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All Journal JAIS (Journal of Applied Intelligent System) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JISA (Jurnal Informatika dan Sains) Jurnal Sistem Informasi dan Sains Teknologi Jurnal Ilmiah Wahana Pendidikan Bulletin of Information Technology (BIT) Academic Journal of Computer Science Research (AJCSR) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Informatika Teknologi dan Sains (Jinteks) JUARA: Jurnal Pengabdian Kepada Masyarakat Jurnal Pelita Pengabdian SWADIMAS: JURNAL PENGABDIAN KEPADA MASYARAKAT
Gatot Tri Pranoto
Universitas Trilogi
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Social Sciences Other

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Analysis of the Use of Particle Swarm Optimization on Naïve Bayes for Classification of Credit Bank Applications Religia, Yoga Religia; Pranoto, Gatot Tri; Suwancita, I Made
JISA(Jurnal Informatika dan Sains) Vol 4, No 2 (2021): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v4i2.946

Abstract

The selection of prospective customers who apply for credit in the banking world is a very important thing to be considered by the marketing department in order to avoid non-performing loans. The website www.kaggle.com currently provides South German Credit data in the form of supervised learning data. The use of data mining techniques makes it possible to find hidden patterns contained in large data sets, one of which is using classification modeling. This study aims to compare the classification of South German Credit data using the Naïve Bayes algorithm and compare the classification of South German Credit data using the Naïve Bayes algorithm with particle swarm optimization (PSO). The test was carried out using a confusion matrix to determine the accuracy, precision and recall values of the research model. Based on the test, it is known that PSO is able to increase the accuracy and recall of Nave Bayes, but PSO has not been able to increase the precision value of Nave Bayes. The test results show that PSO optimization gives Naïve Bayes an increase in the value of accuracy by 0.46%, and gives Naïve Bayes an increase in recall value by 3.02%. 
The Design of a Monitoring Application System for The Production of Foam Products Using the UML And Waterfall Methods Yulianti, Henny; Pranoto, Gatot Tri
JISA(Jurnal Informatika dan Sains) Vol 4, No 2 (2021): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v4i2.1045

Abstract

The development of information technology, which is followed by a higher level of competition in the foam product industry, encouraging companies to manage their company's resources properly and to plan effective, systematic and mature activities within the company. As a company with a variety of products, the most dominant problem is in the productivity process. Production is the most important part of a manufacturing company, where in carrying out its production activities this company produces based on orders from customers (Job Orders). And the problems that often occur are planning revisions in the midst of production and changing production schedules between groups (lines), delays in production planning in terms of prioritizing planning, and still being done manually in making daily reports. By implementing monitoring, which is the supervision and control of an activity where measurements and evaluations are completed repeatedly from time to time, monitoring is carried out for the purposes of the company and to maintain ongoing management. Monitoring will provide information about the status and trend of production activities towards the company's goals. The solution to this production problem is to build a web-based foam product production monitoring system application using the Waterfall method which is integrated with UML the method used is use case diagrams, activity diagrams, sequence diagrams, class diagrams and component diagrams and software development with PHP and MySQL technology. With Black box testing, it is proven that the design of this foam production monitoring system application can assist the company's foam product production activities in fulfilling customer orders and accurate reports so that it becomes effective and efficient. in improving the productivity and performance of the company.
Grouping of Village Status in West Java Province Using the Manhattan, Euclidean and Chebyshev Methods on the K-Mean Algorithm Pranoto, Gatot Tri; Hadikristanto, Wahyu; Religia, Yoga
JISA(Jurnal Informatika dan Sains) Vol 5, No 1 (2022): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v5i1.1097

Abstract

The Ministry of Villages, Development of Disadvantaged Areas and Transmigration (Ministry of Village PDTT) is a ministry within the Indonesian Government in charge of rural and rural development, empowerment of rural communities, accelerated development of disadvantaged areas, and transmigration. Village Potential Data for 2014 (Podes 2014) in West Java Province is data issued by the Central Statistics Agency in collaboration with the Ministry of Village PDTT which is in unsupervised data format, consists of 5319 village data. The Podes 2014 data in West Java Province were made based on the level of village development (village specific) in Indonesia, by making the village as the unit of analysis. Base on the Regulation of the Minister of Villages, Disadvantaged Areas and Transmigration of the Republic of Indonesia number 2 of 2016 concerning the village development index, the Village is classified into 5 village status, namely Very Disadvantaged Village, Disadvantaged Village, Developing Village, Advanced Village and Independent Village based on the ability to manage and increase the potential of social, economic and ecological resources. Village status is in fact inseparable from village development that is under government funding support. However, village development funds have not been distributed effectively and accurately according to the conditions and potential of the village due to the lack of clear information about the status of the village. Therefore, the information regarding the villages priority in term of which villages needs more funding and attention from the government is still lacking. Data mining is a method that can be used to group objects in a data into classes that have the same criteria (clustering). One of the algorithms that can be used for the clustering process is the k-means algorithm. Data grouping using k-means is done by calculating the closest distance from data to a centroid point. In this study, different types of distance calculation in the K-means algorithm are compared. Those types are Manhattan, Euclidean and Chebyshev. Validation tests have been carried out using the execution time and Davies Bouldin index. From this test, the data Village Potential 2014 in West Java province have grouped all the 5 status of the village with the obtained number of villages for each cluster is a cluster village Extremely Backward many as 694 villages, cluster Villages 567 villages, cluster village Evolving as much as 1440 villages, the cluster with Desa Maju1557 villages and the cluster Independent Village for 1061 villages. For distance calculation, Chebyshev has the most efficient accumulation time of 1 second compared to Euclidean 1.6 seconds and Manhattan 2.4 seconds. Meanwhile, the Euclidean method has the value, Davies Index most optimal which is 0.886 compared to the Manhattan method 0.926 and Chebyshev 0.990.
The Knowledge Management Model to Support Academic Activities at SD Al-Imam Islamic School Cileungsi Using the Seci Approach Bajsair, Faik; Pranoto, Gatot Tri
JISA(Jurnal Informatika dan Sains) Vol 6, No 1 (2023): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v6i1.1637

