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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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Predictive Performance Evaluation of ARIMA and Hybrid ARIMA-LSTM Models for Particulate Matter Concentration Kurniawan, Johanes Dian; Parhusip, Hanna Arini; Trihandaru, Suryasatria
JOIN (Jurnal Online Informatika) Vol 9 No 2 (2024)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v9i2.1318

Abstract

This study provides an objective evaluation of prediction performance models for particulate matter policy for industrial stakeholders by comparing the ARIMA and Hybrid ARIMA-LSTM models for predicting air quality data from the industrial environment. In the case of PM 1.0 concentration, we have an RMSE value of 8.29 and an error ratio of 0.45 for the ARIMA model and an RMSE value of 3.54 and an error ratio of 0.22 for the hybrid ARIMA-LSTM model. Meanwhile, for PM 2.5 concentration, we obtain an RMSE value of 6.61, an error ratio of 0.66 for the ARIMA model, an RMSE value of 2.68, and an error ratio of 0.19 for the hybrid ARIMA-LSTM model. According to this study, the ARIMA model, which is found in autoarima and represents the best model, is (2,0,1) for PM1.0 and (1,0,1) for PM2.5. The hybrid ARIMA-LSTM model outperforms the ARIMA model in terms of prediction accuracy, as evidenced by the RMSE and error ratio values, which are improved by approximately 57.30% and 51.11% for PM1.0 and 59.46% and 71.21% for PM2.5, respectively, since the hybrid ARIMA-LSTM model can accommodate variable-length sequences and capture long-term relationships to become noise-resistant, which makes higher prediction accuracy possible.
OVERCOMING OVERFITTING IN MONKEY VOCALIZATION CLASSIFICATION: USING LSTM AND LOGISTIC REGRESSION Trihandaru, Suryasatriya; Parhusip, Hanna Arini; Goni, Abdiel Wilyar
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp973-986

Abstract

The problem of overfitting in a classification task involving animal vocalizations, namely squirrel monkeys, golden lion tamarins, and tailed macaques, is handled in this project. Acoustic features extracted for the audio data used in this research are MFCCs. The classification of subjects was done using the LSTM model. However, several architectures with LSTM also presented the problem of overfitting. To overcome this, a logistic regression model was used, which had a classification accuracy of 100%. These results indicate that for such a classification problem, logistic regression may be more appropriate than the complex architecture of LSTMs. Several LSTM architectures have been presented in this study to give an overall review of the observed challenges. Although the capability of LSTM in handling sequential data is very promising, sometimes simpler models might be preferred, as indicated by the results. This is a single-dataset work, and the findings may not generalize well to other domains. The work contributes much-needed insight into the choice of models for audio classification tasks and identifies the trade-off between model complexity and performance
Analisis Indeks Pembangunan Manusia (IPM) Kabupaten/Kota di Provinsi Maluku Utara Menggunakan Indeks Geary C Berdasarkan Resampling Estimasi Densitas Kernel Fetriks Theo Sarita; Adi Setiawan; Hanna Arini Parhusip
Jurnal Teknik Informatika dan Sistem Informasi Vol 5 No 1 (2019): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v5i1.1582

Abstract

In this study, it is discussed the Regency/City Human Development Index (HDI) in North Maluku Province to determine the spatial influence of HDI. The data used are Regency/City HDI in North Maluku Province from 2013 to 2017. Furthermore, spatial autocorrelation test with Geary C index is used based on resampling by using Estimated Kernel Density. The test is done to determine whether the Regency/City HDI affects the neighboring Regency/City HDI. Based on the results of optimal bandwidth usage (hopt) with a repetition of 10000 times it is obtained p-value 0.0318, 0.0272, 0.0282, 0.0262 and 0.0258, respectively from 2013 to 2017. Thus, there is a spatial between Regencies/Cities in North Maluku Province from 2013 to 2017. In other words, if the HDI in a Regency/City is high then the neighbouring Regency/City HDI tends to be high, and vice versa.
Learning Algorithms of SVR, DTR, RFR, and XGBoost (Case Study: Predictive Maintenance of Fuel Consumption) Parhusip, Hanna Arini; Lea, Lea; Trihandaru, Suryasatriya; Nugroho, Didit Budi; Santosa, Petrus Priyo; Hariadi, Adrianus Herry
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 2 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i2.85657

