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Gata, Windu
STMIK Nusa Mandiri Jakarta
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Indonesian Government Revenue Prediction Using Long Short-Term Memory Mahmud; Windu Gata; Hafifah Bella Novitasari; Sigit Kurniawan; Dedi Dwi Saputra
Inspiration: Jurnal Teknologi Informasi dan Komunikasi Vol. 14 No. 1 (2024): Inspiration: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Pusat Penelitian dan Pengabdian Pada Masyarakat Sekolah Tinggi Manajemen Informatika dan Komputer AKBA Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35585/inspir.v14i1.67

Abstract

Government revenue plays an important role in achieving national development goals. In the context of optimal state treasury management, accurate forecasts of government revenue are needed so that cash can be utilized optimally for the coming period. This study examines the appropriate method for predicting government revenue based on historical data from 2013 to 2022. It proposes applying the Long Short-Term Memory (LSTM) model for this purpose. Experiments show that the LSTM model, using two hidden layers and the right hyperparameters, can produce a Mean Absolute Percentage Error (MAPE) of 11.14% and a Root Mean Square Error (RMSE) of 15.43%. These results are better than those obtained using conventional modeling techniques such as Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA). The findings indicate that the LSTM model offers superior predictive accuracy and can significantly improve the management of government finances. By implementing this advanced predictive model, policymakers can make more informed decisions, enhancing the efficiency of resource allocation and contributing to the overall economic stability of the nation.
Implementasi Algoritma BERT Pada Komentar Layanan Akademik dan Non Akademik Universitas Terbuka di Media Sosial Fatmasari, Rhini; Septiani, Riska Kurnia; Pinem, Tuahta Hasiolan; Fabiyanto, Dedik; Gata, Windu
Sains, Aplikasi, Komputasi dan Teknologi Informasi Vol 5, No 2 (2023): Sains, Aplikasi, Komputasi dan Teknologi Informasi
Publisher : Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jsakti.v5i2.13915

Abstract

Media sosial TikTok dan Twitter (X) merupakan dua media sosial yang memiliki banyak pengguna di Indonesia. Berdasarkan lembaga survei We Are Social pengguna TikTok di Indonesia mencapai 109.9 juta sedangkan pengguna Twitter (X) di Indonesia mencapai 24 juta. Media sosial TikTok dan Twitter (X) seringkali menjadi tempat untuk menyampaikan pendapat atau komentar terhadap suatu hal. Universitas Terbuka merupakan suatu kampus yang memiliki media sosial TikTok dan Twitter (X) dengan ribuan pengikut. Penelitian ini dilakukan untuk mengetahui tingkat layanan bidang akademik dan non-akademik Universitas Terbuka. Data yang dianalisis sebanyak 685 data komentar pada media sosial TikTok dan Twitter (X) dengan kata kunci Universitas Terbuka. Metode yang digunakan adalah analisis menggunakan model pre-trained BERT. Pada model ini diperoleh nilai akurasi sebesar 90% dengan proporsi data latih dan data uji 80:20.
PELATIHAN PEMANFAATAN TEKNOLOGI CHATGPT UNTUK SURAT MENYURAT KADER PKK DESA CIMULANG BOGOR Marlinda, Linda; Gata, Windu; Tutupoly, Taransa Agasya
Jurnal AbdiMas Nusa Mandiri Vol. 6 No. 1 (2024): Periode April 2024
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/abdimas.v6i1.5514

Abstract

The use of technology is the key to increasing effectiveness and efficiency in communication. One technology that is currently developing is chatbot, a computer program that can interact with humans via chat or text messages. Chatbots can speed up the flow of communication, simplify information retrieval, and increase the level of community involvement in various activities. Training in the use of ChatGPT Technology for writing correspondence is important considering the need for efficiency and quality in written communication, especially for Village PKK cadres. This training aims to improve cadres' ability to use ChatGPT technology to compose correspondence more effectively and efficiently. With this training, several significant outcomes were achieved. First, there will be an increase in productivity in the preparation of official and informal letters by Village PKK cadres, because the use of ChatGPT technology will speed up the process of writing and sending letters. Second, it can improve the quality of cadres' written communication, so that the message conveyed can be more clearly and effectively understood by the recipient. Lastly, the use of ChatGPT Technology can expand the scope of messages delivered to the people of Cimulang Village, Bogor, so that important information and education can be broader and more easily accessible to all residents. This training not only supports the efficiency and quality of communication but also strengthens relations between cadres and society as a whole.
Klasifikasi Sentimen Terhadap Kualitas Aplikasi Bahan Ajar Digital Akademik Universitas Terbuka di Google Play Fatmasari, Rhini; Gata, Windu; Kusuma Wardhani, Nia; Prayogi, Kurnia; Binti Husna, Modesta
Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Vol 14 No 1 (2024): Maret 2024
Publisher : STMIK Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33020/saintekom.v14i1.591

