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All Journal JSM (Jurnal SIFO Mikroskil) Jurnal Informatika dan Teknik Elektro Terapan E-Dimas: Jurnal Pengabdian kepada Masyarakat Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Indonesian Journal of Artificial Intelligence and Data Mining MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Yayasan Cita Cendikiawan Al Khwarizmi Journal of Social Responsibility Projects by Higher Education Forum Jurnal Desain dan Analisis Teknologi
Sinaga, Frans Mikael
Univeritas Mikroskil, Medan
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Pelatihan Instalasi Jaringan Komputer Menggunakan Simulasi Cisco pada SMK Methodist Tanjung Morawa Frans Mikael Sinaga; Sio Jurnalis Pipin; Heru Kurniawan
Journal of Social Responsibility Projects by Higher Education Forum Vol 4 No 1 (2023): Juli 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jrespro.v4i1.3633

Abstract

SMK Swasta Methodist Tanjung Morawa is one of the private schools under the auspices of Yayasan Methodist Kasih Imanuel Indonesia, which was established in 2008. Tanjung Morawa Methodist Private Vocational School has various majors, one of which is Network and Computer Engineering (TKJ). Network installation is one of the most interesting subjects to discuss because the students have studied it before and it is already a lesson that is in accordance with the majors of the students of Tanjung Morawa Methodist Private Vocational School, namely Computer Network Engineering (TKJ). The students have learned several computer network simulation applications such as virtual boxes but the network simulation applications studied are still limited, therefore, the Faculty of Informatics Universitas Mikroskil offers activities in the form of computer network installation training using Cisco simulation to improve the ability of students to have better competencies. This training activity lasted for 2 days and was carried out in the computer laboratory of Universitas Mikroskil. During this training activity the students were given pre-test questions, materials and case studies, post-test and final feedback.
Sentiment Analysis Classification of ChatGPT on Twitter Big Data in Indonesia Using Fast R-CNN Sio Jurnalis Pipin; Frans Mikael Sinaga; Sunaryo Winardi; Muhammad Noor Hakim
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6816

Abstract

The advent of OpenAI's ChatGPT, a large language model (LLM) proficient in various fields including artificial intelligence (AI) and natural language processing (NLP), has ignited a plethora of opinions and discussions, especially on social media platforms like Twitter in Indonesia. This research seeks to delve into the intricate dynamics of these discussions, aiming to map both the commendations and criticisms surrounding ChatGPT's technological advancements and potential negative impacts. Utilizing deep learning-based sentiment analysis techniques, the study employs Convolutional Neural Network (CNN) and Fast Region-based Convolutional Network (Fast R-CNN) to analyze a dataset consisting of 7,604 tweets categorized into "Positive", "Negative", and "Neutral" sentiments. The objective is to provide a comprehensive understanding of the societal perceptions towards this artificial intelligence technology in the Indonesian context. The methodology encompasses several stages including data collection from Twitter, data cleaning, and pre-processing, followed by the application of CNN and Fast R-CNN models for sentiment analysis. The findings indicate a superior performance of the Fast R-CNN model, achieving an accuracy rate of 94.5%, compared to the CNN model with an accuracy rate of 86%. In conclusion, the research highlights the effectiveness of integrating Fast R-CNN in sentiment analysis to extract deeper insights from Twitter data in Indonesia. This study not only contributes to the scientific literature in the fields of sentiment analysis and natural language processing but also aids in formulating informed strategies to navigate the challenges and opportunities presented by artificial intelligence technology in the Indonesian landscape. Future research avenues should focus on optimizing this sentiment analysis model and exploring other potential applications of this technology in the dynamically evolving digital landscape in Indonesia.
Optimization of Sentiment Analysis Classification of ChatGPT on Big Data Twitter in Indonesia using BERT Sinaga, Frans Mikael; Purba, Ronsen; Pipin, Sio Jurnalis; Lestari, Wulan Sri; Winardi, Sunaryo
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7861

