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All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) Techno Nusa Mandiri : Journal of Computing and Information Technology Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer
Risca Lusiana Pratiwi
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Computer Science & IT Industrial & Manufacturing Engineering

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IMPLEMENTATION OF DATA MINING ALGORITHM FOR PREDICTING POPULARITY OF PLAYSTORE GAMES IN THE PANDEMIC PERIOD OF COVID-19 Siti Fauziah; Daning Nur Sulistyowati; Norma Yunita; Siti Fauziah; Risca Lusiana Pratiwi
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol 6 No 1 (2020): JITK Issue August 2020
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1535.861 KB) | DOI: 10.33480/jitk.v6i1.1425

Abstract

The existence of the COVID-19 virus makes everyone fill their time at home by doing various activities, one of them playing games on the phone. For the game to develop continuously, it needs an assessment that comes from the community and especially the game lovers themselves. This assessment is used to find out what category of game you want. Therefore the analysis is needed to determine the interests of game lovers by analyzing the popularity of a game. This research was conducted to predict the level of popularity of games in PlayStore applications to find out how many popular and unpopular games and the accuracy obtained with the C4.5 algorithm and Naive Bayes algorithm. The results obtained using the C4.5 algorithm showed 73 popular games and 12 unpopular games with an accuracy value of 85.83% with a precision of 85.83% and a recall of 100% and Naive Bayes showed 23 popular games and 62 unpopular games with an accuracy value of 80% with a precision of 96.11% and a recall of 81.01%. The evaluation results with the ROC curve show the AUC value using the Naive Bayes model of 0.776 and the C4.5 model of 0.500. Of the two models used, one of them is included in the classification of Good classification, namely the Naive Bayes algorithm model, because it has an AUC value between 0.80-0.90. While the C4.5 algorithm model is included in the Fair classification, has an AUC value between 0.70 - 0.80.
FINAL GRADE PREDICTION MODEL BASED ON STUDENT'S ALCOHOL CONSUMPTION rangga ramadhan saelan; Siti Masturoh; Taopik Hidayat; Siti Nurlela; Risca Lusiana Pratiwi; Muhammad Iqbal
Jurnal Techno Nusa Mandiri Vol 19 No 1 (2022): Techno Nusa Mandiri : Journal of Computing and Information Technology Period of
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/techno.v19i1.3056

Abstract

Untuk mengetahui pengaruh konsumsi alcohol dan dan beberapa faktor lainnya yang diperkirakan memiliki peran terhadap tingkat kinerja belajar remaja yang masih bersekolah, maka saat ini dilakukan penelitian terhadap data publik yang telah didapatkan dengan menggunakan teknik machine learning dengan melatih beberapa model untuk memprediksi nilai akhir sebagai acuan kinerja belajar pelajar. Dengan melatih beberapa model machine learning untuk memprediksi nilai tahun akhir dari bahasa portugal dengan melakukan metode komparatif membandingkan model Support Vector Regressor (SVR) dan Random Forest (RF) sehingga akan didapatkan model terbaik untuk memprediksi. Semua model memiliki hyperparameter yang harus disesuaikan. Untuk menyetel hyperparameter ini menggunakan menggunakan Cross Validation. Model terbaik untuk memprediksi nilai akhir G3 adalah Support Vector Regressor (SVR) dan Random Forest (RF), dan memiliki mean absolute error (MAE) masing-masing sekitar 2,24 dan 2,25. Melalui plot MAE, model SVR dan RF bekerja dengan baik. Tetapi, Dengan menganalisis distribusi kesalahan yang dibuat oleh kedua model, dapat disimpulkan bahwa SVR lebih seimbang, yaitu memiliki rasio yang lebih baik antara nilai yang diremehkan dan ditaksir terlalu tinggi, sementara RF berkinerja lebih baik pada outlier.
Analisis Pengelompokan Karakter Pemain Usia Dini di Sekolah Futsal Golden Eagles Berdasarkan Faktor Perilaku Menggunakan Algoritma K-means Abdul Khoir; Muhamad Arifin Ilhan; Kevin Ananda Maas; Risca Lusiana Pratiwi; Euis Widanengsih
Jurnal ilmiah Sistem Informasi dan Ilmu Komputer Vol. 5 No. 3 (2025): November: Jurnal ilmiah Sistem Informasi dan Ilmu Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juisik.v5i3.1709

