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All Journal Jurnal Buana Informatika Biogenesis: Jurnal Ilmiah Biologi JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL ILMIAH INFORMATIKA Journal of Computer System and Informatics (JoSYC) Journal of Information System and Technology (JOINT) Jurnal Pustaka Data : Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) J-SIGN (Journal Of Informatics, Information System, And Artificial Intelligence) JoDENS: Journal of Digital Ecosystem for Natural Sustainability JUTEKOM - Jurnal Teknologi dan Ilmu Komputer
Purnajaya, Akhmad Rezki
Universitas Universal
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Immunology & microbiology Industrial & Manufacturing Engineering Mechanical Engineering

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Implementasi Support Vector Machine dan Radial Basis Function untuk Klasifikasi Makanan Vegetarian Menggunakan Data Image Williams; Gunawan, Fery; Limuel, Patrick; Purnajaya, Akhmad Rezki
Journal of Digital Ecosystem for Natural Sustainability Vol 3 No 1 (2023): Juli 2023
Publisher : Fakultas Komputer - Universitas Universal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63643/jodens.v3i1.123

Abstract

The vegetarian diet has become increasingly popular in the 21st century due to its potential to reduce the risk of chronic and degenerative diseases. Vegetarians are individuals who do not consume animal products, either for religious or health reasons. However, it can be difficult to determine whether a particular food is vegetarian or non-vegetarian based on visual inspection alone. Therefore, this study successfully developed an SVM & RBF model in RStudio that can accurately differentiate between vegetarian and non-vegetarian foods based on image data. The model achieved an accuracy rate of 95%, specificity of 100%, sensitivity of 88.89%, and an AUC value of 94.44%. It can be concluded that the SVM & RBF model is capable of predicting data with high accuracy and effectively distinguishing between vegetarian and non-vegetarian classes.
Metode Analytic Hierarchy Process (AHP) dalam Pemilihan Penerima Beasiswa (Studi Kasus: Prodi Teknik Perangkat Lunak Universitas Universal) Gunawan, Fery; Limuel, Patrick; Tayanto, Vincent; Purnajaya, Akhmad Rezki
Journal of Digital Ecosystem for Natural Sustainability Vol 3 No 2 (2023): Desember 2023
Publisher : Fakultas Komputer - Universitas Universal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63643/jodens.v3i2.191

Abstract

Specific attention in this study is given to Universitas Universal's scholarship programs, which rely on entrance exam results for selection, prompting the exploration of more efficient and objective decision-making processes. The study draws inspiration from existing research, particularly those utilizing the Analytic Hierarchy Process (AHP), but distinguishes itself by evolving criteria to include exam scores, computer literacy, motivation, and program understanding. The primary focus is on addressing selection challenges in the software engineering program for post-admission students at Universitas Universal. By employing AHP, the study aims to provide a comprehensive decision support system, offering a prospective solution for future scholarship selection challenges. From the results of the AHP program that has been researched, it has been found that the scholarship recipient candidate is the first candidate with a score of 0.518.
Mango and Banana Ripeness Detection based on Lightweight YOLOv8 Saragih, Raymond Erz; Purnajaya, Akhmad Rezki; Syafrinal, Ilwan; Pernando, Yonky; Yodi
Jurnal Buana Informatika Vol. 15 No. 2 (2024): Jurnal Buana Informatika, Volume 15, Nomor 02, Oktober 2024
Publisher : Universitas Atma Jaya Yogyakarta

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

Abstract

Fruits like bananas and mangoes are harvested after reaching a specific ripeness stage. Traditionally, farmers rely on manual inspection to determine ripeness, a process that can be tedious, time-consuming, expensive, and subjective. This work proposes an automatic bananas and mangoes ripeness detector utilizing computer vision technology. The detected bananas and mangoes fall into two classes: ripe and unripe. The state-of-the-art YOLOv8 architecture serves as the core of the detector. Three YOLOv8 variants, YOLOv8n, YOLOv8s, and YOLOv8m, were investigated for their performance. Results show that YOLOv8s achieved the highest overall performance, 0.9991 recall, and a mean Average Precision (mAP) of 0.8897. While YOLOv8m achieved the highest precision of 0.9995, YOLOv8n is the most miniature model, making it suitable for deployment on devices with limited resources.
Application of Support Vector Machine for Multi-Class Migraine Classification Purnajaya, Akhmad Rezki; Jaya, Mega
Jurnal Teknologi informasi dan Ilmu Komputer Vol. 1 No. 4 (2025): Oktober 2025
Publisher : Nolsatu Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65258/jutekom.v1.i4.29

