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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jesya (Jurnal Ekonomi dan Ekonomi Syariah) Buletin Ilmiah Sarjana Teknik Elektro Mobile and Forensics Journal of Robotics and Control (JRC) Jurnal Abdi Insani Humanism : Jurnal Pengabdian Masyarakat International Journal of Robotics and Control Systems International Journal of Social Service and Research DECODE: Jurnal Pendidikan Teknologi Informasi SainsTech Innovation Journal Secure And Knowledge-Intelligent Research in Cybersecurity And Multimedia (SAKIRA) IJMST Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Scientific Journal of Engineering Research SainsTech Innovation Journal
Asno Azzawagama Firdaus
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Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Neuroscience Public Health Social Sciences Other

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Future Potential of E-Nose Technology: A Review Furizal Furizal; Alfian Ma'arif; Asno Azzawagama Firdaus; Wahyu Rahmaniar
International Journal of Robotics and Control Systems Vol 3, No 3 (2023)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v3i3.1091

Abstract

Electronic Nose (E-Nose) technology unlocks the fascinating world of electronic detection, identification, and analysis of scents and odors, paving the way for innovative research and promising applications.  E-Nose mimics the human sense of smell and has gained significant attention and is applied in various fields, including the food, health and drug industries, safety and crime, and the environmental and agricultural sectors. This technology has the potential to improve quality control, medical diagnostics, and hazardous material detection processes. The E-Nose consists of a combination of gas sensors that mimic the olfactory receptors of the human nose. These sensors detect and respond to different scent molecules, resulting in unique response patterns that can be interpreted and analyzed. E-Nose has found application in the food industry to assess food quality, detect contamination, and monitor fermentation processes. In the health field, it has been used for disease diagnosis, monitoring patient health, and detecting cancerous tissue. In addition, E-Nose has been used for security purposes, such as detection of explosives and prohibited substances, as well as identification of counterfeit products. In addition, it has been used in environmental monitoring for air quality assessment and agriculture for disease detection in crops.  Despite its promising potential, widespread adoption of E-Nose faces challenges related to sensor sensitivity, data analysis algorithms (complex data interpretation), response diversity, regulatory considerations, implementation complexity, and cost. This article reviews the latest developments in E-Nose technology, explores its applications and future potential, and highlights challenges that need to be addressed.  This is considered important because E-Nose opens up a world of electronic scent identification, and analysis with the potential to improve quality control, diagnosis, and detection.
Analisis Sentimen Pada Proyeksi Pemilihan Presiden 2024 Menggunakan Metode Support Vector Machine Asno Azzawagama Firdaus; Anton Yudhana; Imam Riadi
Decode: Jurnal Pendidikan Teknologi Informasi Vol. 3 No. 2: SEPTEMBER 2023
Publisher : Program Studi Pendidikan Teknologi Infromasi UMK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51454/decode.v3i2.172

Abstract

Indonesia menganut sistem demokrasi dan Pemilihan Umum sebagai penerapan dari sistem tersebut. Pemilihan Presiden dan Wakil Presiden dilaksanakan tahun 2024 dan isu tersebut menjadi fokus perbincangan publik. Calon-calon dan koalisi pengusung terus melakukan kampanye politik secara tradisional maupun melalui media sosial. Twitter menjadi platform media sosial yang banyak digunakan oleh masyarakat untuk membicarakan isu Pemilihan Presiden. Keberpihakan masyarakat dapat diketahui pada diskusi yang ada di Twitter, namun diperlukan pembelajaran komputer yang mampu mengklasifikasi sentimen tersebut. Analisis sentimen digunakan sebagai salah satu teknik untuk mengklasifikasi sentimen masyarakat di Twitter tentang isu Pemilihan Presiden. Metode yang digunakan yaitu Support Vector Machine (SVM) untuk klasifikasi teks. Didapatkan hasil sentimen berdasarkan tiga dataset kandidat yang dipilih, yaitu anies baswedan 65,62%, ganjar pranowo 73,58%, dan prabowo subianto 66,34%. Hasil akurasi metode yang dimiliki oleh ketiga dataset yaitu anies baswedan 73%, ganjar pranowo 79% dan prabowo subianto 79%. Berdasarkan wordcloud popularitas kata yang muncul di Twitter dengan pembahasan Presiden 2024 secara berturut-turut adalah “prabowo subianto”, “presiden ri”, “calon presiden”, “ganjar pranowo”, hingga “anies baswedan”.
Pengenalan Dan Pelatihan UI/UX Serta Jenjang Karir Di Masa Depan untuk Siswa Siswi SMK Informatika Wonosobo Abdul Fadlil; Murinto; Asno Azzawagama Firdaus; Dianda Rifaldi
Humanism : Jurnal Pengabdian Masyarakat Vol 4 No 3 (2023): Desember
Publisher : Universitas Muhammadiyah Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30651/hm.v4i3.20285

