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All Journal Jurnal Studi Manajemen Organisasi Telematika : Jurnal Informatika dan Teknologi Informasi Jiko (Jurnal Informatika dan komputer) International Journal of Advances in Data and Information Systems Journal of Advanced in Information and Industrial Technology (JAIIT) Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika SKANIKA: Sistem Komputer dan Teknik Informatika Nusantara Science and Technology Proceedings Mitra Akademia: Jurnal Pengabdian Masyarakat JoMMiT : Jurnal Multi Media dan IT International Journal of Data Science, Engineering, and Analytics (IJDASEA) Jurnal Abdimas Kartika Wijayakusuma International Journal Of Computer, Network Security and Information System (IJCONSIST) ALINIER: Journal of Artificial Intelligence & Applications Journal of International Conference Proceedings Jurnal Pengabdian Masyarakat Bangsa Software Development Digital Business Intelligence and Computer Engineering Jurnal ilmiah teknologi informasi Asia Jurnal Aplikasi Sains Data
Fahrudin, Tresna Maulana
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ANALISIS SPEECH-TO-TEXT PADA VIDEO MENGANDUNG KATA KASAR DAN UJARAN KEBENCIAN DALAM CERAMAH AGAMA ISLAM MENGGUNAKAN INTERPRETASI AUDIENS DAN VISUALISASI WORD CLOUD Fahrudin, Tresna Maulana; Sari, Allan Ruhui Fatmah; Lisanthoni, Angela; Lestari, Amanda Ayu Dewi
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 5 No 2 (2022): Jurnal SKANIKA Juli 2022
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1170.948 KB) | DOI: 10.36080/skanika.v5i2.2942

Abstract

Di era revolusi industri 4.0 saat ini, penggunaan media sosial sangat berkembang pesat dengan terjadinya interaksi dan komunikasi antarmanusia dalam dunia maya. Namun, terkadang ditemui adanya pengguna media sosial yang menyalahgunakan untuk kepentingan tertentu, salah satunya ceramah agama yang mengandung kata-kata kasar dan ujaran kebencian. Semakin banyak kekeliruan dalam memahami agama dikarenakan apa yang disampaikan oleh penceramah bukanlah tentang agama itu sendiri, tetapi justru menghasut, menghina dan memprovokasi para pendengarnya untuk tujuan tertentu. Oleh karena itu, penelitian ini mengusulkan analisis speech-to-text pada video yang mengandung kata-kata kasar dan ujaran kebencian dalam ceramah agama islam menggunakan interpretasi audiens dan visualisasi word cloud. Hasil penelitian menunjukkan bahwa sebanyak 3 penceramah agama dan total terdapat 9 video di mana masing-masing video berdurasi 3 menit mengandung kata-kata kasar dan ujaran kebencian.
Daily Forecasting for Antam's Certified Gold Bullion Prices in 2018-2020 using Polynomial Regression and Double Exponential Smoothing Fahrudin, Tresna Maulana; Riyantoko, Prismahardi Aji; Hindrayani, Kartika Maulida; Diyasa, I Gede Susrama Mas
Journal of International Conference Proceedings Vol 3, No 4 (2020): Proceedings of the 8th International Conference of Project Management (ICPM) Mal
Publisher : AIBPM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32535/jicp.v3i4.1009

Abstract

Gold investment is currently a trend in society, especially the millennial generation. Gold investment for the younger generation is an advantage for the future. Gold bullion is often used as a promising investment, on other hand, the digital gold is available which it is stored online on the gold trading platform. However, any investment certainly has risks, and the price of gold bullion fluctuates from day to day. People who invest in gold hopes to benefit from the initial purchase price even if they must wait up to five years. The problem is how they can notice the best time to sell and buy gold. Therefore, this research proposes a forecasting approach based on time series data and the selling of gold bullion prices per gram in Indonesia. The experiment reported that Holt’s double exponential smoothing provided better forecasting performance than polynomial regression. Holt’s double exponential smoothing reached the minimum of Mean Absolute Percentage Error (MAPE) 0.056% in the training set, 0.047% in one-step testing, and 0.898% in multi-step testing.
Hybrid Holt Winter-Prophet method to forecast the num-ber of foreign tourist arrivals through Bali's Ngurah Rai Airport Damaliana, Aviolla Terza; Hindrayani , Kartika Maulida; Fahrudin, Tresna Maulana
IJDASEA (International Journal of Data Science, Engineering, and Analytics) Vol. 3 No. 2 (2023): International Journal of Data Science, Engineering, and Analytics Vol 3, No 2,
Publisher : Universitas Pembangunan Nasional Veteran Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijdasea.v3i2.8

