Article Per Year (5 Year)
p-Index From 2021 - 2026
7.55
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Psikologika : Jurnal Pemikiran dan Penelitian Psikologi dCartesian: Jurnal Matematika dan Aplikasi JURNAL SISTEM INFORMASI BISNIS Prosiding KOMMIT Jurnal Sains dan Teknologi Jurnal Buana Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Indonesian Journal of Mathematics and Natural Sciences Jurnal Ilmiah Kursor Noetic Psychology Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Transformatika JUITA : Jurnal Informatika Scientific Journal of Informatics Psikodimensia: Kajian Ilmiah Psikologi Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Jurnal Sains Matematika dan Statistika Proceeding of the Electrical Engineering Computer Science and Informatics MNJ (Malang Neurology Journal) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Mercumatika : Jurnal Penelitian Matematika dan Pendidikan Matematika Inquiry: Jurnal Ilmiah Psikologi BAREKENG: Jurnal Ilmu Matematika dan Terapan IJEBD (International Journal Of Entrepreneurship And Business Development) JOURNAL SPORT AREA Philanthropy: Journal of Psychology MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Evangelikal: Jurnal Teologi Injili dan Pembinaan Warga Jemaat Aptisi Transactions on Technopreneurship (ATT) Insight: Jurnal Ilmiah Psikologi Jurnal Abdi Insani Computer Science and Information Technologies Jurnal Sains dan Edukasi Sains SPEKTA (Jurnal Pengabdian Kepada Masyarakat : Teknologi dan Aplikasi) Indonesian Journal of Applied Research (IJAR) Journal of Science and Science Education JAMBURA JOURNAL OF PROBABILITY AND STATISTICS Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya INJURITY: Journal of Interdisciplinary Studies ENDLESS : International Journal of Future Studies d'Cartesian: Jurnal Matematika dan Aplikasi Tesseract: International Journal of Geometry and Applied Mathematics JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Prismatika: Jurnal Pendidikan dan Riset Matematika
Adi Setiawan
Universitas Kristen Satya Wacana
Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology 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 Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Neuroscience Physics Public Health Social Sciences Transportation Other

Published : 92 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 92 Documents
Search

 6   7   8   9   10 
Comparison of k-Nearest Neighbor and Naive Bayes Methods for SNP Data Classification Denny Indrajaya; Adi Setiawan; Bambang Susanto
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 1 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i1.1758

Abstract

In an accident, sometimes the identity of a person who has an accident is hard to know, so it is necessary to use biological data such as Single Nucleotide Polymorphism (SNP) data to identify the person's origin. This research aims to compare the accuracy and the F1 score of the k-Nearest Neighbor method and the Naive Bayes method in classifying SNP data from 120 people who divide into groups, namely European (CEU) and Yoruba (YRI). Determination of the best method based on the average value of accuracy and the average value of F1 score from 1000 iterations with various percentage distributions of training datasets and testing datasets. In this research, the selection of SNP locations for the classification process was carried out by correlation analysis. The average accuracy obtained for the k-Nearest Neighbor method with the value of k=31 is 98.38% where the average F1 score is 98.39% while the Naive Bayes method obtained the average accuracy of 96.74% and the average F1 score of 96.63%. In this case, the k-Nearest Neighbor method is better than the Naive Bayes method in classifying SNP data to determine the origin of a person's ancestor tends to be from CEU or YRI.
Comparison of the Karney Polygon Method and the Shoelace Method for Calculating Area Vikky Aprelia Windarni; Adi Setiawan; Atina Rahmatalia
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 1 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i1.2929