Abstract

Terbuka University is a leading institution that implements the optimization of digital transformation, especially in distance learning systems. To improve the quality of service to students and stakeholders, Terbuka University has developed the Terbuka University Digital Learning Materials application. This application offers several learning modules that can be accessed through the Google Play Store. This research aims to classify data using different labels related to reviews of the Terbuka University Digital Learning Materials application using the Long Short-Term Memory classification algorithm. Evaluation is conducted to find accuracy, f1-score, precision, and recall values. The research results show that classification with Long Short-Term Memory achieves an accuracy of 76.72% with the Vader label, and the accuracy with the TextBlob label reaches 74.21%. Confusion matrix evaluation shows precision results of 0.91 and recall of 0.78, with an f1-score of 0.84 for the Vader label. For the TextBlob label, the precision is 0.96, recall is 0.45, and the f1-score is 0.61. This research contributes positively to understanding the evaluation and classification of reviews of the Terbuka University Digital Learning application. Implementing the Long Short-Term Memory algorithm with the Vader label can be an effective choice to improve service and learning quality through the application.
TWITTER SENTIMENT ANALYSIS OF POST NATURAL DISASTERS USING COMPARATIVE CLASSIFICATION ALGORITHM SUPPORT VECTOR MACHINE AND NAÏVE BAYES Zumarniansyah, Ainun; Pebrianto, Rangga; Normah, Normah; Gata, Windu
Jurnal Pilar Nusa Mandiri Vol 16 No 2 (2020): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v16i2.1423

Abstract

Natural disasters trigger people, especially Twitter users to provide information or opinions in the form of tweets. The Tweet can be an expression of sadness, concern, or complaint. Processing of data from these tweets will create trends that can be used for information needs such as education, economics, and others. Natural disasters are events that threaten human life caused by nature, including in the form of earthquakes. The method used is the Support Vector Machine and Naive Bayes from the tweet. The data collected is filtered from tweets by deleting duplicate data. In calculating the Natural Disaster sentiment analysis using a comparison of the Support Vector Machine and the Naive Bayes algorithm, the difference in accuracy is 3.07% where the results of the Support Vector Machine are greater than Naive Bayes. The purpose of this research is to analyze sentiment for the distribution of disaster aid that does not flow information due to information & coordination in the field. so as to provide information on the location of natural disasters, natural disaster management, and its presentation to victims that can be shared evenly in an efficient time due to information and natural management so that the distribution of aid is hampered
ANALYSIS OF INTER-RELIGIOUS TOLERANCE SENTIMENTS IN INDONESIA ON CONVERSATIONS ON SOCIAL MEDIA TWITTER Pribadi, Yogie; Hafidz, Noor; Nuryamin, Yamin; Gata, Windu
Jurnal Pilar Nusa Mandiri Vol 16 No 2 (2020): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v16i2.1520

