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Implementation of Orange Data Mining to Predict Student Graduation on Time at Pringsewu Muhammadiyah University Novianto, Roby; Triraharjo, Bambang; Baskoro
Buana Information Technology and Computer Sciences (BIT and CS) Vol 5 No 1 (2024): Buana Information Technology and Computer Sciences (BIT and CS)
Publisher : Information System; Universitas Buana Perjuangan Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36805/bit-cs.v5i1.6073

Thel prolcelss olf molnitolring and elvaluating thel graduatioln olf Muhammadiyah Pringselwu Univelrsity (UMPRI) studelnts relally nelelds tol bel dolnel belcausel thel studelnt graduatioln ratel is an ellelmelnt olf accrelditatioln asselssmelnt that is velry impolrtant folr elach Study Prolgram. Data Mining can bel useld tol classify studelnt graduatioln accuracy. This study aims tol apply thel olrangel data mining applicatioln using thel K-Nelarelst Nelighbolr (K-NN), Delcisioln Trelel and Naivel Bayels moldells and will theln elvaluatel thel accuracy olf elach olf thelsel moldells. This relselarch was colnducteld at Pringselwu Muhammadiyah Univelrsity in selvelral batchels, theln studelnt data will bel analyzeld using thel olrangel data mining applicatioln using thel K-NN, Delcisioln Trelel and Naivel Bayels moldells. Thel data telsting prolcelss appliels K-Folld Crolss Validatioln (K=5), whilel thel elvaluatioln moldell useld is thel Colnfusioln Matrix and ROlC. Thel relsults olf thel colmparisoln olf thel threlel moldells arel as folllolws, K-NN has an accuracy ratel olf 75.7%, Delcisioln Trelel has an accuracy ratel olf 78.1%, and Naivel Bayels has an accuracy ratel olf 77.8%. Thelrelfolrel, folr classifying thel graduatioln ratel olf Muhammadiyah Univelrsity studelnts, Pringselwu relcolmmelnds thel Delcisioln Trelel moldell belcausel it has a belttelr lelvell olf accuracy than K-NN and Naivel Bayels.

Implementation of the C4.5 Algorithm in Predicting the Interest of Prospective Students in Choosing Higher Education Triraharjo, Bambang; Ayu Minarni, Prilian; Baskoro
Buana Information Technology and Computer Sciences (BIT and CS) Vol 6 No 1 (2025): Buana Information Technology and Computer Sciences (BIT and CS) (InProcess)
Publisher : Information System; Universitas Buana Perjuangan Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36805/bit-cs.v6i1.7697

Data governance involves setting internal standards for how data is collected, stored, processed and deleted. In the context of the oil industry, data governance can intervene at the level of oil exploration and production to manipulate data in particular. In our contribution, we explain how Fuzzy c - means based machine learning can be used for oil data governance. This deep artificial intelligence concept, which we will use in addition to fuzzy logic, by applying Fuzzy c - means for good training can enable the decision-maker a better governance policy.

Pendeteksi Suhu dan Kelembaban Ruangan Menggunakan Sensor DHT11 Berbasis Web Server Nugroho, Adam; Wibowo, Adi; Triraharjo, Bambang
Sienna Vol 5 No 2 (2024): Sienna Volume 5 Nomor 2 Desember 2024
Publisher : LPPM Universitas Muhammadiyah Kotabumi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47637/sienna.v5i2.1644

This article discusses the development of an Internet of Things (IoT)-based system to detect room temperature and humidity using a DHT11 sensor whose data is monitored through a web server interface. The research aims to create a real-time monitoring system with effectively integrated data. The research method includes hardware and software design, sensor data collection, and web-based interface development. The results show that this system can detect changes in temperature and humidity with a high level of accuracy and provide easy access via the web. This system has the potential to be applied in the management of indoor environments such as offices, homes, and storage rooms.

Listen to Me: Transforming Learning Accessibility with an Audio-Based Android App for Visually Impaired Children Pratiwi, Dian; Triraharjo, Bambang; Qori'ah, Aisyatul Vidyah; Surya, Ridho Pamungkas Ibnu; Aulia, Mega
Journal of Languages and Language Teaching Vol 13, No 2 (2025)
Publisher : Universitas Pendidikan Mandalika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/jollt.v13i2.14210

This research aims to design and develop an audio-based Android application, Listen to Me app, specifically designed to support the learning needs of visually impaired children. The application seeks to enhance accessibility to educational content by providing audio-based materials, enabling users to engage with learning resources independently. The study adopts a Research and Development (RnD) approach, utilizing the Waterfall Method development model. This structured approach consists of four main stages: conducting a user needs analysis, designing the application, developing a prototype based on inclusive design principles, and testing the usability of the application with visually impaired children. Each stage is meticulously aligned with the goal of creating a user-friendly and effective learning tool. The findings reveal that the app significantly improves learning accessibility through its interactive features, such as voice-guided navigation and customizable audio content tailored to user preferences. These results highlight the potential of Listen to Me app to empower visually impaired children by fostering their independence in accessing and engaging with educational content. The primary contribution of this research is the development of an innovative and inclusive audio-based learning platform. It provides a technological solution that addresses the unique learning challenges faced by visually impaired children, offering them greater autonomy and opportunities in education.