Abstract

In calculating the area of an area, latitude and longitude coordinates are based on data from Global Administrative Region Database and Google Earth can be used. The aim of this research is to calculate the area. This research uses the Karney and Shoelace method to determine its accuracy based on Median Absolute Percentage Error in calculating the area of an area. Median Absolute Percentage Error results use data based on Global Administration The Regional Database by applying the polygon method proposed by Karney is 18.73%, and the percentage is 18.19% by applying the Shoelace method. Based on Google Earth data, implementation the method proposed by Karney obtained a percentage of 19.14%, and the application of shoelaces method obtained a percentage of 19.72%. In this case, Karney polygons and the Shoelace method has good accuracy because the value is below 20%. The proposed Shoelace method is easier to perform understand compared to the Karney method for calculating land area because it uses the Universal Transverse Mercator coordinate system, which projects points on the Earth's surface onto a flat plane.
Athletes’ Sports Orientation Viewed from Parental Social Support and Gender Rumahpasal, Olivia; Kristinawati, Wahyuni; Setiawan, Adi
Journal Sport Area Vol 5 No 2 (2020): December
Publisher : UIR Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/sportarea..vol().4775

Abstract

Sports orientation is one of the important factors for improving an athletes’ performance in achieving sports achievements. This study aims to determine the effect of parental social support and gender on athlete’s sports orientation. Quantitative approach was used as the research method of this study with a correlational research design. The subjects of study were 86 athletes (53.49 % women) at the Students Sports Training Center (SSTC) of DKI Jakarta Province who were selected through convenience sampling technique. Demographic data questionnaires, Parents Social Support Scale and Sport Orientation Questionnaires were used as the measuring instruments. Data analysis uses linear regression (simple and multiple) techniques show results as follows: (1) Parental social support has a significant effect on athletes’ sports orientation (tcount = 4.396 > ttable = 1.988); (2) Gender does not have a significant effect on athletes’ sports orientation (tcount = 1.891 < ttable = 1.988); (3) Parental social support and gender can simultaneously predict athletes’ sports orientation (Fcount = 12.93 > Ftable = 2.49). The effective contribution of parental social support and gender simultaneously toward athletes’ sports orientation is 21.9 % and the rest is influenced by other factors outside the study. The role of parental social support (17.75 %) is more dominant than gender (4.14 %)
THE INFLUENCE OF FRUGALITY ON SUBJECTIVE WELL-BEING WITH RELIGIOSITY AS A MODERATOR VARIABLE: A STUDY ON EARLY ADULT ONLINE SHOPPERS Gloria Meydelina; Wahyuni Kristinawati; Adi Setiawan
Interdiciplinary Journal and Hummanity (INJURITY) Vol. 4 No. 1 (2025): INJURITY: Journal of Interdisciplinary Studies.
Publisher : Pusat Publikasi Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58631/injurity.v4i1.1396

Abstract

This study aims to examine the influence of frugality on subjective well-being (SWB) in early adult online shoppers, with religiosity as a moderator variable. The study participants consisted of men and women aged 18-40 years who are online shoppers in Indonesia. The hypothesis test was carried out using the Multiple Linear Regression technique, and the results of this study showed that the results showed that a positive relationship was found between the three variables. The results of the study also showed that religiousosis positively moderated the relationship between the variables ( = 0.073, p  0.10). In addition, it was found that the relationship was negatively correlated with the two variables (P  0.05). In conclusion, it is suggested that online shoppers can implement a lifestyle of simplicity by making a list of shopping priorities, comparing prices between platforms, and considering the use value of products before making purchases.
ANALISIS KEMANDIRIAN BELAJAR SISWA DALAM PEMBELAJARAN MATEMATIKA DITINJAU DARI GENDER Setiawan, Adi; Sujiwo, Dimas Anditha Cahyo; Panglipur, Indah Rahayu
Prismatika: Jurnal Pendidikan dan Riset Matematika Vol. 7 No. 1 (2024): Prismatika: Jurnal Pendidikan dan Riset Matematika
Publisher : Universitas Insan Budi Utomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33503/prismatika.v7i1.204

