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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 Jurnal Abdimas Kartika Wijayakusuma Journal of Dinda : Data Science, Information Technology, and Data Analytics Jurnal Ilmiah IT CIDA : Diseminasi Teknologi Informasi Jurnal Pengabdian Masyarakat SisInfo : Jurnal Sistem Informasi dan Informatika Jurnal INFOTEL Jurnal Informatika Polinema (JIP) RADIAL: Jurnal Peradaban Sains, Rekayasa dan Teknologi
Nur Ghaniaviyanto Ramadhan
Telkom University
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Public Health Social Sciences Other

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TEKNIK SMOTE DAN GINI SCORE DALAM KLASIFIKASI KANKER PAYUDARA Ramadhan, Nur Ghaniaviyanto; Adhinata, Faisal Dharma
RADIAL : Jurnal Peradaban Sains, Rekayasa dan Teknologi Vol. 9 No. 2 (2021): RADIAL: JuRnal PerADaban SaIns RekAyasan dan TeknoLogi
Publisher : Universitas Bina Taruna Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (453.223 KB) | DOI: 10.37971/radial.v9i2.229

Abstract

Breast cancer is a malignancy in breast tissue that can originate from the epithelium of the ducts and lobules. WHO says 30% - 50% of cancer cases can be prevented. Breast cancer prevention can be done utilizing screening or early diagnosis. The purpose of the initial diagnosis is that if a lump appears, predictions can be made whether it is classified as malignant or benign. Breast cancer prediction can be done using a dataset containing cancer-related parameters. However, sometimes the dataset used also has problems such as the amount of data is not balanced and the use of irrelevant features. This study aims to improve breast cancer prediction results by balancing the number of data classes and using the rank feature. The method used is SMOTE for imbalanced data and Gini score for rank features. The classification model used is random forest and naïve Bayes. The results obtained by the random forest classification model are superior to Naïve Bayes.
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.
Pengembangan Website Desa Wisata Berbasis Partisipasi Masyarakat untuk Penguatan Promosi dan Ekonomi Lokal: Studi Kasus Desa Banjarsari Selviandro, Nungki; Ramadhan, Nur Ghaniaviyanto; Lhaksmana, Kemas Muslim; Erfianto, Bayu
Jurnal Abdimas Kartika Wijayakusuma Vol 7 No 1 (2026): Jurnal Abdimas Kartika Wijayakusuma
Publisher : LPPM Universitas Jenderal Achmad Yani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26874/jakw.v7i1.1283

Abstract

Pengembangan desa wisata berbasis digital menjadi strategi penting dalam memperkuat promosi destinasi dan mendorong penguatan ekonomi lokal. Desa Wisata Banjarsari memiliki potensi wisata alam dan budaya yang beragam, namun pemanfaatan teknologi digital sebagai media promosi masih belum optimal. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk mengembangkan website desa wisata berbasis partisipasi masyarakat guna meningkatkan visibilitas destinasi wisata serta mendukung pemberdayaan ekonomi lokal. Metode yang digunakan adalah pendekatan partisipatif melalui Participatory Action Research (PAR) yang dikombinasikan dengan prinsip Community-Based Tourism (CBT) dan pemberdayaan digital. Kegiatan dilaksanakan melalui tahapan pemetaan potensi, perencanaan partisipatif, pelatihan dan pendampingan pengelolaan konten digital, serta monitoring dan evaluasi. Hasil kegiatan menunjukkan bahwa masyarakat, khususnya pengelola BUMDes, pemuda desa, dan pelaku UMKM, mampu terlibat aktif dalam pengelolaan website desa wisata. Website yang dikembangkan berfungsi sebagai pusat informasi wisata dan etalase digital produk lokal. Kegiatan ini berkontribusi terhadap peningkatan kapasitas masyarakat dalam pengelolaan promosi digital serta membuka peluang penguatan ekonomi lokal berbasis pariwisata.
A Hybrid DenseNet201-SVM for Robust Weed and Potato Plant Classification Muhammad Dzulfikar Fauzi; Faisal Dharma Adhinata; Nur Ghaniaviyanto Ramadhan; Nia Annisa Ferani Tanjung
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 8 No. 2 (2022): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v8i2.23886

