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All Journal International Journal of Electrical and Computer Engineering Techno.Com: Jurnal Teknologi Informasi Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Prosiding SNATIF Jurnal Edukasi dan Penelitian Informatika (JEPIN) International Journal of Advances in Intelligent Informatics Scientific Journal of Informatics Journal of Information Systems Engineering and Business Intelligence JAIS (Journal of Applied Intelligent System) Register: Jurnal Ilmiah Teknologi Sistem Informasi Jurnal Teknologi dan Sistem Komputer Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Prosiding SAKTI (Seminar Ilmu Komputer dan Teknologi Informasi) JISKa (Jurnal Informatika Sunan Kalijaga) APTIKOM Journal on Computer Science and Information Technologies (CSIT) Innovation in Research of Informatics (INNOVATICS) Journal of Soft Computing Exploration Unnes Journal of Mathematics Journal of Student Research Exploration Journal of Information System Exploration and Research Nusantara Journal of Computers and its Applications Jurnal Informatika: Jurnal Pengembangan IT Scientific Journal of Informatics
Much Aziz Muslim
Unknown Affiliation
Aerospace Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Other

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Accuracy of Malaysia Public Response to Economic Factors During the Covid-19 Pandemic Using Vader and Random Forest Jumanto, Jumanto; Muslim, Much Aziz; Dasril, Yosza; Mustaqim, Tanzilal
Journal of Information System Exploration and Research Vol. 1 No. 1 (2023): January 2023
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v1i1.104

Abstract

This study conducted a sentiment analysis of the impact of the Covid-19 pandemic in the economic sector on people's lives through social media Twitter. The analysis was carried out on 23,777 tweet data collected from 13 states in Malaysia from 1 December 2019 to 17 June 2020. The research process went through 3 stages, namely pre-processing, labeling, and modeling. The pre-processing stage is collecting and cleaning data. Labeling in this study uses Vader sentiment polarity detection to provide an assessment of the sentiment of tweet data which is used as training data. The modeling stage means to test the sentiment data using the random forest algorithm plus the extraction count vectorizer and TF-IDF features as well as the N-gram selection feature. The test results show that the polarity of public sentiment in Malaysia is predominantly positive, which is 11,323 positive, 4105 neutral, and 8349 negative based on Vader labeling. The accuracy rate from the random forest modeling results was obtained 93.5 percent with TF-IDF and 1 gram.
Melanoma Skin Cancer Classification Using EfficientNetB7 for Deep Feature Extraction and Ensemble Learning Approach Darmawan, Aditya Yoga; Dullah, Ahmad Ubai; Qohar, Bagus Al; Unjung, Jumanto; Muslim, Much Aziz
Innovation in Research of Informatics (Innovatics) Vol 7, No 1 (2025): March 2025
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v7i1.12764

Abstract

Cancer is one of the deadliest diseases in the world. cancer is caused by the presence of cancer cells due to abnormal conditions during the cell turnover process. One of the dangerous types of cancer is melanoma skin cancer, this cancer attacks the outer skin of humans because skin cells are prone to damage. However, diagnosis for this disease is mostly done manually while there are previous studies that use deep learning approaches with the accuracy that can be improved. The purpose of this study is to find an effective and efficient method for melanoma cancer recognition so that it can be treated more quickly. We propose several methods that we have compared to be able to classify melanoma skin cancer with EfficientNetB7 Feature Extractor and Ensemble Learning. The results of this research model get the highest accuracy of 91.2%. When EfficientNetB7 together with ensemble learning. This research model has better and efficient results when compared to previous research.
Credit Risk Assessment in P2P Lending Using LightGBM and Particle Swarm Optimization Dasril, Yosza; Muslim, Much Aziz; Hakim, M. Faris Al; Jumanto , Jumanto; Prasetiyo, Budi
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 9 No 1 (2023): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v9i1.3060

