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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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Smart Catering Canteen School (SCCS) using Streamlit Suryasatriya Trihandaru; Hanna Arini Parhusip; Mitchella Sinta Larasati
Jurnal Sistem Informasi Bisnis Vol 16, No 1 (2026): Volume 16 Number 1 Year 2026 (In Press)
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/vol15iss4pp%p

Abstract

The school canteen faced service problems of as many as 1000 students in a short break period, especially when everything had to be done manually on business processes, especially payments. The method that has existed so far is to use Google Form to place menu orders and manually pay all verified customers which causes delays and errors. Therefore, this study aims to create a business information system for canteens called the Smart Canteen System (SCCS) which uses Optical Character Recognition (OCR) and Natural Language Processing (NLP) to be able to automate payment verification and provide sequences. This SCCS business information system will convert proof of payment from text to text and processed so that the validity of the proof of payment can be proven. With the Stremlit platform, the management process can be carried out in real time and reports can be carried out immediately. With this verification, SCCS provides the main result, namely efficiency, reducing errors in business processes in the canteen. Work that was originally done manually in 2 days became 5-10 minutes in the same process.
Performance of an AIOT-Particle Device for Air Quality and Environmental Data Prediction in Salatiga Area Using ARIMA Model Johanes Dian Kurniawan; Suryasatriya Trihandaru; Hanna Arini Parhusip
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.
Management of Traditional Business into Modern: from Microsoft Excel to Deep Learning for prototyping classification Swiftlet’s nests Hanna Arini Parhusip; Suryasatriya Trihandaru; Kristoko Dwi Hartomo; Karina Bianca Lewerissa; Linda Ariany Mahastanti; Djoko Hartanto
International Journal Of Community Service Vol. 4 No. 2 (2024): May 2024 (Indonesia - Ethiopia )
Publisher : CV. Inara in Colaboration with www.stie-sampit.ac.id

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.
AI-Enhanced Production Planning: Integrating LSTM Forecasting with Linear Programming Eduardus Albert Winarto; Hanna Arini Parhusip; Suryasatriya Trihandaru
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.
Analisis Resolusi Spasial Citra Ultrasonografi (USG) pada Arah Tangensial Radias Citra menggunakan Phantom Berbasis Silicon Rubber: Victory Immanuel Ratar, Surya Suryasatriya Trihandaru, Giner Maslebu Victory Immanuel Ratar Victory Immanuel Ratar; Surya Suryasatriya Trihandaru Surya Suryasatriya Trihandaru; Giner Maslebu Giner Maslebu
Jurnal Fisika dan Aplikasinya Vol 16 No 1 (2020): January 2020 Edition
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat, LPPM-ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24604682.v16i1.4287

Abstract

Ultrasound is widely used in diagnostic imaging, therefore quality control(QC) of ultrasound images is important. One of the QC parameter is spatial resolution that can be analyzed by calculating the distance between two nearby object in the tangential direction of radiation. The image that is produced by the Curved Array transducer will produce a circular shaped image and the coordinates of the object isn’t in the cartesian coordinate system because it adapts to the surface of the transducer. The image must be transformed with a circle equation approach to calculate the distance between two nearby object. This study was using a Mindray 3D ultrasound model: DP-10 with Transducer model: 35C50EB, and phantom instruments made from a mixture of 99 ml silicon rubber and 1 ml catalyst. The acquisition data used a fixed frequency of 4.5 MHz with a gain variation of 168 dB, 182 dB, 202 dB and a depth variation of 3.3 cm, 4.9 cm, 5.7 cm. The result found that the measurement of objects in A area had an error of 0.8963%(0.087 mm),B area was 1.2979%(0.0779 mm), and C area was 2.6296%( 0.1183 mm). The values obtained still meet the standards set by American Association of Physicist in Medicine(AAPM).
PRELIMINARY MATHEMATICAL MODEL FOR CANCER TREATMENT USING BORON NEUTRON CANCER THERAPY (BNCT) Suryasatriya Trihandaru; Hanna Arini Parhusip; Yohannes Sardjono; Isman Mulyadi Triatmoko; Gede Sutresna Wijaya; Jane Labadin
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1283-1300

Abstract

This article outlines a revolutionary approach to immunotherapy and stem-cell cancer treatments that leverages Boron Neutron Cancer Therapy (BNCT). We formulated two models, one being the immunotherapy-BNCT model and the other featuring a stem-cell model and BNCT therapy. The former simulates the dynamics of the concentration of BNCT with anticancer properties present at the cancer site, the number of cancer cells, and the blood drug concentration, while considering periodicity. Similarly, using boronophenylalanine in the simulation, our stem-cell BNCT model evaluates the drug’s impact on the dynamics of cancer cells, stem cells, effector cells, and BNCT involvement. Using the eigenvalues of the Jacobian matrix calculated from those solutions, each model is examined for the stability of equilibrium solutions. Next, the equilibrium solution is generated and found to be unstable using the simulation parameters given in the literature. Furthermore, one of the equilibrium solutions has a zero-value variable, rendering it practically meaningless. The models have impacted the new approach to utilizing BNCT in immunotherapy and stem-cell therapy, underscoring the need for follow-up in developing stable and balanced model parameters. Such efforts will improve the existing model while also yielding positive results from the BNCT approach.
Studi Komparatif Penerapan Machine Learning Model Dalam Prediksi Harga Rumah Di Wilayah Jabodetabek Fachrurrozi; Trihandaru, Suryasatriya; Parhusip, Hanna Arini
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 13 No 4: Agustus 2026
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.134

Abstract

Penelitian ini bertujuan untuk menguji dan membandingkan efektifitas berbagai model machine learning dalam memprediksi harga rumah di wilayah Jabodetabek, yang merupakan kawasan dinamis dan berkembang pesat di Indonesia. Data dikumpulkan dari marketplace properti di Indonesia dan dilengkapi dengan indikator sosial-ekonomi dari Badan Pusat Statistik (BPS). Penelitian ini menguji lima model yaitu Ridge Regression, Lasso Regression, Elastic Net Regression, Artificial Neural Network (ANN), dan Random Forest (RF) dengan fokus pada kinerja masing-masing di berbagai transformasi data. Di antara kelima model tersebut, Random Forest menunjukkan kinerja paling unggul dengan nilai R² sebesar 0,8715 pada skala log-transformed dan 0,8242 pada skala asli, yang masing-masing menjelaskan sekitar 87% dan 82% variasi harga perumahan. Faktor-faktor penentu utama, seperti luas bangunan, luas tanah, dan lokasi, diidentifikasi sebagai variabel yang paling berpengaruh. Hasil penelitian ini memberikan wawasan berharga bagi pengembang properti, investor, dan pembuat kebijakan untuk menyusun strategi dalam pasar perumahan.   Abstract This study aims to evaluate and compare the effectiveness of various machine learning models in predicting housing prices in the Jabodetabek region, a dynamic and rapidly developing area in Indonesia. Data were collected from Indonesia property marketplace and supplemented with socio-economic indicators from the Central Bureau of Statistics (BPS). The research examines five models—Ridge Regression, Lasso Regression, Elastic Net Regression, Artificial Neural Network (ANN), and Random Forest (RF)—with particular attention to model performance under different data transformations. Among these, the Random Forest model demonstrated superior performance, achieving an R² of 0.8715 on the log-transformed scale and 0.8242 on the original scale, thereby explaining approximately 87% and 82% of the variance in housing prices, respectively. Key determinants of housing prices, such as building area, land area, and location, were identified as the most influential factors. The findings offer valuable insights for property developers, investors, and policymakers to formulate more informed strategies in the housing market.
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