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All Journal AL-ISHLAH: Jurnal Pendidikan JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Sisfokom (Sistem Informasi dan Komputer) JINAV: Journal of Information and Visualization
Prati Hutari Gani
Telkom University
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences

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Application of the TROPOS Method to Development a Website-Based Blood Stock Management System at Palang Merah Indonesia (PMI) in Bandung City Rinda Firma Violita; Sri Widowati; Prati Hutari Gani
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 9, No 2 (2020): JULI
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v9i2.856

Abstract

Palang Merah Indonesia (PMI) is a social organization in the city of Bandung. In the business process, the blood donor section has not used any software that satisfies the need to connect communities and PMI on blood donor information. So it takes software that can help the process of running blood donor business to minimize the problems that occur such as human error and not spread information about blood supply. RE (Requirement Engineering) is an early stage as an important task, as many software failures come from inconsistent, incomplete or just wrong specification requirements. In RE there is a process that is requirement analysis to do analysis of user needs. Goal Oriented Requirements Engineering (GORE) is one of the models that can be used to analyse user needs. One method on the GORE model is the TROPOS method. The use of TROPOS on the development of the blood stock management software to focus on the needs analysis on the stages of modeling early requirement and late requirement. The results of the analysis are implemented into web software design. Software that has been created based on the modeling evaluated using BlackBox Testing with user acceptance test (UAT) by stakeholders. Based on the results of the assessment UAT score, the results of the respondents assessment is 3.55%, so that the blood stock management software can be used as a supporting tool to run the blood stock business process of PMI.
Pengembangan Model Fast Incremental Gaussian Mixture Network (IGMN) pada Interpolasi Spasial Prati Hutari Gani; Gusti Ayu Putri Saptawati
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 1 (2022): Januari 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i1.3490

Abstract

Gathering geospatial information in an organization is one of the most critical processes to support decision-making and business sustainability. However, many obstacles can hinder this process, like uncertain natural conditions and a large geographical area. This problem causes the organization only to obtain a few sample points of observation, resulting in incomplete information. The data incompleteness problem can be solved by applying spatial interpolation to estimate or determine the value of unavailable data. Spatial interpolation generally uses geostatistical methods. These geostatistical methods require a variogram as a model built based on the knowledge and input of geostatistic experts. The existence of this variogram becomes a necessity to implement these methods. However, it becomes less suitable to be applied to organizations that do not have geostatistics experts. This research will develop a Fast IGMN model in solving spatial interpolation. In this study, results of the modified Fast IGMN model in spatial interpolation increase the interpolation accuracy. Fast IGMN without modification produces MSE = 1.234429691, while using Modified Fast IGMN produces MSE = 0.687391. The MSE value of the Fast IGMN-Modification model is smaller, which means that the smaller the MSE value, the higher the accuracy of the interpolation results. This modified Fast IGMN model can solve problems in gathering information for an organization that does not have geostatistics experts in the spatial data modeling process. However, it needs to be developed again with more varied input data.
Personality Classification on Twitter Social Media using BERT Yantrisnandra Akbar Maulino; Warih Maharani; Prati Hutari Gani
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5597

Abstract

In the modern era, social media is a platform often used to interact with people. Twitter is a popular social media, especially for human interaction. Using tweets on Twitter can describe how a person's personality and can also describe characteristics of a person. Humans themselves based on the Big Five Model Nursing Theory (Big Five Personality), have five general personalities, namely openness, conscientiousness, extraversion, agreeableness, and neuroticism. Personality itself influences a person's judgment of many things, knowing the personality of a person can make it easier to know the characteristics, habits, and ways of that person in their daily activities. In addition, understanding someone's personality can be a reference in seeing how someone can interact with others. It can also be used when looking for a job according to their personality. Thus, this research builds a system to classify personality using the BERT model with the dataset used in the form of tweets from Twitter users by making several changes such as parameters and using tests with several ratios in determining test data and also training data. The results acquired in this study are 50%.
Personality Detection On Twitter User With RoBERTa Rianda Khusuma; Warih Maharani; Prati Hutari Gani
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5598

