Article Per Year (5 Year)
p-Index From 2021 - 2026
3.4
P-Index
This Author published in this journals
All Journal Jurnal Sains dan Teknologi Jurnal Pengabdian Masyarakat Bidang Sains dan Teknologi JOMLAI: Journal of Machine Learning and Artificial Intelligence Jurnal Manajemen Informatika dan Bisnis Digital Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS
Victor Asido Elyakim P
Unknown Affiliation
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Transportation Veterinary

Published : 26 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 26 Documents
Search

 1   2   3 
Classification of Village Development Index in North Sumatra Using the Support Vector Machine (SVM) Method Yayang Arum Kemangi; Daniel Desmanto Sihombing; Permaisuri Siregar; Sella Ujani; Victor Asido Elyakim P
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 4 No. 3 (2025): September 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v4i3.6117

Abstract

The classification of the Village Development Index (IDM) status is a fundamental component in formulating targeted and effective village development policies. However, the conventional classification process is often slow and inefficient, thereby reducing the data's relevance for dynamic decision-making. This research aims to design and evaluate an automatic classification model for the IDM status in 5,417 villages in North Sumatra Province using the Support Vector Machine (SVM) method. By utilizing secondary data from 2024, this model uses three main sub-indices—the Social Resilience Index (IKS), the Economic Resilience Index (IKE), and the Environmental Resilience Index (IKL)—as predictor variables to map villages into five status categories. The implementation of the SVM model with a Radial Basis Function (RBF) kernel was chosen to handle the complex non-linear relationships between variables. The evaluation results on the test data show superior performance, with an overall accuracy rate reaching 96.77%. The model's performance proved to be very strong, particularly in identifying the 'Developing' class with a perfect recall (1.00) and the 'Independent' class with perfect precision (1.00). Although minor challenges were found in distinguishing between adjacent classes such as 'Disadvantaged' and 'Developing', the high F1-score across all classes confirms a good balance between precision and recall. This study concludes that the SVM method is a highly reliable and valid approach for automating IDM classification, and it offers significant implications as a fast and accurate evidence-based decision support tool for local government
Diagnosis of Gastric Disease Based on Artificial Neural Network with Hebb Rule Algorithm Victor Asido Elyakim P; Alyah Octafia; Yemima Pepayosa Sembiring; Dony Jordan Pangomoan Sirait; Angga Priandi
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 4 No. 3 (2025): September 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v4i3.6543

Abstract

Gastric disorders are among the most common health problems faced by society, often caused by irregular eating habits, unhealthy lifestyles, and high stress levels. The symptoms are diverse, ranging from abdominal pain and nausea to weight loss, making accurate and timely diagnosis essential to prevent more serious complications. This study aims to develop a diagnostic system for gastric diseases using Artificial Neural Networks (ANN) with the Hebb Rule algorithm, a learning principle that strengthens the connections between neurons when they are activated simultaneously. The research utilized binary-encoded data consisting of ten types of gastric diseases and twenty associated symptoms to establish patterns of correlation between symptoms and diagnoses. The results demonstrate that the system successfully recognized all test data with outcomes consistent with the expected targets, proving that the Hebb Rule is effective in mapping symptom-disease relationships even when applied to simple binary data. These findings highlight the practicality and efficiency of the Hebb Rule in building an intelligent diagnostic framework, while also showing its potential for further development with more complex datasets, such as symptom severity levels or laboratory test results. Ultimately, this research contributes to the advancement of smart medical systems that can support both healthcare professionals and the general public in performing early detection of gastric diseases quickly, accurately, and effectively.
Optimasi Pemilihan Smartphone Gaming Terbaik di Kelas Menengah Menggunakan Metode Simple Additive Weighting (SAW) Berbasis Preferensi Konsumen Samantha Arta Sinuhaji; Sandy Hardiansyah; Candra Harapan Simanjuntak; Yulita Santa Nova Girsang; Victor Asido Elyakim P
JUMINTAL: Jurnal Manajemen Informatika dan Bisnis Digital Vol. 3 No. 2 (2024): November 2024
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jumintal.v3i2.4831

Abstract

The selection of middle-class gaming smartphones is a challenge for consumers because there are many choices with varying specifications. This research aims to help consumers make the best choice by applying the Simple Additive Weighting (SAW) method. This method is used to provide recommendations based on a number of key criteria, such as processor performance, RAM capacity, screen quality, battery life, and price. The initial step in this research is to identify the problem, which is the selection of the best gaming smartphone in the middle-class category. Next, alternatives and relevant criteria were determined, followed by data collection through a questionnaire involving 78 respondents. The data obtained was analyzed using the SAW method to determine the importance weight of each criterion and the overall ranking of each alternative. The analysis results show that Infinix ranks highest as the best choice based on the aggregate score obtained for each criterion. The SAW method allows decision making to be more systematic and objective by considering predetermined weights. The conclusion of this study confirms that a SAW-based approach can assist consumers in choosing a gaming smartphone that suits their needs and preferences. This study also provides insights for manufacturers in devising more effective marketing strategies.
Implementasi Metode Simple Additive Weighting (SAW) dalam Rekomendasi Laptop Ekonomis untuk Mahasiswa Yolanda Victoria Damanik; Endang Kartika; Sabrina Fadillah; Muhammad Robbi Akbar Pohan; Victor Asido Elyakim P
JUMINTAL: Jurnal Manajemen Informatika dan Bisnis Digital Vol. 3 No. 2 (2024): November 2024
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jumintal.v3i2.4836

