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All Journal Faktor Exacta Journal of Novel Engineering Science and Technology IPSSJ
Irwansyah Saputra
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Public Health Social Sciences

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 1 
SISTEM BERBASIS CLOUD COMPUTING UNTUK IDENTIFIKASI RESEP DOKTER “BARSEP” Irwansyah Saputra; ANDI SARYOKO; GANDA WIJAYA; MEILYNDA TRISIANA; ASEP MULYANA; DANDI YUSBIAL BAYANI; DHARMA WINATA; VILSAFA KHOIRUNNISAK
Faktor Exacta Vol 13, No 4 (2020)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v13i4.7569

Abstract

A doctor's prescription is a doctor's written request to the pharmacist to prepare and give medicine to the patient. Prescriptions are made according to the needs of the patient after the doctor has examined and diagnosed the patient. However, doctor’s writing on a prescription that considered unclear can cause errors when compounding / preparing the drug and using prescribed drugs. In fact, the cure rate and life expectancy of patients is directly proportional to the administration of the right medicine. This study aims to prevent errors in the process of identification of prescription drugs by pharmacists. The technology used is cloud computing with the PHP 7.1.3 programming language, Laravel framework, and database storage using MySQL. BarSep application works by adding QR Code on recipe paper. The QR Code contains patient examination information including patient data, prescription drugs, and diagnoses, so that when the pharmacist scans the QR Code, the system will display all patient information that has been inputted by the doctor at the time of the examination. The results obtained from the implementation of the BarSep application at the Rapha Farma Pharmacy is BarSep applications effective for tackling errors in reading doctor's prescriptions that can save patients from medication errors. A doctor's prescription is a doctor's written request to the pharmacist to prepare and give medicine to the patient. Prescriptions are made according to the needs of the patient after the doctor has examined and diagnosed the patient. However, doctor’s writing on a prescription that considered unclear can cause errors when compounding / preparing the drug and using prescribed drugs. In fact, the cure rate and life expectancy of patients is directly proportional to the administration of the right medicine. This study aims to prevent errors in the process of identification of prescription drugs by pharmacists. The technology used is cloud computing with the PHP 7.1.3 programming language, Laravel framework, and database storage using MySQL. BarSep application works by adding QR Code on recipe paper. The QR Code contains patient examination information including patient data, prescription drugs, and diagnoses, so that when the pharmacist scans the QR Code, the system will display all patient information that has been inputted by the doctor at the time of the examination. The results obtained from the implementation of the BarSep application at the Rapha Farma Pharmacy is BarSep applications effective for tackling errors in reading doctor's prescriptions that can save patients from medication errors.                                                 
Analisis Sentimen Pengguna Marketplace Bukalapak dan Tokopedia di Twitter Menggunakan Machine Learning Irwansyah Saputra; RAHMAD SINGGIH AJI PAMBUDI; HANAFI EKO DARONO; FACHRI AMSURY; MUHAMMAD RIZKI FAHDIA; BENNI RAMADHAN; ANGGIE ARDIANSYAH
Faktor Exacta Vol 13, No 4 (2020)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v13i4.7074

Abstract

      A collection of tweets from Twitter users about Marketplace Bukalapak and Tokopedia can be used as a sentiment analysis. The data obtained is processed using data mining techniques, in which there is a process of mining the text, tokenize, transformation, classification, stem, etc. Then calculated into three different algorithms to be compared, the algorithm used is the Decision Tree, K-NN, and Naïve Bayes Classifier with the aim of finding the best accuracy. Rapidminer application is also used to facilitate writers in processing data. The highest results from this study are Decision Tree algorithm with 82% accuracy, 81.95% precision and 86% recall.
Text Mining of PeduliLindungi Application Reviews on Google Play Store Irwansyah Saputra; Taufik Djatna; Riki Ruli A. Siregar; Dinar Ajeng Kristiyanti; Hasbi Rahma Yani; Andri Agung Riyadi
Faktor Exacta Vol 15, No 2 (2022)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v15i2.10629