Abstract

Artikel ini menyajikan kegiatan pengabdian yang dilaksanakan pada 12 Juni 2023 di SMK Informatika Wonosobo, Jawa Tengah. Kegiatan tersebut difokuskan pada pengenalan desain UI/UX dan pelatihan terkait desain UI/UX untuk membantu siswa mempersiapkan karir di bidang tersebut di masa depan. Sebanyak 20 orang siswa ikut serta dalam kegiatan ini yang didampingi oleh pihak sekolah. Peserta menunjukkan antusiasme yang tinggi selama kegiatan berlangsung. Kegiatan berupa sosialisasi dan tanya jawab hingga praktik langsung ini memang baru kali pertama diselenggarakan pada SMK Informatika Wonosobo tersebut sehingga siswa belum memiliki pemahaman mengenai desain UI/UX. Hal tersebut terlihat dari peningkatan skor akhir yang signifikan dalam evaluasi pra dan pasca pembekalan menggunakan pre test dan post test dengan metode perhitungan likert. Skor akhir meningkat dari 44,2% pada pre test menjadi 93,6% pada post test. Hasil ini menunjukkan bahwa kegiatan pengabdian ini berhasil meningkatkan pemahaman dan pengetahuan peserta dalam bidang desain UI/UX. Pihak sekolah mengharapkan kegiatan serupa dapat tetap dilaksanakan di SMK Informatika Wonosobo guna meningkatkan pengetahuan dan pemahaman siswa mengenai dunia kerja.
Prediction of Indonesian Presidential Election Results using Sentiment Analysis with Nave Bayes Method Firdaus, Asno Azzawagama; Yudhana, Anton; Riadi, Imam; Mahsun, Mahsun
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7007

Abstract

Social media serves as a solution for politicians as a campaign tool because it can save costs compared to conventional campaigns. The 2024 Indonesian Presidential Election has drawn public attention, especially among social media users. Twitter, as one of the widely used social media platforms in Indonesia, functions as an effective campaign forum. However, the problem that arises is how to automatically collect social media data related to presidential discussions and provide conclusions on the analysis results. Of course, this is not easy if done manually. Sentiment analysis is one approach that can be used for this in order to draw conclusions and analysis related to the available data. Data was collected shortly after the registration of presidential and vice-presidential candidates in November 2023. This study aims to obtain sentiment results from the latest data obtained, get the best model from the Naive Bayes method, to conduct analysis in predicting presidential election results based on sentiment. However, at the time of data collection, candidate numbers had not been assigned by the Election organizers. The obtained data amounted to 11,569 records using the Valence Aware Dictionary for Sentiment Reasoning (VADER) library for labeling. After removing duplicated tweets, the data was reduced to 4,893 records, with each candidate pair having 1,631 data points. The sentiment analysis classification model was determined using the Nave Bayes method with Term Frequency-Inverse Document Frequency (TF-IDF) feature extraction. Based on the data, the highest percentage of positive sentiment was found in Ganjar Pranowo - Mahfud MD data at 69.16%, and the highest negative sentiment was in Prabowo Subianto - Gibran Rakabuming Raka data at 52.12%. Common words in positive sentiment for Ganjar Pranowo - Mahfud MD include "strong," "corruption," "support," "reward," and others. Meanwhile, frequently appearing negative sentiment words for Prabowo Subianto - Gibran Rakabuming Raka include "child," "eldest," "mk," "young," and others. This research achieved an average accuracy of 76.67% using the Naive Bayes method on the entire dataset, indicating its reliability in similar cases.
PUBLIC STIGMA ABOUT POLYGAMY BASED ON ISLAMIC-MUHAMMADIYAH VIEWS USING SENTIMENT ANALYSIS APPROACH Arqam, Mhd Lailan; Firdaus, Asno Azzawagama; Palahuddin, Palahuddin; Furizal, Furizal; Muis, Alwas; Atmojo, Ahmad Muslih
International Journal of Social Service and Research Vol. 4 No. 8 (2024): International Journal of Social Service and Research
Publisher : Ridwan Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46799/ijssr.v4i8.896