Abstract

Potato plant growth needs to be protected from weeds that grow around it. Currently, the manual spraying of pesticides by farmers is not only precise on weeds but also on cultivated plants. Therefore, we need an intelligent system that can appropriately classify potato plants and weeds. The research contribution combines feature extraction and appropriate classification methods to obtain optimal accuracy. In addition, the small amount of data also contributes to this research. In this research, it is proposed to use a combination of feature extraction using deep learning techniques and classification using machine learning. We use the feature extraction method with the DenseNet201 model because this study's data is not too much. Complex vectors from DenseNet201 were reduced using Principal Component Analysis (PCA). Then we classified it with the Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) classification methods. The experimental results show that the PCA method can reduce the complexity of high-dimensional features into 2 and 3 dimensions. The average of the best classification results using SVM was obtained with a 3-dimensional PCA configuration, but on the contrary, using KNN obtained the best results in a 2-dimensional PCA configuration. The results showed 100% accuracy on the DenseNet201-SVM hybrid. The SVM kernel configuration used is a linear kernel. The results of this study can be an insight into an accurate classification method for separating weeds and potatoes so that agricultural technology can apply this method for classification.
Transformasi Digital Tata Kelola Pemakaman Bersejarah melalui Implementasi TIMGRAVID di Yayasan Sajarah Timbanganten Bandung Nungki Selviandro; Angel Metanosa Afinda; Iga Narendra Pramawijaya; Nur Ghaniaviyanto Ramadhan; Adiwijaya; Indah Novitasari Dwi Saputro; Bayu Satrio Wibowo
Jurnal Pengabdian Masyarakat - PIMAS Vol. 5 No. 3 (2026): Agustus
Publisher : LPPM Universitas Harapan Bangsa Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/pimas.v5i3.2568

Abstract

Yayasan Sajarah Timbanganten Bandung merupakan pengelola kawasan pemakaman bersejarah yang memiliki nilai budaya dan historis bagi masyarakat Kota Bandung. Namun, proses administrasi pemakaman masih dilakukan secara manual sehingga menimbulkan berbagai kendala, seperti kesulitan pengelolaan data, monitoring makam, pencatatan pembayaran, dan penyediaan informasi kepada masyarakat. Kegiatan pengabdian kepada masyarakat ini bertujuan mengembangkan dan mengimplementasikan TIMGRAVID (Timbanganten Grave Digital Information), yaitu sistem informasi manajemen pemakaman berbasis web untuk mendukung digitalisasi tata kelola dan peningkatan kualitas layanan yayasan. Metode yang digunakan adalah Participatory Action Research (PAR) dan User-Centered Design (UCD) melalui tahapan identifikasi kebutuhan, pengembangan sistem, implementasi, pelatihan, pendampingan, dan evaluasi. Sistem yang dikembangkan mengintegrasikan pengelolaan data jenazah, data penanggung jawab, relasi keluarga, pengelolaan blok makam, administrasi pembayaran, monitoring masa berlaku hak penggunaan makam, serta penyediaan informasi publik. Hasil implementasi menunjukkan bahwa TIMGRAVID mampu meningkatkan efektivitas administrasi, mempermudah pencarian data, meningkatkan transparansi pengelolaan pembayaran, serta mendukung monitoring status makam secara lebih terstruktur. Hasil evaluasi dari sembilan responden menunjukkan tingkat kepuasan mitra yang tinggi, dengan nilai 4,89 untuk kesesuaian materi, 4,44 untuk waktu pelaksanaan, 4,78 untuk kejelasan materi, 5,00 untuk pelayanan tim pelaksana, dan 5,00 untuk keberlanjutan program, dengan rata-rata keseluruhan sebesar 4,82. Selain mendukung pengelolaan administrasi, sistem juga berkontribusi terhadap pelestarian digital data sejarah dan genealogis pada kawasan pemakaman bersejarah Timbanganten Bandung.
Co-Authors Abdurrahman Ibnul Rasidi Adil El-Faruqi Adiwijaya Afzal Ziqri Agung Toto Wibowo Agus Supriatman Aji Gautama Putrada Alfian Akbar Gozali Alon Jala Tirta Segara Ananda Aulia Rizky Angel Metanosa Afinda Arief Rais Bahtiar Arif Amrulloh Azka Khoirunnisa Bayu Erfianto Bayu Satrio Wibowo Christoph Quix Diovianto Putra Rakhmadani Diva Angelica Enjeli Cistia Sukmawati Faisal Dharma Adhinata Hashimoto, Takako Hilda Fahlena Iga Narendra Pramawijaya Imelda Atastina Indah Novitasari Dwi Saputro Kemas Muslim Lhaksmana Kridabayu, Adam Nur Kurnianingsih Kurnianingsih, Kurnianingsih Lintang Suryaningrum Maman Abdurohman Merlinda Wibowo Mohammad Fikri Nur Syahbani Muhammad Dzulfikar Fauzi Nabilah Sharfina Nia Annisa Ferani Tanjung Nicolaus Euclides Wahyu Nugroho Novi Rahmawati Novi Rahmawati Nugraha, Narantyo Maulana Adhi Nungki Selviandro 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