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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) RADIASI: Jurnal Berkala Pendidikan Fisika Lontar Komputer: Jurnal Ilmiah Teknologi Informasi BERKALA FISIKA Jurnal Ilmu Lingkungan Jurnal Sains dan Teknologi Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) JURNAL FISIKA Jurnal Teknologi Informasi dan Ilmu Komputer Journal of Mathematical and Fundamental Sciences JUITA : Jurnal Informatika International Journal of Advances in Intelligent Informatics JFA (Jurnal Fisika dan Aplikasinya) Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Jurnal Fisika FLUX JOIN (Jurnal Online Informatika) Science and Technology Indonesia JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Indonesian Journal of Physics and Nuclear Applications Jurnal Penelitian Pendidikan IPA (JPPIPA) INDONESIAN JOURNAL OF APPLIED PHYSICS BAREKENG: Jurnal Ilmu Matematika dan Terapan Indonesian Journal of Chemistry JTAM (Jurnal Teori dan Aplikasi Matematika) Zero : Jurnal Sains, Matematika, dan Terapan Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) MAJAMATH: Jurnal Matematika dan Pendidikan Matematika ComTech: Computer, Mathematics and Engineering Applications Jurnal Linguistik Komputasional Jurnal Abdi Insani Indonesian Journal of Electrical Engineering and Computer Science Jurnal Sains dan Edukasi Sains Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) SPEKTA (Jurnal Pengabdian Kepada Masyarakat : Teknologi dan Aplikasi) Jurnal Teknik Informatika (JUTIF) Journal of Science and Science Education Advance Sustainable Science, Engineering and Technology (ASSET) International Journal of Community Service Proceeding ISETH (International Summit on Science, Technology, and Humanity) Prosiding University Research Colloquium Jurnal Informatika: Jurnal Pengembangan IT SJME (Supremum Journal of Mathematics Education) Lontar Komputer: Jurnal Ilmiah Teknologi Informasi
Suryasatriya Trihandaru
Universitas Kristen Satya Wacana
Religion Agriculture, Biological Sciences & Forestry Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering 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 Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Transportation Other

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Comparison of Convolutional Neural Network (CNN) Models in Face Classification of Papuan and Other Ethnicities Yenusi, Yuni Naomi; Suryasatriya Trihandaru; Setiawan, Adi
JST (Jurnal Sains dan Teknologi) Vol. 12 No. 1 (2023): April
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jstundiksha.v12i1.46861

Abstract

Klasifikasi objek pada citra menjadi salah satu problem dalam visi komputer. Komputer diharapkan dapat meniru kemampuan manusia dalam memahami informasi citra. Salah satu pendekatan yang berhasil yaitu dengan menggunakan Jaringan Syaraf Tiruan (JST) dimana pendekatan ini terinspirasi dari jaringan syaraf pada manuasia yang dikembangkan lebih jauh menjadi Deep Learning. Convolutional Neural Network (CNN) merupakan salah satu jenis Deep Learning yang sangat terkenal dengan keemampuannya dalam melakukan klasifikasi citra. Dengan mengimplementasikan beberapa model CNN akan dilakukan perbandingan antara model arsitektur CNN dalam klasifikasi wajah etnis Papua dan wajah etnis lainnya untuk melihat model dengan akurasi terbaik pada kasus ini. Model CNN yang dipilih yaitu VGG16, VGG-19, ResNet-50 dan MobileNet v1 dan Mobilenet v2. Model terbaik adalah model arsitektur Mobile Net v1 untuk Pengenalan Wajah Papua dan Non Papua dengan akurasi 95%. Pada penelitian ini disimpulkan bahwa MobileNet V1 adalah model yang terbaik. Model ini menghasilkan akurasi, precision, recall, dan f1-score dengan nilai 95%, 99%, 91%, dan 94%. Adapun saran untuk penelitian selanjutnya adalah dilakukan modifikasi terhadap layer pada masing-masing molde untuk meninggkatkan performa model arsitektur CNN.
Automated Property Valuation with Multi-Hazard Risk: Jakarta Metropolitan Area Study Fachrurrozi, Fachrurrozi; Parhusip, Hanna Arini; Trihandaru, Suryasatriya
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5236

