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All Journal Journal of Information System JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JPP IPTEK (Jurnal Pengabdian dan Penerapan IPTEK) EVOLUSI : Jurnal Sains dan Manajemen Jurnal Teknologi Informasi dan Pendidikan Infotekmesin Journal of Innovation Information Technology and Application (JINITA) Madani : Indonesian Journal of Civil Society Jurnal Abdimas: Pengabdian dan Pengembangan Masyarakat Jurnal Inovasi Daerah
Abdul Rohman Supriyono
Politeknik Negeri Cilacap
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Public Health Social Sciences Other

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DAMPAK KNOWLEDGE MANAGEMENT TERHADAP PENINGKATAN KINERJA PEGAWAI PADA PERUSAHAAN HOTEL CILACAP Linda Perdana Wanti; Inka Putri Cahyanti; Abdul Rohman Supriyono
Jurnal Inovasi Daerah Vol. 1 No. 2 (2022): JID: Jurnal Inovasi Daerah, Desember 2022
Publisher : Badan Perencanaan Pembangunan Penelitian dan Pengembangan Daerah Kabupaten Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56655/jid.v1i2.42

Abstract

Indonesia has many tourist destinations that can be used to increase regional income. Among them are Teluk Penyu Beach and the tourist area of Benteng Pendem which is in Cilacap Regency. This will also affect the formation of tourism accommodations such as hotels. The purpose of this study is to improve the service of hotel employees who are influenced by the performance of hotel employees, so the purpose of this study is to analyze the effect of knowledge management on improving employee performance. The data used in this study uses employee data and other data taken at the Whiz Cilacap Hotel which is located on Jln. General Soedirman, Cilacap Regency, Central Java. The method used in this study is a quantitative descriptive method with independent variables including team performance (X1), employee empowerment (X2), and training (X3), while the dependent variable is employee performance (Y). The results of this study are that the independent variables have a positive and significant influence/impact on the dependent variable, which means that team performance, employee empowerment and training have a positive effect on improving the performance of Whiz Cilacap Hotel employees.
Perbandingan Kinerja Antara Gatling dan Apache JMeter pada Uji Beban RESTful API Prih Diantono Abda'u; Agus Susanto; Abdul Rohman Supriyono; Dwi Novia Prasetyanti
Infotekmesin Vol 15 No 1 (2024): Infotekmesin: Januari, 2024
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v15i1.2176

Abstract

This research explores and compares the performance of two popular load testing tools, namely Gatling and Apache JMeter, with a focus on API performance testing. The rapid growth in web and mobile application development highlights the urgent need to ensure optimal API performance. This research was conducted to provide in-depth insight into the advantages and disadvantages of these two testing tools through the use of similar testing scenarios. The experimental method involves implementing test scenarios that include load variations and high demands on both devices. The main parameters observed include API response time, throughput, and latency. In-depth analysis was carried out on the data obtained to evaluate the reliability and efficiency of each tool. The results of this research provide a comprehensive understanding of the performance of Gatling and Apache JMeter in the context of API performance testing. These findings can provide practical guidance for software developers and testing practitioners in selecting load testing tools that suit their project needs. Recommendations for future research include expanding exploration of other load testing tools, comparison with more complex test scenarios, and integration with performance monitoring tools for more holistic analysis. Thus, this research is expected to make a significant contribution to the understanding and selection of effective load testing tools in web and mobile application development.
Development of a Hybrid CNN–SVM-Based Acute Lymphoblastic Leukemia Detection System on Hematology Image Data Linda Perdana Wanti; Annisa Romadloni; Kukuh Muhammad; Abdul Rohman Supriyono; Muhammad Nur Faiz
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 2 (2025): JINITA, December 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v7i2.3002

Abstract

Acute Lymphoblastic Leukemia (ALL) is among the most common pediatric blood cancers and progresses rapidly, necessitating early and accurate detection. Manual diagnosis via microscopic analysis of blood samples is time-consuming and highly dependent on specialist expertise. This study proposes a hybrid model that combines a Convolutional Neural Network (CNN) with a Support Vector Machine (SVM) to automatically detect ALL from blood-cell images. The CNN performs deep feature extraction from images, while the SVM serves as the classifier to determine ALL status. The dataset comprises microscopic images labeled as ALL or normal and is processed through preprocessing steps such as augmentation and normalization. The adopted CNN produces optimized feature representations. Experimental results show that the hybrid CNN–SVM model with an RBF kernel achieves the best performance, with an accuracy of 96.4%, precision of 95.8%, recall of 96.1%, and an F1-score of 96.0%, surpassing pure CNN-based baselines. Training converged at the 41st epoch, with a training accuracy of 97.2%, validation accuracy of 95.9%, training loss of 0.09, and validation loss of 0.11, indicating stable learning without overfitting. The model’s ROC curve lies well above the chance diagonal, with an Area Under the Curve (AUC) of 0.914, means there is a 91.4% chance the model assigns a higher score to a truly positive (leukemia) image than to a negative (normal) image.These findings suggest that the CNN–SVM hybrid approach enhances leukemia detection performance compared with conventional CNN-only methods and holds promise as a fast, accurate, and efficient image-based decision-support tool for early leukemia diagnosis in digital hematology.
Co-Authors Agus Susanto Andesita Prihantara Anggita Dwi Fatimah Annas Setiawan Prabowo Annisa Romadloni Arif Fahrizal Arif Wirawan Muhammad Bambang Sugiantoro Betti Widianingsih Devi Taufiq Nurrohman Dodi Satriawan Dwi Novia Prasetyanti Inka Putri Cahyanti Isa Bahroni, Isa Kukuh Muhammad Laura Sari Linda Perdana Wanti Linda Perdana Wanti Lutfi Syafirullah Muhammad Nur Faiz Oman Somantri Oto Prasadi Prih Diantono Abda`u Ratih Hafsarah Maharrani Riyadi Purwanto Rostika Listyaningrum Sari, Eka Dyah Puspita Yudi prayudi