Abstract

The absence of a processing model, knowledge storage due Knowledge in the form of notes, files, pictures and so on disappears because 17 teachers dump themselves and bring knowledge, files, notes that ever made. Allegedly with the creation of a processing model, knowledge-based storage a website with a SECI approach can provide a managed solution with good knowledge at SD Al-Imam Islamic school." In this study, the authors develop model knowledge management system that is in accordance with the conditions that exist in SD Al-Imam Islamic School, knowledge formation using the SECI model. From the results of testing the experimental system knowledge management system using the Technology Acceptance Model (TAM) method the average result is above 58%, which means that the respondent is in doubt about the application of the system tried the knowledge management system at SD Al-Imam Islamic School.
South German Credit Data Classification Using Random Forest Algorithm to Predict Bank Credit Receipts Religia, Yoga; Pranoto, Gatot Tri; Santosa, Egar Dika
JISA(Jurnal Informatika dan Sains) Vol 3, No 2 (2020): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v3i2.837

Abstract

Normally, most of the bank's wealth is obtained from providing credit loans so that a marketing bank must be able to reduce the risk of non-performing credit loans. The risk of providing loans can be minimized by studying patterns from existing lending data. One technique that can be used to solve this problem is to use data mining techniques. Data mining makes it possible to find hidden information from large data sets by way of classification. The Random Forest (RF) algorithm is a classification algorithm that can be used to deal with data imbalancing problems. The purpose of this study is to discuss the use of the RF algorithm for classification of South German Credit data. This research is needed because currently there is no previous research that applies the RF algorithm to classify South German Credit data specifically. Based on the tests that have been done, the optimal performance of the classification algorithm RF on South German Credit data is the comparison of training data of 85% and testing data of 15% with an accuracy of 78.33%.
Sentiment Analysis Review Threads Google Play Store with RoBERTa Model Natan Kharisma A; Dewi Lestari; Gatot T Pranoto
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 14 No 4: November 2025
Publisher : This journal is published by the Department of Electrical and Information Engineering, Faculty of Engineering, Universitas Gadjah Mada.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v14i4.22038

Abstract

The rapid development of internet technology globally, including in Indonesia, has drastically changed communication and interaction patterns between individuals. One impact is seen in the increasing use of text-based social media applications, such as Threads, developed by Meta. Within a short time, Threads managed to attract millions of users. However, the large number of user reviews on the Google Play Store presents its own challenges, particularly in manual sentiment analysis, which is very time-consuming and prone to bias. This research aims to overcome these challenges by implementing a variant of bidirectional encoder representations from transformers (BERT), the robustly optimized BERT pretraining approach (RoBERTa) model, which has been optimized for natural language processing. The research process followed the cross-industry standard process for data mining (CRISP-DM) framework, including several main stages: understanding the business context, data exploration and model building preparation, performance evaluation, and model deployment. Data were obtained directly from the Google Play Store and then cleaned through deduplication, normalization, and tokenization stages. The RoBERTa model demonstrated strong performance, with an accuracy of 88%. Precision was recorded at 92% for positive sentiment and 81% for negative sentiment, while recall was at 88% and 87%, respectively. The F1 score was also high, at 90% for positive and 84% for negative sentiment. When compared to algorithms like naïve Bayes and support vector machine (SVM), RoBERTa proved superior. This research opens opportunities for exploring other transformer models or using ensembles to improve performance in the future.
Co-Authors Amali Amali Annisa Maulana Majid Bajsair, Faik Baskara, Jaka Adi Budiarto, Eko Dewi Lestari Donny Maulana Edy Widodo Fauzi Baisyir Fida, Syafatul Henifa, Henifa Henny Yulianti Intan Sari Rahayu Ismasari Nawangsih Kaspia, Qinara Azra Puja Majid, Annisa Maulana Maulana Majid, Annisa Maulana, Donny Muh. Darwis Muhamad Ilham Fauzan Muhammad Darwis Muhammad Ramadhan Natan Kharisma A Nawangsih, Ismasari Naya, Candra Nugroho, Agung Nurhadi Surojudin Piliang, Tchinda Eliza Prijanisa, Almira Ayumi Pupung Purnamasari Pupung Purnamasari Putra, Fajar Ariya Putri, Wahyuningdiah Trisari Harsanti Ramadhan, Raka Hikmah romanuddin, ahmad Samidi Samidi, Samidi Santosa, Egar Dika Setiawan, Rifai Ady Suwancita, I Made Syafran Nurrahman Syihab, Faizah Taofik Safrudin Tjahjanto, Tjahjanto Wahyu Hadikristanto Wicaksana, Yusuf Eka Yaddarabullah, Yaddarabullah Yoga Religia Yusman, Roni Zy, Ahmad Turmudi