Abstract

This study crafts a machine learning framework that systematically integrates multi-hazard disaster risk assessments into automated property valuation for the Jakarta Metropolitan Area. The framework addresses 25–30% MAPE typically observed in disaster-prone regions, providing more reliable valuation results. We made 114 prediction features from 42 input variables by using 14,284 property data from Indonesian markets, physical risk data from the Think Hazard platform, and socio-economic data from Central Bureau of Statistics. Elastic Net model performed superior compared to other models which had R² = 0.7922 and a MAPE of 28.27%. We found that some disaster risks had unexpected beneficial effects on property prices. We expected that risks related to the earth (+40.5%) and water (+19.2%) would have positive effects, while risks related to the weather (-66.9%) would have negative effects. These conflicting results suggest that in complex urban markets, the quality of infrastructure, location premiums, and differences in risk perception may outweigh simple risk penalties. The idea gives realistic ideas for property valuation that takes risks into account, but it also points out big problems with how the market judges how likely a disaster is to happen.
Performance of an AIOT-Particle Device for Air Quality and Environmental Data Prediction in Salatiga Area Using ARIMA Model Kurniawan, Johanes Dian; Trihandaru, Suryasatriya; Parhusip, Hanna Arini
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 2 (2024): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i2.28490

Abstract

This study introduces the AIOT-Particle, a compact device designed for comprehensive air quality and environmental monitoring in Tegalrejo, Salatiga, Indonesia. Addressing the need for real-time, multi-parameter environmental data, the device simultaneously tracks PM1.0, PM2.5, temperature, humidity, pressure, and altitude, utilizing a built-in data fusion algorithm to ensure accurate and coherent data collection. Air pollution standards classify air quality as "good" (0–50), "moderate" (51–100), "unhealthy" (101-200), "very unhealthy" (201-300), and "hazardous" (>300). The research contribution is the development and validation of the AIOT-Particle using the ARIMA model for precise environmental monitoring. The methods involved deploying the device in Salatiga and applying the ARIMA model to analyze the collected data for accuracy. The results demonstrated promising accuracy: for PM1.0, the RMSE was 8.13 with an MAE of 6.04; for PM2.5, the RMSE was 6.60 with an MAE of 4.49. Environmental data analysis showed an RMSE of 0.74 for temperature (MAE 0.43), 2.11 for humidity (MAE 1.36), 0.25 for pressure (MAE 0.19), and 2.18 for altitude (MAE 1.70). These findings highlight the device's potential to enhance environmental surveillance and public health assessments, advance the understanding of air quality dynamics, and support targeted interventions to mitigate environmental risks. The novelty of this study lies in the integration of multiple environmental parameters into a single monitoring device, validated for accuracy using the ARIMA model.
Developing Fishpond Control System for School Natural Laboratory Automation Sebastian, Danny; Chandra, Dian Widiyanto; Wijono, Sutarto; Prasetyo, Sri Yulianto Joko; Trihandaru, Suryasatriya; Saputra, Laurentius Kuncoro Probo
Jurnal Informatika: Jurnal Pengembangan IT Vol 9, No 1 (2024)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v9i1.5640

Abstract

Pandemi Covid-19 memaksa kegiatan belajar dilakukan secara daring. Sekolah berusaha melakukan kegiatan secara luring dengan membatasi jumlah siswa atau dengan melaksanakan kegiatan di laboratorium alam. Mengelola laboratorium alam membutuhkan banyak biaya terutama pada kondisi pasca covid-19. Internet of Things adalah teknologi yang memungkinkan kendali jarak jauh dan otomatisasi. Hal ini memungkinkan pengelolaan laboratorium alam dilakukan dari jarak jauh atau secara otomatis. Penelitian ini bertujuan untuk membuat desain dan sistem IoT yang meliputi penentuan modul dasar dan fungsinya, penentuan perangkat sensor dan aktuator yang dibutuhkan. Sistem dibangun menggunakan arsitektur MQTT. Aplikasi Android dibuat untuk mengontrol periferal IoT. Sistem yang telah berhasil dibangun diuji dengan metode blackbox testing. Berdasarkan hasil blackbox testing, aplikasi Android dan periferal IoT dapat berkomunikasi dan berfungsi dengan baik. Penelitian ini masih memiliki keterbatasan yaitu perlu dilakukannya kalibrasi perangkat IoT dan pengujian perangkat keras IoT dalam jangka waktu yang lama.
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.
Comparison Study of Beryllium and Lithium Target System for Beam Shaping Assembly Used in Boron Neutron Capture Therapy Harendza, David; Trihandaru, Suryasatriya; Santosa, Slamet; Sardjono, Yohanes
Proceeding ISETH (International Summit on Science, Technology, and Humanity) 2015: Proceeding ISETH (International Conference on Science, Technology, and Humanity)
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/iseth.2348

Abstract

Boron neutron capture therapy (BNCT) is a selective targeting cancer therapy method which is by radiating epithermal neutron beams to a tumor which has been injected by boron-10 compound. The implementation of this method requires 2 (two) key elements, namely the boron-10 compound and the irradiating neutron beam of epithermal energy level. The neutron source used in this research was 13 MeV 1mA cyclotrons which have been developed by Indonesia National Nuclear Energy Agency. This paper was preoccupied in beam shaping assembly (BSA) target system. BSA target system is used to convert the proton produced by cyclotron into neutron. IAEA generated a recommendation for BNCT neutron beam quality, one of them was epithermal neutron flux which is higher than 109
Simple Forward Finite Difference for Computing Reproduction Number of COVID-19 in Indonesia During the New Normal Trihandaru, Suryasatriya; Parhusip, Hanna Arini; Susanto, Bambang; Sardjono, Yohanes
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 5, No 1 (2021): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v5i1.3468

