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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) 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) 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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Pembelajaran Pemodelan Realistik dengan Fungsi Kuadratik Dua Variabel Parhusip, Hanna Arini
SJME (Supremum Journal of Mathematics Education) Vol 3 No 2 (2019): July 2019
Publisher : Fakultas Keguruan dan Ilmu Pendidikan Universitas Singaperbangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35706/sjme.v3i2.1898

Abstract

dalam melakukan optimasi produksi kedelai Jawa Tengah tahun 2000-2015.Siswa pada umumnya mengenal fungsi kuadratik satu variabel dimana parameter telah diketahui. Sedangkan novelty pada penelitian ini ditunjukkan bagaimana siswa mengenal langsung cara menyusun fungsi kuadratik multivariabel dan parameter fungsi harus dicari berdasarkan data. Proses dimulai dengan memberikan bentuk umum fungsi kuadratik dan kendala yang mungkin terjadi sebagai batasan. Demikian pula penyusunan fungsi Lagrange dijelaskan untuk memberikan penjelasan kepada pembaca cara memproses optimasi secara manual. Data merupakan luas panen,  luas produksi dan produktivitas kedelai pada periode 1, periode 2, periode 3 penanaman sepanjang tahun 2000-2015. Dengan menggunakan pengetahuan menyusun turunan dari fungsi Lagrange yang dibentuk dimana turunan harus nol pada solusi kritis, maka proses pencarian solusi optimal dapat dilakukan.
Pembelajaran Vektor Untuk Klasifikasi Data Pada Bidang Parhusip, Hanna Arini; Susanto, Bambang; Linawati, Lilik; Trihandaru, Suryasatriya; Sardjono, Yohanes
SJME (Supremum Journal of Mathematics Education) Vol 4 No 2 (2020): Supremum Journal of Mahematics Education
Publisher : Fakultas Keguruan dan Ilmu Pendidikan Universitas Singaperbangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35706/sjme.v4i2.3515

Abstract

Tujuan penelitian ini adalah penyusunan hyperplane untukmemisahkan data yang mempunyai 2 kelas dan bersifat linear padabidang datar sebagai pembelajaran vektor untuk klasifikasi data.Adapun metode yang digunakan adalah pre-Support Vector Machine(SVM). Metode ini mencari garis (hyperplane) terbaik yangmemisahkan data dan memberi ruang antar 2 kelas data dimana ruangpemisah tersebut tidak boleh memuat data serta ruang tersebutmerupakan margin maksimal. Langkah awal adalah menduga garispemisah (hyperplane) awal melalui titik O. Dengan mengambil salahsatu titik data yang menjadi titik referensi, disusun vektor dari Oterhadap titik referensi dan garis melalui titik referensi sebagai bataspertama margin. Kemudian dibentuk vektor arah dari titik O yangtegak lulus terhadap garis awal (hyperplane). Selanjutnya vektorproyeksi dibentuk dari titik referensi terhadap vektor arah sehinggavektor arah dan vektor proyeksi berhimpit (searah). Penyusunanmargin diperoleh dengan menyusun garis yang pararel terhadap garisawal sebagai hyperplane serta berjarak 2 kali dengan panjang vektorproyeksi tersebut. Hyperplane terbaik diperoleh secara manual denganmengatur batas kedua dari margin yang diperoleh dengan menggambargaris melalui suatu titik data pada kelas ke-2 dengan jarak terdekat danpararel terhadap garis yang melalui titik referensi dari data kelas ke-1.
Human Capital Decision Intelligence (HCDI) architecture in microbiology laboratory based on machine learning and operations research models Trihandaru, Suryasatriya; Susetyo, Yosia Adi; Parhusip, Hanna Arini; Susanto, Bambang
International Journal of Advances in Intelligent Informatics Vol 11, No 4 (2025): November 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i4.1676

Abstract

The Human Capital Decision Intelligence (HDCI) system integrates human-computer interaction in a microbiology laboratory that uses machine learning and operational research to classify new tasks and then recommend assignments to each person. The models evaluated in building this system are Support Vector Machine, Gaussian Naive Bayes, Multinomial Logistic Regression, and Artificial Neural Network. The results of the research show that the ANN model is the most consistent and reliable across various training ratios, as indicated by the model's goodness parameters. The selected ANN model is combined with a linear programming approach to optimize workload distribution. The integrated system successfully manages new job scenarios and recommends staff based on competencies and availability. It also ensures assignments do not exceed maximum workload limits and finds alternatives when key staff are unavailable. The implementation of the HDCI system has a positive impact on various factors, including the fair distribution of tasks, enhanced staff performance monitoring, and significantly improved operational efficiency and human resource management in the microbiology laboratory. The system is designed to be easy to use and support collaboration between laboratory staff and computational models. The system is not only advanced in supporting personnel management decision-making, but it can also demonstrate how artificial intelligence and operations research systems can be combined to address the needs of the microbiology laboratory environment.
Data Exploration Using Tableau and Principal Component Analysis Parhusip, Hanna Arini; Trihandaru, Suryasatriya; Heriadi, Adrianus Herry; Santosa, Petrus Priyo; Puspasari, Magdalena Dwi
JOIV : International Journal on Informatics Visualization Vol 6, No 4 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.4.952

