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All Journal Jurnal Indonesia Sosial Sains Asian Journal of Social and Humanities
Budi Tjahjono
Universitas Esa Unggul, Indonesia
Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Energy Environmental Science Immunology & microbiology Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Neuroscience Physics Social Sciences

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Risk Management Domain Application Plan Electronic Based Governance System (SPBE) Case Study: Tangerang Government Communications and Informatics Service Bayu Sulistiyanto Ipung Sutejo; Agung Mulyo Widodo; Gerry Firmansyah; Budi Tjahjono
Jurnal Indonesia Sosial Sains Vol. 4 No. 09 (2023): Jurnal Indonesia Sosial Sains
Publisher : CV. Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jiss.v4i09.878

Abstract

Various applications of SPBE have been produced by the Central and Regional Authorities and have contributed to the efficiency and effectiveness of government maintenance. Nevertheless, the results of the SPBE development show a relatively low maturity rate and a high gap between the Central Authority and the Regional Government. Based on the results of the 2018 SPBE evaluation of 616 Central and Regional Government Instances, the National SPBE Index reached a value of 1.98 with a sufficient predicate of the target SPBE index of 2.6 out of 5 levels with a good prediction. Reviewed from the access of Central and Regional Authorities, the average Central Authorities SPBE index was 2.6 with a good predicate, while the average Regional Government SPBE Index was 1.87 with a sufficient Predicate. Reviewed from the target access spread, 13.3% of Central and Regional Authorities have reached or exceeded the target SPBE 2.6 index, while 86.7% have not yet reached the SPBE 2.0 index target. This shows that there are problems in the development of the SPBE nationally. On the other hand, the development of ICT 4.0 trends is a key external factor that can drive the realization of integrated SPBE implementation and improved quality of SPBE services that make it easier for users in accessing government services. The vision is a benchmark in implementing the integrated implementation of SPBE in the Central and Regional Authorities to produce integrative, dynamic, transparent, and innovative government bureaucracy, as well as improving the quality of integrated, effective, responsive, and adaptive public services.
Audit of Information Technology Governance on School Operational Cost Flow in SMKN West Jakarta Using COBIT 2019 Narul Sakron; Gerry Firmansyah; Habibullah Akbar; Budi Tjahjono
Jurnal Indonesia Sosial Sains Vol. 4 No. 09 (2023): Jurnal Indonesia Sosial Sains
Publisher : CV. Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jiss.v4i09.881

Abstract

IT governance is a process that aims to carry out the process of aligning business objectives in an agency in accordance with the business strategy applied to that agency. One sector that is trying to improve IT in improving its governance is the education sector including all SMKNs in West Jakarta. The problem with data management within the West Jakarta Vocational High School is that there is no analysis and research on good and relevant information technology governance in every budget the boss funds issue. This study aims to determine the level of capability and gaps in information technology governance that is currently being implemented, namely the boss fund management information system (Rkas, Arkas, Ready Bop Bos and Headquarters). This study uses analysis based on the 2019 COBIT standard with a focus on the APO12 (Managed Risk) and APO13 (Managed Security) domains to produce a value of capability level that can be used as a reference for analyzing risk management and security management in boss fund management at all West Jakarta Vocational High Schools. The data used in this study came from interviews, questionnaires and direct observation to the research site. The audit results show. APO12 level 2 capability level with a value of 89.29% (Fully Achieved). The capability level of APO12 is level 3 with a value of 77.36% (Largely Achieved) where the capability level of Level 3 APO12 does not reach Fully Achieved so that the capability level of APO12 is at level 3 The capability level of APO13 level 2 with a value of 61.43% (largely Achieved) which does not reach Fully Achieved so The capability level of APO13 is at level 2. The results of this study provide recommendations for aligning the vision, mission, and objectives of boss fund management, so as to improve the function of the boss fund management information system in all SMKN West Jakarta.
Analysis of Time Series Water Level Data Prediction Using Deep Learning Method at the Water Gate of DKI Jakarta Water Resources Office Supriyade Supriyade; Gerry Firmansyah; Habibullah Akbar; Budi Tjahjono
Jurnal Indonesia Sosial Sains Vol. 4 No. 09 (2023): Jurnal Indonesia Sosial Sains
Publisher : CV. Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jiss.v4i09.883