Abstract

The research purpose shown in this article is describing the time dependent reproduction number of coronavirus called by COVID-19 in the new normal period  for 3 types areas, i.e. small, medium and global areas by considering the number of people in these areas.  It is known that in early June 2020, Indonesia has claimed to open activities during the pandemic with the new normal system. Though the number of COVID-19 cases is still increasing in almost infected areas, normal activities are coming back with healty care protocols where public areas are opened as usual with certain restrictions. In order to have observations of spreading impact of COVID-19, the basic reproduction number (Ro)  i.e. the reproduction number (Ro) is the ratio between 2 parameters of SIR model where SIR stands for Susceptible individuals, Infected individuals, and Recovered individuals respectively. The reproduction numbers  are computed as discrete values depending on time. The used research method is  finite difference scheme for computing rate of change parameters in SIR models based on the COVID-19 cases in Indonesia (global area), Jakarta (medium area) and Salatiga (small area) by considering the number of people in these areas respectively. The simple forward finite difference is employed to the SIR model to have time dependent of parameters. The second approach is using the governing linear system to obtain the values of parameter daily. These parameters are computed for each day such that the values of Ro are obtained as function of time. The research result shows that 3 types areas give the same profiles of parameters that the rate of changes of reproduction numbers are decreasing with respect to time. This concludes that the reproduction numbers are most likely decreasing.
Pembelajaran Vektor Untuk Klasifikasi Data Pada Bidang Parhusip, Hanna Arini; Susanto, Bambang; Linawati, Lilik; Trihandaru, Suryasatriya; Sardjono, Yohanes
SJME (Supremum Journal of Mathematics Education) Vol 4 No 2 (2020): Supremum Journal of Mahematics Education
Publisher : Fakultas Keguruan dan Ilmu Pendidikan Universitas Singaperbangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35706/sjme.v4i2.3515

Abstract

Tujuan penelitian ini adalah penyusunan hyperplane untukmemisahkan data yang mempunyai 2 kelas dan bersifat linear padabidang datar sebagai pembelajaran vektor untuk klasifikasi data.Adapun metode yang digunakan adalah pre-Support Vector Machine(SVM). Metode ini mencari garis (hyperplane) terbaik yangmemisahkan data dan memberi ruang antar 2 kelas data dimana ruangpemisah tersebut tidak boleh memuat data serta ruang tersebutmerupakan margin maksimal. Langkah awal adalah menduga garispemisah (hyperplane) awal melalui titik O. Dengan mengambil salahsatu titik data yang menjadi titik referensi, disusun vektor dari Oterhadap titik referensi dan garis melalui titik referensi sebagai bataspertama margin. Kemudian dibentuk vektor arah dari titik O yangtegak lulus terhadap garis awal (hyperplane). Selanjutnya vektorproyeksi dibentuk dari titik referensi terhadap vektor arah sehinggavektor arah dan vektor proyeksi berhimpit (searah). Penyusunanmargin diperoleh dengan menyusun garis yang pararel terhadap garisawal sebagai hyperplane serta berjarak 2 kali dengan panjang vektorproyeksi tersebut. Hyperplane terbaik diperoleh secara manual denganmengatur batas kedua dari margin yang diperoleh dengan menggambargaris melalui suatu titik data pada kelas ke-2 dengan jarak terdekat danpararel terhadap garis yang melalui titik referensi dari data kelas ke-1.
A Hierarchical Bayesian Model of Multi-Hazard Impacts on Property Prices in the Jakarta Metropolitan Area Fachrurrozi; Ambat, Jordi Enal; Parhusip, Hanna Arini; Trihandaru, Suryasatriya
Jurnal Penelitian Pendidikan IPA Vol 11 No 11 (2025): November: In Progress
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i11.12717

