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All Journal Scientific Journal of Informatics JOIN (Jurnal Online Informatika) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JISKa (Jurnal Informatika Sunan Kalijaga) Journal of Soft Computing Exploration Journal of Student Research Exploration Journal of Information System Exploration and Research Scientific Journal of Informatics
Pertiwi, Dwika Ananda Agustina
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Library & Information Science

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Journal : Journal of Student Research Exploration

 1 
Analysis of k-means clustering algorithm in advanced country clustering using rapid miner Prabaswara, Ireneus; Pertiwi, Dwika Ananda Agustina; Jumanto, Jumanto
Journal of Student Research Exploration Vol. 2 No. 2: July 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/josre.v2i2.337

Abstract

In the era of globalization, the understanding of developed countries is no longer limited to the level of per capita income alone. As part of the analysis of developed countries based on aspects of government revenue, income balance, national savings, and domestic output based on sales. This research aims to cluster and to find out how these economic indicators are interrelated and affect the status of a country as a developed country. The K-means algorithm is used to identify patterns of countries with similar economic characteristics. From the research conducted, there are 4 clusters generated based on the characteristics of developed countries.
Sentiment analysis spotify applications on google play store with naïve bayes and neural network methods Syahra, Syahra Audiyani Fitra; Pertiwi, Dwika Ananda Agustina
Journal of Student Research Exploration Vol. 3 No. 2 (2025): July 2025
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/josre.v3i2.416

Abstract

Digital advancements have significantly changed the way music is accessed and enjoyed, with streaming platforms such as Spotify emerging as one of the most widely used applications worldwide. Along with this growth, user reviews on platforms like the Google Play Store have become an important source of information, offering insights into user satisfaction and areas for improvement. In this study, sentiment analysis was conducted on Spotify reviews using two classification methods, Naïve Bayes and Neural Networks. The reviews were collected, processed, and then analyzed with both approaches to evaluate their performance. The results show that Neural Networks outperformed in terms of accuracy, F1-score, and recall, while Naïve Bayes performed better in AUC, precision, and MCC. Analysis of the dataset also revealed that negative reviews dominated at 52.8%, followed by positive at 28.3%, and neutral at 19%. These findings highlight the value of sentiment analysis in understanding user perspectives and can support developers in improving application quality and user experience.
Increasing package delivery efficiency through the application of the prim algorithm to find the shortest route on the expedition route Lestari, Apri Dwi; Pertiwi, Dwika Ananda Agustina; Muslim, Much Aziz
Journal of Student Research Exploration Vol. 1 No. 1: January 2023
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/josre.v1i1.105

Abstract

One of the changes is in terms of shopping. Previously, people shopped through physical stores, but since the emergence of online shopping platforms, people have started to switch to using the marketplace as a place to make buying and selling transactions. This platform utilizes expedition services to send packages in the form of ordered goods from sellers to buyers. This activity presents a new problem, which is related to the efficiency of package delivery by courier services so that goods can arrive as quickly as possible in the hands of buyers. Graph modeling to solve a problem related to the shortest path and the fastest path is adapted in this paper. The algorithm used is Prim's Algorithm, which is an algorithm to determine the minimum spanning tree of a connected weighted graph. The test results show that the algorithm is suitable for increasing packet delivery efficiency by determining the shortest path based on the minimum spanning tree concept. By taking a sample of travel routes on the island of Java, the best route was obtained with a total distance of 1,771 kilometers connecting cities from the city of Jakarta to the city of Banyuwangi.
Enhanced Out-of-Fold Stacking with Feature Grouping and Model-Specific Transformations for Diabetes Prediction Improvement Putro, Ari Nugroho; Kharisma, Sidiq Noor; Al-Zahra, Gea Destadia; Muslim, Much Aziz; Pertiwi, Dwika Ananda Agustina
Journal of Student Research Exploration Vol. 4 No. 1 (2026): January 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/josre.v4i1.674

Abstract

Diabetes mellitus is a chronic disease with serious implications for global health. Early detection is essential to reduce these risks, and machine learning methods are widely used in diabetes prediction. However, improving accuracy remains a major challenge in the development of predictive models. This study proposes a stacking-based ensemble learning approach with an out-of-fold (OOF) scheme to improve classification performance. The proposed method consists of several systematic steps, namely (1) data preprocessing via median imputation of invalid values and feature transformation according to model characteristics, (2) the creation of base learners comprising Logistic Regression, Gaussian Naïve Bayes, Support Vector Machine, Random Forest, and XGBoost, (3) model training using Stratified Cross Validation 5 Fold to generate OOF predictions, (4) combining all OOF predictions into a meta-feature matrix, and (5) training an XGBoost-based meta-model to generate the final prediction. This approach enables the meta-model to optimally learn the relationships among the outputs of the baseline models. Experimental results show that the proposed method achieves an accuracy of 91.15%, precision of 90.65%, recall of 83.21%, and an F1-score of 86.77%. These results indicate that stacking is effective in improving the accuracy of diabetes predictions.
Co-Authors Ahmad, Kamilah Al-Zahra, Gea Destadia Ali, Muazam Annegrat, Ahmed Mohamed Artiyani, Fani Aslamiyah, Sulaibatul Budi Prasetiyo, Budi Budiman, Kholiq Darmawan, Aditya Yoga Dianti, Erika Noor Djuniadi Djuniadi Dullah, Ahmad Ubai Efrilianda, Devi Ajeng Fattah, Ahmad Maulana Malik Hadiq, Hadiq Hidayah, Sayidah Rohmatul Jumanto Jumanto, Jumanto Jumanto Unjung Khan, Atta Ullah Kharisma, Sidiq Noor Khoirunnisa, Oktaria Lestari, Apri Dwi Lodana, Mae Much Aziz Muslim Muhammad Yusuf Muzayanah, Rini N. Nelis Febriani SM Ningsih, Maylinna Rahayu Noraini, Oktafiyani Aisah Nuk Ghurroh Setyoningrum Nurwijayanti Prabaswara, Ireneus Putri Utami, Putri Putro, Ari Nugroho Riliandini, Popy Rofik Rofik, Rofik Salahudin, Shahrul Nizam Santosa, Kevin Septian, M. Rivaldi Ali Solehatin, Solehatin Syahra, Syahra Audiyani Fitra Umar, Fadhil