Abstract

Migraine is a prevalent, debilitating neurological disorder where accurate subtype classification is critical. Machine learning (ML) offers a promising avenue to enhance diagnostic accuracy. This study evaluates a Support Vector Machine (SVM) model for multi-class migraine classification. Utilizing a public Kaggle dataset, data was partitioned into 75% training and 25% testing sets. An SVM with a linear kernel was implemented to classify seven migraine subtypes. Performance was evaluated using overall accuracy, a confusion matrix, and detailed per-class metrics: Precision, Recall, and F1-Score. The model achieved 82.65% overall accuracy and a weighted-average F1-Score of 0.824. However, detailed metrics revealed significant variance. The model achieved perfect F1-Scores (1.000) for 'Migraine Without Aura' and 'Typical Aura without Migraine' but struggled with class confusion. 'Typical Aura With Migraine' exhibited a low Recall (0.533), and 'Basilar-Type Aura' had a poor F1-Score (0.400). Critically, the model completely failed to classify 'Sporadic Hemiplegic Migraine' (0.000 F1-Score), a failure masked by the high overall accuracy. These results suggest the linear SVM is a viable baseline, but its reliability varies drastically across subtypes. The granular F1-Score and Recall metrics are essential, exposing classification failures hidden by overall accuracy. Future work must address class imbalance and symptomatic overlap, likely via non-linear models, before this approach is clinically viable.
Analitik Sampah: Eksplorasi Proyeksi Pengelolaan Sampah Kota Batam Menggunakan Pemrograman R Gita Prajati; Suryo Widiantoro; Akhmad Rezki Purnajaya
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

The increasing amount of waste requires a better waste management plan. Batam is one of the cities in Indonesia with a high population and economic growth rate. Batam will need better waste management planning in the future. This study aims to project waste generation in Batam City. The study will be divided into three stages, namely secondary data collection, correlation analysis, and waste generation projection calculations. Secondary data collection will be carried out by collecting data from relevant agencies. Waste generation projections are made based on sociodemographic and socioeconomic factors using R programming. The results show that there are four variables with negative values, namely population, GDP, length of schooling, and literacy rate. Meanwhile, the variables of population density and economic growth rate have positive values. Waste generation projections were made using the variables of literacy rate, length of schooling, population size, and population density. As a result, waste generation in 2030 is projected to decrease to 0.592 kg/person/day.
Dampak Tantangan dan Potensi Vibe Coding Berbasis AI dalam Lanskap Pendidikan Pemrograman: Tinjauan Literatur Sistematis Akhmad Rezki Purnajaya
Jurnal Teknologi informasi dan Ilmu Komputer Vol. 2 No. 2 (2026): April 2026
Publisher : Nolsatu Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65258/jutekom.v2.i2.52

Abstract

Vibe coding transforms natural language commands into functional code through Artificial Intelligence (AI). This Systematic Literature Review (SLR) evaluates the impacts, challenges, and potential of this approach within education. Adhering to the PRISMA protocol, 17 studies were selected based on inclusion criteria. The distribution encompasses higher education (n=9) and K-12 (n=8), focusing on tools like GitHub Copilot and ChatGPT. Synthesis results from controlled experimental studies among higher education students indicate a significant increase in task completion efficiency of up to 35%. However, a distinction is observed where increased technical productivity does not equate to improved learning achievement; instead, risks concerning diminished conceptual understanding and academic integrity challenges remain. This review is limited by the geographic dominance of studies from North America and Europe and the high heterogeneity of study designs, which necessitated a narrative synthesis. This study recommends a pedagogical shift towards critical human-AI collaboration.
Evaluating Resampling Methods for Imbalanced Necrosis Classification on CT Scans Purnajaya, Akhmad Rezki; Masparudin
Jurnal Teknologi informasi dan Ilmu Komputer Vol. 2 No. 2 (2026): April 2026
Publisher : Nolsatu Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65258/jutekom.v2.i2.56

Abstract

Necrosis, or body tissue death, occurs when there is insufficient blood flow to the tissue, which can be caused by injury, radiation, or chemicals. One of the main challenges in the automated diagnosis of necrosis is data imbalance in medical datasets, where the number of pathological cases is far less than normal cases. To address this issue, this study implements and evaluates various data sampling techniques, including Random Undersampling (RUS), Random Oversampling (ROS), Combination of Over-Undersampling (COUS), Synthetic Minority Over-sampling Technique (SMOTE), and Tomek Link, then using a Support Vector Machine (SVM) as the classifier. The test results show that the best sampling technique is the Synthetic Minority Over-sampling Technique (SMOTE), which successfully achieved an accuracy of 100% and an Area Under Curve (AUC) of 100%, indicating its significant potential in improving the accuracy of necrosis diagnosis from CT scans.
Co-Authors ., Very Alvaro, Charlos Charles Charles Darmawan, Justin Eva Tesvara Fatma Indriani Fery Gunawan Fuad Dwi Hanggara Gita Prajati Gunawan, Fery Ilwan Syafrinal Jaden Gil Salim Jaya, Mega Joni Irwansyah Kaharuddin Limuel, Patrick Masparudin Medria Kusuma Dewi Hardhienata Milson Nestelrody Muhammad Reza Faisal, Muhammad Reza Noverio, Oscar Pangestu, Amora Antonio Patrick Limuel Pernando, Yonky Raymond Erz Saragih Rika Jelita Roza, Yuni Saragih, Raymond Erz Steffany Tayanto, Vincent Vincent Tayanto Vinxencius Lieputra Widiantoro, Suryo Williams Williams Wisnu Ananta Kusuma Yamin, Valerian Yodi Yuni Roza