Abstract

This research is grounded in the emergence of ChatGPT technology, supported by prior and similar studies. The urgency of the issue is highlighted by previous research indicating non-convergent classification outcomes in LSTM (Long Short-Term Memory) methods due to suboptimal hyperparameter settings and limitations in understanding text data within Big Data. The presence of ChatGPT technology brings both benefits and potential misuse, such as copyright infringement, unauthorized news extraction, and violations of accountability principles. Understanding public sentiment towards the presence of ChatGPT technology is crucial. The research aims to implement the BERT (Bidirectional Encoder Representations from Transformers) method to achieve accurate and convergent sentiment analysis classification. This study involves data preprocessing stages using Natural Language Processing (NLP) techniques. Text data, already vectorized, is classified using BERT to determine public sentiment (positive, negative, neutral) towards ChatGPT technology, ensuring greater accuracy, convergence, and contextual relevance. Performance testing of the BERT model is conducted using a Confusion Matrix. With parameters set to Max Sequence Length = 128 and Batch Size = 16, the highest classification accuracy achieved is 93.4%.
Classification of Big Data Stunting Using Support Vector Regression Method at Stella Maris Medan Maternity Hospital Chen, Kelvin; Adriansyah, R. A. Fattah; Juliandy, Carles; Sinaga, Frans Mikael; Liko, Frederick; Angkasa, Aswin
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 2 (2024): September 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i2.31112

Abstract

This study aims to classify big data related to stunting using the Support Vector Regression (SVR) method at Stella Maris Maternity Hospital, Medan. Stunting, a condition of impaired growth in children due to chronic malnutrition and repeated infections, affects physical and cognitive development. With increasing health data, big data processing methods are essential for accurate information. SVR was chosen for handling high-dimensional and non-linear data, providing precise results. The study uses medical information, nutritional history, and socio-economic factors collected from hospital patients. The research process includes data collection, pre-processing to address missing values and outliers, normalization, and SVR application. Final results use SVR with Voting Classifier combining Support Vector Classifier (SVC), Random Forest (RF), and Gradient Boosting (GB), achieving an accuracy of 91.67%. This approach effectively identifies main stunting factors, aiding clinical decision-making and intervention programs. The study showcases big data and machine learning's potential in healthcare, serving as a model for improving health services and monitoring children's health conditions.
Forecasting Climate Change Patterns to Improving Rice Harvest Using SVR for Achieving Green Economy Juliandy, Carles; Kelvin, Kelvin; Halim, Apriyanto; Pipin, Sio Jurnalis; Sinaga, Frans Mikael; Lestari, Wulan Sri
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 2 (2024): September 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i2.32393

Abstract

The consistently declining rice harvest will cause several economic and environmental problems. The unstable and unpredictable climate change was believed as the main problem of the declining rice harvest. We proposed a method for forecasting climate change to help the farmer in their rice cultivation. We used Support Vector Regression (SVR) to improve algorithm steps such as normalizing the data and applying an Adaptive Linear Combiner (ALC) to optimize the dataset before we processed it with the algorithm. Our model gets 95% accuracy as measured with the confusion matrix. We believe our model will help the farmers in their rice cultivation with good climate forecasting. A further benefit of this research we belief that with the well-forecasted climate, the usage of pesticides will decrease and will help the vision of the Indonesian government with a green economy
Exploring New Frontiers: XCEEMDAN, Bidirectional LSTM, Attention Mechanism, and Spline in Stock Price Forecasting Kelvin, Kelvin; Sinaga, Frans Mikael; Winardi, Sunaryo; Susmanto, Susmanto
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 2 (2024): September 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i2.29649