Abstract

Aplikasi PeduliLindungi merupakan aplikasi buatan pemerintah indonesia  untuk melakukan pelacakan dan penghentian  penyebaran Covid-19. Ulasan terkait aplikasi tersebut tidak seluruhnya baik, hal ini dibuktikan dengan beragamnya peringkat bintang yang diberikan pengguna sehingga terjadinya kesulitan dalam melihat sentimen positif atau negatif terkait aplikasi tersebut. Penelitian ini bertujuan untuk mengklasifikasi ulasan mengenai aplikasi PeduliLindungi kepada dua kelas, yakni sentimen positif dan sentimen negatif. Algoritma klasifikasi yang digunakan adalah klasifikasi Naive Bayes Classifier (NBC). Hasil Menunjukkan Accuracy  85%, Precision 77,7%, Recall 98%, dan F1-Score 86,7%.
TRANSPARANSI KEUANGAN PEMERINTAH DAERAH SUMBAWA: MENGHADAPI TANTANGAN DAN MENEMUKAN SOLUSI DALAM AKUNTANSI SEKTOR PUBLIK Irwansyah Saputra; Wiyanda Anggraini; Risma Adekantari; Selfi Pebrianti; Farel Nasril Ilham; Rizky Kamula
Integrative Perspectives of Social and Science Journal Vol. 2 No. 03 Juni (2025): Integrative Perspectives of Social and Science Journal
Publisher : PT Wahana Global Education

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Transparansi keuangan publik merupakan aspek penting dalam mewujudkan tata kelola pemerintahan daerah yang baik, akuntabel dan responsif. Tujuan dari penelitian ini adalah untuk mempelajari masalah dan solusi yang ada dalam meningkatkan transparansi keuangan di Kabupaten Sumbawa dengan menggunakan metode akuntansi sektor publik. Metode yang digunakan adalah studi pustaka dengan pendekatan deskriptif-kualitatif yang menganalisis literatur dan data sekunder dari berbagai jurnal dan dokumen relevan. Hasil penelitian menunjukkan bahwa beberapa tantangan utama yang dihadapi termasuk kurangnya pengetahuan tentang keuangan aparatur, keterbatasan sistem informasi pengelolaan keuangan daerah, dan kurangnya fungsi pengawasan internal dan eksternal. Selain itu, masyarakat masih kurang terlibat dalam proses anggaran. Solusi untuk masalah ini mencakup peningkatan kapasitas sumber daya manusia melalui pelatihan berkelanjutan, penggunaan teknologi informasi untuk mengelola dan menyampaikan data keuangan, dan memperkuat mekanisme akuntabilitas publik dengan mengacu pada akuntansi sektor publik. Solusi ini diharapkan dapat meningkatkan transparansi dan partisipasi dalam tata kelola keuangan daerah.
Implementation of Knowledge-Based Graph Neural Networks for Reasoning and Ranking Medical Entities from CORD-19 Texts Agus Rahmat Fadillah; Irwansyah Saputra
Journal of Novel Engineering Science and Technology Vol. 4 No. 03 (2025): Forthcoming Issue - Journal of Novel Engineering Science and Technology
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/jnest.v4i02.1049

Abstract

The rapid growth of biomedical literature, espe- cially during the COVID-19 pandemic, has introduced new challenges in retrieving clinically relevant information using conventional search methods. This study proposes a novel, interpretable framework for biomedical information retrieval that integrates Named Entity Recognition (NER), knowledge graph construction, and Graph Neural Networks (GNNs) to support semantic reasoning and entity-level ranking. Unlike prior biomedical retrieval systems that operate at document level or perform link prediction over KGs, our framework introduces a novel task formulation contextual entity-level ranking powered by graph-based semantic reasoning. Leveraging the CORD-19 dataset, the system filters abstracts based on user queries, extracts domain-specific entities using SciSpacy, and constructs a semantic graph that captures co-occurrence relationships among medical concepts. A Graph Convolutional Network (GCN) is then employed to prop- agate relevance signals across the graph, enabling context- aware entity ranking. Experimental evaluations using queries such as ”pneumonia” and ”cough” demonstrate superior performance over traditional IR baselines like TF-IDF and BM25, achieving a Mean Average Precision (MAP) of 0.95 and Precision@3 of 1.00. The results confirm the system’s effectiveness in identifying semantically meaningful biomed- ical entities while offering enhanced transparency through graph-based visualizations. This work contributes a scalable and extensible approach to biomedical search and lays the foundation for intelligent literature exploration in medical research and clinical decision support.
Co-Authors Agus Rahmat Fadillah Andi Saryoko Andri Agung Riyadi ANGGIE ARDIANSYAH ASEP MULYANA BENNI RAMADHAN DANDI YUSBIAL BAYANI DHARMA WINATA Dinar Ajeng Kristiyanti Fachri Amsury Farel Nasril Ilham Ganda Wijaya HANAFI EKO DARONO Hasbi Rahma Yani MEILYNDA TRISIANA MUHAMMAD RIZKI FAHDIA RAHMAD SINGGIH AJI PAMBUDI Riki Ruli A. Siregar Risma Adekantari Rizky Kamula Selfi Pebrianti Taufik Djatna VILSAFA KHOIRUNNISAK Wiyanda Anggraini