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Graphology analysis for detecting hexaco personality and character through handwriting images by using convolutional neural networks and particle swarm optimization methods Alvin Barata; Habibullah Akbar; Marzuki Pilliang; Anwar Nasihin
International Journal of Industrial Optimization Vol. 3 No. 2 (2022)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/ijio.v3i2.6242

Graphology or handwriting analysis can be used to infer the traits of the writers by examining each stroke, space, pressure, and pattern of the handwriting. In this study, we infer a six-dimensional model of human personality (HEXACO) using a Convolutional Neural Network supported by Particle Swarm Optimization. These personalities include Honesty-Humility, Emotionality, eXtraversion, Agreeableness (versus Anger), Conscientiousness, and Openness to Experience. A digital handwriting sample data of 293 different individuals associated with 36 types of personalities were collected and derived from the HEXACO space. A convolutional neural network model called GraphoNet is built and optimized using Particle Swarm Optimization (PSO). The PSO is used to optimize epoch, minibatch, and droupout parameters on the GraphoNet. Although predicting 32 personalities is quite challenging, the GraphoNet predicts personalities with 71.88% accuracy using epoch 100, minibatch 30 and dropout 52% while standard AlexNet only achieves 25%. Moreover, GraphoNet can work with lower resolution (32 x 32 pixels) compared to standard AlexNet (227 x 227 pixels).

Risk Management in Software Development Projects: A Systematic Literature Review Marzuki Pilliang; Munawar Munawar
Khazanah Informatika Vol. 8 No. 2 October 2022
Publisher : Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v8i2.17488

Risk Management is an integral part of every project. Risk management must estimate the risks’ significance, especially in the SDLC process, and mitigate those risks. Since 2016, many papers and journals have researched planning, design, and risk control in software development projects over the last five years. This study aims to find the most exciting topics for researchers in risk management, especially in software engineering projects. This paper takes a systematic approach to reviewing articles containing risk management in software development projects. This study collects papers and journals included in the international online library database, then summarizes them according to the stages of the PICOC methodology. This paper results in the focus of research in the last five years on Agile methods. The current issue is that many researchers are trying to explicitly integrate risk management into the Agile development process by creating a comprehensive risk management framework. This SLR helps future research get a theoretical basis to solve the studied problem. The SLR explains the focuses of previous research, analysis of research results, and the weaknesses of the investigation. For further study, take one of the topic papers, do a critical review, and find research gaps.

Studi Komparasi Kerangka Kerja Manajemen Risiko dalam Scrum Marzuki Pilliang; Munawar Munawar; Budi Tjahjono
Jurnal Informatika Universitas Pamulang Vol 7, No 2 (2022): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v7i2.17468

Risk management in software engineering projects describes an integrated design with methods, processes, and artifacts that continuously identify, analyze, control, and monitor risks, to prevent the project from failing. Agile methodology is an alternative to the traditional sequential software development process. Scrum is the most frequently used method based on the 2016 Agile development survey results. In recent years, there have been many studies that have produced a risk management framework for Scrum. However, risk analysis and the selection of responses to risks become a burden for stakeholders, so a framework is needed that can become a support system to help make decisions. This paper uses a comparative study of risk management framework literature and literature that utilizes tools for risk management. The research resulted in a new framework that integrates datasets and machine learning into a risk management framework, so further work can be done to test the effectiveness of the new framework

Studi Komparasi Naive Bayes, K-Nearest Neighbor, dan Random Forest untuk Prediksi Calon Mahasiswa yang Diterima atau Mundur Puteri Sejati; Munawar Munawar; Marzuki Pilliang; Habibullah Akbar
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 9 No 7: Spesial Issue Seminar Nasional Teknologi dan Rekayasa Informasi (SENTRIN) 2022
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2022976737

Penelitian ini bertujuan untuk mendapatkan model prediksi terbaik dari data Penerimaan Mahasiswa Baru tahun 2014 hingga 2019 dengan membandingkan Naive Bayes, K-Nearest Neighbor, dan Random Forest. Penelitian ini menggunakan metode klasifikasi untuk memprediksi calon mahasiswa. Mereka diterima atau mundur. Dalam penelitian ini digunakan 19.603 data latih dan 4.901 data uji. Hasil penelitian menunjukkan bahwa algoritma Random Forest adalah yang terbaik dengan akurasi 73,61%, dibandingkan dengan K-Nearest Neighbor dengan akurasi 72,08%, dan Naive Bayes dengan akurasi 70,47%. Disimpulkan juga bahwa optimasi model dengan teknik Hyperparameter menghasilkan nilai akurasi yang lebih baik. Hasil penelitian ini dapat digunakan untuk mendukung bagian pemasaran dalam meminimalisir jumlah calon mahasiswa yang mengundurkan diri. AbstractThis study aimed to obtain the best predictive model from New Student Admissions data for 2014 to 2019 by comparing Naive Bayes, K-Nearest Neighbor, and Random Forest. This study used the classification method to predict prospective students. They are accepted or withdrawn. In this study, 19,603 training data and 4,901 test data were used. The results showed that the Random Forest algorithm was the best with an accuracy of 73.61%, compared to K-Nearest Neighbor with an accuracy of 72.08%, and Naive Bayes with an accuracy of 70.47%. It is also concluded that optimizing the model with the Hyperparameter technique produces better accuracy values. This study's results can be used to support the marketing department in minimizing the number of withdrawn prospective students.

Studi Komparasi Kerangka Kerja Manajemen Risiko dalam Scrum Marzuki Pilliang; Munawar Munawar; Budi Tjahjono
Jurnal Informatika Universitas Pamulang Vol 7, No 2 (2022): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v7i2.17468

Risk management in software engineering projects describes an integrated design with methods, processes, and artifacts that continuously identify, analyze, control, and monitor risks, to prevent the project from failing. Agile methodology is an alternative to the traditional sequential software development process. Scrum is the most frequently used method based on the 2016 Agile development survey results. In recent years, there have been many studies that have produced a risk management framework for Scrum. However, risk analysis and the selection of responses to risks become a burden for stakeholders, so a framework is needed that can become a support system to help make decisions. This paper uses a comparative study of risk management framework literature and literature that utilizes tools for risk management. The research resulted in a new framework that integrates datasets and machine learning into a risk management framework, so further work can be done to test the effectiveness of the new framework

SENTIMENT ANALYSIS OF CONTENT PERMENKOMINFO NO.5 OF 2020 USING A CLASSIFICATION ALGORITHM Mohammad Amada; Munawar Munawar; Marzuki Pilliang
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 8 No. 2 (2023): JITK Issue February 2023
Publisher : LPPM Nusa Mandiri

This study aims to evaluate the impact of the policy issued by the Minister of Communication and Information Technology (PERMENKOMINFO No.5 of 2020) on the public's ability to access content through Private Scope Electronic System Providers (PSE). The study uses sentiment analysis and data classification methods to analyze the content of PERMENKOMINFO No.5 of 2020 and provides results on the accuracy of sentiment prediction. The results of the study show that the data classification method in sentiment analysis can provide accurate results in predicting the sentiment towards the content of PERMENKOMINFO No.5 of 2020. The study also highlights the need for improvement and better policy to ensure the interests of the public in accessing online information. The negative sentiment of 80.34% obtained through sentiment analysis provides important contributions for policy evaluation and feedback for improvement. This study provides valuable insights into the public's sentiment towards the PERMENKOMINFO No.5 of 2020 policy and its impact on their ability to access content. It also contributes to understanding the legal uncertainty in accessing content and reinforces the case for better policy to ensure the interests of the public.

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