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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) dCartesian: Jurnal Matematika dan Aplikasi MATEMATIKA JURNAL SISTEM INFORMASI BISNIS 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 Pendas : Jurnah Ilmiah Pendidikan Dasar JTAM (Jurnal Teori dan Aplikasi Matematika) 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 International Journal of Computing Science and Applied Mathematics-IJCSAM
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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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.
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 A.A. Ketut Agung Cahyawan W Adi Setiawan Adi Setiawan Adrianus Herry Heriadi Adrianus Herry Heriadi Alfagustina, Yumita Cristin ALOYSIUS JOAKIM FERNANDEZ Atyanta Nika Rukmasari Bambang Susanto Bambang Susanto Beni Utomo Bernadus Aryo Adhi Wicaksono Carolina Febe Ronicha Putri Denny Indrajaya Denny Indrajaya Didit Budi Nugroho Didit Budi Nugroho Didit Budi Nugroho Djoko Hartanto Djoko Hartanto Eduardus Albert Winarto Endang Warsiki Fachrurrozi Fachrurrozi Fachrurrozi Fachrurrozi Faldy Tita Fetriks Theo Sarita Fika Widya Pratama Fitri, Nirmala Ayu Andika Gede Sutresna Wijaya Goni, Abdiel Wilyar Hariadi, Adrianus Herry Heriadi, Adrianus Herry Hindriyanto Dwi Purnomo Indrajaya, Denny Isman Mulyadi Triatmoko Istiarsi Saptuti Sri Kawuryan Istiarsih Saputri Sri Kawuryan Jane Labadin Jane Labadin Johanes Dian Kurniawan Johanes Dian Kurniawan Johanes Dian Kurniawan Jordi Enal Ambat Karina Bianca Lewerissa Karina Bianca Lewerissa Kristia Anggraeni Kristoko Dwi Hartomo Larasati, Mitchella Sinta Lea, Lea Leopoldus Ricky Sasongko Lilik Linawati Linda Ariany Mahastanti Mauliddha Rachmi Melina Tito Wijaya Mitchella Sinta Larasati Mitha Febby R. Donggori Mitha Febby R. Donggori Nafisah Riskya Hasna Nugroho Dwi Susanto Nugroho, Didit B. 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 Urosidin, Nur I. M. Veny M Ningtyas Veny M. Ningtyas Wijaya, Melina Tito Wijayanti, Yunita Puput Wulandari, Nadya Putri Yohanes Sardjono Yohanes Sardjono Yohanes Sardjono Yohanes Sardjono, Yohanes Yohannes Sardjono Yosia Adi Susetyo Yusuf Kurniawan