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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 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 INDONESIAN JOURNAL OF APPLIED PHYSICS 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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Studi Ekstraksi Fitur Data Teks Rencana Pelaksanaan Pembelajaran Memanfaatkan Model Word2Vec Daniel Eliazar Latumaerissa; Suryasatriya Trihandaru; Didit Budi Nugroho
Jurnal Linguistik Komputasional Vol 4 No 2 (2021): Vol. 4, No. 2
Publisher : Indonesia Association of Computational Linguistics (INACL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jlk.v4i2.54

Abstract

Abstrak— Natural Language Processing (NLP) adalah bidang dalam ilmu computer yang mencoba menjembatani mesin dan manusia melalui analisa bahasa manusia, misalnya dalam bentuk teks. Data berupa teks sebelum digunakan dalam pelatihan mesin perlu dirubah terlebih dahulu menjadi vektor (trasnformasi) bermakna sehingga dapat dihitung secara matematis. Pemilihan teknik transformasi atau dikenal juga dengan Vector Space Model (VSM) menjadi penting karena dapat berpengaruh terhadap proses pelatihan mesin. Telah dilakukan uji transformasi teks ke vektor menggunakan model Word2Vec pada dataset RPP dan didapatkan bahwa variasi Bag of Centroids Based Word2Vec adalah pilihan Teknik terbaik untuk melakukan transformasi teks dataset RPP berdasarkan analisa matriks hasil perhitungan cosine similarity.
ANDROID-BASED EDUCATIONAL GAME: RECOGNITION OF PAPUA ENDEMIC ANIMALS Kristia Yuliawan; Gunawan Prayitno; Sutarto Wijono; Sri Yulianto Joko Prasetyo; Suryasatriya Trihandaru
Jurnal Teknik Informatika (Jutif) Vol. 3 No. 4 (2022): JUTIF Volume 3, Number 4, August 2022
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jutif.2022.3.4.319

Abstract

Papua is the largest island in Indonesia; several animals are included in the endemic group. These animals are only found in certain areas and not in other areas. To study the endemic animals of Papua, children can explore them through books that display pictures of endemic animals in Papua. Children often experience difficulties learning from books taught by teachers and parents caused by children who are less enthusiastic about participating in learning. Another problem is that learning about Papua's endemic animals through books is impractical and inefficient because thick books provide a heavy burden for children to carry. Hence, children are reluctant to study them. With educational games, media is a medium that can be used by children so that it is easy to give lessons about the endemic animals of Papua. This educational game increases efficiency and effectiveness in terms of the learning process at home and school. Learning this educational game can be done anywhere at any time so that children can learn about Papua's endemic animals innovatively and efficiently. The method used in making this educational game introducing Papua's endemic animals uses the Agile Development method. Based on testing the educational game application using the black box method, it was found that this educational game was following what was expected because there were no errors found in the menu on the system, so it worked properly.
MobileNetV2-D and multiple cameras for swiftlet nest classification based on feather intensity Denny Indrajaya; Hanna Arini Parhusip; Suryasatriya Trihandaru; Djoko Hartanto
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp1144-1158

Abstract

MobileNetV2-D is a modified version of MobileNetV2, which is the novelty of this article. The algorithm is used to classify swiftlet nests into seven classes. In 2023, PT Waleta Asia Jaya is required to achieve a 7-fold increase in the export quota of swiftlet nests. To meet the quota, the company made a machine that can recognize swiftlet nest objects, which are classified into seven classes based on feather intensity, namely BRS, BR, BST, BS, BBT, BB, and BB2 for the light feathers to the heavy feathers, respectively. The input image is a combination of four images from four cameras with different positions, which adds to the novelty of MobileNetV2-D for the particular problem here. From the evaluation that has been carried out, the accuracy value of the MobileNetV2-D model was better than the MobileNetV2 model, i.e., the accuracy value of the MobileNetV2-D model was 99.9928% for the training dataset and 94.0723% for the testing dataset. Moreover, the speed of MobileNetV2-D is better than MobileNetV2- architecture.
Management of Traditional Business into Modern: from Microsoft Excel to Deep Learning for prototyping classification Swiftlet’s nests Arini Parhusip, Hanna; Trihandaru, Suryasatriya; Hartomo, Kristoko Dwi; Bianca Lewerissa, Karina; Ariany Mahastanti, Linda; Hartanto, Djoko
International Journal Of Community Service Vol. 4 No. 2 (2024): May 2024 (Indonesia - Ethiopia )
Publisher : CV. Inara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51601/ijcs.v4i2.268