Abstract

This study aims to analyze and categorize the character of young futsal players based on the results of coaches' assessments using the K-Means Clustering algorithm. Data were obtained from 70 Golden Eagles Futsal School students aged 5–13 years who were directly observed by coaches during routine training sessions. Assessment aspects included concentration, cooperation, discipline, enthusiasm, emotional control, and response to instructions. The analysis process was carried out using Python with the Scikit-learn library as the main tool for data processing and visualization of results (bar charts and heatmaps). The clustering results formed three main clusters: (1) independent and highly focused children, (2) developing children, and (3) children who require more attention. The average behavioral scores showed clear differences between clusters, with the first group having the highest levels of concentration and discipline. These findings indicate that a data-driven approach can provide a deep understanding of the character of young children in the context of futsal, as well as serve as a reference for coaches in designing more personalized and effective coaching strategies.
Perbandingan Kinerja Algoritma Decision Tree dan Random Forest dalam Memprediksi Kepuasan Penumpang Maskapai Rahma Ayu Silvana; Nadila Anggiani; Athallah Labib; Risca Lusiana Pratiwi; Euis Widanegsih
Jurnal ilmiah Sistem Informasi dan Ilmu Komputer Vol. 5 No. 3 (2025): November: Jurnal ilmiah Sistem Informasi dan Ilmu Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juisik.v5i3.1759

Abstract

This study aims to conduct a comparative analysis of the performance of two classification algorithms, namely Decision Tree and Random Forest, in predicting the level of airline passenger satisfaction. The data used in this research were obtained from the Airline Passenger Satisfaction dataset available on Kaggle, which contains various variables related to passengers’ flight experiences. The research employed a quantitative experimental method using the CRISP-DM (Cross Industry Standard Process for Data Mining) approach, consisting of several stages including data understanding, data preparation, modeling, evaluation, and deployment. The modeling process was carried out using RapidMiner Studio, with the dataset divided into 70% for training and 30% for testing. The experimental results indicate that the Decision Tree algorithm achieved an accuracy rate of 91.77%, while the Random Forest algorithm achieved a higher accuracy of 93.37%. This difference demonstrates that Random Forest possesses better generalization capabilities and more stable performance in handling complex and varied data. Therefore, it can be concluded that the Random Forest algorithm performs more effectively in predicting airline passenger satisfaction levels. Moreover, this study highlights the importance of selecting an appropriate algorithm in data analysis processes to support data-driven decision-making within the aviation industry.
Klasifikasi Tingkat Kemiskinan di Indonesia Menggunakan Naive Bayes dengan RapidMiner Ahmad Rizki Sya’bani; Wahyu Nur Hidayat; Mikhael Valliano Benjamin; Risca Lusiana Pratiwi; Euis Widanegsih
Jurnal ilmiah Sistem Informasi dan Ilmu Komputer Vol. 5 No. 3 (2025): November: Jurnal ilmiah Sistem Informasi dan Ilmu Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juisik.v5i3.1770

Abstract

Poverty is a multidimensional issue that has a significant impact on social and economic development in Indonesia. Accurate analysis of poverty levels is essential to support government policies in distributing aid and planning targeted development programs. This study aims to classify poverty levels in Indonesia using the Naive Bayes algorithm based on machine learning, assisted by the RapidMiner Studio software. The dataset consists of 155 entries with 12 key attributes reflecting social and economic indicators, such as household expenditure, education level, unemployment rate, and the Human Development Index (HDI). The research follows the CRISP-DM methodology, which includes the stages of business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The modeling results show that the Naive Bayes algorithm achieves an accuracy of 94.19%, with high precision and recall values, indicating consistent model performance in classifying poor and non-poor categories. These findings suggest that the Naive Bayes-based machine learning approach can serve as an effective analytical tool to understand poverty patterns in Indonesia. The implementation of this model is expected to assist the government in making data-driven decisions to improve the effectiveness of poverty alleviation programs.
Co-Authors Abdul Khoir Ahmad Rizki Sya’bani Athallah Labib Euis Widanegsih Euis Widanengsih Kevin Ananda Maas M. Rangga Ramadhan Saelan Mikhael Valliano Benjamin Muhamad Arifin Ilhan Nadila Anggiani Norma Yunita Nur Hidayat, Wahyu Nurwijayanti Rahma Ayu Silvana Siti Fauziah Siti Fauziah Siti Masturoh siti nurlela Sulistyowati, Daning Nur Taopik Hidayat