawan; Fajar Syahruddin
Jurnal Teknomatika Vol 16 No 2 (2023): 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.v16i2.1254

Abstract

Abstract - Do Up uses the website as online learning. users complain about the accessibility of the website with some minimal features and an unattractive UI when accessed via a smartphone will make the UX limited and will limit user interaction in using Do Up. Designing UI and UX prototypes of mobile learning at startup Do Up, using the design thinking method to solve problems and find the right solution according to the user's wishes. The author applies design thinking in this research. The author makes an illustration in the form of a Do Up mobile learning UI design that is in accordance with user needs and provides the design to Do Up stakeholders. In SEQ there are 4 scales given by users, namely 4.5, 6 and 7 scale. Most users give a 7 scale on the UI/UX design of the Do Up mobile learning prototype. On SUS which shows that the final score is 87 It means that the prototype has been well received by the users. The author has applied design thinking which consists of empathize, define, ideate, prototype and test stages in this study.
Assessing Bagging-meta Estimator in Imbalanced CT Kidney Disease Classification: A Focus on Sobel and Hu Moment Techniques Rudi Setiawan; Andi Maulidinnawati Abdul Kadir Parewe; Asslia Johar Latipah; Nur Rochmah Dyah Puji Astuti; Aris Wahyu Murdiyanto; Fajri Profesio Putra
International Journal of Artificial Intelligence in Medical Issues Vol. 1 No. 2 (2023): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijaimi.v1i2.100

Abstract

This study investigates the efficacy of the Bagging-meta estimator in classifying CT kidney diseases, focusing on an imbalanced dataset processed through Sobel segmentation and Hu moment feature extraction. The research utilized a quantitative approach, applying the Bagging-meta estimator to a dataset comprising CT images classified into four categories: Normal, Cyst, Tumor, and Stone. These images were preprocessed using Sobel segmentation to highlight critical structures and Hu moment feature extraction for robust classification features. The study employed a 5-fold cross-validation method to evaluate the model's performance, assessing metrics such as accuracy, precision, recall, and F1-Score. The results indicated a significant variation in the model's performance across different folds, with accuracy ranging from 49.86% to 66.17%, precision between 51.86% and 65.93%, recall from 57.95% to 64.44%, and F1-Scores spanning 48.26% to 60.74%. These findings suggest that while the Bagging-meta estimator can achieve reasonable accuracy in classifying kidney diseases from CT images, its performance is affected by the imbalanced nature of the dataset. This study contributes to the understanding of the challenges and potential of machine learning in medical imaging, particularly in the context of imbalanced datasets. It highlights the need for specialized approaches to handle such datasets and underscores the importance of preprocessing techniques in enhancing model performance. Future research directions include exploring methods to address data imbalance, investigating alternative feature extraction techniques, and testing the model on diverse datasets to enhance its generalizability and reliability in clinical settings. This research offers valuable insights into the development of automated diagnostic tools in medical imaging and advances the field of computer-aided diagnosis in nephrology.
Co-Authors Adri Priadana Adri Priadana Agung Purwanto Soedarbe Ahmad Adita Shiddiq Alfun Roehatul Jannah Andi Maulidinnawati Abdul Kadir Parewe Arif Himawan Arif Himawan Dimas Pratama Jati Fajar Syahruddin Fajri Profesio Putra Iqbal Hadi Subekti Kharisma Latipah, Asslia Johar M. Abu Amar Al Badawi Muhamad Arabi Rizki Angkotasan Muhammad Habibi Muhammad Rifqi Ma'arif Nafisa Alfi Sa'diya Nufia Alfi Rohyana Nur Rochmah Dyah Pujiastuti Nurul Fatimah Risky Setyadi Putra Setiawan, Rudi