Abstract

This study aims to describe students' learning independence in mathematics learning in terms of gender. Learning uses the drill method, namely repeating seriously with the aim of understanding what is being learned. The research method used is descriptive qualitative. The research instrument uses a questionnaire to measure students' mathematics learning independence and interviews are used as additional instruments to obtain more in-depth information about students' experiences and views on their learning independence. The research subjects were 12 female and 12 male students of class VII of SMP Kartika IV-6 Ambulu. The data analysis technique used involved the use of a percentage formula for student answers based on indicators of student learning independence, which were then described using the criteria for interpreting the percentage of answers. The results of the study showed that student independence in mathematics learning in terms of gender showed that males had an average of 59.03% with the category of "mostly" independent and females had an average of 63.19% with the category of "mostly" independent. Based on the results of the percentage, most male and female students are able to learn independently.
GLCM-Based Feature Combination for Extraction Model Optimization in Object Detection Using Machine Learning Florentina Tatrin Kurniati; Irwan Sembiring; Adi Setiawan; Iwan Setyawan; Roy Rudolf Huizen
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i4.27842

Abstract

In the era of modern technology, object detection using the Gray Level Co-occurrence Matrix (GLCM) extraction method plays a crucial role in object recognition processes. It finds applications in real-time scenarios such as security surveillance and autonomous vehicle navigation, among others. Computational efficiency becomes a critical factor in achieving real-time object detection. Hence, there is a need for a detection model with low complexity and satisfactory accuracy. This research aims to enhance computational efficiency by selecting appropriate features within the GLCM framework. Two classification models, namely K-Nearest Neighbours (K-NN) and Support Vector Machine (SVM), were employed, with the results indicating that K-Nearest Neighbours (K-NN) outperforms SVM in terms of computational complexity. Specifically, K-NN, when utilizing a combination of Correlation, Energy, and Homogeneity features, achieves a 100% accuracy rate with low complexity. Moreover, when using a combination of Energy and Homogeneity features, K-NN attains an almost perfect accuracy level of 99.9889%, while maintaining low complexity. On the other hand, despite SVM achieving 100% accuracy in certain feature combinations, its high or very high complexity can pose challenges, particularly in real-time applications. Therefore, based on the trade-off between accuracy and complexity, the K-NN model with a combination of Correlation, Energy, and Homogeneity features emerges as a more suitable choice for real-time applications that demand high accuracy and low complexity. This research provides valuable insights for optimizing object detection in various applications requiring both high accuracy and rapid responsiveness.
Improving genomic classification via Pearson-based SNP selection: a comparison of k-NN, SVM, and random forest Prihanto Ngesti Basuki; Sri Yulianto Joko Prasetyo; Adi Setiawan
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.9087

Abstract

Accurate genomic classification is vital for precision health and population studies, yet high-dimensional single-nucleotide polymorphism (SNP) data (pn) amplify noise, redundancy, and overfitting. This study evaluates a simple, model-independent Pearson-based selection that ranks SNPs by feature–label correlation, and assesses k-nearest neighbors (k-NN), linear support vector machine (SVM), and random forest (RF) under leakage-free stratified Monte Carlo cross-validation (MCCV). Performance increases monotonically with |r|: the strongest tiers reach ?99–100% accuracy; SVM leads in mid tiers (RF second), while k-NN is competitive mainly at the extremes. A matched-dimensionality PCA-120 baseline (TRAIN-only) attains parity for SVM/RF and trails slightly for k-NN at the 10% test size. With 120-SNP panels, prediction medians are ?0.30 ms (SVM), 1.81–1.83 ms (k-NN), and 34–35 ms (RF), supporting CPU-only deployment. A consensus panel combining correlation evidence with principal component analysis (PCA) selection frequency yields interpretable Top-20/Top-120 subsets and |r|-based operating thresholds. Overall, Pearson-based selection provides a transparent, reproducible baseline for small-sample SNP classification, offering accuracy competitive with PCA at lower computational complexity and straightforward extensions to broader cohorts and multi-omics integration.
WORKSHOP PENINGKATAN LITERASI DATA SISWA MELALUI PEMBELAJARAN BERBASIS PROYEK DIGITAL DI SMP KRISTEN SATYA WACANA SALATIGA Adi Setiawan; Faldy Tita; Lennox Larwuy; Amazia Ananta Prabaswara; Harfeley Leipary; Daniel Bramantyo Ranu Bagaskoro
Jurnal Abdi Insani Vol 13 No 1 (2026): Jurnal Abdi Insani
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/abdiinsani.v13i1.3440

