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All Journal International Journal of Electrical and Computer Engineering Lontar Komputer: Jurnal Ilmiah Teknologi Informasi 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 MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer 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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Journal : Journal of Soft Computing Exploration

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Improving car price prediction performance using stacking ensemble learning based on ann and random forest Tanga, Yulizchia Malica Pinkan; Simanjuntak, Robert Panca R.; Rofik, Rofik; Muslim, Much Aziz
Journal of Soft Computing Exploration Vol. 5 No. 3 (2024): September 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i3.462

Abstract

Determining the right selling price for a car can be a challenge for car sales companies. The selling price of a car is highly influenced by car characteristics such as brand, type, year of production, fuel type, and mileage. Therefore, the research aims to develop a more accurate model of car price prediction model by using a stacking ensemble technique that combines Random Forest and ANN. Random Forest is effective in handling outliers and reducing the risk of overfitting, while ANN has the advantage of capturing complex nonlinear patterns. The results show that the stacking ensemble model combining ANN and Random Forest can predict car sales prices by achieving an R2 value of 0.97. The results of this study can help distributors in selling cars make the right decisions regarding the sales price of cars. To improve the generalization of the model, future research is recommended to try a combination of different ensemble methods and the use of larger and more diverse datasets.
Support Vector Machine (SVM) Optimization Using Grid Search and Unigram to Improve E-Commerce Review Accuracy Sulistiana; Much Aziz Muslim
Journal of Soft Computing Exploration Vol. 1 No. 1 (2020): September 2020
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v1i1.3

Abstract

Electronic Commerce (E-Commerce) is distributing, buying, selling, and marketing goods and services over electronic systems such as the Internet, television, websites, and other computer networks. E-commerce platforms such as amazon.com and Lazada.co.id offer products with various price and quality. Sentiment analysis used to understand the product’s popularity based on customers’ reviews. There are some approaches in sentiment analysis including machine learning. The part of machine learning that focuses on text processing called text mining. One of the techniques in text mining is classification and Support Vector Machine (SVM) is one of the frequently used algorithms to perform classification. Feature and parameter selection in SVM significantly affecting the classification accuracy. In this study, we chose unigram as the feature extraction and grid search as parameter optimization to improve SVM classification accuracy. Two customer review datasets with different language are used which is Amazon reviews that written in English and Lazada reviews in the Indonesian language. 10-folds cross validation and confusion matrix are used to evaluating the experiment results. The experiment results show that applying unigram and grid search on SVM algorithm can improve Amazon review accuracy by 26,4% and Lazada reviews by 4,26%.
Improved Accuracy of Naive Bayes Classifier for Determination of Customer Churn Uses SMOTE and Genetic Algorithms Afifah Ratna Safitri; Much Aziz Muslim
Journal of Soft Computing Exploration Vol. 1 No. 1 (2020): September 2020
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v1i1.5

Abstract

With increasing competition in the business world, many companies use data mining techniques to determine the level of customer loyalty. The customer data used in this study is the german credit dataset obtained from UCI. Such data have an imbalance problem of class because the amount of data in the loyal class is more than in the churn class. In addition, there are some irrelevant attributes for customer classification, so attributes selection is needed to get more accurate classification results. One classification algorithm is naive bayes. Naive Bayes has been used as an effective classification for years because it is easy to build and give an independent attribute into its structure. The purpose of this study is to improve the accuracy of the Naive Bayes for customer classification. SMOTE and genetic algorithm do for improving the accuracy. The SMOTE is used to handle class imbalance problems, while the genetic algorithm is used for attributes selection. Accuracy using the Naive Bayes is 47.10%, while the mean accuracy results obtained from the Naive Bayes with the application of the SMOTE is 78.15% and the accuracy obtained from the Naive Bayes with the application of the SMOTE and genetic algorithm is 78.46%.
Optimize naïve bayes classifier using chi square and term frequency inverse document frequency for amazon review sentiment analysis Falasari, Anisa; Muslim, Much Aziz
Journal of Soft Computing Exploration Vol. 3 No. 1 (2022): March 2022
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v3i1.68