Abstract

The credit risk evaluation is a vital task in the P2P Lending platform. An effective credit risk assessment method in a P2P lending platform can significantly influence investors' decisions. The machine learning algorithm that can be used to evaluate credit risk as LightGBM, however, the results in evaluating P2P lending need to be improved. The aim of this research is to improve the accuracy of the LightGBM algorithm by combining the Particle Swarm Optimization (PSO) algorithm. The novelty developed in this research is combining LightGBM with PSO for large data from the Lending Club Dataset which can be accessed on Kaggle.com. The highest accuracy also presented satisfactory results with 98.094% of accuracy, 90.514% of Recall, and 97.754% of NPV respectively. The combination of LightGBM and PSO shows better results.
Deep Learning Model Implementation Using Convolutional Neural Network Algorithm for Default P2P Lending Prediction Nikmah, Tiara Lailatul; Jumanto, Jumanto; Prasetiyo, Budi; Fitriani, Nina; Muslim, Much Aziz
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26366

Abstract

Peer-to-peer (P2P) lending is one of the innovations in the field of fintech that offers microloan services through online channels without intermediaries. P2P  lending facilitates the lending and borrowing process between borrowers and lenders, but on the other hand, there is a threat that can harm lenders, namely default.  Defaults on  P2P  lending platforms result in significant losses for lenders and pose a threat to the overall efficiency of the peer-to-peer lending system. So it is essential to have an understanding of such risk management methods. However, designing feature extractors with very complicated information about borrowers and loan products takes a lot of work. In this study, we present a deep convolutional neural network (CNN) architecture for predicting default in P2P lending, with the goal of extracting features automatically and improving performance. CNN is a deep learning technique for classifying complex information that automatically extracts discriminative features from input data using convolutional operations. The dataset used is the Lending Club dataset from P2P lending platforms in America containing 9,578 data. The results of the model performance evaluation got an accuracy of 85.43%. This study shows reasonably decent results in predicting p2p lending based on CNN. This research is expected to contribute to the development of new methods of deep learning that are more complex and effective in predicting risks on P2P lending platforms.
Increasing package delivery efficiency through the application of the prim algorithm to find the shortest route on the expedition route Lestari, Apri Dwi; Pertiwi, Dwika Ananda Agustina; Muslim, Much Aziz
Journal of Student Research Exploration Vol. 1 No. 1: January 2023
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/josre.v1i1.105

Abstract

One of the changes is in terms of shopping. Previously, people shopped through physical stores, but since the emergence of online shopping platforms, people have started to switch to using the marketplace as a place to make buying and selling transactions. This platform utilizes expedition services to send packages in the form of ordered goods from sellers to buyers. This activity presents a new problem, which is related to the efficiency of package delivery by courier services so that goods can arrive as quickly as possible in the hands of buyers. Graph modeling to solve a problem related to the shortest path and the fastest path is adapted in this paper. The algorithm used is Prim's Algorithm, which is an algorithm to determine the minimum spanning tree of a connected weighted graph. The test results show that the algorithm is suitable for increasing packet delivery efficiency by determining the shortest path based on the minimum spanning tree concept. By taking a sample of travel routes on the island of Java, the best route was obtained with a total distance of 1,771 kilometers connecting cities from the city of Jakarta to the city of Banyuwangi.
Sign Language Detection System Using YOLOv5 Algorithm to Promote Communication Equality People with Disabilities Ningsih, Maylinna Rahayu; Nurriski, Yopi Julia; Sanjani, Fathimah Az Zahra; Hakim, M. Faris Al; Unjung, Jumanto; Muslim, Much Aziz
Scientific Journal of Informatics Vol. 11 No. 2: May 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i2.6007