Abstract

Conversations on social media Twitter related to tolerance among religious communities in Indonesia are fascinating. However, it is a sensitive issue. In reality, there is often a war of comments about the implementation of tolerance between religious people in carrying out their own beliefs. The community is not careful in issuing opinions that can result in social insecurity, insecurity, and national instability. This condition will significantly affect the state of the country's economy. In some cases, political problems can be a trigger for intolerance between religious communities. The purpose of this study is to compare the performance of classification accuracy on positive or negative sentiments from conversations that intersect with the problem of tolerance among religious communities during the past year. In this study, we compared the performance of the accuracy of the modeling of sentiment analysis classification on public conversations on social media Twitter related to tolerance between religious communities in Indonesia. Because the text that will be carried out modeling comes from the Indonesian language, to facilitate labeling, translation is carried out into English, then a performance comparison of the sentiment analysis classification modeling with SVM algorithm, Naïve Bayes, Decision Tree, and k-NN. Based on the experiments, it was concluded that the SVM algorithm has the highest performance for the classification of sentiment analysis categories up to 65.03% compared to the Naïve Bayes algorithm, which reached 59.92%, Decision Tree, which reached 63.52% and k-NN which reached 57 66%.
DECISION SUPPORT OF CONTRACT EMPLOYEE PERFORMANCE ASSESSMENT USING SAW METHOD AT PT. AEROFOOD ACS Rahman, Fathur; Syarifa, Naf'a; Hendri, Hendri; Novitasari, Hafifah Bella; Gata, Windu
Jurnal Pilar Nusa Mandiri Vol 17 No 1 (2021): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v17i1.2034

Abstract

Pengelolaan SDM dari suatu perusahaan sangat mempengaruhi banyak aspek penentu keberhasilan kinerja perusahaan tersebut. Jika SDM dapat di organisir dengan baik, maka diharapkan perusahaan dapat menjalankan semua proses bisnisnya dengan baik. Oleh karena hal tersebut, PT. Aerofood ACS yang memiliki banyak karyawan kontrak, perlu adanya penilaian kinerja karyawan dalam menentukan perpanjangan kontrak. Peran sistem pendukung keputusan sangat dibutuhkan guna meningkatkan efisiensi pengambilan keputusan. Dalam hal ini membantu pihak manajemen dalam mencapai tujuan dari penilaian kinerja karyawan kontrak melalui parameter-parameter yang sudah ditentukan oleh pihak perusahaan tersebut, diantaranya Discipline, Integrity, Achievement Orientation, Continnous Learning, Continunous Improvement, Quality Orientation, Customer Service Orientation, dan Teamwork. Untuk mencari solusi dalam menyelesaikan masalah tersebut, metode dalam Sistem Pengambilan Keputusan yang digunakan yaitu dengan metode Simple Additive Weighting (SAW). Semua parameter yang dinyatakan mempunyai pengaruh penting dalam penetapan alternatif keputusan terbaik dalam menentukan perpanjangan kontrak karyawan.
SENTIMENT ANALYSIS OF INDONESIAN COMMUNITY ON COVID-19 VACCINATION ON TWITTER SOCIAL MEDIA Nurmalasari, Nurmalasari; Astuti, Widi; Gata, Windu; Zuniarti, Ida
Jurnal Pilar Nusa Mandiri Vol 18 No 2 (2022): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v18i2.3820

Abstract

In the process, data mining will extract valuable information by analyzing the existence of specific patterns or relationships from extensive data. One of the concerns of the new disease outbreak caused by the coronavirus (2019-nCoV) or commonly referred to as Covid-19, was officially designated as a global pandemic by the World Health Organization (WFO) on March 11, 2020. To break the transmission of Covid-19, the government carried out vaccinations for the Indonesian population. In the first period, the vaccination target will be for health workers with a total of 1.3 million people, public officers with 17.4 million people, and 21.5 million people. 19. The Data processed is only text data from Twitter application reviews that use Indonesian. Using the polarity of the Sentiment class Textblob, the sentiment class is positive, negative, and neutral. The data mining used is SVM, Naive Bayes, and Logistic Regression. As for this research in the form of knowledge of sentiment in the community towards vaccination activities, the results of this study get 43% positive sentiment, 40.8% negative, and 16.2% negative by testing the classification algorithm, Logistic Regression accuracy of 87%, SVM 86, 4%, and Naive Bayes, 40% of these results, can be seen that the Indonesian people have a positive sentiment towards the covid-19 vaccine.
Penerapan Algoritma Random Forest Untuk Menentukan Kualitas Anggur Merah Supriyadi, Riki; Gata, Windu; Maulidah, Nurlaelatul; Fauzi, Ahmad
E-Bisnis : Jurnal Ilmiah Ekonomi dan Bisnis Vol 13 No 2 (2020): Jurnal Ilmiah Ekonomi dan Bisnis
Publisher : STEKOM PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/e-bisnis.v13i2.247

