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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) RADIASI: Jurnal Berkala Pendidikan Fisika 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 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 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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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
Using an LSTM Neural Network to Improve Symmetric and Asymmetric GARCH Volatility Forecast Rahmawanto, Setya Budi; Nugroho, Didit Budi; Trihandaru, Suryasatriya
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 1 (2025): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v9i1.24614

Abstract

Volatility forecasting is crucial for financial risk management, yet traditional models like GARCH struggle with nonlinearities and asymmetric effects. This study leverages Long Short-Term Memory (LSTM) neural networks to enhance symmetric and asymmetric GARCH models, addressing these limitations. By integrating LSTM with GARCH, GARCH-X, and Realized GARCH frameworks, we propose hybrid models (Baseline and Extended versions) to improve forecasting accuracy. Using daily data from FTSE 100, Nikkei 225, and S&P 500 indices (2000–2020), we compared hybrid models against traditional models. Results show that the Extended LSTM hybrid model outperforms both traditional GARCH-type models and the Baseline LSTM, capturing complex volatility patterns more effectively. The Extended model’s architecture, featuring ReLU, GRU, and dropout layers, mitigates over-smoothing and enhances responsiveness to market fluctuations. This research demonstrates LSTM’s potential to refine volatility forecasting, offering valuable insights for investors and risk managers.
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.
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. 
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.
AI-Enhanced Production Planning: Integrating LSTM Forecasting with Linear Programming Winarto, Eduardus Albert; Parhusip, Hanna Arini; Trihandaru, Suryasatriya
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 10, No 2 (2025): Edisi Agustus
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v10i2.924

Abstract

Efficient production planning is crucial in the manufacturing industry, including in the paper sector, where fluctuating demand and limited production capacity pose significant challenges. This study introduces an intelligent optimization system that integrates demand forecasting using Long Short-Term Memory (LSTM) with production scheduling optimization through Linear Programming (LP) in Pyomo. The LSTM model processes historical order data to predict demand for the next 30 days, which is then used as input for the LP model to generate an optimal production schedule while considering machine capacity and operational time constraints. The experimental results indicate that the LSTM model achieves a prediction error (loss) of approximately 0.032, demonstrating high accuracy in capturing demand patterns. Meanwhile, the LP model implemented in Pyomo efficiently allocates production time, ensuring that machine utilization is optimized without exceeding the available working hours. By integrating these approaches, companies can minimize the risks of overproduction and stockouts while maximizing resource efficiency. Furthermore, this method enhances decision-making processes by providing data-driven insights into production scheduling and inventory management. The proposed framework offers a scalable solution for improving operational performance in the paper industry, enabling companies to respond more effectively to market fluctuations and optimize their supply chain strategies.
INTRODUCTION OF PAPUAN AND PAPUA NEW GUINEAN FACE PAINTING USING A CONVOLUTIONAL NEURAL NETWORK Haay, Happy Alyzhya; Trihandaru, Suryasatriya; Susanto, Bambang
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 (576.962 KB) | DOI: 10.30598/barekengvol17iss1pp0211-0224

Abstract

In this research, the face painting recognition of Papua and Papua New Guinea was identified using the Convolutional Neural Network (CNN). This CNN method is one of the deep learning that is very well known and widely used in face recognition. The best training process model is obtained using the CNN architecture, namely ResNet-50, VGG-16, and VGG-19. The results obtained from the training model obtained an accuracy of 80.57% for the ResNet-50 model, 100% for the VGG-16 model, and 99.57% for the VGG-19 model. After the training process, predictions were continued using architectural models with test data. The prediction results obtained show that the accuracy of the ResNet-50 model is 0.70, the VGG-16 model is 0.82, and the VGG-19 model is 0.83. It means that the CNN architectural model that has the best performance in making predictions in identifying the recognition of Papua and Papua New Guinea's face painting is the VGG-19 model because the accuracy value obtained is 0.83.
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.
Co-Authors Abigail Geofani Boham Adi Setiawan Adita Sutresno Adrianus Herry Heriadi Adrianus Herry Heriadi Alvama Pattiserlihun Alvama Pattiserlihun Alvama Pattiserlihun Andreas Setiawan Ariany Mahastanti, Linda Bambang Susanto Bambang Susanto Bernadus Aryo Adhi Wicaksono Carolina Febe Ronicha Putri Daniel Eliazar Latumaerissa Denny Indrajaya Denny Indrajaya Dian Widiyanto Chandra Didit Budi Nugroho Djoko Hartanto Djoko Hartanto Dwi Pangestuti Fachrurrozi Fachrurrozi Ferdy S. Rondonuwu Ferdy Semuel Rondonuwu Ferri Rusady Saputra Gede Sutresna Wijaya 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 Isman Mulyadi Triatmoko, Isman Mulyadi Ivanky Saputra Jane Labadin Johanes Dian Kurniawan Johanes Dian Kurniawan Johanes Dian Kurniawan Karina Bianca Lewerissa Kristoko Dwi Hartomo Kurniawan, Johanes Dian 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 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 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 Yayi Suryo Prabandari Yenusi, Yuni naomi Yohanes Martono Yohanes Sardjono Yohanes Sardjono Yohanes Sardjono Yohanes Sardjono, Yohanes Yohannes Sardjono Yohannes Sardjono Yuliawan, Kristia