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

 5   6   7   8   9 
ANALYSIS OF EARTHQUAKE SEISMICITY IN MALUKU PROVINCE AND ITS SURROUNDING AREAS USING THE MAXIMUM LIKELIHOOD ESTIMATION METHOD Wattimanela, Henry Junus; Setiawan, Adi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss3pp2179-2190

Abstract

Tectonic earthquakes are natural disasters that occur abruptly over a relatively short period, resulting from the movement of tectonic plates. The Maluku region is classified as prone to seismic activity due to its geographical location at the confluence of three tectonic plates: the Eurasian, Pacific, and Indo-Australian. This study aims to analyze the seismic activity of earthquakes in the Maluku region and its surrounding areas. The methodology employed is based on Descriptive Statistics and Maximum Likelihood Estimation. The data set was obtained from the International Seismological Centre (ISC) and comprises earthquakes occurring in the Maluku region and surrounding areas between 1970-2023. The earthquakes were selected based on the criteria of magnitude > 3.8 Mw and depth < 60 km. The research was facilitated by using various software applications, including Microsoft Excel, SPSS, Matlab, GMT, and Z-map. Descriptive statistics were employed to analyze the hypocenter and epicenter of the earthquake distribution. In contrast, the maximum likelihood method was employed to ascertain the seismicity value and earthquake return period. The findings indicate that the earthquake distribution is relatively dense, except in certain regions within the sea area. The results of the seismicity analysis demonstrate that Sub-region I exhibits the highest level of seismic activity. At the same time, the shortest return period is observed at a magnitude of 3.0 Mw, specifically within Sub-region I.
IMPROVEMENT OF LEARNING OUTCOMES OF GRADE VIII STUDENTS OF SMPN 4 SALATIGA THROUGH THE PBL MODEL WITH A CRT APPROACH Dewi, Riana; Pratama, Fika Widya; Setiawan, Adi; Deswita, Yenny
Tesseract: International Journal of Geometry and Applied Mathematics Vol. 2 No. 3 (2024): Tesseract: International Journal of Geometry and Applied Mathematics
Publisher : Nindikayla Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57254/tess.v2i3.42

Abstract

This study aims to improve the mathematics learning outcomes of class VIII G students of SMP Negeri 4 Salatiga in the material Surface Area of Flat Sided Buildings through the PBL (Problem Based Learning) model with the CRT (Culturally Responsive Teaching) approach. This research is classroom action research conducted in two cycles using the Kemmis and MC Taggart models with 4 stages namely planning, implementation, observation/observation, and reflection. The subjects of this study were class VIII G students of SMP Negeri 4 Salatiga for the 2022/2023 academic year, consisting of 30 students (15 boys and 15 girls). The technique of collecting data in this research uses observation sheets of the implementation of learning and learning achievement tests. The criteria for the success of learning outcomes in this study were increasing the class average score in each cycle and the class achieving classical mastery (many students who completed individually  85%). The results of this study showed an increase in class average scores from pre-cycle to cycle 1 (from 62.7 to 77.03) and cycle 1 to cycle 2 (from 77.03 to 84.1) and the achievement of classical mastery of 86. 67%. The conclusion obtained in this study is that the application of the PBL model with the CRT approach has succeeded in increasing the mathematics learning outcomes of class VIII G students of SMP Negeri 4 Salatiga on the material surface area of flat side shapes.
WORKSHOP PERSIAPAN PEMBELAJARAN MATEMATIKA DENGAN BANTUAN PAKET PROGRAM KOMPUTER (GEOGEBRA/R) UNTUK MGMP MATEMATIKA SMA KABUPATEN SEMARANG JAWA TENGAH Setiawan, Adi; Parhusip, Hanna Arini; Nugroho, Didit Budi; Sasongko, Leopoldus Ricky; Rudhito, Andy; Utomo, Beni; Fernandez, Aloysius Joakim
Jurnal Abdi Insani Vol 12 No 2 (2025): Jurnal Abdi Insani
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/abdiinsani.v12i2.2046

Abstract

The world is heading towards the era of Society 5.0, which requires learning Mathematics as much as possible to be easy to understand and interesting for students. Technology-based visualization, such as the use of Geogebra, can help students understand Mathematics formulas better. Therefore, teachers who are members of the High School Mathematics MGMP need to have insight into the importance of technology-based learning as a strategy to improve teaching quality. This activity aims to open teachers' insights into visualization and technology-based Mathematics learning, and help them develop creative learning modules and tools. The activity method includes four meetings consisting of webinars and workshops. The first webinar contains an introduction to the importance of visualization-based learning; the second webinar provides training in using Geogebra/R onsite or hybrid; the third webinar trains the creation of learning modules; and the fourth webinar presents the theory of writing scientific papers and using Mendeley. The results show that teachers are able to make creative and interesting lesson plans and learning modules using Geogebra/R, and motivate students to learn Mathematics independently or in groups. Some of the modules produced have been tested at school, although none of the participants have succeeded in making papers ready for publication. This activity succeeded in improving the ability of teachers to utilize technology for learning Mathematics.
Perbandingan Kinerja Metode Support Vector Regression dan Metode Regresi Linier Berganda dalam Memprediksi BMI pada Dataset ASTHMA Kurniawan, Titus Antonius David; Setiawan, Adi; Tita, Faldy
Jurnal Sains dan Edukasi Sains Vol. 8 No. 2 (2025): 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.v8i2p133-142