Abstract

In this article, the transformation of traditional management of Swiftlet’s nests into modern business is proposed. Traditional business means that data management of Swiftlet’s nests is done manually, sorted by recording in Microsoft Excel. This is done by PT Waleta Asia Jaya, a company engaged in processing Swiftlet’s nests. This sorting is done because the number of feathers in the Swiftlet’s nests determines the price and cost of workers in processing feather cleaning. In addition, the shape of the Swiftlet’s nests needs attention. However, because it is complex, sorting is done simpler. Originally, Swiftlet’s nests were sorted into 50 categories. To facilitate sorting, deep learning is used with the SSD Mobile Net V2 algorithm as an algorithm to classify into 7 categories based on feather intensity. The device is still a prototype that shows an 85% accuracy rate but has been quite helpful in the process of purchasing Swiftlet’s nests before processing.
Implementation of YOLOv8-seg on store products to speed up the scanning process at point of sales Arini Parhusip, Hanna; Trihandaru, Suryasatriya; Indrajaya, Denny; Labadin, Jane
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp3291-3305

Abstract

You only look once v8 (YOLOv8)-seg and its variants are implemented to accelerate the collection of goods for a store for selling activity in Indonesia. The method used here is object detection and segmentation of these objects, a combination of detection and segmentation called instance segmentation. The novelty lies in the customization and optimization of YOLOv8-seg for detecting and segmenting 30 specific Indonesian products. The use of augmented data (125 images augmented into 1,250 images) enhances the model's ability to generalize and perform well in various scenarios. The small number of data points and the small number of epochs have proven reliable algorithms to implement on store products instead of using QR codes in a digital manner. Five models are examined, i.e., YOLOv8-seg, YOLOv8s-seg, YOLOv8m-seg, YOLOv8l-seg, and YOLOv8x-seg, with a data distribution of 64% for the training dataset, 16% for the validation dataset, and 20% for the testing dataset. The best model, YOLOv8l-seg, was obtained with the highest mean average precision (mAP) box value of 99.372% and a mAPmask value of 99.372% from testing the testing dataset. However, the YOLOv8mseg model can be the best alternative model with a mAPbox value of 99.330% since the number of parameters and the computational speed are the best compared to other models.
NH4 Modelling with ARIMA and LSTM Hanna Arini Parhusip; Suryasatriya Trihandaru; Johanes Dian Kurniawan
Jurnal Ilmu Lingkungan Vol 22, No 6 (2024): November 2024
Publisher : School of Postgraduate Studies, Diponegoro Univer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jil.22.6.1670-1678

Abstract

AI-Mining is a prototype designed to detect various environmental gases, including CO2, NH3, NH4, and hydrogen, alongside temperature, pressure, and humidity. This study emphasizes the importance of modeling NH4 time series data due to its critical role in environmental and health monitoring. Accurate NH4 predictions facilitate early pollution detection and timely greenhouse gas control interventions. The study investigates the effectiveness of AI-Mining in detecting and predicting gas levels, focusing on data collection and analysis. Initial data analysis employed the Autoregressive Moving Average (ARIMA) model, specifically ARIMA (1,1,1), described by the equation yt = 0.0311 - 0.0750yt-1 + 0.3842εt-1. Despite its use, ARIMA's Root Mean Square Error (RMSE) performance was found lacking compared to more advanced methods. Given the classification of the obtained data as big data and time series, the Long Short-Term Memory (LSTM) method was also applied. The LSTM model initially used two layers with tanh and relu activation functions, and its performance was further explored by adding a third layer and varying the number of neurons (64, 128, and 256). The Adam optimizer was consistently used across all LSTM variations. Results indicated that increasing layers and neurons did not significantly impact LSTM's performance, with RMSE values around 0.023. However, LSTM consistently outperformed ARIMA in prediction accuracy, highlighting its robustness and reliability. Consequently, the study recommends using LSTM for predicting other recorded data in AI-Mining, underscoring its superiority in handling complex environmental datasets.
Prototype of Swiftlet Nest Moisture Content Measurement Using Resistance Sensor and Machine Learning Parung, Ratu Anggriani Tangke; Parhusip, Hanna Arini; Trihandaru, Suryasatriya
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 5 (2024): October 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i5.5923