Abstract

The Indonesian is an archipelago rich in culture and natural resources. The Government of Indonesia utilizes this wealth by maximizing the tourism potential to earn sizeable foreign exchange. As a major destination, the Indonesian government needs a strategy to ensure foreign tourists continue to increase in terms of health, cleanliness, a sustainable environment and infrastructure. When we can forecast the number of foreign tourists, it is hoped that the government can establish appropriate policies to develop tourism. Based on this, an appropriate forecasting method is needed. This study will use a hybrid model with the Holt-Winter and the Prophet method. The data used is the number of foreign tourists to Bali through Ngurah Rai Airport from January 2009 to December 2019. This study will use stages based on the OSEMN Framework. These stages are Obtain, Scrub, Explore, Model, and Interpret. The result of this study is that the MAPE value for the Hybrid Method is 2.5880%. This result means the Hybrid Holt Winter-Prophet is better than the Holt Winter Method
Social Media Analysis and Topic Modeling: Case Study of Stunting in Indonesia Muhaimin, Amri; Fahrudin, Tresna Maulana; Alamiyah, Syifa Syarifah; Arviani, Heidy; Kusuma, Ade; Sari, Allan Ruhui Fatmah; Lisanthoni, Angela
Telematika Vol 20, No 3 (2023): Edisi Oktober 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i3.10797

Abstract

Purpose: Stunting is a problem that currently requires special attention in Indonesia. The stunting rate in 2022 will drop to 21.6%, and for the future, the government has set a target of up to 14% in 2024. Rapid technological developments and freedom of expression on the internet produce review text data that can be analyzed for evaluation. This study analyzes the text data of Twitter users' reviews on stunting. The method used is a text-mining approach and topic modeling based on Latent Dirichlet Allocation.Design/methodology/approach: The methodology used in this study is Latent Dirichlet Allocation. The data was collected from twitter with the keyword 'stunting'. After, the data was cleaned and then modeled using the Latent Dirichlet Allocation.Findings/results: The results show that negative sentiment dominates by 60.6%, positive sentiment by 31.5%, and neutral by 7.9%. In addition, this research shows that 'children', 'decrease', 'number', 'prevention', and 'nutrition' are among the words that often appear on stunting.Originality/value/state of the art: This study uses the keyword stunting and analyzes it. Social media analytics show that the people of Indonesia are primarily aware of stunting. Also, the Latent Dirichlet Analysis can be used to create the model.
Sentiment Analysis in Social Media: Case Study in Indonesia Muhaimin, Amri; Fahrudin, Tresna Maulana; Alamiyah, Syifa Syarifah; Arviani, Heidy; Kusuma, Ade; Sari, Allan Ruhui Fatmah; Lisanthoni, Angela
Nusantara Science and Technology Proceedings 8th International Seminar of Research Month 2023
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2024.4106

Abstract

Stunting is a problem that currently requires special attention in Indonesia. The stunting rate in 2022 will drop to 21.6% and for the future, the government has set a target of up to 14% in 2024. There have been many government efforts in implementing programs to reduce stunting rates. However, not everything runs optimally. Rapid technological developments and freedom of expression in the internet world produce review text data that can be analyzed for evaluation. This study aims to analyze the text data of Twitter users' reviews on stunting. The method used is a text-mining approach and topic modeling based on Latent Dirichlet Allocation (LDA). The results show that negative sentiment dominates by 60.6%, positive sentiment by 31.5%, and neutral by 7.9%. In addition, this research shows that 'anak', 'turun', 'angka', 'cegah' and 'gizi' are among the words that often appear on the topic of stunting.
Bibliometric Analysis and Literature Review of Big Data Research Fields using Publish or Perish and VOSviewer Fahrudin, Tresna Maulana
Nusantara Science and Technology Proceedings 8th International Seminar of Research Month 2023
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2024.4140

Abstract

The evolution of data develops from homogeneous data to heterogeneous data, resulting in the emergence of structured, semi-structured, and unstructured data. The current trend in research methods is not only based on qualitative methods but is shifting towards quantitative with the spread of social media data which can be analyzed using Big Data technology. Explore Big Data ideas and research requires supporting software to collect bibliography data and literature review. Therefore, the research implements bibliometric analysis and literature review in Big Data fields using Publish or Perish to collect bibliography data and using VOSviewer to visualize bibliometric networks. The results of research using keywords containing the term Big Data and Hadoop software produced around 200 titles of scientific articles from the Scopus Journal to produce bibliography data visualizations based on network visualization, overlay visualization, and density visualization. Network visualization displays Big Data research clusters in the fields of data mining, health, education, data security, sentiment analysis, spatial temporal, machine learning, and blockchain. Literature reviews are carried out by identifying problem objects, data samples, methods, and tools, as well as evaluating of the scientific articles collected. In future research, further bibliometric analysis is needed using data from scientific article authors and looking at the author's connection to their research field.
Design and Implementation of a Database and RBAC for Scientific and Competency Mapping Web-based Information System for Lecturers at UPN “Veteran” Jawa Timur Fahrudin, Tresna Maulana; Al Makruf, Achmad Yusuf
Nusantara Science and Technology Proceedings 8th International Seminar of Research Month 2023
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2024.4148