Abstract

Body Mass Index (BMI) merupakan metode untuk mengevaluasi apakah seseorang memiliki berat badan ideal, yang berperan penting dalam mengidentifikasi risiko kesehatan seperti penyakit jantung dan diabetes. Dalam penelitian ini, BMI digunakan sebagai variabel prediktor untuk menentukan kemungkinan seseorang menderita asma, yaitu penyakit kronis yang memengaruhi saluran pernapasan. Untuk memprediksi BMI berdasarkan variabel-variabel lain dalam dataset Asthma, penelitian ini membandingkan dua metode regresi, yaitu regresi linier berganda dan Support Vector Regression (SVR). Evaluasi akurasi model dilakukan menggunakan Mean Absolute Percentage Error (MAPE), yang mengukur kesalahan prediksi dalam bentuk persentase, di mana nilai MAPE yang lebih rendah menunjukkan tingkat akurasi prediksi yang lebih baik. Data uji dibagi menjadi empat skenario, yaitu 10%, 20%, 30%, dan 40% dari keseluruhan data. Hasil perhitungan menunjukkan bahwa metode regresi linier berganda menghasilkan nilai MAPE sebesar 15,42%; 15,44%; 15,51%; dan 15,63% secara berturut-turut. Sementara itu, metode SVR menghasilkan nilai MAPE sebesar 15,77%; 15,74%; 15,77%; dan 15,81%. Berdasarkan hasil tersebut, regresi linier berganda terbukti memberikan prediksi yang lebih akurat dibandingkan SVR dalam konteks dataset Asthma. Dengan demikian, regresi linier berganda lebih efektif dalam memodelkan BMI dibandingkan SVR dan dapat menjadi pertimbangan penting dalam pengembangan model prediktif untuk mendukung pengambilan keputusan terkait risiko penyakit pernapasan seperti asma.
ANALISIS REGRESI NON LINEAR PADA DATA PASIEN COVID-19 MENGGUNAKAN METODE BOOTSRAP Pradani, Wynona Adita; Setiawan, Adi; Parhusip, Hanna Arini
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 15 No 3 (2021): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (606.139 KB) | DOI: 10.30598/barekengvol15iss3pp453-466

Abstract

Dalam penelitian ini membahas tentang analisis regresi non linier dengan menggunakan data statistic perkembangan pasien positif Covid-19 di Indonesia. Penyakit Covid-19 sangat mudah berkembang penyebarannya sehingga WHO menyatakan penyakit ini sebagai pandemi, dalam penelitian ini menggunakan lima model analisis regresi non linier yaitu model Weibull 3 parameter, Gompertz 3 parameter, Log-logistic 3 parameter, Log-Logistic 4 parameter dan model Logistic 3 parameter. Analisis yang terbaik dalam memprediksi yaitu Log-logistic 3 parameter dengan nilai AIC = 6527.434 dan RMSE = 6836.79, dan diperoleh nilai parameter , dan A= 19477000, sehingga pengestimasian parameter dengan menggunakan metode Bootstrap B = 10000 dengan interval kepercayaan 95% untuk parameter , dan A berturut-turut adalah maka diperoleh nilai rata-rata estimas Bootstrap , dan . Pada data prediksi pasien yang positif Covid-19 akan dibandingkan dengan data pengamatan, dari hasil perbandingan diperoleh nilai MAPE = 9%, sehingga dapat dikatakan pemodelan Log-logistic 3 parameter sangat baik dalam memprediksi.
LATENT DIRICHLET ALLOCATION (LDA) METHOD ANALYSIS ABOUT COVID-19 VACCINE ON TWITTER SOCIAL MEDIA Haay, Happy Alyzhya; Setiawan, Adi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 1 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (788.349 KB) | DOI: 10.30598/barekengvol16iss1pp189-196

Abstract

Twitter is one social media that often provides much information for its users, one of which is information regarding the COVID-19 vaccination. This study aimed to explore and find out what topics are often discussed on Twitter social media. One of which is the topic of COVID-19 vaccination using the Latent Dirichlet Allocation (LDA) method and analysis of the frequency of keywords that often appear with this topic. The Tweet data used in this study was taken from Twitter users worldwide in November 2021. In this study, the results of sentiment analysis were obtained from the tweet data taken, which was divided into positive sentiment and negative sentiment, namely "vaccination" with 40 words and "'Covid19" with 35 words
CALCULATION OF CENTRAL JAVA PROVINCE REGION AREA USING SHOELACE FORMULA BASED ON THE GADM DATABASE Setiawan, Adi; Sediyono, Eko; Mahatma, Tundjung
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 2 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (511.692 KB) | DOI: 10.30598/barekengvol16iss2pp597-606

