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All Journal Jurnal Masyarakat Informatika Techno.Com: Jurnal Teknologi Informasi Prosiding SNATIF JAIS (Journal of Applied Intelligent System) Journal of Information System Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Infotekmesin Abdimasku : Jurnal Pengabdian Masyarakat Journal of Soft Computing Exploration Community: Jurnal Pengabdian Pada Masyarakat Jurnal Informatika Polinema (JIP) Jurnal Abdi Negeri
Lalang Erawan
Universitas Dian Nuswantoro
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Social Sciences

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Improving Cervical Cancer Classification Using ADASYN and Random Forest with GridSearchCV Optimization Saputra, Resha Mahardhika; Alzami, Farrikh; Pramudi, Yuventius Tyas Catur; Erawan, Lalang; Megantara, Rama Aria; Pramunendar, Ricardus Anggi; Yusuf, Moh.
Infotekmesin Vol 16 No 1 (2025): Infotekmesin: Januari 2025
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v16i1.2552

Abstract

Cervical cancer is a leading cause of death among women, with over 300,000 deaths recorded in 2020. This study aims to improve the accuracy of cervical cancer diagnosis classification through a combination of Adaptive Synthetic Sampling (ADASYN) and Random Forest algorithm. The research data was obtained from the Cervical Cancer dataset in the UCI Machine Learning Repository with an imbalanced data distribution of 95% negative class and 5% positive class. ADASYN method was chosen for its ability to handle imbalanced data by focusing on minority data points that are difficult to classify. The Random Forest algorithm was optimized using GridSearchCV to achieve maximum performance. Results show that this combination improved accuracy from 96.5% to 96.8% and recall from 93.7% to 94.3%. Feature importance analysis identified key risk factors such as number of pregnancies, age at first sexual intercourse, and hormonal contraceptive use that significantly influence diagnosis. This research demonstrates the effectiveness of combining ADASYN and Random Forest in enhancing classification performance for early cervical cancer detection.
Pelatihan Pemanfaatan Google Sites Untuk Pembuatan Media Pembelajaran Berbasis Website Untuk Guru Dan Dosen Pada Perkumpulanprofesi Multimedia Dan Teknologi Informasi (PPMULTINDO) Jatmoko, Cahaya; Rakasiwi, Sindhu; Widya Laksana, Deddi Award; Erawan, Lalang; Rizqa, Ifan; Astuti, Erna Zuni
Community : Jurnal Pengabdian Pada Masyarakat Vol. 4 No. 2 (2024): Juli : Jurnal Pengabdian Pada Masyarakat
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/community.v4i2.535

Abstract

Conveying appropriate information to be understood quickly and accurately is very important in various areas of life, both academic and non-academic. A teacher or lecturer is a teacher whose job is to educate and provide instruction to students or students. Data visualization is one way that can be used to present data. The advantage of this method is the availability of statistical graphics which can enrich the display of information so that the results are more interactive for the audience. Google Sites is a service owned by the Google company that can be used for e-learning. That way, the information becomes more appropriate to understand quickly and accurately.
Pemanfaatan Google Drive Untuk Backup dan Akses File Bersama Pada CV Berkah Cimandiri Makmur erawan, lalang; Agus Winarno; Candra Irawan
Jurnal Abdi Negeri Vol 1 No 2 (2023): September 2023
Publisher : Informa Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63350/jan.v1i2.11