Abstract

The Attention Mechanism is acknowledged as a machine learning method proficient in managing relationships within sequential data, surpassing traditional models in this regard. However, the unique characteristics of stock data, including substantial volatility, multidimensionality, and non-linear patterns, present challenges in attaining accurate forecasts of stock prices. This research aims to tackle these hurdles by enhancing a prior model through the incorporation of an Attention Mechanism, resulting in an enhanced model. The forecasted data are standardized and prepared for analysis before undergoing signal decomposition into high and low-frequency components. Subsequently, the Attention Mechanism processes the high-frequency signals. Evaluation entails comparing the performance of the proposed model with that of the previous model using identical parameters. The findings indicate that the proposed model achieves a reduced RMSE value of 0.5708777053 compared to the previous model's average RMSE value of 0.5823726212, indicating enhanced accuracy in stock price prediction. This approach is anticipated to make a substantial contribution to the advancement of more dependable and effective stock price prediction models, addressing the limitations of prior methodologies
Membangun Fondasi Pemrograman dengan Python pada SMA Swasta Methodist Tanjung Morawa Kelvin; Sinaga, Frans Mikael; Kurniawan, Heru; Winardi, Sunaryo; Saragih, Yuni Marlina
Dedikasi Sains dan Teknologi (DST) Vol. 4 No. 2 (2024): Artikel Riset Nopember 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/dst.v4i2.4875

Abstract

SMA Swasta Methodist Tanjung Morawa adalah salah satu sekolah swasta di bawah naungan Yayasan Methodist Kasih Imanuel Indonesia, yang berdiri sejak tahun 2008. SMA Swasta Methodist Tanjung Morawa memiliki ketertarikan terhadap teknologi robot yang merupakan teknologi yang saat ini sedang banyak-banyaknya diterapkan diberbagai sector. Salah satu bekal yang dapat dipersiapkan untuk para murid adalah pemahaman dalam menggunakan Bahasa pemrograman, sehingga para siswa nantinya dapat membangun sendiri berbagai instruksi dalam membuat robot atau bahkan sekedar program sederhana. Untuk mendukung keinginan tersebut, Fakultas Informatika Universitas Mikroskil menawarkan kegiatan berupa pelatihan pengenalan salah satu Bahasa pemrograman yaitu python. Kegiatan pelatihan ini berlangsung selama 1 hari dan dilaksanakan di laboratorium komputer Universitas Mikroskil. Selama kegiatan pelatihan ini para siswa akan mendengarkan pemaparan materi, mengerjakan latihan-latihan sederhana dan quiz pada akhir pelatihan sebagai evaluasi untuk menilai sejauh mana pemahaman siswa tentang bahasa pemrograman setelah pelatihan.
Optimization of Sentiment Analysis Classification of ChatGPT on Big Data Twitter in Indonesia using BERT Sinaga, Frans Mikael; Purba, Ronsen; Pipin, Sio Jurnalis; Lestari, Wulan Sri; Winardi, Sunaryo
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7861

Abstract

This research is grounded in the emergence of ChatGPT technology, supported by prior and similar studies. The urgency of the issue is highlighted by previous research indicating non-convergent classification outcomes in LSTM (Long Short-Term Memory) methods due to suboptimal hyperparameter settings and limitations in understanding text data within Big Data. The presence of ChatGPT technology brings both benefits and potential misuse, such as copyright infringement, unauthorized news extraction, and violations of accountability principles. Understanding public sentiment towards the presence of ChatGPT technology is crucial. The research aims to implement the BERT (Bidirectional Encoder Representations from Transformers) method to achieve accurate and convergent sentiment analysis classification. This study involves data preprocessing stages using Natural Language Processing (NLP) techniques. Text data, already vectorized, is classified using BERT to determine public sentiment (positive, negative, neutral) towards ChatGPT technology, ensuring greater accuracy, convergence, and contextual relevance. Performance testing of the BERT model is conducted using a Confusion Matrix. With parameters set to Max Sequence Length = 128 and Batch Size = 16, the highest classification accuracy achieved is 93.4%.
Enhancing Image Quality for the Detection of Underwater Debris with Adaptive Fuzzy Filter Halim, Apriyanto; Ulina, Mustika; Tanti, Tanti; Sinaga, Frans Mikael
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 1 (2025): March 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i1.33010