Abstract

Swiftlet nests are highly valued for their health and cosmetic benefits, with moisture content crucial in determining their quality. Traditional moisture measurement methods are often slow and can potentially damage the samples. This study introduces PRORESKA, an innovative system utilizing resistance sensors and Machine Learning (ML) for non-destructive, and real-time moisture measurement. The system incorporates a voltage divider circuit to establish a correlation between resistance data and moisture content. Three mathematical models (linear, exponential, and modulated exponential) and a neural network were employed to predict moisture content. Validation tests conducted on paper and swiftlet nests indicated that the neural network model, enhanced through transfer learning, achieved superior accuracy. The results demonstrated a strong correlation between predicted and actual moisture content (R² = 0.9759), with the neural network model attaining a mean squared error (MSE) of 0.01. This method holds significant potential to improve the efficiency and cost-effectiveness of moisture measurement for swiftlet nests and similar applications.
A Novel Machine Learning for Ethanol and Methanol Classification with Capacitive Soil Moisture (CSM) Sensors Sari, Devina Intan; Trihandaru, Suryasatriya; Parhusip, Hanna Arini
ComTech: Computer, Mathematics and Engineering Applications Vol. 15 No. 2 (2024): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v15i2.12051

Abstract

Although Gas Chromatography (GC) is highly accurate, it is costly, highlighting the need for a more affordable method for alcohol detection. Ethanol and methanol have different evaporation rates and dielectric constants, suggesting the potential for classification as an alternative initial step to GC based on differences in dielectric due to evaporation using Capacitive Soil Moisture (CSM) sensors, although it has not been previously attempted. The research aimed to present a novel machine learning for ethanol and methanol classification with CSM sensors. The method involved placing evaporated samples on CSM plates and measuring the change in evaporative dielectric properties over time. The data were then processed using Python, preprocessing data, splitting data, and training various classifiers with key differentiators based on standard deviation, mean, difference, and cumulative summary. Then, model accuracy was evaluated. The research results show that the approach can distinguish between pure ethanol and methanol based on the dielectric differences in each substance's evaporation rate using machine learning training methods with classifiers such as Random Forest, Extra Trees, Gaussian Naive Bayes, AdaBoost, and Logistic Regression with seven folds in cross-validation, L2 regularization, and Newton-Cholesky solver, with accuracies of 96.67%, 96.67%, 96.67%, 93.33%, and 93.33%, respectively. Although the research is limited to the classification of two types of alcohol, the novel approach can classify methanol and ethanol, leading to a potential initial step in determining alcohol content in the future. It can be an alternative to GC with a simpler and more affordable setup using CSM sensors.
Introduction of Artificial Intelligence to Students Using AIOT-kit Based on ThingSpeak Trihandaru, Suryasatriya; Parhusip, Hanna Arini; Susanto, Bambang; Setiawan, Adi; Nugroho, Didit Budi; Kurniawan, Johanes Dian
SPEKTA (Jurnal Pengabdian Kepada Masyarakat : Teknologi dan Aplikasi) Vol. 5 No. 2 (2024)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/spekta.v5i2.9462

Abstract

Background: Schools struggle to engage students in science and technology, highlighting the need for innovative, tech-driven teaching methods to meet 21st-century educational demands. Contribution: An AIOT kit was developed to introduce middle school students to Artificial Intelligence (AI) and the Internet of Things (IoT). The kit measures environmental factors like temperature, humidity, pressure, and light, providing real-time data. Method: Students received training in mathematical and coding fundamentals, programmed the AIOT kit to collect data, and displayed it on the ThingSpeak dashboard. They also designed and assembled the kit, fostering peer-to-peer learning in future activities. Results: Students visualized data effectively and successfully connected the AIOT kit to the dashboard, confirming its functionality. Conclusion: The project enhanced students' understanding of AI and IoT, providing hands-on learning and boosting engagement in science and technology
LSTM-IOT (LSTM-based IoT) untuk Mengatasi Kehilangan Data Akibat Kegagalan Koneksi Susetyo, Yosia Adi; Parhusip, Hanna Arini; Trihandaru, Suryasatriya; Susanto, Bambang
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 12 No 1: Februari 2025
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2025129157