Abstract

The development of the digital era demands data literacy skills as a basic 21st-century skill that is important for junior high school students. Based on observations at SMPK Satya Wacana Salatiga, most students still experience difficulties in understanding, interpreting, and using data in learning. This community service activity aims to improve students' data literacy through hands-on practice-based training with an interactive and contextual learning approach. The implementation method includes socialization, data concept training, data processing simulations using spreadsheets and R software, and evaluation through pre-tests and post-tests. The results showed a significant increase in data analysis skills. The average pre-test score of 3.11 increased to 3.84 in the post-test, with an average normalized gain (K) of 0.40, which is included in the medium category. The paired t-test produced a value of t = -12.49 and p-value = 0.000 (p-value < 0.05), indicating a significant difference between the results before and after the training. A total of 44 students were in the medium improvement category, 38 students were low, and 19 students were high. These findings indicate that R-based data literacy training is effective in improving junior high school students' conceptual understanding, self-confidence, and analytical skills, and is relevant to be developed as a 21st-century learning model.
Komparasi Support Vector Machine dengan Logistic Regression terhadap klasifikasi pada data asma Tiara Utary Grace Moelyono; Adi Setiawan; Rachel Wulan Nirmalasari Wijaya
Jurnal Sains dan Edukasi Sains Vol. 9 No. 1 (2026): Jurnal Sains dan Edukasi Sains
Publisher : Faculty of Science and Mathematics, Universitas Kristen Satya Wacana, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/juses.v9i1p1-8

Abstract

Single Nucleotide Polymorphisms (SNP) atau Polimorfisme Nukleotida Tunggal merupakan salah satu jenis variasi genetik yang paling umum pada genom manusia. Salah satu contoh data SNP adalah data asma yang diperoleh dari paket program R yaitu SNPassoc. Penyakit asma  diantaranya dipengaruhi oleh kondisi genetik manusia. Untuk melihat pengaruh SNP pada genetik manusia dapat dilakukan klasifikasi yang merupakan sebuah proses untuk memisahkan kelas data satu dengan yang lainnya. Penelitian ini bertujuan membandingkan kinerja (performance) metode Support Vector Machine (SVM) dan Logistic Regression (LR) untuk klasifikasi terkena penyakit asma atau tidak pada data asma. Empat statistik digunakan guna melihat tingkat kebaikan metode atau kinerja dari metode yaitu akurasi (accuracy), presisi (precision), recall, dan F1-Score. Pada proporsi data uji 20%, metode SVM lebih unggul dengan akurasi, presisi, recall, dan F1-Score sebesar 78,76%, 78,85%, 100%, 88,12% dibandingkan metode LR dengan hasil 77,09%, 79,34%, 96,09%, 78,14%. Demikian juga untuk proporsi data uji yang lain, memberikan hasil yang analog. Berdasarkan hasil yang diperoleh dapat disimpulkan bahwa pada kasus ini metode SVM cenderung lebih baik dari pada metode LR.
Deep Learning-Based Visualization of Network Threat Patterns Using GAN-Generated Infographic Mars Caroline Wibowo; Iwan Setyawan; Adi Setiawan; Irwan Sembiring
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6717

