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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) dCartesian: Jurnal Matematika dan Aplikasi MATEMATIKA JURNAL SISTEM INFORMASI BISNIS Jurnal Ilmu Lingkungan Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Indonesian Journal of Mathematics and Natural Sciences Kreano, Jurnal Matematika Kreatif-Inovatif Jurnal Teknologi Informasi dan Ilmu Komputer JUITA : Jurnal Informatika International Journal of Advances in Intelligent Informatics Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Jurnal Fourier JOIN (Jurnal Online Informatika) Science and Technology Indonesia JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Penelitian Pendidikan IPA (JPPIPA) Desimal: Jurnal Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan Pendas : Jurnah Ilmiah Pendidikan Dasar JTAM (Jurnal Teori dan Aplikasi Matematika) International Journal on Emerging Mathematics Education SJME (Supremum Journal of Mathematics Education) Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Journal on Education Jambura Journal of Mathematics ComTech: Computer, Mathematics and Engineering Applications KAIBON ABHINAYA : JURNAL PENGABDIAN MASYARAKAT 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 International Journal of Community Service Jurnal Ilmiah Sains Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya d'Cartesian: Jurnal Matematika dan Aplikasi JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Limits: Journal of Mathematics and Its Applications SJME (Supremum Journal of Mathematics Education) Lontar Komputer: Jurnal Ilmiah Teknologi Informasi International Journal of Computing Science and Applied Mathematics-IJCSAM
Hanna Arini Parhusip
Universitas Kristen Satya Wacana
Religion Agriculture, Biological Sciences & Forestry Humanities 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 Physics Public Health Social Sciences Transportation Other

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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.
Modeling and Estimating GARCH-X and Realized GARCH Using ARWM and GRG Methods Didit Budi Nugroho; Melina Tito Wijaya; Hanna Arini Parhusip
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol. 11 No. 1 (2025)
Publisher : LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24775401.ijcsam.v11i1.4309

Abstract

This study evaluates the fitting performance of GARCH-X(1,1) and RealGARCH(1,1) models, which are extensions of GARCH(1,1) model by adding the Realized Kernel measure as an exogenous component, on real data, namely the Financial Times Stock Exchange 100 and Hang Seng stock indices over the period from January 2000 to December 2017. The models assume that the return error follows Normal and Student- t distributions. The parameters of models are estimated by using the Adaptive Random Walk Metropolis (ARWM) method implemented in Matlab and the Generalized Reduced Gradient (GRG) method. The comparison of estimation results shows that the GRG method has a good ability to estimate the models because it provides the estimation results that are close to the results of the ARWM method in terms of relative error. On the basis of Akaike Information Criterion, the RealGARCH models perform better than the GARCH-X models, where the RealGARCH model with Student- t distribution provides the best fit.
Evaluating the Fitting Performance of AGARCH(1,1), NAGARCH(1,1), and VGARCH(1,1) Models Didit Budi Nugroho; Veny M. Ningtyas; Hanna A. Parhusip
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol. 9 No. 2 (2023)
Publisher : LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study compares the performance of the GARCH(1,1), AGARCH(1,1), NAGARCH(1,1), and VGARCH(1,1) models fitted to real data. The observed real data are the USD exchange rate against IDR in the daily period from January 2010 to December 2017. To identify the superiority and evaluate the performance of those models in capturing the heavy-tailed and skewed character in exchange rate distribution, the return error is assumed to be the Normal, Skew Normal (SN), Skew Curved Normal (SCN), and Student-t distributions. The model's parameters are estimated using the GRG Non-Linear method in Excel Solver and the ARWM method in the MCMC scheme implemented in the Scilab program. Estimation results using Excel's Solver have similar values to the estimates obtained using MCMC, concluding that Excel's Solver has a good ability in estimating the model's parameters. Based on AIC values, this study concludes that the NAGARCH(1,1) model under Student-t distribution performs the best.
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.
Model Regresi untuk Return Aset dengan Volatilitas Mengikuti Model GARCH(1,1) Berdistribusi Epsilon-Skew Normal dan Student-t Didit Budi Nugroho; Kristia Anggraeni; Hanna Arini Parhusip
Limits: Journal of Mathematics and Its Applications Vol. 17 No. 2 (2020): Limits: Journal of Mathematics and Its Applications Volume 17 Nomor 2 Edisi De
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Studi ini mendiskusikan dua perluasan dari model GARCH(1,1), yaitu AR(1)-GARCH(1,1) dan MA(1)-GARCH(1,1), yang diperoleh dengan cara menambahkan Autoregression tingkat 1 atau Moving Average tingkat 1 pada persamaan return . Untuk kasus ini, error dari return diasumsikan berdistribusi Normal, Skew Normal (SN), Epsilon Skew Normal (ESN), dan Student- t . Analisis terhadap model didasarkan pada pencocokan model untuk return dari indeks saham FTSE100 periode harian dari Januari 2000 sampai Desember 2017 dan indeks saham TOPIX periode harian dari Januari 2000 sampai Desember 2014. Model yang dipelajari diestimasi menggunakan metode GRG ( Generalized Reduced Gradient ) Non Linear yang tersedia di Solver Excel dan juga metode Adaptive Random Walk Metropolis (ARWM) yang diimplementasikan pada program Scilab. Hasil estimasi dari kedua alat bantu tersebut menunjukkan nilai-nilai yang hampir sama, mengindikasikan bahwa Solver Excel mempunyai kemampuan yang handal dalam mengestimasi parameter model. Uji rasio log- likelihood dan AIC ( Akaike Information Criterion ) menunjukkan bahwa model dengan distribusi ESN lebih unggul dibandingkan dengan model-model berdistribusi tipe normal lainnya untuk setiap kasus model dan data pengamatan, bahkan ini bisa mengungguli distribusi Student- t pada suatu model dan data pengamatan. Lebih lanjut, model-model dengan penambahan proses regresi di persamaan return menyediakan pencocokan yang lebih baik daripada model dasar, dimana pencocokan terbaik untuk kedua data pengamatan diberikan oleh model AR(1)-GARCH(1,1) berdistribusi Student- t .
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.
Smart Catering Canteen School (SCCS) using Streamlit Suryasatriya Trihandaru; Hanna Arini Parhusip; Mitchella Sinta Larasati
Jurnal Sistem Informasi Bisnis Vol 16, No 1 (2026): Volume 16 Number 1 Year 2026 (In Press)
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/vol15iss4pp%p

