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All Journal Jiko (Jurnal Informatika dan komputer) INFORMASI (Jurnal Informatika dan Sistem Informasi) Journal of Applied Data Sciences Jurnal Ilmiah Teknik Informatika dan Komunikasi Jurnal Teknologi dan Informatika JISTECH: Journal of Information Science and Technology
Kristia Yuliawan
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice

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COMPARISON OF DECISION TREE AND NAÏVE BAYES ALGORITHMS IN PREDICTING STUDENT GRADUATION AT YPK JUNIOR HIGH SCHOOL, NABIRE REGENCY Kristia Yuliawan; Stevanus Murib
JIKO (Jurnal Informatika dan Komputer) Vol 7 No 2 (2024)
Publisher : Program Studi Teknik Informatika Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i2.8506

Abstract

This study aims to compare the accuracy of the Decision Tree C4.5 and Naive Bayes algorithms in predicting student graduation at YPK Immanuel Nabire Junior High School, Central Papua. Student data from the 2022 and 2023 school years were used as training data, whereas student data for the 2024 school year were used as testing data. Data collection methods included field studies, interviews with schools, and literature studies. The implementation of the algorithm is carried out using the Orange software, which simplifies the process of data visualization and analysis. Both algorithms are applied to data processed through stages of cleaning and normalization to ensure the quality and relevance of the data used. The results show that the Decision Tree C4.5 algorithm has a prediction accuracy of 90.91%, while the Naive Bayes algorithm has an accuracy of 63.64%. The C4.5 Decision Tree algorithm is superior in predicting student graduation compared to Naive Bayes, which means that the C4.5 Decision Tree is more effective in identifying students who are likely to pass or not pass. The implementation of the C4.5 Decision Tree algorithm also helps schools make better decisions to support students who require additional attention. The study concluded that the Decision Tree C4.5 algorithm is recommended for use in predicting student graduation because it provides higher accuracy. The results of this research can be used by schools to improve the efficiency of the graduation prediction process and develop more effective and efficient learning programs. Using the right algorithms, schools can be more proactive in identifying students who need additional support, which can reduce academic failure rates and improve the overall quality of education
Comparative Analysis of YOLOv8s and Faster R-CNN for High-Resolution UAV RGB Oil Palm Health Detection: Accuracy versus Inference Speed Trade-Off Kristia Yuliawan; Danny Manongga; Hendry Hendry
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1430

Abstract

Accurate and rapid detection of oil palm health conditions using UAV imagery is essential for supporting precision agriculture and large-scale plantation monitoring. However, challenges such as overlapping canopies, complex background textures, varying illumination, and severe class imbalance often reduce the reliability of automated detection systems. This study presents a comparative evaluation between YOLOv8s, a one-stage object detector, and Faster R-CNN, a two-stage detector, for identifying healthy and unhealthy oil palm trees using high-resolution UAV RGB imagery. The dataset consists of 2,303 annotated images collected from drone surveys and divided into training (70%), validation (20%), and testing (10%) subsets under a controlled experimental design. Both models were trained and evaluated using identical preprocessing pipelines and annotation formats to ensure fairness in comparison. Performance was assessed using precision, recall, F1-score, mean Average Precision (mAP@50 and mAP@50–95), and inference time. Experimental results show that YOLOv8s achieves superior performance with 0.987 precision, 0.998 recall, 0.977 mAP@50–95, and extremely fast inference speed of 1.1 ms per image. In contrast, Faster R-CNN achieves comparable detection accuracy at 0.981 precision and 0.993 recall but with significantly higher computational cost, reaching 875 ms per image. These findings indicate that YOLOv8s provides an optimal balance between accuracy and efficiency, making it more suitable for real-time UAV-based monitoring systems, while Faster R-CNN is more appropriate for offline and high-precision analytical tasks. The study contributes a standardized benchmarking framework for deep learning-based oil palm health detection and provides practical insights for selecting appropriate models in smart agricultural applications.
Implementasi dan Pendampingan Sistem Pengelolaan Stok BBM Berbasis Web pada PT. Gunung Selatan Kabupaten Nabire Suryani, Lili; Kristia Yuliawan
Jurnal Teknologi dan Informatika Vol. 4 No. 1 (2026): Agustus : Jurnal Teknologi dan Informatika
Publisher : STMIK Pesat Nabire

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70539/jti.v4i1.111

Abstract

Fuel inventory management at PT. Gunung Selatan, Nabire Regency, was previously conducted manually, resulting in transaction recording delays, stock calculation errors, difficulties in inventory monitoring, and inefficiencies in report preparation. This community service activity aimed to implement and assist the utilization of a web-based fuel inventory management system to improve the effectiveness, efficiency, and accuracy of fuel stock management. The methods employed included needs identification through observation, interviews, and documentation studies, system development using the prototype approach, system implementation, user training, and evaluation through Black Box Testing. The results demonstrated that all system features functioned properly, including fuel data management, fuel receipt and distribution transactions, real-time stock monitoring, and automated reporting. The implemented system successfully accelerated transaction recording, improved stock accuracy, simplified monitoring processes, and enhanced users’ ability to operate the system independently. Therefore, the system effectively supports the digital transformation of fuel inventory management within the company.
PENGEMBANGAN SISTEM INFORMASI MAHASISWA KULIAH KERJA NYATA (KKN) UNIVERSITAS PAPUA MENGGUNAKAN PHP DAN MYSQL: INFORMATION SYSTEM DEVELOPMENT REAL WORK COLLEGE STUDENTS (KKN) PAPUA UNIVERSITY USES PHP AND MYSQL Kristia Yuliawan; Adie Purnama Putra
JISTECH: Journal of Information Science and Technology Vol 10 No 2 (2018): Oktober 2018
Publisher : Universitas Papua

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

KKN is a form of academic activities related to community service activities that arecarried out by interdisciplinary and intraculicular activities. The implementation iscoordinated by research institutions and community service (LPPM). Data from PapuaState University students who have been modest so far, besides that there is no digitallyrecorded data, in the form of student numbers, student names, location of assignmentsor supervision. So there needs to be a MySQL database application that can facilitateLPPM and help students in the KKN registration process.The research method used is the Development of KKN Information System datacollection, analysis, design, implementation and testing. System design uses Data FlowDiagrams (DFD) and ERD (Entity Relationship Diagrams). The development of theKKN information system development using PHP version 5.6.23 and MySQL. Theadded feature is the student registration feature, KKN photo gallery upload feature,input KKN value, message input to LPPM, input announcement and upload fileattached to the announcement. This research facilitates students and LPPM inregistering KKN and group division so that it can be far more effective and efficienKeywords: Application Development, KKN, PHP, MySQL, Web.
Co-Authors Abigael Jenifer Simopiaref Adie Purnama Putra AGUNG HARDIANSYAH Alya Tasya Indriani Andre Duma Arief Rahman Hakim Cici Kamalia Danny Manongga Diana Wonda Donny Irwanto Ester Ayuk Pusvita Gerry Novand Kaunang Gloriyana Dwijayanti Hurulean Gunawan Prayitno Hendry . Jevan Pilenaung Juridno Wilson Kelvin Kelvin Lili Suryani Lusia Esterina Konsita Maria Claudia Letsoin Mei Herba Natalia Paisey Olvan Nadia Manikome Randi Tandiele Raymond, Christian Paul Stevanus Murib Sulastri Arung Sombolinggi Usman Arfan Wisna Bara’ Padalingan