Abstract

Indonesia has 2 seasons, namely the dry season and the rainy season. During the rainy season, many points in the DKI Jakarta area experience flooding or inundation. The reason why Jakarta often experiences flooding is caused by several factors, including local rain floods, shipment floods and tidal floods. The DKI Jakarta Water Resources Agency currently does not have a system that can predict future water levels by referring to past and present water level data. Through this background, the author tries to conduct research in one of the floodgates in the northern area of DKI Jakarta in predicting water levels using deep learning methods , namely Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM). The purpose of this research is to analyze the best deep learning models and predict water level time series data. From the results of the analysis carried out, the best deep learning model is Long Short Term Memory (LSTM) using several tests such as n-input, split data with a composition of 90.33% train data and 9.67% test data , as well as testing of different parameters including epoch, batch size, learning rate, dropout , so the results obtained are the lowest error values with RMSE (17.65), MAPE (0.29), MAE (3.37) and the time needed in the process (runtime) is 39 minutes
Analysis of Data Mining Applications for Determining Credit Eligibility Using Classification Algorithms C4.5, Naïve Bayes, K-NN, and Random Forest Yessy Oktafriani; Gerry Firmansyah; Budi Tjahjono; Agung Mulyo Widodo
Asian Journal of Social and Humanities Vol. 1 No. 12 (2023): Asian Journal of Social and Humanities
Publisher : Pelopor Publikasi Akademika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59888/ajosh.v1i12.119

Abstract

This study aims to enhance the credit evaluation process within Credit Union (CU) Karya Bersama Lestari (KABARI). The study leveraged four distinct algorithms, namely Decision Tree C4.5, Naive Bayes, K-Nearest Neighbors (K-NN), and Random Forest, to predict the suitability of extending loans to potential borrowers. Rapid Miner was employed as a tool to maximize accuracy by analyzing the Confusion matrix. Testing was conducted on a dataset consisting of 459 member loan submissions. The results of the analysis revealed that the K-Nearest Neighbors (K-NN) algorithm achieved the highest accuracy among the evaluated algorithms. Specifically, the Decision Tree algorithm demonstrated an accuracy rate of 95.65%, along with a precision and recall of 94.12%. The Naive Bayes algorithm achieved an accuracy rate of 95.65%, supported by precision and recall values of 100% and 88.24%, respectively. The K-Nearest Neighbors algorithm displayed the highest accuracy rate of 97.83%, accompanied by 100% precision and 94.12% recall. Meanwhile, the Random Forest algorithm exhibited an accuracy rate of 93.48%, complemented by precision and recall values of 100% and 82.35%, respectively. The study's conclusions bear relevance for refining loan approval processes and fostering improved lending practices within financial institutions like CU KABARI.  
Performance Evaluation of Business Continuity Plan in Dealing with Threats and Risks in Cilegon Companies Use ISO 22301:2019 & NIST Sp 800-30 R1 Frameworks Case Study: PT. X Hendaryatna Hendaryatna; Gerry Firmansyah; Budi Tjahjono; Agung Mulyo Widodo
Asian Journal of Social and Humanities Vol. 1 No. 12 (2023): Asian Journal of Social and Humanities
Publisher : Pelopor Publikasi Akademika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59888/ajosh.v1i12.120

Abstract

This research was conducted at PT.X which is located in Cilegon, Merak-Banten. Seeing the geographical location of PT.X which is in a disaster-prone area, the company must ensure an effective business continuity process. In accordance with government regulations on Electronic-Based Government Systems (SPBE) related to corporate and government business activities, companies must be able to ensure business continuity in every condition that poses a threat and risk, but with no specific obligation that is the basis for the company's business continuity if it does not have a Business Continuity Plan (BCP) process, it will get a sanction. The purpose of this research is to evaluate the existing BCP process at PT X Cilegon and provide recommendations for a standardized BCP framework in the company to ensure business continuity as the company's Business Continuity Management System (BCMS) to avoid all threats and risks. BCP has standards regulated in ISO 22301: 2019 as its framework, and in BCP there is a risk analysis process and this research will be carried out using the NIST SP 800-30 Revision 1 method as its best practice. The evaluation results show that the previous BCP process at PT X Cilegon was not in accordance with the standards and the risk analysis carried out was still based on the ISO that the company had implemented but not ISO 31000 which is the risk management standard, so this study provides recommendations for a BCP framework that is in accordance with the standards and risk analysis with risk analysis methods that produce risk priorities.
Analysis of School Community Sentiment towards Personal Data Protection Law Using Support Vector Machine (SVM) Method Gusti Fachman Pramudi; Gerry Firmansyah; Budi Tjahjono; Agung Mulyo Widodo
Asian Journal of Social and Humanities Vol. 1 No. 12 (2023): Asian Journal of Social and Humanities
Publisher : Pelopor Publikasi Akademika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59888/ajosh.v1i12.121