Abstract

The most complex aspect of predictive maintenance (PdM) for heavy vehicles is accurately forecasting fuel consumption as it is both critical and challenging to achieve optimal efficiency while minimizing expenses. Overfitting and failure to capture the existing data's linear relationships seem to remain the most persistent issues with traditional methods. In order to achieve this, the following techniques were analyzed to choose the best fuel consumption forecaster: Support Vector Regression (SVR), Decision Tree Regression (DTR), Random Forest Regression (RFT), and XGBoost. The models were implemented and their performance measured using Mean Squared Error (MSE). The analysis revealed that SVR surpassed the others with a linear kernel (C=10) achieving the lowest MSE rates of 0.26, while DTR, RFR, and XGBoost earned significantly higher 3.375, 2.857, and 3.857 (MSEs). The other models lagged behind SVR because SVR was more effective in capturing linear relations and managing overfitting, a dominating issue with decision-tree based models. This points out another important aspect of predictive maintenance (PdM) : the appropriate machine learning technique plays a very important role in accurately predicting fuel consumption of heavy trucks, which improves precision and fuel efficiency.
Modern Ethnomathematics Mainstreaming through Mathematics Entrepreneurship Using Mathematical Ornaments Parhusip, Hanna Arini; Purnomo, Hindriyanto Dwi; Nugroho, Didit Budi; Sri Kawuryan, Istiarsi Saptuti Sri
International Journal on Emerging Mathematics Education IJEME, Vol. 5 No. 2, September 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/ijeme.v5i2.15118

Abstract

Modern ethnomathematics is proposed in this article by introducing curves and surfaces to objects based on commonly used mathematics. There are 2 types of objects, batik and ornament. The object is known as Batima, which means a mathematical motif made in a batik stamp. The same design can be used to design ornaments, souvenirs, accessories or other household items such as glasses, t-shirts and other materials. The formation of ethnomathematics is driven by entrepreneurial activities. The method starts with the expansion of the circular and spherical equations based on the variation of the power form which was originally 2 in the equation to be valued at random (say p). The other used equations are parametric equations, especially the hypocycloid which is extended to both curves and surfaces with spherical coordinates. In addition, derivative operators can be applied. Product manufacturing is carried out by at least 10 household businesses around Salatiga and Jogjakarta and its surroundings. In order to sustain the mainstreaming of modern ethnomathematics, entrepreneurial activities are carried out with existing materials through exhibitions and competitions that are followed. Likewise, the use of social media and marketplaces are explored to mainstream the modern ethnomathematics into society.
Learning geometry through surface creation from the hypocycloid curves expansion with derivative operators for ornaments Parhusip, Hanna Arini; Purnomo, Hindriyanto Dwi; Nugroho, Didit Budi; Kawuryan, Istiarsi Saptuti Sri
Desimal: Jurnal Matematika Vol. 4 No. 1 (2021): Desimal: Jurnal Matematika
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/djm.v4i1.7385

Abstract

Geometry is one of the particular problems for students. Therefore, several methods have been developed to attract students to learn geometry. For undergraduate students, learning geometry through surface visualization is introduced. One topic is studying parametric curves called the hypocycloid curve. This paper presents the generalization of the hypocycloid curve. The curve is known in calculus and usually is not studied further. Therefore, the research's novelty is introducing the spherical coordinate to the equation to obtain new surfaces. Initially, two parameters are indicating the radius of 2 circles governing the curves in the hypocycloid equations. The generalization idea here means that the physical meaning of parameters is not considered allowing any real numbers, including negative values. Hence, many new curves are observed infinitely. After implementing the spherical coordinates to the equations and varying the parameters, various surfaces had been obtained. Additionally, the differential operator was also implemented to have several other new curves and surfaces. The obtained surfaces are useful for learning by creating ornaments. Some examples of ornaments are presented in this paper.
Selection Dominant Features Using Principal Component Analysis for Predictive Maintenance of Heave Engines Trihandaru, Suryasatriya; Parhusip, Hanna Arini; Heriadi, Adrianus Herry; Santosa, Petrus Priyo; Sardjono, Yohanes; Lea, Lea
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 4 (2024): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i4.22854

