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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) dCartesian: Jurnal Matematika dan Aplikasi MATEMATIKA 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 JTAM (Jurnal Teori dan Aplikasi Matematika) International Journal of Computing Science and Applied Mathematics 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
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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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.
Teaching at The Right Level pada Model PBL Berbasis Digital untuk Meningkatkan Hasil Belajar Matematika dan Kesehatan Mental Siswa SMK Mauliddha Rachmi; Nugroho Dwi Susanto; Hanna Arini Parhusip; Fika Widya Pratama
Journal on Education Vol 6 No 4 (2024): Journal on Education: Volume 6 Nomor 4 Mei-Agustus 2024
Publisher : Departement of Mathematics Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/joe.v6i4.6154

Abstract

This research aims to improve mathematics learning outcomes and mental health of class with the digital-based Problem Based Learning model and Teaching at The Right Level approach. This research method is collaborative classroom action research with 3 stages, namely planning, acting and observing, as well as reflecting. The data sources for this research are the results of material knowledge tests and the MHC-SF mental health questionnaire. This research consisted of pre-cycle, cycle I, and cycle II with the percentage of mathematics learning outcomes increasing from 22.2% in the pre-cycle to 83.3% in cycle I and 86.1% in cycle II. Meanwhile, the percentage of student’s mental health in the flourishing category was 19.4% in the pre-cycle, increasing to 50% in cycle I and 69.4% in cycle II.
Model Koreksi Kesalahan pada Data Runtun Waktu Indeks Harga Konsumen Kota-kota di Papua Mitha Febby R. Donggori; Adi Setiawan; Hanna Arini Parhusip
d\'Cartesian: Jurnal Matematika dan Aplikasi Vol. 3 No. 1 (2014): Maret, 2014
Publisher : Sam Ratulangi University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35799/dc.3.1.2014.4011

Abstract

Abstract The Consumer Price Index is used as a measure of inflation. Consumer Price Index data is time series data are often not stationary, causing decision-making related to the data becomes invalid. Consumer Price Index has a different rate of change in each region, as well as for the city of Jayapura, Sorong and Manokwari in Papua. In this paper, Error Correction Model is used to correct short-term imbalances and establish a long term relationship models Consumer Price Index cities - cities in Papua. We use time period : January 2009 to May 2013. To test stationarity  of the data, we use Phillips - Perron unit root test. Engle - Granger cointegration test is performed to determine whether there is a long-term relationship among cities in Papua. Furthermore, the model established by using the Error Correction Method by Domowitz - Elbadawi to correct short- term imbalances and establish long-term relationships model. The obtained Error Correction Models were compared to the results obtained with the bootstrap method . . Keywords : consumer price index, stationarity test, co integration test, error correction model, the bootstrap method Abstrak Indeks Harga Konsumen digunakan sebagai tolok ukur inflasi. Data Indeks Harga Konsumen merupakan data runtun waktu yang seringkali tidak stasioner sehingga menyebabkan pengambilan keputusan yang berkaitan dengan data menjadi tidak valid. Indeks Harga Konsumen memiliki tingkat perubahan yang berbeda di setiap daerah, begitu juga untuk kota Jayapura, Sorong dan Manokwari di Papua. Model koreksi kesalahan digunakan untuk mengoreksi ketidakseimbangan jangka pendek dan membentuk model hubungan jangka panjang Indeks Harga Konsumen kota – kota di Papua pada makalah ini. Periode waktu yang diamati adalah bulan Januari 2009 sampai dengan bulan Mei 2013. Uji stasioneritas data dengan uji akar unit Phillips-Perron, uji kointegrasi Engle-Granger yang dilakukan untuk mengetahui ada tidaknya hubungan jangka panjang di antara kota – kota tersebut. Lebih lanjut, dibentuk model koreksi kesalahan dengan metode Domowitz-Elbadawi untuk mengoreksi ketidakseimbangan jangka pendek dan membentuk model hubungan jangka panjang. Model koreksi kesalahan yang diperoleh dibandingkan dengan hasil yang diperoleh dengan metode bootstrap.   Kata kunci: indeks harga konsumen, uji stasioneritas, uji kointegrasi, model koreksi kesalahan, metode bootstrap
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
Model Biaya Garansi dengan Peubah Acak Skala Komposit Sederhana sebagai Model Reduksi Biaya Garansi Dua Dimensi dengan Strategi Penggantian Fitri, Nirmala Ayu Andika; Sasongko, Leopoldus Ricky; Parhusip, Hanna Arini
Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya 2019: Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (553.071 KB)

Abstract

Penelitian ini mempelajari tentang bagaimana memperoleh model biaya garansi dengan peubah acak skala komposit sederhana sebagai reduksi model biaya garansi dua dimensi. Peubah acak skala komposit sederhana dapat mereduksi model biaya garansi dua dimensi menjadi model biaya garansi satu dimensi yang selanjutnya estimasi biaya garansi dapat diperoleh berdasarkan ekspektasi banyak kegagalan model yang dihitung melalui metode MeVTI (Mean Value Theorem for Integrals). Data yang dibahas dalam penelitian ini adalah data penggantian komponen spark plug pada produk mobil, dimana data didapatkan dari rekaman data servis mobil. Hasil penelitian, yang berdasarkan penghitungan taksiran parameter distribusi melalui MLE (Maximum Log Likelihood) dan tes uji kecocokan Kolmogorov-Smirnov untuk beberapa model distribusi, menunjukkan bahwa model distribusi Burr memiliki p-value terbesar sehingga dapat dikatakan model distribusi Burr terbaik dari model distribusi yang lainnya. Model distribusi Burr sebagai bentuk dari perilaku data, sedangkan nilai MSE (Mean Square Error) dari ekspektasi banyak kegagalan yang diperoleh melalui model distribusi Lognormal adalah yang terkecil.
Co-Authors A.A. Ketut Agung Cahyawan W Adi Setiawan Adi Setiawan Adrianus Herry Heriadi Alfagustina, Yumita Cristin ALOYSIUS JOAKIM FERNANDEZ Ambat, Jordi Enal Ariany Mahastanti, Linda Atyanta Nika Rukmasari Bambang Susanto Bambang Susanto Beni Utomo Bernadus Aryo Adhi Wicaksono Carolina Febe Ronicha Putri Denny Indrajaya Denny Indrajaya Didit Budi Nugroho Djoko Hartanto Djoko Hartanto Endang Warsiki Fachrurrozi Fachrurrozi Fachrurrozi Faldy Tita Fetriks Theo Sarita Fika Widya Pratama Fitri, Nirmala Ayu Andika Goni, Abdiel Wilyar Hariadi, Adrianus Herry Heriadi, Adrianus Herry Hindriyanto Dwi Purnomo Indrajaya, Denny Istiarsi Saptuti Sri Kawuryan Istiarsih Saputri Sri Kawuryan Jane Labadin Johanes Dian Kurniawan Johanes Dian Kurniawan Karina Bianca Lewerissa Kristia Anggraeni Kristoko Dwi Hartomo Kurniawan, Johanes Dian Lea, Lea Leopoldus Ricky Sasongko Lilik Linawati Linda Ariany Mahastanti Mauliddha Rachmi Mitha Febby R. Donggori Mitha Febby R. Donggori Nafisah Riskya Hasna Nugroho Dwi Susanto 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 Veny M Ningtyas Wijaya, Melina Tito Wijayanti, Yunita Puput Winarto, Eduardus Albert Wulandari, Nadya Putri Yohanes Sardjono Yohanes Sardjono Yohanes Sardjono, Yohanes Yusuf Kurniawan