Abstract

Abstract In this study that was used as the object of research in classifying red wine based on the quality influenced by each red wine or red wine based on the content of each type of wine, from each attribute containing the composition in the wine seen which attributes most affect the quality of red wine, so that it will be known ingridents that can improve the quality of the wine, in this study was carried out by the application of Machine learning by comparing three algorithms of mining data that is , Decission Tree, Random Forest and Support Vector Machine (SVM), from the results of research that has been done by comparing the three algorithms, Random Forest produced the best accuracy among other algorithms that have been tested. Random Forest with accuracy results of 0.7468 makes this algorithm best used to classify the quality of red wine. And in the second order Decission Tree with accuracy results of 0.7031, while Support Vector Machine (SVM) get an accuracy result of 0.65. So in the research that has been done to classify the quality of red wine based on its composition Random Forest becomes the best algorithm to use..
ALGORITMA KLASIFIKASI DECISION TREE UNTUK REKOMENDASI BUKU BERDASARKAN KATEGORI BUKU Maulidah, Mawadatul; Windu Gata; Rizki Aulianita; Cucu Ika Agustyaningrum
E-Bisnis : Jurnal Ilmiah Ekonomi dan Bisnis Vol 13 No 2 (2020): Jurnal Ilmiah Ekonomi dan Bisnis
Publisher : STEKOM PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/e-bisnis.v13i2.251

