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All Journal International Journal of Evaluation and Research in Education (IJERE) Bulletin of Electrical Engineering and Informatics Jurnal technoscientia Jurnal Script Jurnal Jarkom Telematika : Jurnal Informatika dan Teknologi Informasi SMARTICS Journal Jurnal TAM (Technology Acceptance Model) Jusikom : Jurnal Sistem Komputer Musirawas Dharma Bakti IJISCS (International Journal Of Information System and Computer Science) Jurnal Teknologi Komputer dan Sistem Informasi Jurnal ICT : Information Communication & Technology JISKa (Jurnal Informatika Sunan Kalijaga) International Journal of Applied Sciences and Smart Technologies Infotech: Journal of Technology Information JURNAL TEKNOLOGI TECHNOSCIENTIA Jurnal PkM: Pemberdayaan Masyarakat Jurnal Pengabdian Masyarakat (ABDIRA) NEAR: Jurnal Pengabdian kepada Masyarakat Journal of Applied Business and Technology International Journal of Multidisciplinary Approach Research and Science Jurnal Riset Multidisiplin dan Inovasi Teknologi Jurnal Pengabdian Kepada Masyarakat Radisi Jurnal TAM (Technology Acceptance Model) IJISCS (International Journal of Information System and Computer Science) JTKSI (Jurnal Teknologi Komputer dan Sistem Informasi)
Uning Lestari
Department Of Informatics, Universitas AKPRIND Indonesia, Yogyakarta,
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Nursing Public Health Social Sciences Transportation Other

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Augmented Reality Technology for the Introduction of Mobile-Based Spaces with the Hough Transform Method (Case Study: Akprind Institute of Science and Technology Campus Locations) Galuh Ayu Novilia; Uning Lestari
JTKSI (Jurnal Teknologi Komputer dan Sistem Informasi) Vol 4, No 1 (2021): JTKSI
Publisher : Institut Bakti Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/jtksi.v4i1.978

Abstract

AKPRIND Institute of Science Technology (IST AKPRIND) Yogyakarta is one of the private universities in Yogyakarta which has 3 different building locations so that it has many rooms and buildings in a large area. In addition, the shape of the room is similar and the lack of information and the location of the campus which is divided into three, often makes it difficult for new students to find a room to carry out the lecture process later. Augmented Reality (AR) is a concept of combining virtual reality with world reality (real life), so that 2-dimensional (2D) or 3-dimensional (3D) virtual objects seem to look real and blend into the real world. The AR camera will capture and identify markers and then position and place a virtual data object on the marker. In the process of creating an AR application, Unity3D tools and a database using Vuforia are required. The results of the application will be tested using the Standard Hough Transform (SHT) method. SHT is tested to get a conclusion about the detection distance value at the distance between the image and the camera. The test was carried out using 3 marker sizes with a tilt angle of 0 °, 15 °, 30 °, 45 °, 60 °, 75 °, and 90 ° carried out with 20 watts of lamp lighting and a distance of 2.5 meters between the lamp and the marker. Based on testing with the SHT method, the larger the marker size, the farther the marker distance can be detected.
Effect of Hyperparameter Tuning on Performance on Classification model Sholeh, Muhammad; Lestari, Uning; Andayati, Dina
International Journal of Applied Sciences and Smart Technologies Volume 07, Issue 1, June 2025
Publisher : Universitas Sanata Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24071/ijasst.v7i1.11735

Abstract

This research aims to analyze the effect of hyperparameter tuning on the performance of Logistic Regression, K-Nearest Neighbours, Support Vector Machine, Decision Tree, Random Forest, Random Forest Classifier, Naive Bayes algorithms.  These six algorithms were tested both using hyperparameter tuning and not using hyperparameter tuning. The dataset used in this research is a public dataset, namely the heart datasheet. This datasheet contains information about features related to the diagnosis of heart disease. Hyperparameter tuning is performed using a grid search technique to determine the best combination of hyperparameter values that can improve model accuracy. Performance comparison is done by measuring the accuracy, precision, recall, and F1-score of each algorithm before and after tuning. The research method follows the stages in the Knowledge Discovery in Databases (KDD) methodology. The KDD methodology consists of several stages of data collection, data cleaning to remove errors, data integration from various sources, and data selection and transformation to be ready for analysis. Next, data mining is performed to find patterns or relationships in the data and evaluation and interpretation of the results to ensure their validity. The results show that hyperparameter tuning applied to the six algorithms does not necessarily improve performance. In the algorithm. SVM and decision tree algorithms, the performance results before hyperparameter tuning actually have a higher accuracy value. The performance of algorithms that experienced an increase after hyperparameter tuning was logistic regression and K-Nearest neighbours. The same performance results are generated in the Random Forest and Naive Bayes algorithms. Based on testing the six algorithms and using the heart datasheet, the hyperparameter tuning process does not always result in a better performance value.
ANALISIS PREDIKSI TUMBUH KEMBANG ANAK DENGAN MACHINE LEARNING Nugraheni, Murien; Widodo, Widodo; Lestari, Uning; Effendy, Vina Ardelia; Yunanto, Prasetyo Wibowo; Amannu, Ramadhan
Infotech: Journal of Technology Information Vol 11, No 1 (2025): JUNI
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v11i1.389

