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All Journal Jurnal Pekommas Telematika : Jurnal Informatika dan Teknologi Informasi Jurnal Teknologi dan Sistem Komputer Compiler Journal of Dedicators Community Jurnal Penelitian Pers dan Komunikasi Pembangunan JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal Berdaya Mandiri Indonesian Journal of Data and Science Jurnal Abdimas Kartika Wijayakusuma PELS (Procedia of Engineering and Life Science) 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
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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.
Performance Analysis of the Decision Tree Classification Algorithm on the Water Quality and Potability Dataset Zaky, Umar; Naswin, Ahmad; Sumiyatun, Sumiyatun; Murdiyanto, Aris Wahyu
Indonesian Journal of Data and Science Vol. 4 No. 3 (2023): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v4i3.113

Abstract

Ensuring water potability is paramount for public health and safety. This research aimed to assess the efficacy of the Decision Tree classification algorithm in predicting water potability using the Water Quality and Potability dataset. Employing a 5-fold cross-validation technique, the model showcased a moderate performance with an average accuracy of approximately 54.33%. While the Decision Tree provides a baseline and interpretable mechanism for classification, the results emphasize the need for further exploration using more intricate models or ensemble methods. This study contributes to the broader effort of leveraging machine learning techniques for water quality assessment and provides insights into the potential and limitations of such models in predicting water safety
Assessing the Predictive Power of Logistic Regression on Liver Disease Prevalence in the Indian Context Alwiah, Izmy; Zaky, Umar; Murdiyanto, Aris Wahyu
Indonesian Journal of Data and Science Vol. 5 No. 1 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i1.121

Abstract

This study delves into the application of Logistic Regression through a Voting Classifier to predict liver disease prevalence within the Indian demographic, specifically analyzing data from the NorthEast of Andhra Pradesh. Employing a dataset encompassing 584 patient records, the research utilizes a 5-fold cross-validation approach to evaluate the model's performance across accuracy, precision, recall, and F1-Score metrics. The findings reveal accuracy rates ranging from 69.23% to 74.14%, with variable precision and recall, indicating a promising yet improvable predictive capability of the model. The study significantly contributes to the existing body of knowledge by demonstrating the potential of Logistic Regression in medical diagnostics, especially in the context of liver disease, and highlighting the critical role of machine learning models in enhancing diagnostic processes. Through a detailed discussion, the research aligns with previous studies on the efficacy of machine learning in healthcare, advocating for the integration of more comprehensive data and suggesting further exploration into the model's applicability across diverse populations. The study's implications extend to healthcare professionals and policymakers, underscoring the necessity for advanced diagnostic tools in the early detection of liver diseases.
PENERAPAN TEKNOLOGI MESIN MIXER UNTUK MENINGKATKAN KUANTITAS DAN KUALITAS PRODUK PUPUK KOMPOS DI GAPOKTAN NGUDI MAKMUR, BANJARARUM, KULON PROGO Edhy Sutanta; Suparni Setyowati Rahayu; Raden Wisnu Nurcahyo; Samuel Kristiyana; Aris Wahyu Murdiyanto; Mukasi Wahyu Kurniawati
Jurnal Berdaya Mandiri Vol. 6 No. 1 (2024): JURNAL BERDAYA MANDIRI (JBM)
Publisher : Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/jbm.v6i1.6074

Abstract

The Ngudi Makmur Farmer Group (Gapoktan) is one of the productive economic groups located in Banjararum Village, Kulon Progo, Yogyakarta. Gapoktan Ngudi Makmur faces several issues, some of are deemed important and need to be resolved. The mentioned issues include environmental pollution risks and unpleasant odors arising from piles of livestock manure. Livestock manure is generally processed into compost fertilizer to nourish crops managed by the group. The process of turning livestock manure into compost fertilizer is done with the hand tool, resulted in relatively low production quantity and quality due to non-standard/uniform compost grain sizes. This Community Service Activity (PkM) is part of Kosabangsa Program implementation in 2023. The activity is carried out by a combined team from 2 (two) Higher Education Institutions (PT), namely Universitas Jenderal Achmad Yani Yogyakarta (UNJAYA), and Universitas AKPRIND Indonesia. This activity is conducted through 5 (five) stages which is: socialization, training, technology application, mentoring, as well as program evaluation and sustainability. The total duration of the activity is 4 (four) months. The appropriate technology (TTG) applied is a compost mixer machine. Based on the evaluation results after the activity, there was an increase in the quantity of compost production by 16.67% and an improvement in quality of compost with standard/uniform grain sizes. Keywords: mixer machine, quality, quantity, compost, appropriate technology
Co-Authors -, Purnawan Adri Priadana Adri Priadana Agung Purwanto Soedarbe Agung Satria Panca Ahmad Adita Shiddiq Ahmad Adita Shiddiq Ahmad Hanafi Ahmad Hanafi Alfun Roehatul Jannah Alfun Roehatul Jannah Almayanti Susillia Ningrum Alwiah, Izmy Angkotasan, Muhamad Arabi Rizki Arbintarso, Ellyawan Setyo Arif Himawan Arif Himawan Arif Himawan, Arif Aulia Puji Rahayu Bara Falah Adikaputra Catur Iswahyudi David Sulistiyantoro David Sulistiyantoro, David Sulistiyantoro Dewi, Tika Sari Dian Hafidh Zulfikar Dimas Pratama Jati Edhy Sutanta (Jurusan Teknik Informatika IST AKPRIND Yogyakarta) Fitriatul Hasanah Gerlan Haha Nusa Gilang Argya Dyaksa Haha Nusa, Gerlan Hamada Zein Hariyanto, Satriawan Dini Ida Ristiana Iqbal Hadi Subekti Iqbal Hadi Subekti Kadir Parewe, Andi Maulidinnawati Abdul Kharisma Kharisma Kusumaningtyas, Kartikadyota Latipah, Asslia Johar M. Abu Amar Al Badawi Marausna, Gaguk Muhammad Habibi Muhammad Habibi Muhammad Luqman Bukhori Muhammad Rifqi Ma'arif Mukasi Wahyu Kurniawati Nafisa Alfi Sa'diya Naswin, Ahmad Nufia Alfi Rohyana Nufia Alfi Rohyana Nurcahyo, Raden Wisnu Nurul Fatimah Poetro, Bagus Satrio Waluyo Prasetiyo, Erwan Eko Puji Astuti, Nur Rochmah Dyah Purbobinuko, Zakharias Kurnia Purnawan Purnawan Putra, Fajri Profesio Putra, Ikbal Rizki Raden Wisnu Nurcahyo Risky Setyadi Putra Rosid, Ibnu Abdul Rudi Setiawan Samuel Kristiyana Septiyati Purwandari Siregar, Alda Cendekia Sisilia Endah Lestari, Sisilia Endah Sugeng Santoso Sumiyatun Suparni Setyowati Rahayu Surya Rizki Syahruddin, Fajar Tarigan, Thomas Edyson Umar Zaky Yulianto Prabowo, Fajar Zennul Mubarrok, Zennul Mubarrok