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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) RADIASI: Jurnal Berkala Pendidikan Fisika BERKALA FISIKA JURNAL SISTEM INFORMASI BISNIS 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) BAREKENG: Jurnal Ilmu Matematika dan Terapan Indonesian Journal of Chemistry Pendas : Jurnah Ilmiah Pendidikan Dasar 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 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.
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.
Human Capital Decision Intelligence (HCDI) architecture in microbiology laboratory based on machine learning and operations research models Trihandaru, Suryasatriya; Susetyo, Yosia Adi; Parhusip, Hanna Arini; Susanto, Bambang
International Journal of Advances in Intelligent Informatics Vol 11, No 4 (2025): November 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i4.1676

Abstract

The Human Capital Decision Intelligence (HDCI) system integrates human-computer interaction in a microbiology laboratory that uses machine learning and operational research to classify new tasks and then recommend assignments to each person. The models evaluated in building this system are Support Vector Machine, Gaussian Naive Bayes, Multinomial Logistic Regression, and Artificial Neural Network. The results of the research show that the ANN model is the most consistent and reliable across various training ratios, as indicated by the model's goodness parameters. The selected ANN model is combined with a linear programming approach to optimize workload distribution. The integrated system successfully manages new job scenarios and recommends staff based on competencies and availability. It also ensures assignments do not exceed maximum workload limits and finds alternatives when key staff are unavailable. The implementation of the HDCI system has a positive impact on various factors, including the fair distribution of tasks, enhanced staff performance monitoring, and significantly improved operational efficiency and human resource management in the microbiology laboratory. The system is designed to be easy to use and support collaboration between laboratory staff and computational models. The system is not only advanced in supporting personnel management decision-making, but it can also demonstrate how artificial intelligence and operations research systems can be combined to address the needs of the microbiology laboratory environment.
Data Exploration Using Tableau and Principal Component Analysis Parhusip, Hanna Arini; Trihandaru, Suryasatriya; Heriadi, Adrianus Herry; Santosa, Petrus Priyo; Puspasari, Magdalena Dwi
JOIV : International Journal on Informatics Visualization Vol 6, No 4 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.4.952

Abstract

This study aims to determine the dominant chemical elements that may improve the monitoring of the productivity and efficiency of heavy engines in 2015-2021 in the company. The method used is usually Scheduled Oil Sampling. This article proposes a new approach. The research problems are analyzing the recorded chemical elements that are produced by heavy engines and visualizing them through the Tableau program. The basic design of the study is learning the given data after visualization and using the Principal Component Analysis. This method is to obtain chemical elements that affect engine wear during each engine's use in the 2015-2021 period. Because there are three categories in each element in the oil sample, namely wear metals, contaminants, and oil additives, a technique is needed to obtain these elements using Principal Component Analysis. Therefore, Oil Sampling Analysis through data exploration using Tableau resulted in a new approach to data analysis of elements recorded by heavy vehicles. The main findings as a result of the analysis are given by the visualization of Tableau, in which there are five machines analyzed to obtain the main components that cause engine wear. From the visualization results, it is shown that there is one engine coded MSD 012 that experienced wear and tear in 2018 and 2019. This shows where two main components, Ca and Mg, dominate engine wear. These results have been confirmed with the related companies. The company then carried out further studies on the machine to get special treatment because of these results.
Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Principal Component Analysis (PCA) for Particulate Matter (PM) Anomaly Detection Hanna Arini Parhusip; Suryasatriya Trihandaru; Bambang Susanto; Johanes Dian Kurniawan; Adrianus Herry Heriadi; Petrus Priyo Santosa; Yohanes Sardjono
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol. 15 No. 02 (2024): Vol. 15, No. 2 August 2024
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2024.v15.i02.p01

Abstract

This research addresses a critical issue in industrial environments: air quality, specifically regarding PM 1.0 and PM 2.5. High concentrations of these particles pose significant health risks. The study measures temperature, humidity, pressure, altitude, PM 1.0, and PM 2.5 and shows the effectiveness of using AIOT-Particle devices to analyze these features with Principal Component Analysis (PCA). Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is used to detect anomalies during the observation period. Anomalies occur when the altitude ranges from 65 to 70 units, according to PM 1.0 and PM 2.5 values. The positions where anomalies occur are illustrated based on altitude, temperature, pressure, and concentration. The results demonstrate that altitude dominates as the first feature. Finally, the research concludes that altitude, PM 1.0, and PM 2.5 are the dominant features. The study confirms the effectiveness of PCA and recommends using these three features for anomaly detection in DBSCAN. Overall, the research highlights the novelty and success of AIOT-Particle in industrial environments.
SISTEM OTOMATIS KLASIFIKASI BUKTI PEMBAYARAN MENGGUNAKAN OCR DAN EMBEDDING BERT DENGAN PENDEKATAN MULTI-MODEL PEMBELAJARAN MESIN Larasati, Mitchella Sinta; Suryasatriya Trihandaru; Hanna Arini Parhusip
Pendas : Jurnal Ilmiah Pendidikan Dasar Vol. 11 No. 01 (2026): Volume 11 No. 01 Maret 2026 Published
Publisher : Program Studi Pendidikan Guru Sekolah Dasar FKIP Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/jp.v11i01.40994