Abstract

Despite the growing sophistication of cyberattacks, current network traffic analysis tools often lack intuitive visual support, limiting human analysts’ ability to interpret complex threat behaviors. To address this gap, this study proposes a novel deep learning-based visualization framework using a Deep Convolutional Generative Adversarial Network (DCGAN) to synthesize threat-specific infographics from structured numerical features in the CICIDS 2017 dataset. Unlike conventional methods, such as PCA or static dashboards, which often result in abstract or non-adaptive visuals, our approach generates class-distinct grayscale images that preserve the behavioral patterns of various attacks, including denial-of-service, brute force, and port scanning. The preprocessing pipeline reshapes the selected flow-based features into 28×28 matrices to train the generative model. Evaluation using the Frechet Inception Distance (FID) yielded a score of 28.4, whereas a CNN classifier trained on the generated images achieved 91.2% accuracy, confirming visual fidelity and semantic integrity. Additionally, a panel of human experts rated the interpretability of the generated images at 4.3 out of 5.0. These findings demonstrate that generative visualization can enhance human-centered threat analysis by bridging raw data with interpretable imagery, thereby offering a scalable and explainable approach for integrating AI into real-time security workflows.
Co-Authors Adella Septiana Mugirahayu Aldian Umbu Tamu Ama Alfida Tegar Nurani Alicia Anggelia Lumbantoruan ALOYSIUS JOAKIM FERNANDEZ Amazia Ananta Prabaswara Ariani, Dwi Setya Atina Rahmatalia Bambang Susanto Baskoro Arie Nugroho Bayu Wijayanto Beni Utomo Christiana Hari Soetjiningsih Christina Maya Indah Susilowati D. B. Nugroho, D. B. Daivi Wardani, Daivi Daniel Bramantyo Ranu Bagaskoro Delsylia Tresnawaty Ufi Denny Indrajaya Denny Indrajaya Deswita, Yenny Dewi Anisa Istiqomah Dewi Lukitasari Didit Budi Nugroho Dimas Anditha Cahyo Sujiwo Djoko Hartanto E. D. Saputri, E. D. Eko Sediyono Elsa Septyana Endang Sulistyaningsih Faldy Tita Faldy Tita Fika Widya Pratama Florentina Tatrin Kurniati Gloria Meydelina Haay, Happy Alyzhya Hanna Arini Parhusip Harfeley Leipary Hari Slamet Trianto Hari Slamet Trianto Hariyanto Hariyanto Hartiningsih, Tri Henderi . Henry Junus Wattimanela Ignatius Agus Supriyono Ilham Hizbuloh Irwan Sembiring Iwan Setiawan Iwan Setyawan Iwan Setyawan Joko Siswanto JT Lobby Loekmono Keo, Jitro Jemryes Kurniawan, Titus Antonius David Leipary, Harfely Lenox Larwuy Leonardo Refialy Leonardo Refialy, Leonardo Leopoldus Ricky Sasongko Lilik Linawati Lindin Anderson Lydia Soepriyani Fallo masipupu, Frangky Aristiadi Migunani Migunani Mitha Febby R. Donggori Mitha Febby R. Donggori Modjo, Marchella Ellena Mohammad Ridwan Nafisah Riskya Hasna Ninda Lutfiani Olivia Rumahpasal panglipur, indah rahayu Pariama, Aprillia Mauren Pradani, Wynona Adita Priatna , Wowon Prihanto Ngesti Basuki Purbaratri, Winny Purwoko, Agus Qurotul Aini Rachayu, Laras Andriani Rachel Wulan Nirmalasari Wijaya Rachel Wulan Nirmalasari Wijaya Riana Dewi Romauli Basaria Roy Rudolf Huizen Rudhito, Andy Rumahpasal, Olivia Salomina Patty SARI, EMMA NOVITA Setivani, Febi Sri Suwartiningsih Sri Yulianto Joko Prasetyo Sulistio Sulistio Suryasatriya Trihandaru Sutarto Wijono Tamaela, Jemaictry Theo Sarita, Fetriks Theophilus Wellem Tiara Utary Grace Moelyono Tri Wahyuningsih Tundjung Mahatma Untung Rahardja Untung Rahardja Vikky Aprelia Windarni Vikky Aprelia Windarni Vincentia Pawestri Wahyuni Kristinawati Waney, Natalia Christy Wattimanela, Henry Junus Wibowo, Mars Caroline Wijayanti, Yunita Puput Windarni, Vikky Aprelia Wisnu Anendya Sekti Yenusi, Yuni naomi Yulius Yusak Ranimpi