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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) CommIT (Communication & Information Technology) Journal of ICT Research and Applications International Journal of Advances in Intelligent Informatics Scientific Journal of Informatics Journal of Information Systems Engineering and Business Intelligence Indonesian Journal on Computing (Indo-JC) IJoICT (International Journal on Information and Communication Technology) JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research Journal of Information Technology and Computer Science (JOINTECS) JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JURIKOM (Jurnal Riset Komputer) Building of Informatics, Technology and Science Journal of Information Systems and Informatics RADIAL: JuRnal PerADaban SaIns RekAyasan dan TeknoLogi Indonesian Journal of Electrical Engineering and Computer Science Journal of Computer System and Informatics (JoSYC) Madani : Indonesian Journal of Civil Society Teknika Journal of Applied Data Sciences KLIK: Kajian Ilmiah Informatika dan Komputer Journal of Dinda : Data Science, Information Technology, and Data Analytics Jurnal Ilmiah IT CIDA : Diseminasi Teknologi Informasi SisInfo : Jurnal Sistem Informasi dan Informatika Jurnal INFOTEL RADIAL: Jurnal Peradaban Sains, Rekayasa dan Teknologi
Nur Ghaniaviyanto Ramadhan
Institut Teknologi Telkom Purwokerto
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering

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A Combination of Transfer Learning and Support Vector Machine for Robust Classification on Small Weed and Potato Datasets Adhinata, Faisal Dharma; Ramadhan, Nur Ghaniaviyanto; Fauzi, Muhammad Dzulfikar; Tanjung, Nia Annisa Ferani
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1164

Abstract

Agriculture is the primary sector in Indonesia for meeting people's daily food demands. One of the agricultural commodities that replace rice is potatoes. Potato growth needs to be protected from weeds that compete for nutrients. Spraying using pesticides can cause environmental pollution, affecting cultivated plants. Currently, agricultural technology is being developed using an Artificial Intelligence (AI) approach to classifying crops. The classification process using AI depends on the number of datasets obtained. The number of datasets obtained in this research is not too large, so it requires a particular approach regarding the AI method used. This research aims to use a combination of feature extraction methods with local and deep feature approaches with supervised machine learning to classify of small datasets. The local feature method used in this research is Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG), while the deep feature method used is MobileNet and MobileNetV2. The famous Support Vector Machine (SVM) uses the classification method to separate two data classes. The experimental results showed that the local feature HOG method was the fastest in the training process. However, the most accurate result was using the MobileNetV2 deep feature method with an accuracy of 98%. Deep features produced the best accuracy because the feature extraction process went through many neural network layers. This research can provide insight on how to analyze a small number of datasets by combining several strategies
A Hybrid ROS-SVM Model for Detecting Target Multiple Drug Types Ramadhan, Nur Ghaniaviyanto; Khoirunnisa, Azka; Kurnianingsih, Kurnianingsih; Hashimoto, Takako
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1171

Abstract

Misleading in determining the decision to use the target drug will be fatal, even to death. This study examines five pharmacological targets designated as types A, B, C, X, and Y. Early detection of misleading drug targeting will reduce the risk of death. This study aims to develop hybrid random oversampling techniques (ROS) and support vector machine (SVM) methods. The use of the oversampling technique in this study aims to balance classes in the dataset; due to the data collection in each class, there is a relatively large gap. This study applies five schemes to see which combination of models produces the highest accuracy. This study also uses five types of SVM kernels, linear, polynomial, gaussian, RBF, and sigmoid, combined with the ROS oversampling technique. Our proposed model combines the ROS oversampling technique with a linear SVM kernel. We evaluated the proposed model and resulted in an accuracy of 97% and compared it with several experiments, including the ROS technique with a sigmoid kernel which only resulted in 50% accuracy. It can be seen from the results obtained that the linear kernel is very adaptive to data types in the form of numeric and nominal compared to other kernels. The method proposed in this study can be applied to other medical problems. Future research can be carried out using a combination of other sampling techniques with deep learning-based methods on this issue.
An Evaluation of SMOTE Effectiveness in Handling Class Imbalance in Public Opinion Data on the MBG Program Ramadhan, Nur Ghaniaviyanto; Khoirunnisa, Azka
International Journal of Artificial Intelligence Research Vol 9, No 1.1 (2025)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i1.1.1495

Abstract

The “Makan Bergizi Gratis” (MBG) Program is one of the strategic policies of the Government of Indonesia that reaps various opinions from the public, especially through social media. This study aims to classify public sentiment towards the MBG program with an ensemble learning-based machine learning approach, as well as evaluate the effectiveness of the SMOTE algorithm in dealing with class imbalance in opinion data. The dataset was collected from platform X (formerly Twitter) for the January–April 2025 period, totaling 4,374 tweets with label distributions: 1,783 positive, 1,634 negative, and 957 neutral. The preprocessing process includes data cleansing, normalization, stemming, and vectorization with TF-IDF. Five ensemble algorithms were used, namely Random Forest, AdaBoost, Bagging, Stacking, and Voting, tested in two scenarios: with and without the implementation of SMOTE. The results of the experiments showed that Random Forest provided the best and most consistent performance, with the F1-score increasing from 72.03% to 72.66% after the implementation of SMOTE. However, not all models benefit from SMOTE, such as Voting which experienced a drop in F1-score. These findings suggest that SMOTE is effective in increasing the sensitivity of the model to minority classes, but its success depends on the characteristics of the algorithm used. This study suggests the selective selection of balancing methods as well as the development of a more adaptive approach to handle unstructured opinion data.
Co-Authors Abdurrahman Ibnul Rasidi Adil El-Faruqi Adiwijaya Agung Toto Wibowo Agus Supriatman Aji Gautama Putrada Alfian Akbar Gozali Alon Jala Tirta Segara Ananda Aulia Rizky Arief Rais Bahtiar Arif Amrulloh Azka Khoirunnisa Christoph Quix Diovianto Putra Rakhmadani Diva Angelica Enjeli Cistia Sukmawati Faisal Dharma Adhinata Fauzi, Muhammad Dzulfikar Hashimoto, Takako Hilda Fahlena Imelda Atastina Kridabayu, Adam Nur Kurnianingsih Kurnianingsih, Kurnianingsih Lintang Suryaningrum Maman Abdurohman Merlinda Wibowo Mohammad Fikri Nur Syahbani Nabilah Sharfina Nia Annisa Ferani Tanjung Nicolaus Euclides Wahyu Nugroho Novi Rahmawati Novi Rahmawati Nugraha, Narantyo Maulana Adhi Nur Fatin Liyana Mohd Rosely Purnama Dileon Yamora Nainggolan Purwitasari, Rachma Wukir Rachma Wukir Purwitasari Rahardian, Reva Ramadhan, Teguh Ikhlas Rifqi Akmal Saputra Rifqi Alfinnur Charisma Salsabila, Nisrinia Eka Sayyid Yakan Khomsi Pane Sigit Pramono Sofiyudin Pamungkas Warih Maharani Wibowo, Merlinda Yohani Setiya Rafika Nur