Abstract

Social media provides a service where users can make status updates about themselves. One of the social media that has such a facility is twitter. Twitter allows its users to express themselves easily by uploading tweets to their Twitter accounts. These activities on social media can indirectly describe the personality of the account owner. One form of personality classification that can be used is the big five personality. This theory classifies individual characters into five personality types, namely openness, conscientiousness, extraversion, agreeableness, and neuroticism. In the work environment, personality will significantly affect the work that is suitable for someone to do. To do a personality test, a test that is done manually, certainly takes longer and costs more. Therefore the use of machine learning to detect personality from social media is needed. By using the RoBERTa model to perform personality classification and dataset support from Twitter tweets, a system can be formed to detect personality. In the RoBERTa model, by determining the optimal ratio of training data and test data, as well as performing hyperparameter tuning, accuracy results can be obtained in classification activities, reaching 57.14%.
Comparative Analysis of Personality Detection using Random Forest and Multinomial Naive Bayes Azka Zainur Azifa; Warih Maharani; Prati Hutari Gani
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5592

Abstract

Personality is a difference that is owned by each individual in thinking, feeling, and behaving. Personality is an individual characteristic that is formed based on biological parents and environmental influences. Personality type is one of the determinants of the type of work performed. The Big Five personality is a method used to detect personality. This theory divides characteristics into five dimensions, namely Openness, Conscientiousness, Extraversion, Neuroticism, and Agreeableness. Several studies have shown that personality identification can be done through social media, one of which is by using Twitter. Much research related to personality detection has been carried out using machine learning, but only focuses on one machine learning model. In the case of text detection, multinomial naive bayes have a more stable performance than random forest, while random forest has better accuracy than multinomial naive bayes. therefore this study focuses on conducting a comparative analysis using random forest and multinomial naive Bayes. the best accuracy is produced by a system with a random forest model of 60.71% and a precision value of 62% for openness personality and 57% for agreeableness personality.
Depression Detection on Twitter Social Media Platform using Bidirectional Long-Short Term Memory Andre Agasi Simanungkalit; Warih Maharani; Prati Hutari Gani
JINAV: Journal of Information and Visualization Vol. 3 No. 2 (2022)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.jinav1503

Abstract

Depression is one of the mental disorders that are often experienced by a person in daily life. Social media platforms is a new thing as an alternative to tell stories and express current feelings by people today. Twitter is one of the social media that is often used to express feelings and opinions through tweets posts, including tweets that contain hate speech which indirectly shows symptoms of depressive disorder through statements uploaded. It also requires modeling that can recognize users with the potential to experience depression so that they can get initial treatment. This can be implemented using the BiLSTM (Bidirectional Long Short-Term Memory) method and the Word2Vec feature. It can be concluded that the dimensional size of the large feature word2vec, LSTM, and Conv1d layers influenced the model in detecting depression which can be seen in the testing accuracy and F-1 score according to the split data used.
Capturing the Distance Learning in Indonesian Higher Education: Lecturers and Students’ Perspectives during Pandemic for Post-Pandemic Arfive Gandhi; Prati Hutari Gani; Indra Lukmana Sardi
AL-ISHLAH: Jurnal Pendidikan Vol 15, No 3 (2023): AL-ISHLAH: JURNAL PENDIDIKAN
Publisher : STAI Hubbulwathan Duri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35445/alishlah.v15i3.3513

Abstract

Since the pandemic, some universities can act swiftly to develop distance learning mechanisms, but others organize distance learning sporadically by allowing each lecturer to manage without standards. This research used a qualitative survey involving 66 students and 57 lecturers from various universities in Indonesia. It delivered dual perspectives between students and lecturers to improve previous research that did one perspective only. Most recognized the benefits of time and location flexibility when participating in distance learning but were dissatisfied with its implementation. During the pandemic, students were forced to explore independently by reading slides, listening to lecturers, watching videos, discussing in the online room, and doing independent assignments without any preparation. The pandemic also significantly changed the lecturers' teaching styles. They performed several variations in delivering material, increasing interaction in discussion forums, and evaluating the learning. Generally, distance learning without complete preparation since the pandemic was relatively good, as indicated by (1) lecturers' satisfaction with the course material delivery, (2) students' activeness according to the lecturer, and (3) learning outcomes achievement. These findings become insight for university stakeholders to enhance distance learning processes in the post-pandemic era since students and lecturers have felt its benefits but require many improvements.
Co-Authors Andre Agasi Simanungkalit Arfive Gandhi Azka Zainur Azifa Gusti Ayu Putri Saptawati Indra Lukmana Sardi Rianda Khusuma Rinda Firma Violita Sri Widowati Warih Maharani Yantrisnandra Akbar Maulino