Abstract

Social media is very important to control the development of issues that occur today. With social shifts and changing societal values, polygamy has become a complex issue and attracts the attention of many people around the world discussed through social media platforms. This research contributes to the field by applying a sentiment analysis approach to automatically detect and analyze public sentiment regarding polygamiy content on Twitter, particularly in the context of Islamic-Muhammadiyah views. This study used decision tree classification methods, support vector machines, and random forests with the best analysis accuracy obtained at SVM 77.4%. Furthermore, the results of the sentiment class obtained were analyzed according to the views of Muhammadiyah. The results obtained in the analysis 77% commented negatively and 23% commented positively. In addition, this research can be used as a reference for future research on sentiment analysis cases to training and testing classroom models.
Capability of Hybrid Long Short-Term Memory in Stock Price Prediction: A Comprehensive Literature Review Furizal, Furizal; Ma'arif, Alfian; Firdaus, Asno Azzawagama; Suwarno, Iswanto
International Journal of Robotics and Control Systems Vol 4, No 3 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i3.1489

Abstract

Stocks are financial instruments representing ownership in a company. They provide holders with rights to a portion of the company's assets and earnings. The stock market serves as a means for companies to raise capital. By selling shares to the public, companies can obtain funds needed for expansion, research and development, as well as various other investments. Though significant, predicting stock prices poses a challenge for investors due to their unpredictable nature. Stock price prediction is also an intriguing topic in finance and economics due to its potential for significant financial gains. However, manually predicting stock prices is complex and requires in-depth analysis of various factors influencing stock price movements. Moreover, human limitations in processing and interpreting information quickly can lead to prediction errors, while psychological factors such as bias and emotion can also affect investment decisions, reducing prediction objectivity and accuracy. Therefore, machine processing methods become an alternative to expedite and reduce errors in processing large amounts of data. This study attempts to review one of the commonly used prediction algorithms in time series forecasting, namely hybrid LSTM. This approach combines the LSTM model with other methods such as optimization algorithms, statistical techniques, or feature processing to enhance the accuracy of stock price prediction. The results of this literature review indicate that the hybrid LSTM method in stock price prediction shows promise in improving prediction accuracy. The use of optimization algorithms such as GA, AGA, and APSO has successfully produced models with low RMSE values, indicating minimal prediction errors. However, some methods such as LSTM-EMD and LSTM-RNN-LSTM still require further development to improve their performance.
Long Short-Term Memory vs Gated Recurrent Unit: A Literature Review on the Performance of Deep Learning Methods in Temperature Time Series Forecasting Furizal, Furizal; Fawait, Aldi Bastiatul; Maghfiroh, Hari; Ma’arif, Alfian; Firdaus, Asno Azzawagama; Suwarno, Iswanto
International Journal of Robotics and Control Systems Vol 4, No 3 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i3.1546