Abstract

This study examines the complex relationship between multi-hazard disaster risks and property prices in the Jakarta Metropolitan Area, one of the world's most disaster-prone urban regions. The research investigates how various natural hazards, including floods, earthquakes, and other environmental risks, influence real estate values across 138 districts encompassing 15,758 property data. This study pioneers the integration of hierarchical Bayesian modeling with causal machine learning techniques to quantify multi-hazard impacts on property prices, providing the first comprehensive analysis of disaster risk interactions in Indonesian real estate markets. We employ methodological triangulation across Bayesian inference, causal forests, and spatial econometrics to ensure robust causal identification. We employ a multi-methodological approach combining spatial analysis, hierarchical Bayesian modeling, and causal forest algorithms on a dataset of 15,758 properties. The analysis includes Moran's I for spatial autocorrelation (0.73 for risks, 0.65 for prices), PyMC for Bayesian inference with 12,000 MCMC samples, and EconML for causal effect estimation with heterogeneous treatment effects. Properties with high disaster risk experience an 12.2% price discount (95% CI: -20.5%, -3.7%), with each unit increase in average risk score reducing prices by 4.3% (95% CI: -7.9%, -0.4%). Spatial clustering is highly significant (Moran's I = 0.73, p < 0.001). Heterogeneous effects reveal progressive impacts from 3.2% in bottom quintile to 9.4% in top quintile. Policy simulation demonstrates that comprehensive flood mitigation could increase total property values by 840.6 billion IDR, generating an average price increase of 14.8% with benefit-cost ratio exceeding 3:1.
Artificial Neural Network for Classifying Injected Materials under Ultrasonography Utari, Galuh Retno; Maslebu, Giner; Trihandaru, Suryasatriya
Advance Sustainable Science, Engineering and Technology Vol 3, No 1 (2021): November-April
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v3i1.8324

Abstract

We have constructed an artificial neural network (ANN) architecture to classify four different classes of ultrasonography recorded from a jelly box phantom that was injected by iron, glass, or plastic marble, or without any injection. This jelly box was made as a phantom of a human body, and the injected materials were the cancers. The small size of the injected materials caused only little disturbances those could not easily distinguished by human eyes. Therefore, ANN was used for classifying the different kind of the injected materials. The number of original imagestaken from ultrasonographs were not so many, therefore we did data augmentation for providing large enough dataset that fed into ANN. The data augmentation was constructed by pixel shifting in horizontal and vertical directions. The procedure proposed here produced 98.2% accuracy for predicting test dataset, though the result was sensitive to the choice of augmentation area.
Co-Authors Abigail Geofani Boham Adi Setiawan Adita Sutresno Adrianus Herry Heriadi Adrianus Herry Heriadi Alvama Pattiserlihun Alvama Pattiserlihun Alvama Pattiserlihun Ambat, Jordi Enal Andreas Setiawan Ariany Mahastanti, Linda Bambang Susanto Bambang Susanto Bernadus Aryo Adhi Wicaksono Carolina Febe Ronicha Putri Daniel Eliazar Latumaerissa Denny Indrajaya Denny Indrajaya Desman P. Gulo Dian Widiyanto Chandra Didit Budi Nugroho Djoko Hartanto Djoko Hartanto Dwi Pangestuti Fachrurrozi Fachrurrozi Fachrurrozi Ferdy S. Rondonuwu Ferdy Semuel Rondonuwu Ferri Rusady Saputra Giner Maslebu Goni, Abdiel Wilyar Haay, Happy Alyzhya Hanna Arini Parhusip Hanna Arini Parhusip Harendza, David Hariadi, Adrianus Herry Harry Budiharjo Sulistyarso Heriadi, Adrianus Herry Heriyanto Heriyanto Indrajaya, Denny Inti Mustika Ivanky Saputra Jane Labadin Johanes Dian Kurniawan Johanes Dian Kurniawan Johanes Dian Kurniawan Karina Bianca Lewerissa Kristoko Dwi Hartomo Kurniawan, Johanes Dian Laurentius Kuncoro Probo Saputra, Laurentius Kuncoro Probo Lea, Lea Leenawaty Limantara Leksono Mucharam Lilik Linawati Linda Ariany Mahastanti Made Rai Suci Shanti Nurani Ayub Mohamad Hidayatullah Muninggar, Puput Retno Natalia Diyaning Gulita Om Prakash Vyas Parung, Ratu Anggriani Tangke Petrus Priyo Santosa Prayitno, Gunawan Puspasari, Magdalena Dwi Rahmawanto, Setya Budi Riana Amalia Rony, Zahara Tussoleha Santosa, Petrus Priyo Sari, Devina Intan Sebastian, Danny Septoratno Siregar Silamai Tya Mariani Famani Sinatra Canggih Siti Fatimah Slamet Santosa Slamet Santosa Sri Yulianto Joko Prasetyo Susetyo, Yosia Adi Sutarto Wijono Utari, Galuh Retno Victory Immanuel Ratar Wahyu Kurniawan Wahyu Kurniawan Wandi Wantoro wendelina anggriani Winarto, Eduardus Albert Y. Sardjono Yayi Suryo Prabandari Yenusi, Yuni naomi Yohanes Martono Yohanes Sardjono Yohanes Sardjono Yohanes Sardjono Yohanes Sardjono, Yohanes Yohannes Sardjono Yuliawan, Kristia