Abstract

In today's digital era, laptops are a major necessity for students to support academic activities. However, budget limitations are often an obstacle in choosing a laptop that suits the needs and financial capabilities. This research aims to implement the Simple Additive Weighting (SAW) method in an economical laptop recommendation system for students. The way this method works is by calculating alternative choices based on predetermined weights and criteria then sorting to find the best alternative from all existing criteria. So that the results will get a laptop recommendation that is closest to the suitability of the needs of prospective laptop users. The criteria used in the study include price, processor, RAM, storage capacity, and battery life. Sample data is taken from 8 laptop models with a price range of 3-7 million available on the market. The results showed that the application of the SAW method successfully provided laptop recommendations that were in accordance with the needs and budget limitations of students. With this recommendation system, students can more easily choose a laptop that suits their academic needs and budget limitations. This research is expected to be a reference for the development of decision support systems in the selection of efficient and appropriate technology devices.
Analysis of Airline Passenger Satisfaction Using the Rough Set Method Alisa Putri Amanda Nasution; Auralia Izmi; Aprillya Zahra Iswandy Lubis; Haya Atiqah Tampubolon; Victor Asido Elyakim P
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 4 No. 3 (2025): September 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v4i3.5946

Abstract

This study analyzes airline passenger satisfaction using the Rough Set method, an effective approach in handling complex data without requiring additional information such as probability. The main factors influencing customer satisfaction are identified based on data collected through questionnaires and analyzed using the attribute reduction method. The results show that flight punctuality, cabin crew service quality, and flight class type have a significant influence on customer satisfaction. From the survey results, 72% of respondents stated that they were satisfied, 18% were quite satisfied, and 10% were dissatisfied, with dissatisfaction generally related to flight delays and lack of comfortable facilities. The application of the Rough Set method has been proven to be able to identify passenger satisfaction patterns more accurately, so that it can be used by airlines to improve their service strategies.
Public Sentiment Analysis of the Agrarian Conflict between PT TPL and the Toba Simalungun Indigenous Community Using the SVM Method Dian Yusri Andira; Deswita Maharani Harahap; Vibiola Br Damanik; Indah Frian Sari; Victor Asido Elyakim P
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 4 No. 3 (2025): September 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v4i3.6116

Abstract

The agrarian conflict between PT Toba Pulp Lestari and the Toba Simalungun indigenous community has generated diverse public opinions on social media. This study aims to analyze public sentiment regarding the conflict using the Support Vector Machine (SVM) method based on TikTok comment data. A total of 1,751 comments were collected via the TikTok API and processed through cleaning, normalization, stopword removal, and stemming. Sentiment labeling was performed automatically with a lexical-based approach, followed by feature weighting using Term Frequency-Inverse Document Frequency (TF-IDF). The SVM model was used to classify public sentiment into two main categories, namely positive and negative. The results of the testing showed that the SVM model was able to achieve an accuracy of 80%, with excellent performance in detecting negative sentiment. Additional analysis through wordcloud visualization shows the dominant words in each sentiment category, which reinforces the model's classification results. The findingsof this study provide an objective picture of public opinion patterns on social media, while also demonstrating the potential application of machine learning-based sentiment analysis methods to understand public perceptions of other social issues in the future.
Co-Authors Abdul Ghani Ardiansyah Adinda Febiola Aklima Laduna Ramadya Alisa Putri Amanda Nasution Aliya Firanti Alyah Octafia Angga Priandi Anya Nailah Aurellia Aprillya Zahra Iswandy Lubis Auralia Izmi Ayu Utari Nasution Bagas Adi Nata Baginda Faustine Sinaga Bagus Arya Atmaja Candra Harapan Simanjuntak Damar Arya Prayoga Daniel Desmanto Sihombing Darwin Nathaniel Dermawan Perangin-angin Deswita Maharani Harahap Dian Yusri Andira Dika Chryston Purba Dimas Prayogi Dongan Parulian Hutasoit Dony Jordan Pangomoan Sirait Dwi Safitri Ramadhani Ega Wahyu Andani Eko Prima Ambarita Endang Kartika Fahriya Ardiningrum Ferdinand Saragih Fernando Siahaan Fery Padli Pratama Gery Samuel Gultom Hafizah Rahmi Lubis Haya Atiqah Tampubolon Heba Elsisy Fadlia Immanuel Christian Manalu Indah Frian Sari Indra Lesmana Putra Jeremi Sibarani Jhon Hansen Manurung Khafifah Dwi Meilianasari Khaswa Giovani Simanungkalit Lulu Hidayah Harahap M Arif Y Sinaga M Ilham Azharsum M.Alfathan Haris Mhd. Fauzal Pratama Michael Orlando A. Purba Mirza Afif Pradivta Muhammad Farhan Muhammad Fikri Azhari Muhammad ihsan Raditya Muhammad Rizky Ramadhan Muhammad Robbi Akbar Pohan Mustika Almuthi Mawardani Novi Hariyanti Novianty Khairani Nurhidayanti, Nurhidayanti Olivia Diwani Saragih P.A.M. Zidane R.W.P.P. Zer Panggabean, Josua Alfa Viando Permaisuri Siregar Putri Aulia Harahap Putri Ayu Ningsih Rahma Dhea Safitri Ratih Manalu Regita Audyna Siregar Riko ILham Nandika Rizky Nurhasanah Sabrina Fadillah Samantha Arta Sinuhaji Sandy Hardiansyah Sella Ujani Siti Asiah Siti Nurdiana Wijaya Sydah Wanju Tiara Dwi Lestari Purba Vibiola Br Damanik Wanda Eka Nugraha Wanda Riana Wilman Arif Telaumbanua Yayang Arum Kemangi Yemima Pepayosa Sembiring Yolanda Victoria Damanik Yuegilion Pranayama Purba Yulita Santa Nova Girsang Zaskia Aulia Zahra