Listen to Me: Transforming Learning Accessibility with an Audio-Based Android App for Visually Impaired Children Pratiwi, Dian; Triraharjo, Bambang; Qori'ah, Aisyatul Vidyah; Surya, Ridho Pamungkas Ibnu; Aulia, Mega
Journal of Languages and Language Teaching Vol. 13 No. 2 (2025): April
Publisher : Universitas Pendidikan Mandalika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/jollt.v13i2.14210

This research aims to design and develop an audio-based Android application, Listen to Me app, specifically designed to support the learning needs of visually impaired children. The application seeks to enhance accessibility to educational content by providing audio-based materials, enabling users to engage with learning resources independently. The study adopts a Research and Development (RnD) approach, utilizing the Waterfall Method development model. This structured approach consists of four main stages: conducting a user needs analysis, designing the application, developing a prototype based on inclusive design principles, and testing the usability of the application with visually impaired children. Each stage is meticulously aligned with the goal of creating a user-friendly and effective learning tool. The findings reveal that the app significantly improves learning accessibility through its interactive features, such as voice-guided navigation and customizable audio content tailored to user preferences. These results highlight the potential of Listen to Me app to empower visually impaired children by fostering their independence in accessing and engaging with educational content. The primary contribution of this research is the development of an innovative and inclusive audio-based learning platform. It provides a technological solution that addresses the unique learning challenges faced by visually impaired children, offering them greater autonomy and opportunities in education.

Comparison Of The Performance Of C4.5 And Naive Bayes Algorithms For Student Graduation Prediction baskoro, baskoro; Triraharjo, Bambang; Wibowo, Adi
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 9, No 2 (2023): December 2023
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v9i2.24931

Along with the development of technology, especially the development of increasingly large data storage. One organization that has large data storage is an educational organization. Educational organizations use data to obtain information, especially information about students. Student data has many attributes so that we can make predictions such as predictions of student performance, predictions of scholarship recipients and predictions of student graduation. Data mining methods in education are classified into five dimensions, one of which is prediction, such as predicting output values based on input data. From the results of the research conducted from the initial stage to the testing stage of the application of the C4.5 Algorithm, the accuracy results are higher than Naïve Bayes because in its classification stage, C4.5 processes attribute data one by one. The difference is with naïve Bayes which is influenced by the amount of data used, the comparison of the amount of training and testing data. The feasibility of the model obtained is supported by the high accuracy, precision, recall and AUC obtained from the two algorithms that have been tested. The C4.5 algorithm has an accuracy rate of 79.91%, 89.06% precision and 81.38% recall and an AUC value of 0.823. Meanwhile, Naïve Bayes has an accuracy rate of 76.95%, precision of 75.95% and recall of 98.38% and an AUC value of 0.838.Keywords: Graduation, Prediction, Data Mining, C4.5, Naïve Bayes

Comparison Of The Performance Of K-Nearest Neighbors And Naive Bayes Algorithms For Stroke Disease Prediction baskoro, baskoro; Novianto, Roby; Triraharjo, Bambang
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 11, No 2 (2025): December 2025
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v11i2.37542

Purpose: Stroke is a critical global health issue requiring early and accurate prediction to mitigate severe outcomes. This study aims to compare the performance of the K-Nearest Neighbors (KNN) and Naive Bayes algorithms in predicting stroke disease, addressing the challenge of imbalanced datasets and improving prediction accuracy for better clinical decision-making.Methods/Study design/approach: The research followed the CRISP-DM model, utilizing a dataset of 5,110 patient records with 12 attributes from Kaggle. Data preprocessing included handling missing values and normalization. The KNN and Naive Bayes algorithms were implemented using RapidMiner, with performance evaluated through cross-validation, confusion matrices, and ROC-AUC curves.Result/Findings: The KNN algorithm achieved an accuracy of 94.50%, but exhibited low precision (7.89%) and recall (1.20%) for stroke-positive cases due to dataset imbalance. Naive Bayes yielded an accuracy of 88.83% with an AUC of 0.767, demonstrating better probability modeling but similar challenges in minority class detection. Both algorithms highlighted the impact of data imbalance on predictive performance.Novelty/Originality/Value: This study provides a comparative analysis of KNN and Naive Bayes for stroke prediction, emphasizing the need for data balancing and optimization techniques. The findings underscore the potential of these algorithms in healthcare applications while suggesting future improvements through ensemble methods or alternative algorithms like Random Forest.

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