Abstract

Temperature forecasting is a crucial aspect of meteorology and climate change studies, but challenges arise due to the complexity of time series data involving seasonal patterns and long-term trends. Traditional methods often fall short in handling this variability, necessitating more advanced solutions to enhance prediction accuracy. LSTM and GRU models have emerged as promising alternatives for modeling temperature data. This study is a literature review comparing the effectiveness of LSTM and GRU based on previous research in temperature forecasting. The goal of this review is to evaluate the performance of both models using various evaluation metrics such as MSE, RMSE, and MAE to identify gaps in previous research and suggest improvements for future studies. The method involves a comprehensive analysis of previous studies using LSTM and GRU for temperature forecasting. Assessment is based on RMSE values and other metrics to compare the accuracy and consistency of both models across different conditions and temperature datasets. The analysis results show that LSTM has an RMSE range of 0.37 to 2.28. While LSTM demonstrates good performance in handling long-term dependencies, GRU provides more stable and accurate performance with an RMSE range of 0.03 to 2.00. This review underscores the importance of selecting the appropriate model based on data characteristics to improve the reliability of temperature forecasting.
Application of Sentiment Analysis as an Innovative Approach to Policy Making: A review Firdaus, Asno Azzawagama; Saputro, Joko Slamet; Anwar, Miftahul; Adriyanto, Feri; Maghfiroh, Hari; Ma'arif, Alfian; Syuhada, Fahmi; Hidayat, Rahmad
Journal of Robotics and Control (JRC) Vol 5, No 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.22573

Abstract

This literature review comprehensively explains the role of sentiment analysis as a policymaking solution in companies, organizations, and individuals. The issue at hand is how sentiment analysis can be effectively applied in decision making. The solution is to integrate sentiment analysis with the latest NLP trends. The contribution of this research is the assessment of 100-200 recent studies in the period 2020-2024 with a sample of more than 5,000 data, as well as the impact of the resulting policy recommendations. The methods used include evaluation of techniques such as Deep Learning, lexicon-based, and Machine Learning, using evaluation matrices such as F1-score, precision, recall, and accuracy. The results showed that Deep Learning techniques achieved an average accuracy of 93.04%, followed by lexicon-based approaches with 88.3% accuracy and Machine Learning with 83.58% accuracy. The findings also highlight the importance of data privacy and algorithmic bias in supporting more responsive and data-driven policymaking. In conclusion, sentiment analysis is reliable in areas such as e-commerce, healthcare, education, and social media for policy-making recommendations. However, special attention should be paid to challenges such as language differences, data bias, and context ambiguity which can be addressed with models such as mBERT, model auditing, and proper tokenization.
Public Opinion Analysis of Presidential Candidate Using Naïve Bayes Method Firdaus, Asno Azzawagama; Yudhana, Anton; Riadi, Imam
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 8, No. 2, May 2023
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v8i2.1686

Abstract

Elections for president and vice president will take place in 2024. Heading into the election, promoted candidates were vying for public sympathy. People often discussed as presidential candidates are Anies Baswedan, Ganjar Pranowo, and Prabowo Subianto. Therefore, we need a way to predict potential candidates and voter demographics from public opinion on Twitter using sentiment analysis. One of his methods commonly used to classify sentiment analysis is Naive Bayes. This study used the naive Bayes classifier and the TF-IDF extraction function to add weights to the text. Use the scikit-learn Python library to help determine the polarity of negative and positive sentiment classes in your dataset. The datasets used were Twitter datasets acquired from October to December 2022, for a total of 15,000 datasets. The best test scenario obtained by splitting the test and training data is 70% test data and 30% training data, with the highest accuracy generated from the 95% Ganjar dataset. Using the Anies, Ganjar, and Prabowo test data, the positive mood scores for each candidate were 833, 77, and 524, respectively, while the negative mood scores were 637, 1423, and 976, respectively. The test was performed using a confusion matrix and k-fold cross-validation, and the best results were obtained on the Ganjar data set. That is a confusion matrix of 94.93% and a k-fold cross-validation of 94.46%. The lowest f1-score for the positive class is 67% for the Anies dataset and 27% for the negative class for the Ganjar dataset.
Comparison of Convolutional Neural Networks and Support Vector Machines on Medical Data: A Review Furizal, Furizal; Ma'arif, Alfian; Rifaldi, Dianda; Firdaus, Asno Azzawagama
International Journal of Robotics and Control Systems Vol 4, No 1 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i1.1375