Abstract

This research aims to analyze the awareness status of the school community regarding the right to personal data protection, test and analyze the sentiments of the school community towards the implementation of the Personal Data Protection Law and as a means of outreach regarding personal data protection laws in the world of education, especially school community besides that to find out whether the Support Vector Machine method can be used as a method in conducting Sentiment Analysis research. The results of this study can be concluded that only 38% of the school community knows about this regulation while the other 62% still don't know much about this rule. Then for the use of the Support Vector Machine method which has been carried out five (5) trials using different variations of training data and test data produces an average accuracy rate of 85.97% with the highest results on training data and test data 50% - 50% that is equal to 88.00% and the lowest result is in the experiment of training data and test data of 90% - 10% which is equal to 84.44%. For the school community's sentiment towards the Personal Data Protection Act, it was 56% or as many as 496 of 887 words. which shows a neutral response and 8% or as many as 72 out of 887 sentiment words show a negative response.
Emotional Classification Based on Facial Expression Recognition Using Convolutional Neural Network Method Arif Pami Setiaji; Gerry Firmansyah; Habibullah Akbar; Budi Tjahjono; Agung Mulyo Widodo
Asian Journal of Social and Humanities Vol. 1 No. 12 (2023): Asian Journal of Social and Humanities
Publisher : Pelopor Publikasi Akademika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59888/ajosh.v1i12.139

Abstract

In recent years, the development of human-computer interaction technology has reached remarkable levels, particularly in the field of facial expression recognition. This technology utilizes human facial images to identify and classify emotional expressions such as happiness, sadness, fear, and more through computer image processing. Active research in facial expression recognition yields substantial benefits for individual and societal advancement, especially in the context of its application within Smart City environments. This study demonstrates that well- configured Convolutional Neural Network (CNN) models empowered by TensorFlow exhibit higher accuracy compared to models utilizing PyTorch. The TensorFlow model achieves the highest accuracy of 93% in recognizing emotional expressions, whereas the PyTorch model achieves 69% accuracy. The TensorFlow model also displays lower accuracy loss and shorter training times compared to the PyTorch model. In the context of calculating happiness indices within Smart City environments, the appropriate choice of technology significantly influences measurement accuracy and efficiency. Therefore, the TensorFlow platform, proven to deliver superior performance in this study, can be a strategic choice for integrating facial expression detection technology into happiness index measurements in such locations
Utilization of LSTM (Long Short Term Memory) Based Sentiment Analysis for Stock Price Prediction Muhammad Fajrul Aslim; Gerry Firmansyah; Budi Tjahjono; Habibullah Akbar; Agung Mulyo Widodo
Asian Journal of Social and Humanities Vol. 1 No. 12 (2023): Asian Journal of Social and Humanities
Publisher : Pelopor Publikasi Akademika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59888/ajosh.v1i12.141

Abstract

This study aims to utilize sentiment analysis in predicting stock price movements. Sentiment analysis can provide information to investors to understand market sentiment. This study uses a text-based approach by pre-processing data, constructing a sentiment analysis model and evaluating model performance. The collected data is analyzed to identify the text's positive, negative, or neutral sentiments. The approach used in scoring sentiment analysis is the Text blob approach and the Lexicon approach. Differences in the results of the accuracy of the two Sentiment Analysis approaches with the LSTM model have an influence on the prediction results with a better increase in accuracy using the Lexicon Sentiment Analysis approach. Then the LSTM model is implemented to classify texts into the desired sentiment categories. The results of this study are insight into the use of sentiment analysis in predicting stock price movements. The implemented sentiment analysis model can be a useful predictive tool for investors and stock practitioners in making investment decisions.
Assessment of the level of student understanding in the distance learning process using Machine Learning Adilah Widiasti; Agung Mulyo Widodo; Gerry Firmansyah; Budi Tjahjono
Asian Journal of Social and Humanities Vol. 2 No. 6 (2024): Asian Journal of Social and Humanities
Publisher : Pelopor Publikasi Akademika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59888/ajosh.v2i6.272

Abstract

As technology develops, data mining technology is created which is used to analyse the level of understanding of students. This analysis is conducted to group students according to their ability to understand and master the subject matter. This research can provide guidance and insight for educators, as well as artificial intelligence, machine learning, association techniques, and classification techniques. Researchers and policymakers are working to optimise learning and improve the quality of student understanding. This study aims to analyse the level of student understanding in simple and structured terms. Using the Machine learning method to analyse the level of student understanding has the potential to impact the quality of education significantly. In addition, machine learning categories are qualified to be applied to the concept of data mining. The data mining techniques used are association and classification. Association techniques are used to determine the pattern of distance student learning. The following process of classification techniques is used to determine the variables to be used in this study using the Logistic Regression model where data that have been classified are grouped or clustered using the K-Means algorithm into three, namely the level of understanding is excellent, sound, and lacking, based on student activity, assignment scores, quiz scores, UTS scores, and UAS scores.
Co-Authors Adilah Widiasti Arif Pami Setiaji Bayu Sulistiyanto Ipung Sutejo Gerry Firmansyah Gusti Fachman Pramudi Habibullah Akbar Hendaryatna Hendaryatna Muhammad Fajrul Aslim Narul Sakron Supriyade Supriyade Widodo, Agung Mulyo Yessy Oktafriani