Abstract

Purpose: Communication is an important asset in human interaction, but not everyone has equal access to this key asset. Some of us have limitations such as hearing or speech impairments, which require a different communicative approach, namely sign language. These limitations often present accessibility gaps in various sectors, including education and employment, in line with Sustainable Development Goals (SDGs) numbers 4, 8, and 10. This research responds to these challenges by proposing a BISINDO sign language detection system using YOLOv5-NAS-S. The research aims to develop a sign language detection model that is accurate and fast, meets the communicative needs of people with disabilities, and supports the SDGs in reducing the accessibility gap. Methods: The research adopted a transfer learning approach with YOLOv5-NAS-S using BISINDO sign language data against a background of data diversity. Data pre-processing involved Super-Gradients and Roboflow augmentation, while model training was conducted with the Trainer of SuperGradients. Result: The results show that the model achieves a mAP of 97,2% and Recall of 99.6% which indicates a solid ability in separating sign language image classes. This model not only identifies sign language classes but can also predict complex conditions consistently. Novelty: The YOLOv5-NAS-S algorithm shows significant advantages compared to previous studies. The success of this performance is expected to make a positive contribution to efforts to create a more inclusive society, in accordance with the Sustainable Development Goals (SDGs). Further development related to predictive and real-time integration, as well as investigation of possible practical applications in various industries, are some suggestions for further research.
A Performance Comparison of Data Balancing Model to Improve Credit Risk Prediction in P2P Lending Pertiwi, Dwika Ananda Agustina; Ahmad, Kamilah; Unjung, Jumanto; Muslim, Much Aziz
Scientific Journal of Informatics Vol. 11 No. 4: November 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i4.14018

Abstract

Purpose: The problem of imbalanced datasets often affects the performance of classification models for prediction, one of which is credit risk prediction in P2P lending. To overcome this problem, several data balancing models have been applied in the existing literature. However, existing research only evaluates performance based on classification model performance. Thus, in addition to measuring the performance of classification models, this study involves the contribution of the performance of data balancing models including Random Oversampling (ROS), Random Undersampling (RUS), and Synthetic Minority Oversampling (SMOTE). Methods: This research uses the Lending Club dataset with an imbalanced ratio (IR) of 4.098, and 2 classifiers such as LightGBM and XGBoost, as well as 10 cross-validation to assess the performance of the data balancing model including Random Oversampling (ROS), Random Undersampling (RUS), and Synthetic Minority Oversampling (SMOTE). Then the model is evaluated using the metrics of accuracy, recall, precision, and F1-score. Result: The research results show that SMOTE has superior performance as a data balancing model in P2P lending, with an accuracy of the LightGBM+SMOTE model of 92.56% and the XGBoost+SMOTE model of 92.32%, where this performance is better than other models. Novelty: This research concludes that SMOTE as a data balancing model to improve credit risk prediction in P2P lending has superior performance. Apart from that, in this case, we find that the larger the data size used as a model training sample, the superior performance obtained by the classification model in predicting credit risk in P2P lending.
Enhancing Abusive Language Detection on Twitter Using Stacking Ensemble Learning Utami, Putri; Tanga, Yulizchia Malica Pinkan; Unjung, Jumanto; Muslim, Much Aziz
Journal of Information System Exploration and Research Vol. 3 No. 2 (2025): July 2025
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v3i2.594

Abstract

Detecting abusive language on Twitter is an important step in reducing the prevalence of negative content and harassment. This study aims to improve the accuracy and effectiveness of abusive language detection on Twitter by addressing the limitations of the single model commonly used previously. The stacking method is employed by combining Term Frequency-Inverse Document Frequency (TF-IDF) as the feature extraction method, along with the Naive Bayes and XGBoost algorithms as classification models. Naive Bayes is known for its simplicity in handling text classification, while XGBoost excels in processing complex data and achieving high accuracy. The combination of these two models is expected to improve performance in detecting coarse language. The research results show that the proposed model outperforms the methods in previous studies, with an accuracy of 91.91% and an AUC of 96.76%. These findings demonstrate the effectiveness of the stacking approach in reducing classification errors in coarse language detection. Further research could explore the use of larger datasets or more complex models to improve detection accuracy.
Optimizing Support Vector Machine Performance for Parkinson's Disease Diagnosis Using GridSearchCV and PCA-Based Feature Extraction Jumanto, Jumanto; Rofik, Rofik; Sugiharti, Endang; Alamsyah, Alamsyah; Arifudin, Riza; Prasetiyo, Budi; Muslim, Much Aziz
Journal of Information Systems Engineering and Business Intelligence Vol. 10 No. 1 (2024): February
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.10.1.38-50