Abstract

Medical image processing has become an integral part of disease diagnosis, where technological advancements have brought significant changes to this approach. In this review, a comprehensive comparison between Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) in processing medical images is conducted. Automated medical analysis is becoming increasingly important due to issues of subjectivity in manual diagnosis and potential treatment delays. This research aims to compare the performance of Machine Learning (ML) in medical contexts using MRI, CT scan, and X-ray data. The comparison includes the accuracy rates of CNN and SVM algorithms, sourced from various studies conducted between 2018 and 2022. The results of the comparison show that CNN has higher average accuracy in processing MRI and X-ray data, with average values of 98.05% and 97.27%, respectively. On the other hand, SVM exhibits higher average accuracy for CT scan data, reaching 91.78%. However, overall, CNN achieves an average accuracy of 95.58%, while SVM's average accuracy is at 94.72%. These findings indicate that both algorithms perform well in processing medical data with high accuracy. Although based on these average accuracy rates, CNN demonstrates slightly better capabilities than SVM. Further research and development of more complex models are expected to continue improving the effectiveness of both approaches in disease diagnosis and patient care in the future.
Co-Authors Abao , Anrom G. Abdul Fadlil Abdul Fadlil Adipta, Marazaenal Adriyanto, Feri Afriansyah, M. Aldi Bastiatul Fawait Fawait Alfian Ma’arif Anggarista, Randa Anton Yudhana Anton Yudhana Anwar, Miftahul Ariska Fitriyana Ningrum Arqam, Mhd Lailan Aryandani, Aisyah Atmojo, Ahmad Muslih B. Talirongan, Florence Jean Costa, Apolonia Diana Sherly da Daniel Santoso Dianda Rifaldi Ega Silpia Aulia Elmalky , Ahmed Mahmoud Zaki Fahmi Syuhada Faresta, Rangga Alif Furizal Furizal Furizal Furizal Furizal Furizal, Furizal Gabriel Diemesor Gunawan, Rahmad Habibi , Ahmad Rizky Nusantara Hasanah, Rakyatol Imam Riadi Imam Riadi Insyroh, Nazaruddin Joko Slamet Saputro Joni Saputra Kariyamin, Kariyamin Khairanis Yulita Kumoro, Danang Tejo Maghfiroh, Hari Mahsun Mahsun Maulana , Adrian Maulana, Adrian Muhajir Yunus Muhamad Yamin Muhammad Ilham Muhammad Mujahid Dakwah Muhammad Yusuf Muis, Alwas Munzir, Misbahul Murinto Nakib, Arman Mohammad Palahuddin, Palahuddin Pramesti, Retta Farah Pratama, Ramadhana Agung Putra, Ahmad Fatoni Dwi Rangga Alif Faresta Rangga Faresta Sani SR, Sahrul Sherly da Costa, Apolonia Diana Sitti Rahmah Soekarta, Rendra SULISTIANI Sulton, Chaerus Suwarno, Iswanto Syahr, Khaeru Alfi Syahrani Lonang Syuhada, Fahmi Syuhada, Fahmi Syuhada Talirongan , Hidear Thoyyibah, Mazraatin Visman, Javan Wahyu Rahmaniar Wijaya, Setiawan Ardi Yusuf Hendra Pratama