Abstract

This article aims to identify the dominant features that have a significant impact on the health of a heavy machine that relates to the digital infrastructure of a company. The importance of this research is that the authors define predictive maintenance based on Principal Component Analysis (PCA), which is the novelty of this article. The novel contribution of this research lies in the application of Principal Component Analysis (PCA) for predictive maintenance of heavy machinery, which has not been integrated into the Scheduled Oil Sampling (SOS) procedures. The recorded data are called Scheduled Oil Sampling (SOS) and historical data from an equipment called CoreDataQ, which works for recording many features from heavy machine activities. The data contain two sets data. The method is Principal Component Analysis (PCA). This method leads to obtain a maximum of 20 significant features on data based on SOS. The results have been confirmed and agreed upon by the manager who owned CoreDataQ to consider the selected dominant features for further related maintenance. 
Improvement of Real-GJR Model using Jump Variables on High Frequency Data Nugroho, Didit Budi; Wulandari, Nadya Putri; Alfagustina, Yumita Cristin; Parhusip, Hanna Arini; Tita, Faldy; Susanto, Bambang
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 4 (2024): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i4.24294

Abstract

Volatility is a key indicator in assessing risk when making investment decisions. In the world of financial markets, volatility reflects the degree to which the value of a financial asset fluctuates over a given period. The most common way to measure the future loss potential of an investment is through volatility. Focusing on the Realized GJR (RealGJR) volatility model, which consists of return, conditional volatility, and measurement equations, this study proposes the RealGJR-CJ model developed by decomposing the exogenous variable in the volatility equation of RealGJR into continuous C and discontinuous (jump) J variables. The decomposition of exogenous variables makes the RealGJR-CJ model follow realistic financial markets, where the asset volatility is a continuous process with some jump components. As an empirical illustration, the models are applied to an index in the Japanese stock market, namely Tokyo Stock Price Index, covering from January 2004 to December 2011. The observed exogenous variable in the volatility equation of RealGJR models is Realized Volatility (RV), which is calculated using intraday data with time intervals of 1 and 5 minutes. Adaptive Random Walk Metropolis method was employed in Markov Chain Monte Carlo algorithm to estimate the model parameters by updating the parameters during sampling based on previous samples from the chain. From the results of running the MCMC algorithm 20 times, the mean of the information criteria of competing models is significantly different based on standard deviation and the result suggests that the model with continuous and jump variables can improve the model without jump. The best fit model is provided by RealGJR-CJ with the adoption of 1-minute RV data. 
PENGUJIAN NESS-APP UNTUK DETEKSI SARANG BURUNG WALET TESTING OF NESS-APP FOR DETECTING SWIFTLET NESTS Parhusip, Hanna Arini; Trihandaru, Suryasatriya; Indrajaya, Denny; Hartomo, Kristoko Dwi; Lewerissa, Karina Bianca; Mahastanti, Linda Ariany
Jurnal Abdi Insani Vol 11 No 4 (2024): Jurnal Abdi Insani
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/abdiinsani.v11i4.1786

Abstract

This article discusses the development and testing of the Ness-App application, designed to detect and assess the quality of swallow nests effectively and efficiently. The main issue addressed is the difficulty in determining the quality of swallow nests through photos or videos in buying and selling transactions. The purpose of this research is to develop an Android application using object detection technology to assist PT. Waleta Asia Jaya in assessing the quality of swallow nests. The method used involves creating an object detection model using Convolutional Neural Network (CNN) and SSD MobileNet architecture. The results indicate that the Ness-App application can improve transaction efficiency and quality, providing a better understanding of swallow nest conditions for collectors and farmers. In conclusion, Ness-App supports digitalization and technological advancement in the swallow nest industry by providing an effective tool for quality assessment and accelerating the transaction process.
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.
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