Abstract

With the increasing development of technology the more variety of books circulating on the internet. As is the recommendation system on online book sites that provide books relevantly and as needed with one's preferences. One alternative is GoodReads, a social networking site that specializes in cataloging books and users can share reading book recommendations with each other by rating, reviewing, and commenting. As a large book recommendation site, it has a lot of data that can be processed by applying machine learning methods, but still not known as the most accurate model. By using the right model, we can provide more accurate recommendations. Therefore, this study will analyze the data obtained from the www.kaggle.com namely the goodreads-books dataset. This study proposed a data mining classification model to get the best model in recommending books on GoodReads. The algorithms used are Decision Tree, K-Nearest Neighbor, Naïve Bayes, Random Forest, and Support Vector Classifier, then for model evaluation using accuracy, precision, recall, f1-score, confusion matrix, AUC, and Mean Error Absolute. The test results of several classification algorithms found that Decision Tree has the highest accuracy among the methods presented by 99.95%, precision by 100%, recall by 96%, f1-score of 98% with MAE of 0.05 and AUC of 99.96%. This is proof that decision tree algorithms can be used as book recommendations based on book categories on GoodReads.
Co-Authors Achmad Bayhaqy Ade Priyatna Aditya Adiguna Agung Sudrajat Agustiani, Sarifah Ahmad Bayhaqi Ali Ahmad Alizah, Muhammad Dwison Angga Ardiansyah Angga Ardiansyah - Anton . Ardiansyah Ardiansyah Ari Abdilah Ari Saputro Arif Budiarto Arifin Nugroho Awalloedin, Niki Badariatul Lailiah Balla, Imanuel Basri Basri Bayhaqy, Achmad Binti Husna, Modesta Bobby Suryo Prakoso Cucu Ika Agustyaningrum Daniati Uki Eka Saputri Dany Pratmanto Dedi Dwi Saputra Deny Robyanto Destiana Putri Diantika, Sri Dika Putri Metalica Dinar Ismunandar DWI SURYANTO Dwiza Riana Dwiza Riana Eko Supriyanto Elah Nurlelah Ellis Ermawati Eni Heni Hermaliani Eni Heni Hermaliani, Eni Heni F Lia Dwi Cahyanti Fabiyanto, Dedik Fahrul Rozi Faisal, Anas Fajar Sarasati Fathur Rahman Fatiha, Zulfati Dinul Fauzi Ahmad Muda Fauzi, Ahmad Ferda Ernawan Firmansyah, Maman Fitra Septia Nugraha Franseda, Afrilio Frieyadie Fuad Nur Hasan Grace Gata, Grace Hafez Aditya Hafidz, Noor Hafifah Bella Novitasari Hakim, Valianda Hamdan Hari Prasetyo, Basuki Harianto, Sony Harsono, Muhammad Luthfiy Kurniawan Hasan, Rosmani Hendra Budi Kusnawan HENDRA SETIAWAN Hendra Setiawan Hendri Hendri Hiya Nalatissifa iboy, rahmat satria buana Ida Ayu Putu Sri Widnyani Ida Zuniarti, Ida Imam Budiawan Ismaya, Fikri Jamil, Muh. Jordy Lasmana Putra Kadafi, Abdul Rahman Kartika Handayani Ketut Sakho Parthama Kholifah, Desiana Nur Khuluq, Anjahul Krisnandi, Dwi Kurniawan, Triadi Kusuma, Muhammad Romadhona Laela Kurniawati Laela Kurniawati, Laela Lawa Rizky, Joy Linda Marlinda Lindung Parningotan Manik M Ardiansyah M. Iqbal Alifudin M. Rangga Ramadhan Saelan Maghfiroh Maulani Manik, Lindung Parningotan Maria Irmina Prasetiyowati Maria Irmina Prasetiyowati Marta Dinata, Riadi Mawadatul Maulidah Mayangky, Nissa Almira Muchamad Bachram Shidiq Muchamad Bachram Shidiq Mudinillah, Adam Muhamad Azhar, Muhamad Muhammad Anif Muhammad Anif Muhammad Dwison Alizah Muhammad Haris MUHAMMAD HARIS Muhammad Rifqi Firdaus Mulyani, Astriana Nadiyah Hidayati Nawawi, Hendri Mahmud Nia Kusuma Wardhani Nia Kusuma Wardhani, Nia Kusuma Nida Umi Latifah Nila Hardi Nita Merlina Nita Merlina, Nita Noor, Mohamad Normah Normah, Normah Novitasari, Hafifah Bella Nufus, Fina Sifaul Nugraha, Fitra Septia Nugraha, Ranu Agastya Nugroho, Arifin Nurajijah Nurhasanah, Fitri Yani Nurlaelatul Maulidah Nurlaelatul Maulidah Nurmalasari Nurmalasari Nurul Qomariyah Panggabean, Supriadi Pinem, Tuahta Hasiolan Popon Handayani Prasetya, Arfhan Pratiwi, Risca Lusiana Prayogi, Kurnia Pribadi, Yogie Purnomo, Niko Putra, Septian Ade Rabiatus Sa’adah Rachmaliya Joi, Suciaty Rahayu, Cicih Sri Rangga Pebrianto Rhini Fatmasari, Rhini Rian Ardianto Ricko Anugrah Mulya Pratama Ridan Nurfalah Ridwan Muhammad Riefky Sungkar Riki Supriyadi Risnandar, Risnandar Ristyani Slamet rivan, almay faiz Rivanie, Tri Rizki Aulianita Rizky, Joy Lawa Rizmayanti, Ade Irma Romadhona Kusuma, Muhammad Ronny Tanjung Rousyati, Rousyati Rudianto, Biktra S Siswanto Salsabila, Nurul Jannah Saputra, Dedi Dwi Saputra, Surya Fajar Saragih, Gabriel Vangeran Septiani, Riska Kurnia Sidik Sidik Sigit Kurniawan Sigit Kurniawan Simatupang, Lamria Siswanto Siswanto, Siswanto Sita Anggraeni, Sita Siti Helmyati Siti Khotimatul Wildah SRI RAHAYU STMIK, Author Super Sukmawati Anggraeni Putri, Sukmawati Anggraeni Sulaeman, Okky Robiana Sulistyowati, Daning Nur Supriadi Panggabean Sutrisno Wanda, Sulistianto Syarifa, Naf'a SYUAIB, SYUAIB Taopik Hidayat Taufan, Resi Taufik Asra Thira, Indra Jiwana Tika Adilah M Tri Rivanie Trihardo, Rendra Triyanto, M.Kom., Toeko Triyanto, Toeko Tutopoli, Taranza Tutupoly, Taransa Agasya Ummu Radiyah Ummu Radiyah, Ummu Verra Sofica Waeisul Bismi warjiyono Wawan Kurniawan Wawan Kurniawan Widi Astuti Yamin Nuryamin YANTO YANTO Yuliazmi, Yuliazmi Yuris Alkahfi Yuris Alkhalifi