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All Journal Nuansa Informatika Sistemasi: Jurnal Sistem Informasi Jurnal Informatika Jurnal Pilar Nusa Mandiri Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JITK (Jurnal Ilmu Pengetahuan dan Komputer) Techno Nusa Mandiri : Journal of Computing and Information Technology Technologia: Jurnal Ilmiah Journal of Empowerment Community Tematik : Jurnal Teknologi Informasi Komunikasi Innovation in Research of Informatics (INNOVATICS) Jurnal Pengabdian dan Edukasi Sekolah (Jubaedah) International Journal of Quantitative Research and Modeling Joutica : Journal of Informatic Unisla Formosa Journal of Applied Sciences (FJAS) Paradigma International Journal of Ethno-Sciences and Education Research Jurnal Pengabdian Masyarakat Tapis Berseri SisInfo : Jurnal Sistem Informasi dan Informatika Nuansa Informatika Jurnal Bhakti Karya dan Inovatif In Search (Informatic, Science, Entrepreneur, Applied Art, Research, Humanism) International Journal of Computer Technology and Science International Journal of Information Engineering and Science
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Fakultas Teknologi Dan Informatika Universitas Informatika Dan Bisnis Indonesia
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Physics Social Sciences Transportation Other

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Journal : Jurnal Pilar Nusa Mandiri

 1 
XGBOOST HYPERPARAMETER OPTIMIZATION USING RANDOMIZEDSEARCHCV FOR ACCURATE FOREST FIRE DROUGHT CONDITION PREDICTION Alamsyah, Nur; Budiman, Budiman; Yoga, Titan Parama; Alamsyah, R Yadi Rakhman
Jurnal Pilar Nusa Mandiri Vol. 20 No. 2 (2024): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v20i2.5569

Abstract

Climate change and increasing global temperatures have increased the frequency and intensity of forest fires, making fire risk evaluation increasingly important. This study aims to improve the accuracy of predicting forest fuel drought conditions (Drought Code) by using the XGBoost algorithm optimized with RandomizedSearchCV. The research methods include collecting data related to forest fires, preprocessing data to ensure quality and consistency, and using RandomizedSearchCV for XGBoost hyperparameter optimization. The results showed that the optimized XGBoost model resulted in a decrease in Mean Squared Error (MSE) and an increase in R-squared value compared to the default model. The optimized model achieved an MSE of 0.0210 and R2 of 0.9820 on the test data, indicating significantly improved prediction accuracy for forest fuel drought conditions. These findings emphasize the importance of hyperparameter optimization in improving the accuracy of predictive models for forest fire risk assessment.
ISOLATION FOREST PARAMETER TUNING FOR MOBILE APP ANOMALY DETECTION BASED ON PERMISSION REQUESTS Kaunang, Valencia Claudia Jennifer; Alamsyah, Nur; Nursyanti, Reni; Budiman, Budiman; Danestiara, Venia R; Setiana, Elia
Jurnal Pilar Nusa Mandiri Vol. 21 No. 2 (2025): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v21i2.6647

Abstract

Ensuring mobile app security needs the capability to detect apps that request excessive or inappropriate permissions. This research proposes an anomaly detection approach using Isolation Forest, enhanced through hyperparameter tuning, to identify suspect apps based on permission request patterns. The dataset is processed into binary features, followed by exploratory data analysis (EDA) to examine the distribution and highlight sensitive permissions. The Isolation Forest model is then optimized by tuning parameters such as contamination level, number of estimators, and sample size. The fine-tuned model achieved a more accurate separation between normal and anomaly applications, detecting 10 anomalies out of 200 applications, with anomaly applications averaging 125.10 permits compared to 42.76 in normal applications. These anomalies often requested permissions related to network, storage, contacts and microphone, indicating potential privacy risks. The results show that parameter tuning improves the detection performance of Isolation Forest, providing a practical solution for mobile security monitoring. After tuning, the number of false positives decreased by 50%, and the model successfully reduced detected anomalies from 20 to 10, increasing the precision of anomaly detection from 70% to 90%. Future work could include improving feature selection and integration into real-time detection systems. 
Co-Authors Acep Hendra Aggi Panigoro Sarifiyono Akbar, Imannudin Alamsyah, R Yadi Rakhman AlFauzi, Ihsan Amos Duan Nugroho Anto Widianto Ari Rizki Fauzi Bayu Wibisono, Gunthur Cahya Miftahul Falah Catherin Rumambo Mogot Pandin Chairul Habibi Chairul Habibi Danestiara, Venia R Dani Rizky Zaelani Darsiti . Dirham Triyadi Dirham Triyadi Erpurini, Wala Fauzi Ramadhan, Ahmad Fikri Rizqillah Hasani Gelar, Trisna Habibi, Chairul Hani Fitria Rahmani Hasan Nuraripin Hernawan, Kartika Nursyabanita Ismi Nur Muhamad Januantara Prima, Alif Karlina, Nichi Hana Kaunang, Valencia Kaunang, Valencia Claudia Jennifer Niqotaini, Zatin NUR ALAMSYAH Nur Alamsyah Nur Alamsyah, Nur Nursyanti, Reni PARAMA YOGA, TITAN Permata Hati, Tiara R. Yadi Rakhman A4 R. Yadi Rakhman Alamsyah R. Yadi Rakhman Alamsyah Rakhman Alamsyah, Rd. Yadi Ramadhan, Muhammad Rizki Rd. Zidni Rizan Al-Zhahir Yanuar Reni Nursyanti Reni Nursyanti Reni Nursyanti Reynaldy Gimnastiar Rijwan Rijwan S.W. Manurip, Atanasius Angga Setiana, Elia Silvana Anggraeni, Zulmeida Sophian Ramadhan Suci Fitriani Setiawan Titan Parama Titan Parama Yoga Tutik Ultsa Rahmatika Valencia Claudia Jennifer Valencia Claudia Jennifer Kaunang Vani Maharani Nasution Venia Restreva Danestiara Wulandari Wulandari Yoga Rizki Rahmawan Zein Suna Arfigan Said