Abstract

The image quality improvement process plays a very important role. This is because the process can increase the clarity and accuracy of image detection. One type of image detection that exists is the detection of garbage found under the sea. One of the image quality improvement processes is related to noise removal. Noise is a sudden increase in pixel intensity in an image. This can cause various problems that occur such as in medical photos, satellites, and photography. One method used to remove noise from images is using Adaptive Fuzzy Filter (AFF). This method is carried out by first finding the average value of the mean fuzzy set and the gray level fuzzy. After that, the value comparison process is carried out. From the results of the research conducted on 689 images from the dataset obtained, there is a decrease in the amount of noise of around 96,23% of the total noise obtained previously. This can certainly provide good results in terms of changes in noise that have been made.
RANCANG BANGUN SISTEM INFORMASI LES PRIVAT BERBASIS WEB PADA LES PRIVAT MEDAN Ng, Poi Wong; Vanessa Leonora; Filbert; Jerry Johnson; Sinaga, Frans Mikael
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 11 No. 1 (2025): Volume 11 Nomor 1 Tahun 2025
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v11i1.3680

Abstract

Les Privat Medan merupakan salah satu usaha kecil yang bergerak di bidang pendidikan yang memberikan les privat, dimana manajemen kegiatan pembelajarannya masih dilakukan secara manual mulai dari proses pendaftaran, pengaksesan paket, bahan belajar dan penyusunan jadwal, proses pembayaran, hingga pemberian maupun penerimaan informasi seperti berita atau pengumuman. Pencatatan data masih direkap dengan menggunakan microsoft excel, sehingga menyebabkan proses menjadi tidak efisien dan rentan terjadinya kesalahan. Untuk menyelesaikan permasalahan tersebut, maka dirancang dan dibangun sistem informasi les privat dengan metodologi waterfall, analisis sistem dengan diagram use case, dan pengujian dengan metode black-box. Hasil dari perancangan dan pengembangan ini adalah sistem informasi berbasis web yang mampu menyelesaikan permasalahan yang masih dilakukan secara manual dalam manajemen kegiatan pembelajaran pada Les Privat Medan dengan lebih efisien dan terstruktur.
Co-Authors Adriansyah, R. A. Fattah Ananda Putra Brahmana Angkasa, Aswin Apriyanto Halim Arwin Halim Bate'e, David Belferik, Ronald Br Ginting, Geby Laurent Br. Sitorus, Rotua Eka Wati Carles Juliandy Chan, Jacky Chen, Kelvin Edmund, Edmund Fa, Farrell Rio Fandi Presly Simamora Felix Felix Ferawaty, Ferawaty Filbert Filbert, Filbert Gunawan Gunawan Gunawan, Erin Hardy Hardy Heru Kurniawan Heru Kurniawan Hui, David Christian Hutabarat, Yonatan Hutagaol, Grace Helena Irpan Pardosi Jerry Johnson Joy Nasten Sinaga Karina Mannita Tarigan Kelvin Kelvin Kelvin Liko, Frederick Lim, Justine Limbong, Mila Rachmafika Lubis, Muhammad Wahyu Pratama Manullang, Mangasa A.S. Marpaung, Dhea Romantika Maynius, Veilind Mhd Adi Setiawan Aritonang Michael Michael Muhammad Noor Hakim Neveline, Neveline Ng Poi Wong Nuraina, Nuraina Nurhayati Nurhayati Panjaitan, Erwin Setiawan Pipin, Sio Jurnalis Purba, Michael Emmanuel Purba, Rizki Azzura Saifani Rambe, Khairul Hawani Revelin, Dylan Ronsen Purba Saragih, Yuni Marlina Satria, Krismulia Wignya Sherina Sherina, Sherina Siahaan, Alfa Ulina Stefani Simanullang, Maradona Jonas Sinaga, Tetti Sintiya, Cindy Sio Jurnalis Pipin Sio Jurnalis Pipin Siregar, Roy Antoni Sitompul, Indra Maruba Situmorang, Angga Zefanya Sophya Hadini Marpaung Suhindra, Wahyu Sunaryo Winardi Susmanto, Susmanto Syahrul R, Syahrul Syanti Irviantina Tanidi, Davin Tanti Tanti Ulina, Mustika Vanessa Leonora Wijaya, Grace Putri Winardi, Sunaryo Wulan Sri Lestari