Abstract

This study proposes the use of the shoelace formula to determine the area of the regencies and towns/cities in the province of Central Java, which is based on the boundaries of the cities, regencies, (sub-) districts, and villages using the database from the GADM (Global Administrative Area). The results obtained are then compared with the Karney polygon method. With the shoelace formula, the area of Central Java province is 34365.40 km2 (4.77% wider than the reference area), while the Karney polygon method yields 34379.48 km2 (4.81% wider than the reference area). The area calculated using the boundaries of sub-districts is closer to the reference area if compared to using the boundaries of the regencies/cities and of villages. MdAPE values of 6.46 % and 6.54 % are obtained using the shoelace formula and the Karney polygon method respectively.
COMPARATIVE ANALYSIS OF VINCENTY AND GEODESIC METHOD APPROACHES IN MEASURING THE DISTANCE BETWEEN SUBDISTRICT OFFICES IN SALATIGA CITY Windarni, Vikky Aprelia; Setiawan, Adi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 4 (2022): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (627.267 KB) | DOI: 10.30598/barekengvol16iss4pp1207-1220

Abstract

Salatiga city has four subdistrict offices, namely Argomulyo Subdistrict, Sidorejo Subdistrict, Sidomukti Subdistrict and Tingkir Subdistrict. In this study, a comparative analysis of the distance between subdistrict offices in Salatiga city was conducted using the Vincenty method and Geodesic method with distance obtained from Google Maps. The data is the geographical coordinates of the Earth's surface (latitude and longitude) obtained from Google Earth. The results showed that both Vincenty and Geodesic methods compared with Google Maps calculation results between 95% -105%, so it can be said to be good. The geodesic method gives relatively better results than the Vincenty method because it has an average percentage of 99.58 %. In comparison, the Vincenty method has an average percentage of 99.48 %. However, the results obtained still use relatively less data.
COMPARISON OF ANN METHOD AND LOGISTIC REGRESSION METHOD ON SINGLE NUCLEOTIDE POLYMORPHISM GENETIC DATA Setiawan, Adi; Wijaya, Rachel Wulan Nirmalasari
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (494.456 KB) | DOI: 10.30598/barekengvol17iss1pp0197-0210

Abstract

This study aims to determine the goodness of classification using the ANN method on Asthma genetic data in the R program package, namely SNPassoc. SNP genetic data was transformed using codominant genetic traits, namely for genetic data AA, AC, CC were given a score of 0, 0.5 and 1, respectively, while CC, CT and TT were scored 0, 0.5 and 1, respectively. The scoring is based on the smallest alphabetical order given a low score. The average accuracy, precision, recall and F1 score were determined using the neural network method if the genetic code was used with variations in the proportion of test data 10%, 20%, 30% and 40% and repeated B = 1000 times. The results obtained were compared with the logistic regression method. If 20% test data is used and the ANN method is used, the accuracy, precision, recall and F1 scores are 0.7756, 0.7844, 0.9844 and 0.8728, respectively. When all information from various countries is used in the Asthma genetic data, the logistic regression method gives higher average accuracy, precision and F1 scores than the ANN method, but the average recall is the opposite. When a separate analysis is performed for each country, the logistic regression method gives higher accuracy, precision, recall and F1 scores in the ANN method compared to the logistic regression method.
PERFORMANCE COMPARISON OF DECISION TREE AND LOGISTIC REGRESSION METHODS FOR CLASSIFICATION OF SNP GENETIC DATA Setiawan, Adi; Setivani, Febi; Mahatma, Tundjung
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0403-0412

Abstract

This research was conducted to compare the accuracy when decision tree and logistic regression methods are used on some data. Decision tree is one method of classification techniques in data mining. In the decision tree method, very large data samples will be represented as smaller rules, and logistic regression is a method that aims to determine the effect of an independent variable on other variables, namely dichotomous dependent variables. Both algorithms were written and analyzed using R software to see which method is better between the decision tree method and the logistic regression method applied to SNP (Single Nucleotide Polymorphism) genetic data, namely Asthma data. SNP Genetic Data was obtained from R software with the package name "SNPassoc" and the data name "asthma". Asthma data has 57 features, namely Country, Gender, Age, BMI, Smoke, Case control, and SNP (Single Nucleotide Polymorphism) genetic code. Comparative analysis was carried out based on the results of the accuracy values obtained in the two methods. Variations in the proportion of the test data used were 40%, 30%, 20% and 10% and were simulated 1000 times on the grounds of obtaining a better accuracy value. The results obtained show that the decision tree method obtains an accuracy value of 0.5793, 0.5777, 0.5745, 0.5526, respectively, while the logistic regression method is 0.7696, 0.7729, 0.7763, 0.7788, respectively and they are achieved at the proportion of test data of 40%, 30%, 20%, 10%. Thus it can be concluded that in this case the logistic regression method is better than the decision tree method in classifying Asthma data.
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