Abstract

Background: Parkinson's disease (PD) is a critical neurodegenerative disorder affecting the central nervous system and often causing impaired movement and cognitive function in patients. In addition, its diagnosis in the early stages requires a complex and time-consuming process because all existing tests such as electroencephalography or blood examinations lack effectiveness and accuracy. Several studies explored PD prediction using sound, with a specific focus on the development of classification models to enhance accuracy. The majority of these neglected crucial aspects including feature extraction and proper parameter tuning, leading to low accuracy. Objective: This study aims to optimize performance of voice-based PD prediction through feature extraction, with the goal of reducing data dimensions and improving model computational efficiency. Additionally, appropriate parameters will be selected for enhancement of the ability of the model to identify both PD cases and healthy individuals. Methods: The proposed new model applied an OpenML dataset comprising voice recordings from 31 individuals, namely 23 PD patients and 8 healthy participants. The experimental process included the initial use of the SVM algorithm, followed by implementing PCA for feature extraction to enhance machine learning accuracy. Subsequently, data balancing with SMOTE was conducted, and GridSearchCV was used to identify the best parameter combination based on the predicted model characteristics.  Result: Evaluation of the proposed model showed an impressive accuracy of 97.44%, sensitivity of 100%, and specificity of 85.71%. This excellent result was achieved with a limited dataset and a 10-fold cross-validation tuning, rendering the model sensitive to the training data. Conclusion: This study successfully enhanced the prediction model accuracy through the SVM+PCA+GridSearchCV+CV method. However, future investigations should consider an appropriate number of folds for a small dataset, explore alternative cross-validation methods, and expand the dataset to enhance model generalizability.   Keywords: GridSearchCV, Parkinson Disaese, SVM, PCA, SMOTE, Voice/Speech
UTAUT and WebQual Models for Measuring User Acceptance of Text Minutes from Video Conferencing Services Ningsih, Dewi Handayani Untari; Nurdin, Alya aulia; Muslim, Much Aziz
Scientific Journal of Informatics Vol 10, No 3 (2023): August 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v10i3.46651