Abstract

The rapid development of the internet has made information flow rapidly wich has an impact on the world of commerce. Some people who have bought a product will write their opinion on social media or other online site. Long-text buyer reviews need a machine to recognize opinions. Sentiment analysis applies the text mining method. One of the methods applied in sentiment analysis is classification. One of the classification algorithms is the naïve bayes classifier. Naïve bayes classifier is a classification method with good efficiency and performance. However, it is very sensitive with too many features, wich makes the accuracy low. To improve the accuracy of the naïve bayes classifier algorithm it can be done by selecting features. One of the feature selection is chi square. The selection of features with chi square calculation based on the top-K value that has been determined, namely 450. In addition, weighting features can also improve the accuracy of the naïve bayes classifier algorithm. One of the feature weighting techniques is term frequency inverse document frequency (TF-IDF). In this study, using sentiment labelled dataset (field amazon_labelled) obtained from UCI Machine Learning. This dataset has 500 positive reviews and 500 negative reviews. The accuracy of the naïve bayes classifier in the amazon review sentiment analysis was 82%. Meanwhile, the accuracy of the naïve bayes classifier by applying chi square and TF-IDF is 83%.
Mix histogram and gray level co-occurrence matrix to improve glaucoma prediction machine learning Jumanto, Jumanto; Nugraha, Faizal Widya; Harjoko, Agus; Muslim, Much Aziz; Alabid, Noralhuda N.
Journal of Soft Computing Exploration Vol. 4 No. 1 (2023): March 2023
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v4i1.99

Abstract

Glaucoma is an eye disease that is the second leading cause of blindness. Examination of glaucoma by an ophthalmologist is usually done by observing the retinal image directly. Observations from one doctor to another may differ, depending on their educational background, experience, and psychological condition. Therefore, a glaucoma detection system based on digital image processing is needed. The detection or classification of glaucoma with digital image processing is strongly influenced by the feature extraction method, feature selection, and the type of features used. Many researchers have carried out various kinds of feature extraction for glaucoma detection systems whose accuracy needs to be improved. In general, there are two groups of features, namely morphological features and non-morphological features (image-based features). In this study, it is proposed to detect glaucoma using texture features, namely the GLCM feature extraction method, histograms, and the combined GLCM-histogram extraction method. The GLCM method uses 5 features and the Histogram uses 6 features. To distinguish between glaucoma and non-glaucoma eyes, the multi-layer perceptron (MLP) artificial neural network model serves as a classifier. The data used in this study consisted of 136 fundus images (66 normal images and 70 images affected by glaucoma). The performance obtained with this approach is an accuracy of 93.4%, a sensitivity of 86.6%, and a specificity of 100%.
Optimization of support vector machine using information gain and adaboost to improve accuracy of chronic kidney disease diagnosis Listiana, Eka; Muzayanah, Rini; Muslim, Much Aziz; Sugiharti, Endang
Journal of Soft Computing Exploration Vol. 4 No. 3 (2023): September 2023
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v4i3.218

Abstract

Today's database is growing very rapidly, especially in the field of health. The data if not processed properly then it will be a pile of data that is not useful, so the need for data mining process to process the data. One method of data mining used to predict a decision in any case is classification, where in the classification method there is a support vector machine algorithm that can be used to diagnose chronic kidney disease. The purpose of this study is to determine the level of accuracy of the application of information gain and AdaBoost on the support vector machine algorithm in diagnosing chronic kidney disease. The use of information gain is to select the attributes that are not relevant while AdaBoost is used as an ensemble method commonly known as the method of classifier combination. In this study the data used are chronic kidney disease (CKD) dataset obtained from UCI repository of machine learning. The result of experiment using MATLAB applying information gain and AdaBoost on vector machine support algorithm with k-fold cross validation default k = 10 shows an accuracy increase of 0.50% with the exposure of the result as follows, the support vector machine algorithm has accuracy of 99.25 %, if by applying AdaBoost on the support vector machine has an accuracy of 99.50%, whereas if applying AdaBoost and information gain on the support vector machine has an accuracy of 99.75%.
Comparison of the suitability of the otsu method thresholding and multilevel thresholding for flower image segmentation Hadiq, Hadiq; Solehatin, Solehatin; Djuniharto, Djuniharto; Muslim, Much Aziz; Salahudin, Shahrul Nizam
Journal of Soft Computing Exploration Vol. 4 No. 4 (2023): December 2023
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v4i4.266