Abstract

Masalah dalam industri terkait kehilangan data suhu dan kelembaban sering terjadi akibat gangguan perangkat atau hilangnya koneksi. Data ini penting untuk menentukan kelayakan produk yang akan didistribusikan. Untuk mengatasi permasalahan tersebut, dikembangkan inovasi LSTM-IOT, yaitu perangkat IoT yang terintegrasi dengan model Long Short-Term Memory (LSTM) dalam arsitektur Environment Intelligence. Arsitektur ini telah dioptimalkan melalui eksperimen menggunakan berbagai jenis optimizer, seperti Adam, RMSprop, AdaGrad, SGD, Nadam, dan Adadelta. Dari hasil optimasi, kombinasi Nadam Optimizer dengan arsitektur terpilih menunjukkan kinerja unggul dengan nilai Mean Square Error (MSE) sebesar 5,844 x10⁻⁵, Mean Absolute Error (MAE) sebesar 0,005971, dan Root Mean Square Error (RMSE) sebesar 0, 007645. Arsitektur Environment Intelligence versi (a) dengan Nadam Optimizer terbukti paling efektif dalam memproses data sensor, sehingga dipilih untuk integrasi dengan perangkat LSTM-IOT. Implementasi LSTM-IOT dalam skenario dunia nyata dilakukan pada wadah web lokal yang memungkinkan akses real-time ke data suhu dan kelembaban di berbagai lokasi. Halaman web berbasis Streamlit ini menampilkan visualisasi data, performa LSTM, dan hasil prediksi. Uji fungsional menunjukkan bahwa LSTM-IOT memenuhi kebutuhan perusahaan, termasuk penyimpanan data dalam database internal serta prediksi kondisi lingkungan hingga 150 menit ke depan. Dengan fitur prediksi dan pemantauan yang canggih, perangkat ini memberikan solusi efisien dan bernilai tinggi bagi perusahaan dalam memantau kondisi lingkungan secara akurat dan proaktif.   Abstract Problems in the industry related to temperature and humidity data loss are often caused by device interference or loss of connection. This data is important to determine the feasibility of the product to be distributed. To overcome these problems, an LSTM-IOT innovation was developed, namely an IoT device that is integrated with the Long Short-Term Memory (LSTM) model in the Environment Intelligence architecture. This architecture has been optimized through experiments using different types of optimizers, such as Adam, RMSprop, AdaGrad, SGD, Nadam, and Adadelta. From the optimization results, the combination of Nadam Optimizer with the selected architecture shows superior performance with a mean square error (MSE) value of 5.844 x 10⁻⁵, a mean absolute error (MAE) of 0.005971, and a root mean square error (RMSE) of 0.007645. The Environment Intelligence architecture version (a) with Nadam Optimizer proved to be the most effective in processing sensor data, so it was chosen for integration with LSTM-IOT devices. The implementation of LSTM-IOT in real-world scenarios is carried out on a local web container that allows real-time access to temperature and humidity data in various locations. This Streamlit-based webpage displays data visualizations, LSTM performance, and prediction results. Functional tests show that LSTM-IOT meets the needs of the company, including data storage in an internal database and prediction of environmental conditions for up to the next 150 minutes. With advanced prediction and monitoring features, these devices provide efficient and high-value solutions for companies to monitor environmental conditions accurately and proactively.
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 Desman P. Gulo 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 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 Y. Sardjono Yayi Suryo Prabandari Yenusi, Yuni naomi Yohanes Martono Yohanes Sardjono Yohanes Sardjono Yohanes Sardjono Yohanes Sardjono, Yohanes Yohannes Sardjono Yohannes Sardjono Yuliawan, Kristia