Article Per Year (5 Year)
p-Index From 2021 - 2026
4.72
P-Index
This Author published in this journals
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

Published : 85 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 85 Documents
Search

 4   5   6   7   8 
Integrasi Chatbot Berbasis Telegram Untuk Sistem Notifikasi Website moh minhajul mubarok; Much Aziz Muslim
NJCA (Nusantara Journal of Computers and Its Applications) Vol 6, No 1 (2021): Juni 2021
Publisher : Computer Society of Nahdlatul Ulama (CSNU) Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36564/njca.v6i1.121

Abstract

Website merupakan salah satu gerbang yang menghubungkan antara administrator dengan client. Website yang baik adalah website yang dapat berkomunikasi dua arah antara administrator dengan client, tidak hanya memberi komunikasi tapi juga dapat menerima komunikasi. Salah satu contoh bentuk komunikasi client dengan administrator adalah adanya halaman contact perusahaan, halaman karir, dll. Namun seiring berjalannya waktu administrator atau orang yang bertugas memenejemen website mengalami permasalahan yaitu terlambat menerima informasi dari user karena administrator harus terlebih dahulu membuka halaman admin website untuk bisa membuka info terbaru. Oleh karena itu diperlukan sistem notifikasi yang baik yang memungkinkan administrator dapat menerima notifikasi terbaru dari website dengan cara yang mudah dan cepat. Tujuannya yaitu agar dapat memudahkan komunikasi antara perusahaan dengan client khususnya dari sisi administrator  supaya lebih mudah dalam menerima informasi penting dari website menggunakan sistem notifikasi berbasis telegram chatbot. Metode yang digunakan dalam pembuatan sistem ini adalah metode waterfall. Adapun metode pengumpulan data untuk membuat sistem tersebut adalah dengan wawancara. Setelah dilakukan testing, sistem notifikasi website ini dapat berjalan dengan baik dan administrator dapat menerima informasi lebih cepat.
SILATIP Dit Reskrimsus Polda Jateng Unit Cyber Crime Menggunakan Metode Extreme Programming Fadli Dony Pradana; Much Aziz Muslim
Jurnal Informatika: Jurnal Pengembangan IT Vol 3, No 3 (2018): JPIT, September 2018
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v3i3.979

Abstract

The Special Criminal Investigation Directorate is the executing element of the main task under the Regional Police Chief, led by Dir Reskrimsus with the rank of Chief of the Police Commissioner (Kombes Pol) / Echelon II-B, tasked with conducting investigations and investigating special crimes, coordinate, supervise, and administer PPNS investigations in accordance with statutory regulations. So far, the Directorate of Criminal Investigation of the Central Java Regional Police, especially the Cyber Crime Unit, in its service still uses manual methods. This was felt to be inefficient because of the large number of complaints coming in and the lack of police personnel who handled complaints from reporters so that servants to the community were judged to be less than optimal and needed a long time in a complaint report. This study uses the Extreme Programming method in designing the developed system. Based on the analysis and discussion, it can be concluded that Extreme Programming is one method that supports the acceleration of the development of a small to medium scale system and can improve the quality of the system. Unfortunately, Extreme Programming does not have formal documentation so there is no measuring instrument that shows that the system has been completed.
Classification Email Spam using Naive Bayes Algorithm and Chi-Squared Feature Selection Ningsih, Maylinna Rahayu; Unjung, Jumanto; Farih, Habib al; Muslim, Much Aziz
(JAIS) Journal of Applied Intelligent System Vol. 9 No. 1 (2024): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jais.v9i1.9695

Abstract

Spam email is a problem that disturbs and harms the recipient. Machine learning is widely used in overcoming email spam because of its ability to classify emails into spam or non-spam. In this research, the Naïve Bayes algorithm is initiated with the Chi-Squared selection feature to classify spam emails. So that the implementation is able to increase accuracy for better performance in classification. The feature selection method is used to direct the model's attention to features that are related to the target variable. In this study, the chi squared feature uses a value of K = 2500, with an accuracy of 98.83% which shows an increase in model performance compared to previous research. So that the Naïve Bayes model with the Chi-Squared selection feature is proven to provide better performance. 
Random State Initialized Logistic Regression for Improved Heart Attack Prediction Wibowo, Kevyn Alifian Hernanda; Putri, Salma Aprilia Huda; Jumanto, Jumanto; Muslim, Much Aziz
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 7 No. 2 (2023)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v7i2.822

Abstract

One of the primary causes of death in Indonesia is heart attacks. Therefore, an effective method of pre-diction is required to determine whether a patient is experiencing a heart attack. One efficient approach is to use machine learning models. However, it is still rare to find machine learning models that have good performance in predicting heart attacks. This study aims to develop a machine learning model on Logistic Regression algorithm in predicting heart attack. Logistic Regression is one of the machine learning meth-ods that can be used to study the relationship between a binary response variable [0,1] and a set of pre-dictor variables, and can be used directly to calculate probabilities. In this study, a random state is ini-tialized in the Logistic Regression model in order to stabilize the training of the machine learning model and increase the precision of the proposed method. The results of this study show that the proposed model can be a method that has good performance in predicting heart attack disease.
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.
Online payment fraud prediction with machine learning approach using naive bayes algorithm Rahman, Raihan Muhammad Rizki; Muslim, Much Aziz
Journal of Student Research Exploration Vol. 2 No. 2: July 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/josre.v2i2.343

Abstract

The increase in e-commerce has provided easy access for the public, but it also opens up opportunities for fraud in online transactions. Payment fraud is also a problem that often arises in transactions through electronic media. This research aims to analyze payment fraud in e-commerce transactions. This research uses a machine learning approach using the Naive Bayes algorithm. This research uses online transaction datasets involving various attributes such as payment and shipping methods. The developed Naive Bayes model achieved an accuracy of 61.03% with K = 7. The evaluation shows a balance between precision (59.46%) and recall (62.05%), although this study is limited by data quality and basic assumptions of Naive Bayes. In future research, it is worth considering the use of additional features or more complex data processing to improve the performance of fraud detection in online transactions. This research provides important insights in the fight against financial crime in the context of electronic commerce.
Optimized Support Vector Machine with Particle Swarm Optimization to Improve the Accuracy Amazon Sentiment Analysis Classification Ningsih, Maylinna Rahayu; Unjung, Jumanto; Pertiwi, Dwika Ananda Agustina; Prasetiyo, Budi; Muslim, Much Aziz
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 9, No. 1, February 2024
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v9i1.1888

Abstract

Text mining is a valuable technique that empowers users to gain a deeper understanding of existing textual data, ultimately allowing them to make more informed decisions. One important application of text mining is in the field of sentiment analysis, which has gained significant traction among companies aiming to understand how customers perceive their products and services. In response to this growing need, various research efforts have been made to improve the accuracy of sentiment analysis classification models. The purpose of this article is to discuss a specific approach using the Support Vector Machine (SVM) algorithm, which is often used in machine learning for text classification tasks and then combined with the application of Particle Swarm Optimization (PSO), which optimizes the SVM model parameters to achieve the best classification results. This dynamic combination not only improves accuracy but also enhances the model's ability to efficiently handle large amounts of text data to achieve better results. The research findings highlight the effectiveness of this approach. The application of the SVM algorithm with PSO resulted in an outstanding accuracy performance of 94.92%. The substantial increase in accuracy compared to previous studies shows the promising potential of this methodology. This proves that the SVM algorithm model approach with Particle Swarm Optimization provides good performance.
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%.
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