Abstract

The verification process of payment receipts in school environments is still predominantly conducted manually, leading to inefficiency and a high potential for human error. This study proposes an automated system for classifying the validity of digital payment receipts by combining Optical Character Recognition (OCR), BERT (Bidirectional Encoder Representations from Transformers) embeddings, and multi-model machine learning approaches. The system integrates EasyOCR for text extraction from payment receipts, BERT for generating semantic text representations, and four classification algorithms: Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), Naive Bayes (NB), and Logistic Regression (LR). The dataset consists of 185 payment receipt samples, comprising 149 valid and 36 invalid instances, collected via Google Forms and stored in a SQLite database. Experimental results demonstrate that the Multi-Layer Perceptron (MLP) model achieves the highest accuracy of 97% with a test size of 0.2, followed by Logistic Regression with an accuracy of 96.2%, while Naive Bayes exhibits the lowest performance with an accuracy of 85.7%. The proposed system is successfully implemented in a Streamlit-based application, enabling real-time verification of payment receipts with an average processing time of 1.16 seconds per sample.
A Hierarchical Bayesian Model of Multi-Hazard Impacts on Property Prices in the Jakarta Metropolitan Area Fachrurrozi; Jordi Enal Ambat; Hanna Arini Parhusip; Suryasatriya Trihandaru
Jurnal Penelitian Pendidikan IPA Vol 11 No 11 (2025): November
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.
Development of Staff Evaluation Software Based on Association Matrix Methods and Data Mining Using the Streamlit Framework Yosia Adi Susetyo; Hanna Arini Parhusip; Suryasatriya Trihandaru
JUITA: Jurnal Informatika JUITA Vol. 12 No. 2, November 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i2.23300

Abstract

This study discusses evaluating employee performance in microbiology laboratories using an association matrix implemented in web-based software with the Streamlit framework. The purpose of the research is to improve the employee performance evaluation process, which previously used conventional methods. This software is built from a sample receipt recording history data stored in a MySQL database. The initially unstructured data was processed using Python libraries such as NumPy, Matplotlib, Pandas, and Difflib to generate personnel evaluation information such as specialization, task duration, workload, and individual competencies. This software can provide a fast and accurate performance assessment according to the evaluation period. In a test with the System Usability Scale (SUS), the software scored 75.83, which was rated "good.". These results show that the software is easy to use and can improve the efficiency of employee performance evaluation. Follow-up tests with questionnaires given to 18 users showed that this system was preferable to previous conventional methods. This software helps laboratory managers evaluate employee performance effectively and efficiently.
Co-Authors Abigail Geofani Boham Adi Setiawan Adita Sutresno Adrianus Herry Heriadi Adrianus Herry Heriadi Alvama Pattiserlihun Alvama Pattiserlihun Alvama Pattiserlihun Andreas Setiawan Bambang Susanto Bambang Susanto Bernadus Aryo Adhi Wicaksono Boham, Abigail Geofani Carolina Febe Ronicha Putri Daniel Eliazar Latumaerissa Denny Indrajaya Denny Indrajaya Dian Widiyanto Chandra Didit Budi Nugroho Djoko Hartanto Djoko Hartanto Dwi Pangestuti Eduardus Albert Winarto Fachrurrozi Fachrurrozi Fachrurrozi Fachrurrozi Ferdy Semuel Rondonuwu Ferri Rusady Saputra Gede Sutresna Wijaya Giner Maslebu Giner Maslebu Giner Maslebu Goni, Abdiel Wilyar Haay, Happy Alyzhya Hanna Arini Parhusip Harendza, David Hariadi, Adrianus Herry Harry Budiharjo Sulistyarso Hasian P. Septoratno Siregar Heriadi, Adrianus Herry Heriyanto Heriyanto Indrajaya, Denny Inti Mustika Isman Mulyadi Triatmoko Ivanky Saputra Jane Labadin Jane Labadin Johanes Dian Kurniawan Johanes Dian Kurniawan Johanes Dian Kurniawan Jordi Enal Ambat Karina Bianca Lewerissa Karina Bianca Lewerissa Kristoko Dwi Hartomo Larasati, Mitchella Sinta Laurentius Kuncoro Probo Saputra, Laurentius Kuncoro Probo Lea, Lea Leenawaty Limantara Leksono Mucharam Lilik Linawati Linda Ariany Mahastanti Made Rai Suci Shanti Nurani Ayub Mitchella Sinta Larasati 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 Silamai Tya Mariani Famani Sinatra Canggih Siti Fatimah Slamet Santosa Sri Yulianto Joko Prasetyo Susetyo, Yosia Adi Sutarto Wijono Utari, Galuh Retno Victory Immanuel Ratar Victory Immanuel Ratar Victory Immanuel Ratar Wahyu Kurniawan Wahyu Kurniawan Wandi Wantoro wendelina anggriani Yayi Suryo Prabandari Yenusi, Yuni naomi Yohanes Martono Yohanes Sardjono Yohanes Sardjono Yohanes Sardjono Yohanes Sardjono, Yohanes Yohannes Sardjono Yohannes Sardjono Yosia Adi Susetyo Yuliawan, Kristia