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All Journal Jurnal Informatika dan Teknik Elektro Terapan Register: Jurnal Ilmiah Teknologi Sistem Informasi Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Indonesian Journal of Artificial Intelligence and Data Mining Building of Informatics, Technology and Science Dinasti International Journal of Education Management and Social Science Jurnal Teknologi Dan Sistem Informasi Bisnis Abdimas: Jurnal Pengabdian Masyarakat Universitas Merdeka Malang Journal of Computer System and Informatics (JoSYC) Jurnal Teknik Informatika (JUTIF) Instal : Jurnal Komputer International Journal Of Computer, Network Security and Information System (IJCONSIST) Kaunia: Integration and Interconnection of Islam and Science Journal Jurnal Sains dan Teknologi Jurnal Sistem Informasi dan Bisnis Cerdas Journal of Educational Management Research Ekasakti Pareso Jurnal Akuntansi SmartComp Intechno Journal : Information Technology Journal Edu Komputika Journal
Pratama, Irfan
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Religion Aerospace Engineering Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences

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Journal : Building of Informatics, Technology and Science

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Analisis Perbandingan Algoritma Random Forest dan K-Nearest Neighbors pada Klasifikasi Tingkat Stres Pekerja Manurung, Syalom Kristian; Pratama, Irfan
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.7589

Abstract

Work stress has become a prominent concern in the modern professional landscape, as it can lead to reduced productivity, diminished work quality, and decreased mental well-being among employees. This study aims to evaluate and compare the performance of two machine learning algorithms, namely Random Forest and K-Nearest Neighbors (KNN), in classifying levels of work stress. The data were obtained through an online questionnaire completed by 212 respondents from various employment sectors in Indonesia. The responses were converted from Likert scale to numerical values, grouped using the K-Means clustering method, and categorized into five levels of stress, ranging from no stress to very high stress. To address data imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied. The modeling process was conducted using three different data split scenarios, namely 90:10, 80:20, and 70:30, and evaluated using metrics such as accuracy, precision, recall, f1-score, and cross-validation. The findings indicate that the Random Forest algorithm consistently outperformed KNN across all scenarios. After applying SMOTE, both algorithms showed improved performance, with the Balanced Random Forest model achieving the highest accuracy and f1-score of 92 percent in the 70:30 scenario. These results suggest that combining Random Forest with SMOTE offers an effective and reliable solution for classifying work stress levels and could be developed as an objective and efficient early detection system.
Klasifikasi Kanker Payudara Berdasarkan Gambar Histopatologi Menggunakan Metode Convolutional Neural Network Dengan Arsitektur VGG-16 Nandasari, Dayang; Pratama, Irfan
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7377

Abstract

Breast cancer is one of the deadliest diseases with a high prevalence worldwide, especially in women. Breast cancer is the third leading cause of death in Indonesia. Based on Globocan Center data, there will be approximately 408,661 new cases and nearly 242,099 deaths in Indonesia by 2022. Early detection through histopathology images is very important to increase the patient's chances of recovery. However, the diagnosis process carried out manually by pathologists is quite time consuming and affects subjectivity. This study aims to develop a histopathology image-based breast cancer classification system using VGG-16. The dataset to be used consists of histopathology images that are grouped into 2 classes, namely benign and malignant. The data went through several preprocessing stages, including splitting and augmentation, to improve data quality. Test results show that this model achieves 91% accuracy, along with high precision, recall, and F1-scores on the test data. The performance of this model compares favorably with ensemble architectures such as, MobileNet, MobileNetV2. These findings indicate that the proposed approach can be an effective solution as a histopathology image-based breast cancer diagnosis tool.
Co-Authors Ade Kurnia, Dian Akbar, Muhammad Rizky Mawandhyka Albert Yakobus Chandra Alifudin, Arif Andre Bustari Aprila , Zulma Aris Munandar Arobi, M. Rizki Asanah, Nurul Aulia, Indira Dhekawanti Ayu Aprilia, Veni Ayuningsih, Ekatri Bacun, Julio Francisco Dadang Sudrajat Dwi Krisbiantoro, Dwi Edi Wibowo Eva wulandari, Eva Guno Wibowo, M Ioni Abdurrahman Hubu, Muhammad Rizki Iqbal, Sayid Muhammad Kurniawan, Akbar Sidiq Kurniawan, Muhammad Rifki Lestari, Fia Manurung, Syalom Kristian Nandasari, Dayang Nugroho Budi Wibowo Odi Nurdiawan Paneo, Anto Farwanto Pohan, Sulistyawati Pradana, Michael Anggun Kado Prasetyaningrum, Putri Taqwa Pratama, George Recksy Sandy Prayuda, Arya Priatama, Candra Rina Asmeri, Rina Sarmini Sarmini Suria, Ozzi Swapurba, Gangsar Taqwa Prasetyaningrun, Putri Uswatun Hasanah Waluyo, Retno Widianti, Anisa Wuri Handayani