Abstract

The school canteen faced service problems of as many as 1000 students in a short break period, especially when everything had to be done manually on business processes, especially payments. The method that has existed so far is to use Google Form to place menu orders and manually pay all verified customers which causes delays and errors. Therefore, this study aims to create a business information system for canteens called the Smart Canteen System (SCCS) which uses Optical Character Recognition (OCR) and Natural Language Processing (NLP) to be able to automate payment verification and provide sequences. This SCCS business information system will convert proof of payment from text to text and processed so that the validity of the proof of payment can be proven. With the Stremlit platform, the management process can be carried out in real time and reports can be carried out immediately. With this verification, SCCS provides the main result, namely efficiency, reducing errors in business processes in the canteen. Work that was originally done manually in 2 days became 5-10 minutes in the same process.
Performance of an AIOT-Particle Device for Air Quality and Environmental Data Prediction in Salatiga Area Using ARIMA Model Johanes Dian Kurniawan; Suryasatriya Trihandaru; Hanna Arini Parhusip
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.
Co-Authors A.A. Ketut Agung Cahyawan W Adi Setiawan Adi Setiawan Adrianus Herry Heriadi Adrianus Herry Heriadi Alfagustina, Yumita Cristin ALOYSIUS JOAKIM FERNANDEZ Atyanta Nika Rukmasari Bambang Susanto Bambang Susanto Beni Utomo Bernadus Aryo Adhi Wicaksono Carolina Febe Ronicha Putri Denny Indrajaya Denny Indrajaya Didit Budi Nugroho Didit Budi Nugroho Didit Budi Nugroho Djoko Hartanto Djoko Hartanto Eduardus Albert Winarto Endang Warsiki Fachrurrozi Fachrurrozi Fachrurrozi Fachrurrozi Faldy Tita Fetriks Theo Sarita Fika Widya Pratama Fitri, Nirmala Ayu Andika Gede Sutresna Wijaya Goni, Abdiel Wilyar Hariadi, Adrianus Herry Heriadi, Adrianus Herry Hindriyanto Dwi Purnomo Indrajaya, Denny Isman Mulyadi Triatmoko Istiarsi Saptuti Sri Kawuryan Istiarsih Saputri Sri Kawuryan Jane Labadin Jane Labadin Johanes Dian Kurniawan Johanes Dian Kurniawan Johanes Dian Kurniawan Jordi Enal Ambat Karina Bianca Lewerissa Karina Bianca Lewerissa Kristia Anggraeni Kristoko Dwi Hartomo Larasati, Mitchella Sinta Lea, Lea Leopoldus Ricky Sasongko Lilik Linawati Linda Ariany Mahastanti Mauliddha Rachmi Melina Tito Wijaya Mitchella Sinta Larasati Mitha Febby R. Donggori Mitha Febby R. Donggori Nafisah Riskya Hasna Nugroho Dwi Susanto Nugroho, Didit B. Obed Christian Dimitrio Om Prakash Vyas Parung, Ratu Anggriani Tangke Petrus Priyo Santosa Pradani, Wynona Adita Puput Retno Muninggar Purwoko, Agus Puspasari, Magdalena Dwi Rudhito, Andy Santosa, Petrus Priyo Sari, Devina Intan Sri Kawuryan, Istiarsi Saptuti Sri Suryasatriya Trihandaru Susetyo, Yosia Adi Theo Sarita, Fetriks Titilias, Y A Urosidin, Nur I. M. Veny M Ningtyas Veny M. Ningtyas Wijaya, Melina Tito Wijayanti, Yunita Puput Wulandari, Nadya Putri Yohanes Sardjono Yohanes Sardjono Yohanes Sardjono Yohanes Sardjono, Yohanes Yohannes Sardjono Yosia Adi Susetyo Yusuf Kurniawan