Abstract

Stunting is a major chronic nutritional issue that remains a significant challenge in Indonesia. This study aims to predict the risk of stunting in children and enhance prevention efforts by analyzing the health and nutritional status of parents. The research employs Machine Learning methods by comparing the performance of the Decision Tree and Gaussian Naive Bayes algorithms. The dataset was obtained from open data sources and analyzed using Google Colab, with a Technology Readiness Level (TRL) of level 3. Evaluation results show that both algorithms achieved an accuracy of 95.35% based on the confusion matrix. The model accurately identified 2 stunting cases (True Positive) and 41 non-stunting cases (True Negative), indicating a high level of classification reliability. These findings suggest that Machine Learning approaches can be effectively utilized as early detection tools to support stunting prevention strategies in children.
Prediction And Detection Of Type II Diabetes Mellitus Using The K-Nearest Neighbor Algorithm Lestari, Uning; hamzah, amir; Paays, Franco Albertino Karel
Telematika Vol 21 No 2 (2024): Edisi Juni 2024
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v21i2.12384

Abstract

Purpose: High blood sugar causes Mellitus (DM), a metabolic disorder. DM affects human metabolism and causes many complications, such as heart disease, kidney problems, skin disorders, and slow healing. Therefore, using machine learning algorithms to implement an automatic diabetes diagnosis system is crucial for predicting DM.Design/methodology/approach: This research created a DM disease prediction system using machine learning with the K-Nearest Neighbor algorithm. The National Institute of Diabetes and Digestive and Kidney Diseases, Hospital Frankfurt, Germany, and the results of health surveys and medical research are the sources of two separate datasets used in the Kaggle platform data. The stages in Machine Learning include data merging, data cleaning, and data splittingFindings/result: This research produces the best prediction model at a ratio of 70:30, with the lowest MSE value on testing data, 0.217. With K Folding Cross-validation, it makes an average accuracy of 73.88%.Originality/value/state of the art: This research creates a prediction model for diabetes mellitus type 2 using two different datasets with 9 features. It makes a Machine Learning model using the KNN algorithm by importing the KneighborClassifier and evaluating it using the MSE (Mean Square Error) matrix and K Folding cross-validation to determine modelling accuracy
Hyperparameter Optimization Using Grid Search and Random Search to Improve the Performance of Prediction Models with Decision Trees Sholeh, Muhammad; Lestari, Uning; Andayati, Dina
Jurnal Riset Multidisiplin dan Inovasi Teknologi Том 3 № 03 (2025): Jurnal Riset Multidisiplin dan Inovasi Teknologi
Publisher : PT. Riset Press International

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59653/jimat.v3i03.2025

Abstract

Hyperparameter selection to obtain optimal accuracy results is an important factor in improving model performance in data science. This study discusses a comparison of two hyperparameter optimization methods, namely Grid Search and Random Search, in the Decision Tree Classifier algorithm using the Breast Cancer Wisconsin (Diagnostic) Dataset from the UCI Machine Learning Repository. The dataset contains 569 samples with 30 numerical features describing the characteristics of breast cancer cells, such as mean radius, texture, perimeter, area, and smoothness, which are classified into two classes, namely malignant and benign. This study uses the CRISP-DM approach, which includes the stages of business understanding, data understanding, data preparation, modeling, and evaluation. In the modeling stage, three testing scenarios were conducted, namely the Decision Tree model without tuning, the model with Grid Search optimization, and the model with Random Search optimization. Performance evaluation was carried out using accuracy, precision, recall, and F1-score metrics. The results showed that hyperparameter optimization had a significant effect on model performance. The Decision Tree model without tuning produced an accuracy of 92.98%, while the model with Grid Search achieved the highest accuracy of 95.61%, and Random Search obtained an accuracy of 97.37%. Thus, it can be concluded that Grid Search provides the most optimal results in finding the best parameter combination, even though it requires longer computation time compared to Random Search.
SINGLE-LABEL LEARNING STYLE CLASSIFICATION USING MACHINE LEARNING WITH GRIDSEARCH-BASED HYPERPARAMETER TUNING ON LMS BEHAVIORAL DATA Uning Lestari; Sazilah Salam; Yun Huoy Choo
IJISCS (International Journal of Information System and Computer Science) Vol 9, No 3 (2025): IJISCS (International Journal of Information System and Computer Science)
Publisher : Bakti Nusantara Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/ijiscs.v9i3.1876