Abstract

This study aims to determine the dominant chemical elements that may improve the monitoring of the productivity and efficiency of heavy engines in 2015-2021 in the company. The method used is usually Scheduled Oil Sampling. This article proposes a new approach. The research problems are analyzing the recorded chemical elements that are produced by heavy engines and visualizing them through the Tableau program. The basic design of the study is learning the given data after visualization and using the Principal Component Analysis. This method is to obtain chemical elements that affect engine wear during each engine's use in the 2015-2021 period. Because there are three categories in each element in the oil sample, namely wear metals, contaminants, and oil additives, a technique is needed to obtain these elements using Principal Component Analysis. Therefore, Oil Sampling Analysis through data exploration using Tableau resulted in a new approach to data analysis of elements recorded by heavy vehicles. The main findings as a result of the analysis are given by the visualization of Tableau, in which there are five machines analyzed to obtain the main components that cause engine wear. From the visualization results, it is shown that there is one engine coded MSD 012 that experienced wear and tear in 2018 and 2019. This shows where two main components, Ca and Mg, dominate engine wear. These results have been confirmed with the related companies. The company then carried out further studies on the machine to get special treatment because of these results.
Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Principal Component Analysis (PCA) for Particulate Matter (PM) Anomaly Detection Hanna Arini Parhusip; Suryasatriya Trihandaru; Bambang Susanto; Johanes Dian Kurniawan; Adrianus Herry Heriadi; Petrus Priyo Santosa; Yohanes Sardjono
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol. 15 No. 02 (2024): Vol. 15, No. 2 August 2024
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2024.v15.i02.p01

Abstract

This research addresses a critical issue in industrial environments: air quality, specifically regarding PM 1.0 and PM 2.5. High concentrations of these particles pose significant health risks. The study measures temperature, humidity, pressure, altitude, PM 1.0, and PM 2.5 and shows the effectiveness of using AIOT-Particle devices to analyze these features with Principal Component Analysis (PCA). Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is used to detect anomalies during the observation period. Anomalies occur when the altitude ranges from 65 to 70 units, according to PM 1.0 and PM 2.5 values. The positions where anomalies occur are illustrated based on altitude, temperature, pressure, and concentration. The results demonstrate that altitude dominates as the first feature. Finally, the research concludes that altitude, PM 1.0, and PM 2.5 are the dominant features. The study confirms the effectiveness of PCA and recommends using these three features for anomaly detection in DBSCAN. Overall, the research highlights the novelty and success of AIOT-Particle in industrial environments.
PRELIMINARY MATHEMATICAL MODEL FOR CANCER TREATMENT USING BORON NEUTRON CANCER THERAPY (BNCT) Trihandaru, Suryasatriya; Parhusip, Hanna Arini; Sardjono, Yohannes; Triatmoko, Isman Mulyadi; Wijaya, Gede Sutresna; Labadin, Jane
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.
SISTEM OTOMATIS KLASIFIKASI BUKTI PEMBAYARAN MENGGUNAKAN OCR DAN EMBEDDING BERT DENGAN PENDEKATAN MULTI-MODEL PEMBELAJARAN MESIN Larasati, Mitchella Sinta; Suryasatriya Trihandaru; Hanna Arini Parhusip
Pendas : Jurnal Ilmiah Pendidikan Dasar Vol. 11 No. 01 (2026): Volume 11 No. 01 Maret 2026 Published
Publisher : Program Studi Pendidikan Guru Sekolah Dasar FKIP Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/jp.v11i01.40994

Abstract

The verification process of payment receipts in school environments is still predominantly conducted manually, leading to inefficiency and a high potential for human error. This study proposes an automated system for classifying the validity of digital payment receipts by combining Optical Character Recognition (OCR), BERT (Bidirectional Encoder Representations from Transformers) embeddings, and multi-model machine learning approaches. The system integrates EasyOCR for text extraction from payment receipts, BERT for generating semantic text representations, and four classification algorithms: Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), Naive Bayes (NB), and Logistic Regression (LR). The dataset consists of 185 payment receipt samples, comprising 149 valid and 36 invalid instances, collected via Google Forms and stored in a SQLite database. Experimental results demonstrate that the Multi-Layer Perceptron (MLP) model achieves the highest accuracy of 97% with a test size of 0.2, followed by Logistic Regression with an accuracy of 96.2%, while Naive Bayes exhibits the lowest performance with an accuracy of 85.7%. The proposed system is successfully implemented in a Streamlit-based application, enabling real-time verification of payment receipts with an average processing time of 1.16 seconds per sample.
Co-Authors A.A. Ketut Agung Cahyawan W Adi Setiawan Adi Setiawan Adrianus Herry Heriadi Alfagustina, Yumita Cristin ALOYSIUS JOAKIM FERNANDEZ 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 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, Isman Mulyadi Istiarsi Saptuti Sri Kawuryan Istiarsih Saputri Sri Kawuryan Jane Labadin Johanes Dian Kurniawan Johanes Dian Kurniawan Karina Bianca Lewerissa Kristoko Dwi Hartomo Kurniawan, Johanes Dian Larasati, Mitchella Sinta 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 Yohannes Sardjono Yusuf Kurniawan