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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 Indonesian Journal of Mathematics and Natural Sciences Jurnal Ilmiah Kursor Jurnal Produksi Tanaman 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

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PENGABDIAN MASYARAKAT UNTUK PEMBELAJARAN CODING ARTIFICIAL INTELLIGENCE KEPADA SISWA SMP KRISTEN WONOSOBO Trihandaru, Suryasatriya; Parhusip, Hanna Arini; Kurniawan, Johanes Dian; Susanto, Bambang; Setiawan, Adi; Nugroho, Didit Budi
Jurnal Abdi Insani Vol 11 No 2 (2024): Jurnal Abdi Insani
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/abdiinsani.v11i2.1536

Abstract

Artificial intelligence and the Internet of Things (AIOT) have been widely used by various activities, especially in the millennial generation. However, scientific technology has not been widely introduced in education. Additionally, schools experience a decline in student enrollment every year, so it is necessary to carry out innovative learning actions that can be introduced to the community through students. Innovation learning is demonstrated by providing coding lessons that students have never done before so that AIOT becomes part of the learning. Therefore, coding as a learning method is  introduced to junior students so they can get to know AIOT early. The method used is making a device called AIOT-kit with training to be able to directly monitor environmental parameters such as temperature and humidity. The Internet of Things was introduced, which uses ThinkSpeak as a dashboard for making observations. This device was made by students so that they could follow the process from making the AIOT-kit hardware and related coding to utilization. It is shown that AIOT-kit is not yet known to students, including how to code in it. AIOT is an urgent need to access developing related technology. This activity is part of the service team's efforts to make a positive contribution to the community and school environment. After carrying out this activity, there was a change in how students could make their own AIOT-kit devices while also coding. The school even received an award from the local government for the innovation activities carried out during that period.
Analysis of Attack Detection on Log Access Servers Using Machine Learning Classification: Integrating Expert Labeling and Optimal Model Selection Ridwan, Mohammad; Sembiring, Irwan; Setiawan, Adi; Setyawan, Iwan
Scientific Journal of Informatics Vol 11, No 1 (2024): February 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i1.49424

Abstract

Purpose: As the complexity and diversity of cyberattacks continue to grow, traditional security measures fall short in effectively countering these threats within web-based environments. Therefore, there is an urgent need to develop and implement innovative, advanced techniques tailored specifically to detect and address these evolving security risks within web applications.Methods: This research focuses on analyzing attack detection in log access servers using machine learning classification with two primary approaches: expert labeling integration and best model selection. Expert labeling determines whether log entries are safe or indicate an attack.Result: Validation in labeling was applied using different datasets to minimize errors and increase confidence in the resulting dataset. Experimental results show that the Decision Tree and Random Forest models have nearly identical accuracy rates, around 89.3%-89.4%, while the ANN model has an accuracy of 81%.Novelty: This study proposes a fusion of expert knowledge in labeling log entries with a rigorous process of selecting the best classification model. This integration has not been extensively explored in previous research, offering a novel approach to enhancing attack detection within web applications. The research contribution lies in the integration of expert security assessment and the selection of the best model for detecting attacks on server access logs, along with validating labels using various datasets from different log devices to enhance confidence in the analysis results.
MINDFULNESS DAN PENERIMAAN DIRI PADA REMAJA DI ERA DIGITAL Waney, Natalia Christy; Kristinawati, Wahyuni; Setiawan, Adi
Insight: Jurnal Ilmiah Psikologi Vol. 22 No. 2: Agustus 2020
Publisher : Universitas Mercu Buana Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26486/psikologi.v22i2.969

Abstract

Di era digital ini, remaja melakukan eksplorasi dan mengekspresikan diri di media sosial. Media sosial menyebabkan remaja rentan terlibat dalam cyberbullying . Hal ini membuat remaja malu dengan diri sendiri dan berusaha menampilkan citra ideal di media sosial sehingga kurang mampu menerima diri apa adanya. Studi ini merupakan studi literatur yang mencoba menelusuri bagaimana mindfulness dan penerimaan diri pada remaja di era digital. Mesin pencari (search engine) digunakan sebagai alat mencari data. Ditemukan 13 literatur dan penelitian dan digunakan sebagai sumber data. Penelitian ini menyimpulkan bahwa latihan mindfulness bisa dijadikan alternatif dalam meningkatkan penerimaan diri pada remaja, dan mindfulness dapat dipraktikkan dengan memanfaatkan aplikasi smartphone. Namun, belum ditemukan penelitian yang secara khusus membuktikan efektivitas penggunaan aplikasi mindfulness dalam meningkatkan penerimaan diri pada remaja di Indonesia.
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.
Komparasi Support Vector Machine dengan Logistic Regression terhadap klasifikasi pada data asma Moelyono, Tiara Utary Grace; Setiawan, Adi; Wijaya, Rachel Wulan Nirmalasari
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.
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.
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.
Co-Authors Adella Septiana Mugirahayu Aldian Umbu Tamu Ama Alfida Tegar Nurani Alicia Anggelia Lumbantoruan ALOYSIUS JOAKIM FERNANDEZ 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 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 Fika Widya Pratama Gloria Meydelina Haay, Happy Alyzhya Hanna Arini Parhusip 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 Joko Siswanto JT Lobby Loekmono Keo, Jitro Jemryes Kurniawan, Titus Antonius David Leipary, Harfely 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 Moelyono, Tiara Utary Grace 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 Qulubina, Allif Bayna Qurotul Aini Rachayu, Laras Andriani Rachel Wulan Nirmalasari Wijaya Riana Dewi Romauli Basaria 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 Theopillus J. H. Wellem 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