Abstract

The rapid growth of online learning environments has increased the importance of Learning Management Systems (LMS) as a rich source of behavioral data for learning analytics. One learner characteristic that strongly influences learning effectiveness is learning style; however, traditional questionnaire based identification approaches suffer from subjectivity, limited scalability, and static representation. To address these limitations, this study proposes a machine learning-based approach for automatic learning style classification using LMS behavioral data grounded in the Felder–Silverman Learning Style Model (FSLSM). This study utilizes LMS activity log data collected from Universitas Siber Asia over three academic years (2022–2024). The dataset consists of 5,633 student interaction records with 72 raw behavioral attributes, which were preprocessed, aggregated, and transformed into 12 representative behavioral features reflecting students’ interactions with learning materials, assessments, discussions, multimedia resources, and navigation patterns. A rule-based FSLSM mapping mechanism was applied to generate 16 learning style profiles, which were treated as targets in a single-label classification setting. Support Vector Machine (SVM) and Gradient Boosting (GB) classifiers were implemented and optimized using feature selection and GridSearch-based hyperparameter tuning. The dataset was divided into 75% training data and 25% testing data using a stratified split to preserve class distribution. Experimental results show that Gradient Boosting consistently outperforms SVM across all evaluation metrics. The GB model achieved an accuracy of 0.84 and a macro F1-score of 0.79, demonstrating strong generalization capability and robustness to class imbalance. In contrast, SVM exhibited lower and less stable performance, particularly on minority learning style classes. These findings confirm that ensemble-based methods such as Gradient Boosting are more effective for LMS-based single-label learning style classification and support the feasibility of automatic FSLSM-based learning style detection for data-driven adaptive learning systems.
Co-Authors -, Marwoto -, Marwoto Abdullah, Asniyani Nur Haidar Abdulloh, Yusuf Agusalim Syamsudin Pure Ahmad Fesol, Siti Feirusz Ahmad Zarkasi Akhir, Muhammad Al Qallab, Kholoud Alomoush, Ashraf Amannu, Ramadhan Amir Hamzah Amir Hamzah Amir Hamzah Andri Harsono Andri Harsono, Andri Andung Febi Prakoso Anggraeni, Ari Puspratini Annafi’ Franz Aprilianti, Yunis Aprilianti Ardiansyah - Arga, Dwi Asih Sapta Arifuddin, Arham Ariyana, Renna Yanwastika Asti Widyaningsih Aziz Nurwahidin Bondan Prawiro Yudo, Bondan Prawiro Cardoso, Noel Adriano Catur Iswahyudi Choo, Yun-Huoy Dani Heriyanto Dani Yulkarnain Debby Anugrahni Deby Saputra, Deby Dede Hernowo Deserius Marianus Oenunu Dina Andayati Dina Andayati Dini Pujiatin Edhy Sutanta (Jurusan Teknik Informatika IST AKPRIND Yogyakarta) Effendy, Vina Ardelia Eko Budianto Erfanti Fatkhiyah Erfanty Fatkhiyah Erma Susanti Erna Kumalasari Erna Kumalasari Nurnawati Erna Kumalasari Nurnawati Erna Kumalasari Nurnawati Erni Astuti Firmansyah Surwa Adi L Galuh Ayu Novilia Hari Wibowo Hendrati, Rr. Dina Oktavia Indra Kurniawan Irene Sri Morina Ismail, Nurmaisarah iswanto Iswayudi, Catur Jepri Ardianto Joko Triyono Juliyanti, Nur Arifah Kar Mee, Cheong Laksono Trisnantoro Lip, Rashidah Listyaningrum, Desti Arghina Luay Nabila El Suffa M. Abdul Alim Alami Mitra Hasibuan Mohamad, Siti Nurul Mahfuzah Mohd Yusoff, Azizul Muchamad Rizal Rinaldi MUHAMMAD SHOLEH Muhammad Targiono Muntaha Nega Murien Nugraheni Musa, Mohd Hafizan Naniek Widyastuti Naniek Widyastuti Norasikin, Mohd Adili Nurmansyah Oktavina Marlina Roma Paays, Franco Albertino Karel Parasian D.P Silitonga Poh Ee, Tan Prastika, Dika Priska Prihsmoro1, Catur Dwi Prita Haryani Pujiatin, Dini Rendi Saputra Rr Yuliana Rachmawati K RR. Yuliana Rachmawati Salam, Sazilah Saldanha, Paulino Sazilah Salam Sholeh, Muhammad Silitonga, Parasian DP Siti Saudah Sony Cahyo Wibisono Sony Cahyo Wibisono, Sony Cahyo Sugiyatno Sugiyatno Supriyanri, Sri Suraya Suraya Suraya Suraya Suwanto Raharjo Tri Romadhani Triyono, Joko Utami Hayati Victor Motumona Wafikulinuha Wafikulinuha Waliadi, Julfikar Wandy Damarullah Widodo Widodo Wiwik Handayani Yeremias Budi Liman Hege Yeremias Budi Liman Hege, Yeremias Budi Liman Yun Huoy Choo Yunanto, Prasetyo Wibowo Yunis Aprilianti Yusron - Zulfikar .L, Fauzul Rachman