Article Per Year (5 Year)
p-Index From 2021 - 2026
4.699
P-Index
This Author published in this journals
All Journal Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Jurnal Teknik Komputer AMIK BSI JURNAL MEDIA INFORMATIKA BUDIDARMA JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Sisfokom (Sistem Informasi dan Komputer) JSiI (Jurnal Sistem Informasi) Krea-TIF: Jurnal Teknik Informatika IJISTECH (International Journal Of Information System & Technology) Jurnal ICT : Information Communication & Technology Health Sciences and Pharmacy Journal Building of Informatics, Technology and Science Madani : Indonesian Journal of Civil Society Jurnal Pengabdian kepada Masyarakat Nusantara Teknika IJISTECH Journal of Soft Computing Exploration Batara Wisnu : Indonesian Journal of Community Services The Indonesian Journal of Computer Science Research SmartComp The Indonesian Journal of Computer Science
Acihmah Sidauruk
Universitas AMIKOM Yogyakarta
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Library & Information Science Medicine & Pharmacology Nursing Public Health Social Sciences

Published : 32 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : JOIV : International Journal on Informatics Visualization

 1 
Comparative Analysis of Weighted-KNN, Random Forest, and Support Vector Machine Models for Beef and Pork Image Classification Using Machine Learning Satria, Budy; Afrianto, Nurdi; Ningsih, Lidya; Sakinah, Putri; Sidauruk, Acihmah; Mayola, Liga
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3736

Abstract

The actual problem that occurs in the sale of meat by some conventional market traders is mixing beef with pork because of the high selling price. The difference between pork and beef lies in the color and texture of the meat. However, many people do not understand this difference. This study aims to provide a solution to distinguish the two types of beef through a classification process by obtaining the best accuracy using the W-KNN, RF, and SVM models based on machine learning. This study compares the model's performance based on the number of datasets, comprising 400 original images (200 beef and 200 pork images), using a 80:20 ratio for training and test data. The extraction process uses two algorithms: HSV (Hue, Saturation, Value) and RGB (Red, Green, Blue). The model evaluation uses a confusion matrix that includes accuracy, Precision, Recall, and F1-score. Based on the results of the model testing, it was found that the random forest algorithm gave the best overall results, with the highest accuracy of 98.75%, Precision of 97%, F1-score of 98%, and recall of 99% on the number of decision trees of 400. This shows the stability and generalization of the superior model. The random forest algorithm is the most effective for classifying beef and pork data with minimal errors. Implications for further research include using a deep learning approach, especially for image processing, to detect differences in each meat characteristic and increase accuracy.
Optimization and Collaboration of Fuzzy C-Mean, K-Mean, and Naïve Bayes Algorithms Using the Elbow Method for Micro, Small, and Medium Enterprises Norhikmah, -; Nurastuti, Wiji; Aminuddin, Afrig; Sidauruk, Acihmah; Gunawan, Puguh Hasta
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3292

Abstract

Micro, Small, and Medium Enterprises (SMEs) have a vital role in Indonesia’s economy. However, IT-based marketing strategies among SMEs receive limited support from the government due to the lack of sufficient data to inform policy. This study aims to (1) identify the needs of SMEs for social media promotion training as part of their digital capacity building, (2) develop and compare the effectiveness of classification models that combine Fuzzy C-Means and K-Means clustering algorithms with the Naïve Bayes algorithm to group SMEs based on business characteristics, (3) analyze the relationships between business variables—such as business type, marketing media, funding sources, and financial aspects—and SME performance through regression analysis, and (4) provide data-driven foundations for designing targeted digital interventions and policy strategies to support SME development in Indonesia. This study used UPPKS data from 133 SMEs in seven districts in the Special Region of Yogyakarta. Data analysis covered business types, marketing platforms used, funding sources, and financial performance indicators. Data pre-processing involved cleaning, normalization, and integration to ensure consistency and readiness for analysis. The researcher used the Elbow method to determine the optimal number of clusters. Then, it also used Fuzzy C-Means (FCM) and K-Means to categorize SMEs into three groups: high, medium, and low. The classification was based on the Naïve Bayes algorithm. The evaluation of the model performance used a confusion matrix, cross-validation, and regression analysis to examine inter-variable relationships. The results showed that the combination of FCM and Naïve Bayes achieved an accuracy of 85% based on the confusion matrix and 97% based on cross-validation. Meanwhile, the combination of K-Means and Naïve Bayes respectively achieved an accuracy of 96% and 94.7%. These findings demonstrate the effectiveness of the proposed approaches in classifying SMEs based on their characteristics and performance. This research provides important insights for policymakers and SME development agencies in designing more targeted digital training and support programs. Future studies should explore the integration of other algorithms, such as Support Vector Machines (SVM) and Decision Trees, while incorporating market trends and customer engagement to enhance SME classification and provide ongoing support.
Co-Authors Abdullah Abdullah Abdussalam Al Akbar Achmad Lukman, Achmad Ade Pujianto Ade Pujianto, Ade Afifah Nur Latifah Afrianto, Nurdi Afrig Aminuddin Alvy Chasanatussalma Anggita, Sharazita Dyah Arif Dwi Laksito Asro Nasiri Atik Nurmasani Bernadhed, Bernadhed Bister Purba Budy Satria Burhanudin, Nazil Ilham Danang Wijayanto Darmawan, Briga Dwi Rahayu Elsafira Budi Dewantari Erni Seniwati Febri Liantoni, Febri Gita Cahyani Gunawan, Puguh Hasta Hairul Januar Haryoko, Haryoko Hasyim, Jaka Wardana Hendra Kurniawan Ihsan, M. Arinal Ikmah Ikmah Ilyas Ferry Ceasar Widayanto Jamroni Jamroni Khoiruddin, Lukman Liga Mayola M. Arinal Ihsan Miftakhurrokhmat Miftakhurrokhmat, Miftakhurrokhmat Muhammad Iqbal Kusrachmansyah Muhammad Resa Arif Yudianto Mulia Sulistiyono Mulyatun, Sri Ningsih, Lidya Norhikmah Norhikmah Norhikmah, - Nurcholis, Muhammad Tofa Panggih Suseno Pirmansah, Imam Ainudin Proboningrum, Sandyea Rabiatul Adawiyah Raditya Wardhana Raditya Wardhana Rahmi, Alfie Nur Ria Andriani Riza Yulina Amry Robert Marco, Robert Rumini Rumini SAKINAH, PUTRI Salmuasih, Salmuasih Sarni Anggoro Satria, Dhimas Adi Seniwati, Erni Shofi Putri Ekadewi Sutarni, Sutarni Tri Widayanti Trisa Ayunanda Saputri Wardhana, Raditya Waskito, Bimo Wiji Nurastuti Wiji Nurastuti, Wiji Wiwi Widayani Wulandari, Evita Sekar Yoga Pristyanto Yudiyanto, Muhammad Resa Arif Yuli Astuti Yuli Astuti