Text Mining of UU-ITE Implementation in Indonesia

Hakim, Lawrence S.; Kusumasari, Tien Fabrianti; Lubis, Muharman

doi:10.1088/1742-6596/1007/1/012038

Cited by 16 publications

(5 citation statements)

References 16 publications

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“…Importantly, the method stages determine the success of the research to assure its validity and reliability [16][17][18][19][24][25][26][27][28][29][30][31]. Figure 1 is a flowchart that describe the detail of the steps undertaken to complete this research, which is include NPC Agent Design, Design of Environment, Design of Enemy, Design of HFSM for NPC Agent and Design of FSM for Enemies, then continue with various test scenarios.…”

Section: Methodsmentioning

confidence: 99%

Defense behavior of real time strategy games: comparison between HFSM and FSM

Fauzi

Hariadi

Lubis

et al. 2019

IJEECS

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<span>RTS Game is one of the popular genre in PC gaming, which has been played by various type of players frequently. In RTS game, NPC Defense Building (Tower) has attacking behavior to the closest enemy without considering certain enemy parameters. This causes the NPC Tower to be more predictable by the opponent and easily defeated if NPC attacked by enemies in the group. Thus, this research simulates NPC Tower using Hierarchical Finite State Machine (HFSM) method compared with Finite State Machine (FSM). In this study, NPC Tower detects enemies by seeing at four parameters namely NPC Tower Health, Enemy's Health, Enemy Type, and Tower Distance to enemies. NPC Tower will attack the most dangerous enemy according to the ‘Degree of Danger’ parameter. Then use the decision-making logic of the rule-based system. The output of NPC Tower are three type of behaviors namely Aggressive Attacking, Regular Attacking, and Attack with Special Skill. From the test results of 3 NPC Tower, Kamandaka NPC Tower with HFSM method is winning 8.92% compare to Kamandaka Tower with FSM method. For Gayatri Tower NPC obtained equal results using both HFSM and FSM. Meanwhile, Adikara NPC with HFSM method is 4.62% superior to Adikara Tower with FSM method.</span>

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Section: Methodsmentioning

confidence: 99%

Defense behavior of real time strategy games: comparison between HFSM and FSM

Fauzi

Hariadi

Lubis

et al. 2019

IJEECS

Self Cite

View full text Add to dashboard Cite

show abstract

“…In this process there are four main activities performed: punctuation removal, stop words removal, tokenization, and stemming. Stop words removal is the process of removing the words that have no significant meaning [15]. Tokenizing is the process of breaking sentences into words.…”

Section: Fig 1 Conceptual Modelmentioning

confidence: 99%

Sentiment Analysis to Measure Celebrity Endorsment’s Effect using Support Vector Machine Algorithm

Pinem¹,

Andreswari²,

Hasibuan³

2018

EECSI

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Celebrity endorsement is a phenomenon in which companies advertises their products by using celebrity services, and celebrities take advantage of their popularity to promote a brand or product of the company through social media. In this study, KFC did a celebrity endorsement to make their menu more popular. KFC choose to work with Raditya Dika to promote their latest menu, KFC Salted Egg Chicken. This study will examine whether in such cases there is a change in public sentiment towards the product after the celebrity endorsement. It can be done using text mining and sentiment analysis. There are several algorithms that can be used to perform sentiment analysis, one of them is Support Vector Machine. Support Vector Machine (SVM) was chosen because this method is quite accurate in various studies. SVM also takes into account various features of the document, including features that often do not appear on the document, so it can reduce the loss of information from the data. The data used in this research are taken from YouTube and Twitter comment about KFC Salted Egg Chicken. Several step was done in this sentiment analysis research, that are preprocessing text, feature extraction, classification, and evaluation. The result model is tested and evaluated before and after endorsement by looking at the value of accuracy, precision, recall, and f1-measure. The test result of accuracy, precision, recall, and f-measure before endorsement were 67,83%, 69%, 68%, and 66%. After the endorsement, the test results were 74.06%, 74%, 74%, and 74% respectively. The results of this study indicate that SVM has an accurate measurement in sentiment analysis studies. Moreover, this study found that there was not significant change in public sentiment regarding the product before and after the celebrity endorsement.

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“…This approach yielded an accuracy result of 83.33% for recall, 50% for precision, and 62.5% for f-measure. Another research endeavor by Hakim et al [17] explored the clustering of cybercrime cases under the ITE Law, utilizing the k-means algorithm to classify the cases into five distinct clusters.…”

Section: Introductionmentioning

confidence: 99%

“…In contrast, the current investigation uses mutual information, TF-IDF, and the SVM algorithm to classify cybercrime cases, presenting a different approach. Another study by Hakim et al [17] used text mining to cluster cases linked to the ITE Law, with data sourced from Twitter posts. They applied TF-IDF for this purpose.…”

Section: Introductionmentioning

confidence: 99%

Classifying Indonesian Cyber Crime Cases under ITE Law Using a Hybrid of Mutual Information and Support Vector Machine

Rahmat,

Aziira,

Purnamawati

et al. 2023

IJSSE

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In Indonesia, the process of identifying and categorizing cyberlaw infringements traditionally involves manual procedures administered by experts, lawyers, or law enforcement personnel. This study introduces a method to enhance the analysis and processing of case chronological data through the application of text mining. Using the Support Vector Machine for classification, alongside feature extraction both with and without Mutual Information, the study aims to automate the classification of cybercrime cases. The preprocessing phase encompasses text cleaning, case folding, stop word removal, stemming, and tokenization and weighting with TF-IDF. The model achieved an accuracy rate of 95.45% during evaluation and 91.42% when tested on 35 data points with 1500 selected features. This performance surpasses the classification accuracy obtained in previous research.

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Text Mining of UU-ITE Implementation in Indonesia

Cited by 16 publications

References 16 publications

Defense behavior of real time strategy games: comparison between HFSM and FSM

Defense behavior of real time strategy games: comparison between HFSM and FSM

Sentiment Analysis to Measure Celebrity Endorsment’s Effect using Support Vector Machine Algorithm

Classifying Indonesian Cyber Crime Cases under ITE Law Using a Hybrid of Mutual Information and Support Vector Machine

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