Article Per Year (5 Year)
p-Index From 2020 - 2025
1.405
P-Index
This Author published in this journals
All Journal SEIKO : Journal of Management & Business JUKI : Jurnal Komputer dan Informatika Journal of Artificial Intelligence and Engineering Applications (JAIEA) Repeater: Publikasi Teknik Informatika dan Jaringan Merkurius: Jurnal Riset Sistem Informasi dan Teknik Informatika Saturnus: Jurnal Teknologi dan Sistem Informasi
Muammar Khadapi
Unknown Affiliation
Automotive Engineering Computer Science & IT Control & Systems Engineering Social Sciences

Published : 8 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Merkurius: Jurnal Riset Sistem Informasi dan Teknik Informatika

 1 
Analisis Sentimen Masyarakat terhadap Program Makan Siang Gratis di Indonesia Tahun 2024 Menggunakan Long Short-Term Memory (LSTM) Silvia Amara; Novriyenni, Novriyenni; Muammar Khadapi
Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika Vol. 3 No. 4 (2025): Juli : Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/merkurius.v3i4.930

Abstract

The free lunch program is a goverment initiative aimed at addressing the issue of stunting in Indonesia. This program focuses on toddlers, school-age children and pregnant women. Various opinions have emerged from the public regarding this initiative, especially through sosial media platform X (Twitter) and news portals. In this research, sentiment analysis was conducted to understand public responses to the program, whether they are positive, neutral or negative. To evaluate the accuracy of the sentiment analysis perfomed, a deep learning approach was applied using the Long Short-Term Memory (LSTM) algorithm. The results show that public sentiment varies responses, on social media X tend to be negative, while those on news portals tend to be positive toward the free lunch program in Indonesia. Through LSTM-based testing, sentiment analysis on tweet data achieved an accuracy of 88.6%, with a precision of 84.6%, recall of 88.6% and an F1-Score of 86.3%. Meanwhile, sentiment analysis on news portal data reached an accuracy of 89%, with a precision of 81.7%, recall of 89% and an F1-Score of 85.1%.
Diagnosa Penyakit Syndrome pada Anak menggunakan Metode Case Base Reasoning (CBR) Amysa Putri Sitepu; Novriyenni Novriyenni; Muammar Khadapi
Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika Vol. 3 No. 6 (2025): November: Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/merkurius.v3i6.1139

Abstract

Syndrome is a serious problem in children's health because it has a major impact on growth and development, especially in terms of intelligence and daily activities. Down Syndrome, as one of the most well-known chromosomal disorders, is often the main cause of intellectual developmental disorders, hypotonia, facial dysmorphism, early onset of Alzheimer's disease, and various behavioral disorders. Diagnosing syndrome diseases in children is often difficult due to complex and varied symptoms, requiring lengthy, costly, and time-consuming medical evaluations. This study aims to design a Case-Based Reasoning (CBR)-based expert system for diagnosing syndromes in children, which is expected to help accelerate the disease identification process and provide more effective and efficient solutions. The method used is the development of an expert system with a CBR approach, in which the system performs calculations and matching based on the symptoms selected by the user against the available case base. The results of the study show that from symptom inputs such as wide hands with short fingers, short stature, small head, stunted growth, small lower jaw, abnormal body appearance, and weak joints, the system was able to diagnose Klinefelter syndrome with a percentage of 43.58%. This system can be an alternative for patients or families who have limited time and funds to obtain medical consultations, so that diagnosis and follow-up can be carried out more quickly and efficiently.
Co-Authors Amysa Putri Sitepu Assya Harnita Lubis Buaton, Relita Herdina Putri Ahmadi Khair, Husnul Magdalena Simanjuntak Muhammad Arif Ridho Novriyenni Novriyenni Novriyenni, Novriyenni Pakpahan, Victor Maruli Saharuddin Silvia Amara Usman Usman