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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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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.
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.
Simulasi Komputasi Lokasi dan Orientasi Kanker pada ORNL-MIRD Boham, Abigail Geofani; Trihandaru, Suyasatriya; Setiawan, Andreas
Prosiding University Research Colloquium Proceeding of The 11th University Research Colloquium 2020: Bidang Sains dan Teknologi
Publisher : Konsorsium Lembaga Penelitian dan Pengabdian kepada Masyarakat Perguruan Tinggi Muhammadiyah 'Aisyiyah (PTMA) Koordinator Wilayah Jawa Tengah - DIY

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

Abstract

Treatment Planning System (TPS) harus dilakukan sebelum fraksi radioterapi. Dalam tahap ini dilakukan simulasi untuk pendistribusian dosis radiasi, salah satunya adalah pemodelan computational phantom ORNL- MIRD (Oak Ridge National Laboratory- Medical Internal Radiation Dose) model manusia, yang paling sering digunakan pada kebutuhan simulasi Monte Carlo. Terkadang simulasi letak dan orientasi kanker sering diabaikan pada saat penginputan file kode MCNP dalam perhitungan Monte Carlo, yang mempengaruhi sudut penyinaran dan juga efisiensi waktu TPS. Penelitian ini bertujuan untuk mesimulasikan orientasi dan lokasi kanker pada simulasi ORNL-MIRD yang diasumsikan sebagai bentuk ellipsoid tunggal terhadap jantung. Dengan melakukan pendekatan geometri ellipsoid, maka didapatkan suatu formula baru dimana konstanta di dalamnya merupakan kebutuhan file kode MCNP. Formula baru ini mengandung nilai rotasi dan translasi dari ellipsoid yang diasumsikan merupakan bentuk geometri dari kanker. Sebagai uji simulasi diaplikasikan pada phantom ORNL-MIRD, dihasilkan konstanta Surface Card mnemonic GQ pada kode MCNP untuk kebutuhan simulasi sebesar A = 0.036; B = 0.0044; C = 0.0019; D = 0; E = 0; F = 0.0028; G = -1.5445; H = 0; I = -1.1154; J = 215.3605 dalam pixel dengan jarak titik pusat kanker ke jantung adalah (x,y) adalah sebesar 95.566, 216.689 dan orientasi sebesar -59.680 rad .
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.
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