Abstract

Scientific and competency mapping web-based information system aims to address several relevant issues within the context of the Tri Dharma framework in higher education, specifically at UPN "Veteran" Jawa Timur. One of the primary issues is how to review and evaluate the implementation of Tri Dharma, especially in the areas of Education and Teaching, Research, Community Service, and publications, in alignment with the parent-subfields of study and competencies mapping in each study program. Therefore, the research proposes to create an innovative solution that can review and evaluate the alignment of implementation of Tri Dharma in higher education. The research developed a database and Role Base Access Control (RBAC) for web-based information system that lecturers can input education and teaching, research, community service activities, and publications based on the available academic fields, while also helping to identify lecturer expertise according to the knowledge mapping in their respective study programs through a visualization dashboard. The development plan for this application begins with the planning phase, including analyze the system's business processes to understand the system needs of users, designing the necessary features, completing master data such as a list of parent field of study and subfield of study for each study program. This is essential for obtaining an initial understanding of the data structure for the database design. After the database design has been completed, the next step is to implement the database design into an actual database structure using MySQL and then proceed with the development of the web-based application. The system was designed to generate curriculum vitae and visualization in the spider chart to make it easier for the user to understand their scientific and competencies.
Pelatihan Pengolahan Limbah Minyak Jelantah Menjadi Lilin Aromaterapi di SMA Islam Al Azhaar Tulungagung Kotijah, Siti; Trisnawati, Ananda Eka Putri; Damayanti, Camilla Alifia; Salsabila, Olivia; Maharani, Berlian Surya; Ardiani, Fira; Tasya, Anindya Nur; Fahrudin, Tresna Maulana
Jurnal Pengabdian Masyarakat Bangsa Vol. 2 No. 7 (2024): September
Publisher : Amirul Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59837/jpmba.v2i7.1383

Abstract

Minyak jelantah merupakan sisa minyak yang dihasilkan dari sisa penggunaan kebutuhan rumah tangga untuk penggorengan yang berasal dari berbagai jenis minyak goreng seperti minyak jagung. Dapur Pondok Pesantren Al Azhaar Tulungagung sebagai salah satu pondok yang juga mengelola dan menyediakan menu makanan harian menghasilkan minyak jelantah dalam jumlah besar. Sisa minyak penggorengan ini belum digunakan secara efisien, sehingga berpotensi menimbulkan dampak negatif bagi lingkungan maupun kesehatan jika digunakan kembali. Pada dasarnya sisa minyak penggorengan seperti minyak jelantah ini memiliki potensi ekonomi apabila dimanfaatkan dengan tepat. Untuk mengatasi permasalahan tersebut, mahasiswa Kuliah Kerja Nyata Tematik Inovasi Pesantren Universitas Pembangunan Nasional “Veteran” Jawa Timur menyelenggarakan pelatihan untuk membuat lilin dari minyak jelantah yang ditujukan kepada siswa kelas XI SMA Islam Al Azhaar Tulungagung. Pelatihan ini tidak hanya bertujuan untuk mengurangi dampak negatif dari limbah dapur, tetapi juga untuk memberikan pendidikan praktis kepada siswa mengenai pengelolaan limbah rumah tangga serta cara memanfaatkannya menjadi produk yang berguna. Pelatihan ini menggunakan metode workshop dengan memberikan pelatihan secara langsung kepada peserta. Sebelum pelaksanaan pelatihan, mahasiswa melakukan observasi untuk mengidentifikasi permasalahan yang ada, serta memberikan pretest dan posttest kepada peserta untuk mengukur pemahaman mereka sebelum dan setelah pelatihan. Analisis data menunjukkan adanya peningkatan signifikan dalam pengetahuan peserta tentang cara memanfaatkan limbah minyak jelantah dengan skor semula dari 64% pada saat pretest menjadi 84% pada saat posttest. Hasil ini menunjukkan bahwa program pelatihan pembuatan lilin dari minyak jelantah di SMA Islam Al Azhaar Tulungagung berhasil memperluas pemahaman dan meningkatkan keterampilan peserta dalam memanfaatkan limbah minyak jelantah secara efektif.
IMAGE CLASSIFICATION OF VINE LEAF DISEASES USING COMPLEX-VALUED NEURAL NETWORK Putri, Irma Amanda; Prasetya, Dwi Arman; Fahrudin, Tresna Maulana
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 1 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i1.7809