Abstract

One of the issues encountered during the post-COVID-19 and new normal period is in the field of higher education and employment that require education and online or virtual meetings. Automatic speech recognition (ASR) is one of the technologies used to simplify recording through speech recognition into text minutes for the effectiveness of online meetings. This study aims to determine user acceptance in adopting text minutes on the Zoom Meeting video conference service for online meetings so that it can provide insights for video conferencing service provider companies in developing text minutes on their platforms. In this study, a total of 156 respondents participated in the study. The obtained data were analyzed using PLS-SEM. As a result, the construct of information, interaction, and performance expectancy has been shown to affect user satisfaction with text minutes in video conferencing services with an R square value of satisfaction of 0.516 (moderate). The quality of information is an important factor, information from the text minutes on video conferencing services must be accurate, reliable, timely, relevant, easy to understand, and presented in the appropriate format.Purpose: This observes ambitions to determine consumer popularity in adopting text minutes at the Zoom assembly video conference provider for online meetings in order that it could offer insights for video conferencing carrier company organizations in developing text minutes on their structures.Methods/Study design/approach: This study uses a mixture of UTAUT and WebQual. A studies instrument becomes prepared inside the shape of a web questionnaire containing query objects. All the items had been adapted from previous research. Then, data became amassed by way of administering an online questionnaire through diverse social media structures, along with WhatsApp and Telegram to respondents with several demographic questions and a five-factor Likert scale from strongly disagree to strongly agree (scored from 1 to 5) from eleven constructs. The obtained facts had been analyzed the usage of PLS-SEM.Result/Findings: The existence of text minutes on the Zoom Meeting video conference service has not been fully adopted by users in carrying out learning activities or online meetings. Primarily based on the studies that have been executed, there are four hypotheses accepted, namely H2, H3, H5, and H10. Meanwhile, six different hypotheses, namely H1, H4, H6, H7, H8, and H9, have been rejected. Elements influencing person adoption of the text of the Zoom assembly video conferencing carrier depend on the nice of the information generated from the text minutes from video conferencing in which the statistics ought to be accurate, reliable, well timed, relevant, easy to apprehend, and provided in the right format. Novelty/Originality/Value: As a result, the construct of information, interaction, and performance expectancy has been shown to affect user satisfaction with text minutes in video conferencing services with an R square value of satisfaction of 0.516 (moderate). From the outcomes of the research conducted, video conferencing service providers can improve and expand text mins capabilities extra sophisticatedly but nonetheless easy in order that overall customers sense greater satisfied with video conference textual content mins and could reuse and endorse others to apply the same era. In the next look at, its miles predicted that the scope of the survey will be wider globally with the growth within the wide variety of respondents, in addition to including a few constructs that have now not been used
Co-Authors Afifah Ratna Safitri Agus Harjoko Ahmad, Kamilah Alabid, Noralhuda N. Alamsyah - Aldi Nurzahputra Aldi Nurzahputra, Aldi Alfatah, Abdul Muis Alfatah, Abdul Muis Ali, Muazam Amanah Febrian Indriani Aminuyati Anggyi Trisnawan Putra Annegrat, Ahmed Mohamed Astuti, Winda Try Astuti, Winda Try Atikah Ari Pramesti, Atikah Ari Budi Prasetiyo Budi Prasetiyo, Budi Darmawan, Aditya Yoga Dewi Handayani Untari Ningsih Dinova, Dony Benaya Djuniharto Djun Doni Aprilianto Dullah, Ahmad Ubai Eka Listiana Endang Sugiharti, Endang Fadhilah, Muhammad Syafiq Fadli Dony Pradana Falasari, Anisa Farih, Habib al Florentina Yuni Arini, Florentina Yuni Hadiq, Hadiq Hakim, M. Faris Al Hendi Susanto Imam Ahmad Ashari, Imam Ahmad Irfan, Mohammad Syarif Jeffry Nur Rifa’i Jumanto , Jumanto Jumanto Jumanto, Jumanto Jumanto Unjung Khan, Atta Ullah Larasati, Ukhti Ikhsani Larasati, Ukhti Ikhsani Lestari, Apri Dwi Listiana, Eka Listiana, Eka Lodana, Mae Maulana, Muhamad Irvan Miranita Khusniati moh minhajul mubarok Muhamad Anbiya Nur Islam Mustaqim, Amirul Muzayanah, Rini N. Nelis Febriani SM Nikmah, Tiara Lailatul Nina Fitriani, Nina Ningsih, Maylinna Rahayu Nugraha, Faizal Widya Nuk Ghurroh Setyoningrum Nur Astri Retno, Nur Astri Nurdin, Alya Aulia Nurriski, Yopi Julia Perbawawati, Anna Adi Perbawawati, Anna Adi Pertiwi, Dwika Ananda Agustina Priliani, Erlin Mega Priliani, Erlin Mega Purnawan, Dedy Putri Utami, Putri Putri, Salma Aprilia Huda Putriaji Hendikawati Putro, Ari Nugroho Qohar, Bagus Al Raharjo, Bagus Purbo Rahman, Raihan Muhammad Rizki Rahmanda, Primana Oky Rahmanda, Primana Oky Riza Arifudin Rofik Rofik, Rofik Roni Kurniawan Rukmana, Siti Hardiyanti Ryo Pambudi S.Pd. M Kes I Ketut Sudiana . Safri, Yofi Firdan Safri, Yofi Firdan Saiful Arifin Salahudin, Shahrul Nizam Sanjani, Fathimah Az Zahra Seivany, Ravenia Simanjuntak, Robert Panca R. Solehatin, Solehatin Sugiman Sugiman Sulistiana Syarifah, Aulia Tanga , Yulizchia Malica Pinkan Tanga, Yulizchia Malica Pinkan Tanzilal Mustaqim Trihanto, Wandha Budhi Trihanto, Wandha Budhi Triyana Fadila Varindya Ditta Iswari Vedayoko, Lucky Gagah Vedayoko, Lucky Gagah Wargo, Franki Setyo Wibowo, Ceorido Ghalib Wibowo, Kevyn Alifian Hernanda Yosza Dasril Yosza Dasril