Abstract

The digital representation of flowers, characterized by their vivid chromatic attributes, establishes them as viable candidates for deployment as input imagery within the object recognition paradigm. Within the context of object recognition, the imperative of a proficient image segmentation process is underscored, serving to effectively discern the object from its background and, consequently, optimizing the efficacy of the object recognition process. This research unfolds through a methodologically structured tripartite framework, encompassing the initial stage involving input imagery, the subsequent intermediate phase dedicated to image segmentation, and a conclusive stage centered on the quantitative evaluation of methodological outcomes. The second stage, focusing on image segmentation, employs the Otsu thresholding and multilevel thresholding methods. The subsequent third stage involves a thorough assessment of segmentation outcomes through the application of quantitative metrics, including Peak signal-to-oise ratio (PSNR) and Root Mean Square Error (RMSE). Empirical investigations, incorporating a diverse array of floral input images, reveal a conspicuous inclination towards a specific segmentation methodology. Specifically, the Otsu Thresholding method emerges as the more judicious choice relative to multilevel Thresholding, demonstrating superior performance with a diminished RMSE value and an augmented PSNR value, substantiated by an average RMSE value. This research is propelled by the overarching objective of discerning the most optimal method for the segmentation of flower images, particularly in the face of diverse input images. Its significant contribution lies in providing nuanced insights into the discerning selection of segmentation methodologies, attuned to the variability inherent in diverse forms of input imagery, thereby culminating in optimized outcomes within the domain of flower image recognition. Where did these results come from? please show it in the sub-discussion.
Measuring the usability effectiveness of using card menus and tree menus in school web applications Hadiq, Hadiq; Solehatin, Solehatin; Djuniharto, Djuniharto; Muslim, Much Aziz
Journal of Soft Computing Exploration Vol. 5 No. 1 (2024): March 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i1.299

Abstract

The aim of this research is to measure the usability effectiveness of a web application by using card menus and tree menus using user-friendly criteria and access speed as indicated by the number of clicks made by the user. The method used in this research is the Task-centered User Interface method, where this method allows for planning and evaluating the arrangement of the interface according to user needs. There are four stages in this method, including user identification by conducting needs analysis, the second phase is user interface design. The third phase is the implementation of the card menu and tree menu design, and the fourth face is testing the usability and effectiveness requirements. From the research that has been carried out regarding measuring the effectiveness of using card menus, it is more effective to use than tree menus because you can directly lift the menu and access it. Meanwhile, for usability, the card menus have a higher usability index than the tree menus. Meanwhile, for usability measurements carried out by direct observation and distributing questionnaires, the resulting percentage of user understanding, ease, and speed for the card menu display was 87% and for the tree menu was 60% so that the card menu display was more accepted by users than the tree menu. The new thing provided by the results of this research is in the form of suggestions that can be used by web application developers to use the right type of menu in building web-based applications with the same specifications as in the case of school finance applications.
Using genetic algorithm feature selection to optimize XGBoost performance in Australian credit Pertiwi, Dwika Ananda Agustina; Ahmad, Kamilah; Salahudin, Shahrul Nizam; Annegrat, Ahmed Mohamed; Muslim, Much Aziz
Journal of Soft Computing Exploration Vol. 5 No. 1 (2024): March 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i1.302

Abstract

To reduce credit risk in credit institutions, credit risk management practices need to be implemented so that lending institutions can survive in the long term. Data mining is one of the techniques used for credit risk management. Where data mining can find information patterns from big data using classification techniques with the resulting level of accuracy. This research aims to increase the accuracy of classification algorithms in predicting credit risk by applying genetic algorithms as the best feature selection method. Thus, the most important feature will be used to search for credit risk information. This research applies a classification method using the XGBoost classifier on the Australian credit dataset, then carries out an evaluation by measuring the level of accuracy and AUC. The results show an increase in accuracy of 2.24%, with an accuracy value of 89.93% after optimization using a genetic algorithm. So, through research on genetic algorithm feature selection, we can improve the accuracy performance of the XGBoost algorithm on the Australian credit dataset.
A new CNN model integrated in onion and garlic sorting robot to improve classification accuracy Lestari, Apri Dwi; Khan, Atta Ullah; Pertiwi, Dwika Ananda Agustina; Muslim, Much Aziz
Journal of Soft Computing Exploration Vol. 5 No. 1 (2024): March 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i1.304

Abstract

The profit share of the vegetable market, which is quite large in the agricultural industry, needs to be equipped with the ability to classify types of vegetables quickly and accurately. Some vegetables have a similar shape, such as onions and garlic, which can lead to misidentification of these types of vegetables. Through the use of computer vision and machine learning, vegetables, especially onions, can be classified based on the characteristics of shape, size, and color. In classifying shallot and garlic images, the CNN model was developed using 4 convolutional layers, with each layer having a kernel matrix of 2x2 and a total of 914,242 train parameters. The activation function on the convolutional layer uses ReLu and the activation function on the output layer is softmax. Model accuracy on training data is 0.9833 with a loss value of 0.762.
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 Hakim, Roshan Aland 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 Maulana, Muhamad Irvan Miranita Khusniati moh minhajul mubarok Muhamad Anbiya Nur Islam Mustaqim, Amirul Muzayanah, Rini Nikmah, Tiara Lailatul Nina Fitriani, Nina Ningsih, Maylinna Rahayu Nugraha, Faizal Widya 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 Wibowo, Kevyn Alifian Hernanda Yosza Dasril Yosza Dasril