Abstract

Leaf diseases are a serious challenge in the agricultural industry affecting crop quality and yield especially in grapevines. Early recognition and classification of grape leaf diseases is crucial to enable farmers to take appropriate preventive measures in maintaining the health of their crops. The research utilized an innovative approach based on Complex-Valued Neural Network (CVNN) to address the problem. Using Complex-Valued Neural Network (CVNN) this research seeks to identify and classify grape leaf diseases through a series of experiments. A total of 100 images divided into 4 classes namely Black Rot, ESCA, Leaf Blight, and Healthy were collected to train the model. The results show that the trained CVNN model successfully achieved a training accuracy of 100% and a testing accuracy of 97%, demonstrating excellent performance in classifying grape leaf diseases. This states that the proposed approach has great potential to be an effective tool in helping growers manage their vineyards more efficiently and effectively. The developed image processing method is expected to be applied in designing a system to perform image classification of diseases on grape leaves.
ANALISIS SENTIMEN KEPUASAN PELAYANAN TRANSPORTASI ONLINE GOJEK MENGGUNAKAN ALGORITMA EXTREME LEARNING MACHINE Riskiyah, Ameliyah; Fahrudin, Tresna Maulana; Hindrayani, Kartika Maulida
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 5 No. 2 (2024): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v5i2.714

Abstract

With the rapid advancement of technology, online transportation has become the main solution for many people in Indonesia to travel easily and efficiently. Companies such as GOJEK are constantly innovating to improve their services, resulting in many responses and reviews from users. This research aims to analyze customer satisfaction with these online transportation services by analyzing the sentiment of user opinions on the Twitter platform. Sentiment analysis plays a very important role in decision making by classifying user reviews. Data was retrieved through a crawling process using specific keywords related to each service. The data preprocessing process includes case folding, tokenizing, normalization, stemming, filtering, and convert negation. This aims to clean and prepare the data so that it can be processed using the algorithm better. This process includes removing irrelevant elements from the text data, converting the text into a consistent or more standardized form, reducing the number of features in the data by stemming, and converting the text into numbers or vectors so that it can be processed by the algorithm. Feature extraction is performed using the Word2Vec model to convert text into a numerical vector representation that can later be processed by ELM. Converts words into numeric vectors in a high-dimensional space, where words that have the same context in the text are close to each other in that space. The ELM (Extreme Learning Machine) algorithm is used as a classification model due to its high training speed and good generalization ability. Model evaluation is done using confusion matrix which measures classification performance through accuracy, precision, recall matrix. The results of this study show that the ELM algorithm with Word2Vec feature extraction is able to classify user sentiment with a high level of accuracy. This research provides insight into user satisfaction with online transportation services and can be a reference for companies to improve their service quality
Co-Authors Agustin, Sesillia Agvenia, Keisya Al Makruf, Achmad Yusuf Alamiyah, Syifa Syarifah Alamsyah, Ryan Badai Ardiani, Ardia Eva Ardiani, Fira Arianto, Fachrizal Aryananda, Rangga Laksana Atnanda, Primus Akbar Azizah, Alisa Jihan Belardo, Valentino Damaliana, Aviolla Terza Damayanti, Camilla Alifia Diyasa, I Gede Susrama Mas Dwi Arman Prasetya Gunawan, Ellexia Leonie Halim, Christina Hartono, Verina Dewanti Heidy Arviani Hindrayani , Kartika Maulida Hindrayani, Kartika Maulida idhom, Mohammad Illahi, Zabda Dimas Anugrah Isfatayati, Nur Ita Kurniawati Kamisutara, Made Kusuma, Ade Lestari, Amanda Ayu Dewi Lestari, Rizky Dwi Ayu Lisanthoni, Angela Maharani, Berlian Surya Marcelia, Firsta Much. afif masykur mughni Muhaimin, Amri Niken Sulistyowati Nugraha, Isna Putri, Irma Amanda Putri, Serlinda Mareta Putri, Shafira Amanda Putricia, Andini Mutiara Riskiyah, Ameliyah Riwangga, Rikhi Putra Riyantoko, Prismahardi Aji Rizqina, Azza Sa'diyah, Ilmatus Salsabila, Olivia Santosa, Bernadheta Donabella Ega Sari, Allan Ruhui Fatmah Selayanti, Nabilah Sigit, Syauqita Sitanggang, Desi Daomara Siti Kotijah Sugiarto Tasya, Anindya Nur Trimono, Trimono Trisnawati, Ananda Eka Putri Utari, Komang Mega Ilda Utomo, Setyobudi Varqa Ansori, Nine Alvariqati Yasin, Andrew Arjunanda