Abstract

CV. Berkah Cimandiri Makmur merupakan perusahaan pengiriman barang sembako dan kebutuhan pokok masyarakat Kalimantan yang melalui jalur laut dengan armada Dump truk melalui kapal laut dan sebaliknya dari kalimantan membawa barang barang hasil hutan seperti rotan, kayu serta yang lain untuk dikirim ke Semarang dengan memanfatkan Ekspedisi melalui kapal laut. Dalam pelaksanaan operasional administrasinya dibutuhkan penggunaan file yang terintegrasi yang tersimpan di perangkat apapun pada satu tempat yang aman serta sinkronisasi dan bagikan file yang tak terbatas dan fleksibel. Dengan memanfaatkan Google Drive yang memiliki fungsi antara lain dapat berbagi file, menyimpan link, membuat catatan dari Google keep, backup file, backup Chart WhatsApp, dan mengedit file merupakan solusi yang paling murah dan mudah dipahami. Tujuan Program Kemitraan Masyarakat ini untuk memberikan peningkatan pengetahuan, keterampilan para karyawan, sehingga akan meningkatkan kinerja dan pelayanan operasional perusahaan. Luaran Program Kemitraan Masyarakat ini antara lain peningkatan pengetahuan dalam bidang Teknologi Informasi, Jurnal Nasional serta Dokumentasi Video dan menggunakan 3 tahap pelaksanaan yaitu tahap pretest, pengkayaan dan pendalaman materi, dan post test.
Optimization of Heart Failure Classification on Imbalanced Data Using a Supervised Learning Approach Based on Logistic Regression, Random Forest, and K-Nearest Neighbor: Optimalisasi Klasifikasi Gagal Jantung pada Data Imbalanced Menggunakan Pendekatan Supervised Learning Berbasis Regresi Logistik, Random Forest, dan K-Nearest Neighbor agustina, feri; Irawan, Candra; Erawan, Lalang; Suprayogi; Award Widya Laksana, Deddy; Jatmoko, Cahaya; Sinaga, Daurat; Lestiawan, Heru
Jurnal Informatika Polinema Vol. 12 No. 1 (2025): Vol. 12 No. 1 (2025)
Publisher : UPT P2M State Polytechnic of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jip.v12i1.9071

Abstract

Heart failure remains one of the leading causes of mortality worldwide, posing significant challenges for early diagnosis and patient management. One of the major obstacles in developing predictive models for heart failure is the class imbalance problem, where the number of surviving patients far exceeds those who experience death events. This imbalance often leads machine learning algorithms to bias toward the majority class, reducing sensitivity to critical minority cases. To address this issue, this study applies the Synthetic Minority Oversampling Technique (SMOTE) to balance the dataset and improve model performance. Three supervised learning algorithms, namely Logistic Regression (LR), Random Forest (RF), and K-Nearest Neighbor (KNN), were implemented and compared on the UCI Heart Failure Clinical Records dataset containing 299 patient samples with 13 clinical attributes. Experimental results show that the Random Forest model achieved the highest performance with 90% accuracy, precision, recall, and F1-score, outperforming both LR and KNN. The findings demonstrate that combining data balancing with ensemble learning effectively enhances prediction accuracy and sensitivity toward minority classes. The main contribution of this research lies in optimizing supervised models for medical data with skewed class distributions, providing a more reliable and interpretable approach for early heart failure detection. Future research may extend this work by integrating advanced ensemble or hybrid deep learning models and expanding the dataset for multi-institutional validation
Co-Authors Afif, Rizal Naufal Agus Winarno Agus Winarno Agus Winarno, Agus Agustina, Feri Ajib Susanto Ali, Rabei Raad Alzami, Farrikh Award Widya Laksana, Deddy Cahaya Jatmoko Candra Irawan Candra Irawan Christy Atika Sari De Rosal Ignatius Moses Setiadi Dhia Adikusuma Adikusuma Eko Hari Rachmawanto Erna Zuni Astuti Fahmi Amiq Heru Lestiawan Ifan Rizqa Krismawan, Andi Danang Laksana, Deddy Award Widya Mahardika, Nur Raditya Marshela Dinda Amalia Maulydea Ramayanti Megantara, Rama Aria Moh. Yusuf, Moh. Muslih Muslih Pratama, Reza Arista Priskila Penny Ardiana Ricardus Anggi Pramunendar Saputra, Resha Mahardhika Sinaga, Daurat Sindhu Rakasiwi Sintia Rahma Herdajanti Suharnawi Suharnawi Suharnawi Suharnawi Suharnawi Sumarni Adi, Sumarni suprayogi Tan Samuel Permana Wellia Shinta Sari Widya Laksana, Deddi Award Yaacob, Noorayisahbe Mohd Yudistira Dian Hastiti Yuventius Tyas Catur Pramudi Zaenal Arifin