Article Per Year (5 Year)
p-Index From 2020 - 2025
0.659
P-Index
This Author published in this journals
All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
Nur Rokhman
Department of Computer Science and Electronics, FMIPA UGM, Yogyakarta
Computer Science & IT Control & Systems Engineering

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
Detection Of Spam Comments On Instagram Using Complementary Naïve Bayes Nur Azizul Haqimi; Nur Rokhman; Sigit Priyanta
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 13, No 3 (2019): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.47046

Abstract

Instagram (IG) is a web-based and mobile social media application where users can share photos or videos with available features. Upload photos or videos with captions that contain an explanation of the photo or video that can reap spam comments. Comments on spam containing comments that are not relevant to the caption and photos. The problem that arises when identifying spam is non-spam comments are more dominant than spam comments so that it leads to the problem of the imbalanced dataset. A balanced dataset can influence the performance of a classification method. This is the focus of research related to the implementation of the CNB method in dealing with imbalance datasets for the detection of Instagram spam comments. The study used TF-IDF weighting with Support Vector Machine (SVM) as a comparison classification. Based on the test results with 2500 training data and 100 test data on the imbalanced dataset (25% spam and 75% non-spam), the CNB accuracy was 92%, precision 86% and f-measure 93%. Whereas SVM produces 87% accuracy, 79% precision, 88% f-measure. In conclusion, the CNB method is more suitable for detecting spam comments in cases of imbalanced datasets.
Case-Based Reasoning Using The Nearest Neighbor Method For Detection Of Equipment Damage To PLN Power Plant Riska Amalia Praptiwi; Nur Rokhman; Wahyono Wahyono
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 14, No 4 (2020): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.57434

Abstract

Predictive Maintenance (PdM) at the PLN Power Plant is a periodic monitoring of equipment activities before the equipment is damaged in more severe conditions. According to an expert or PdM owner that maintenance analysis is not appropriate and efficiency has an impact on maintenance costs that are not small. In real conditions, the PdM owner analyzes equipment damage based on previous cases of damage equipment. Then we need a computer-based intelligent system that can help detect damage to equipment.Based on the Literature Review that has been done, Case-Based Reasoning can solve new problems using answers or experiences from old problems such as imitating human abilities. Case-Based Reasoning Process there is the most important step, which is to find the highest similarity value or the level of similarity between new cases and old cases by adapting solutions from old cases that have occurred (Sankar, 2004). In this study the process of similarity or approach using Nearest Neighbor.Testing on the system uses 20 test data and the measurement of system performance uses confusion matrix. Evaluation of testing using confusion matrix can be seen how accurately the system can classify data correctly that is equal to 97.98%. Then the precision value of 95% represents the number of positive categorized data that is correctly divided by the total data classified as positive. Furthermore, the test results of the equipment damage detection test data at the PLN plant with a threshold value of 0.75 using the nearest neighbor, the system has a performance with a 95% sensitivity level.
Anomaly Detection in Hospital Claims Using K-Means and Linear Regression Hendri Kurniawan Prakosa; Nur Rokhman
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 15, No 4 (2021): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.68160

Abstract

 BPJS Kesehatan, which has been in existence for almost a decade, is still experiencing a deficit in the process of guaranteeing participants. One of the factors that causes this is a discrepancy in the claim process which tends to harm BPJS Kesehatan. For example, by increasing the diagnostic coding so that the claim becomes bigger, making double claims or even recording false claims. These actions are based on government regulations is including fraud. Fraud can be detected by looking at the anomalies that appear in the claim data.This research aims to determine the anomaly of hospital claim to BPJS Kesehatan. The data used is BPJS claim data for 2015-2016. While the algorithm used is a combination of K-Means algorithm and Linear Regression. For optimal clustering results, density canopy algorithm was used to determine the initial centroid.Evaluation using silhouete index resulted in value of 0.82 with number of clusters 5 and RMSE value from simple linear regression modeling of 0.49 for billing costs and 0.97 for  length of stay. Based on that, there are 435 anomaly points out of 10,000 data or 4.35%. It is hoped that with the identification of these, more effective follow-up can be carried out.
C Source code Obfuscation using Hash Function and Encryption Algorithm Sarah Rosdiana Tambunan; Nur Rokhman
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 17, No 3 (2023): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.86118

Abstract

Obfuscation is a technique for transforming program code into a different form that is more difficult to understand. Several obfuscation methods are used to obfuscate source code, including dead code insertion, code transposition, and string encryption. In this research, the development of an obfuscator that can work on C language source code uses the code transposition method, namely randomizing the arrangement of lines of code with a hash function and then using the DES encryption algorithm to hide the parameters of the hash function so that it is increasingly difficult to find the original format. This obfuscator is specifically used to maintain the security of source code in C language from plagiarism and piracy. In order to evaluate this obfuscator, nine respondents who understand the C programming language were asked to deobfuscate the obfuscated source code manually. Then the percentage of correctness and the average time needed to perform the manual deobfuscation are observed. The evaluation results show that the obfuscator effectively maintains security and complicates the source code analysis.
Co-Authors Hendri Kurniawan Prakosa Nur Azizul Haqimi Riska Amalia Praptiwi Sarah Rosdiana Tambunan Sigit Priyanta Wahyono Wahyono