Article Per Year (5 Year)
p-Index From 2021 - 2026
8.137
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Dinamik Seminar Nasional Aplikasi Teknologi Informasi (SNATI) ELKHA : Jurnal Teknik Elektro Techno.Com: Jurnal Teknologi Informasi Jurnal Simetris Elkom: Jurnal Elektronika dan Komputer TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Prosiding SNATIF Jurnal Transformatika Scientific Journal of Informatics Seminar Nasional Informatika (SEMNASIF) Sisforma: Journal of Information Systems JAIS (Journal of Applied Intelligent System) Proceeding SENDI_U Jurnal Ilmiah Dinamika Rekayasa (DINAREK) Journal of Information System Jurnal Informatika Upgris Jurnal Teknologi dan Sistem Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika SISFOTENIKA JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Wikrama Parahita : Jurnal Pengabdian Masyarakat Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Eksplora Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING J-SAKTI (Jurnal Sains Komputer dan Informatika) Building of Informatics, Technology and Science Jurnal Informatika dan Rekayasa Perangkat Lunak JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Teknik Informatika (JUTIF) JUDIMAS (Jurnal Inovasi Pengabdian Kepada Masyarakat) Jurnal Program Kemitraan dan Pengabdian Kepada Masyarakat Advance Sustainable Science, Engineering and Technology (ASSET) J-SAKTI (Jurnal Sains Komputer dan Informatika) Prosiding Seminar Nasional Hasil-hasil Penelitian dan Pengabdian Pada Masyarakat Jurnal Maklumatika Prosiding Seminar Nasional Teknologi Informasi dan Bisnis Journal of Computing Theories and Applications Seminar Nasional Teknologi dan Multidisiplin Ilmu Scientific Journal of Informatics Journal of Future Artificial Intelligence and Technologies Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) INOVTEK Polbeng - Seri Informatika
Ajib Susanto
Universitas Dian Nuswantoro
Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Physics Public Health Social Sciences Other

Published : 113 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 113 Documents
Search

 8   9   10   11   12 
Analisis Sentimen Twitter Terhadap Isu Royalti Lagu di Industri Musik Indonesia Menggunakan Naive Bayes dan Support Vector Machine Berbasis TF-IDF Alif Fadhil Wibowo; Ajib Susanto
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.9842

Abstract

The development of digital platforms in Indonesia’s music industry has triggered various debates regarding the song royalty system, particularly those related to copyright and income distribution for songwriters. Public opinions on these issues are widely expressed through Twitter, making it a valuable data source for sentiment analysis. This study aims to analyze public sentiment toward song royalty issues in the Indonesian music industry and compare the performance of Multinomial Naive Bayes and Support Vector Machine (SVM) algorithms using TF-IDF weighting. This study contributes through the implementation of semi-manual labeling, the use of a stratified 5-fold cross-validation approach, and multi-metric evaluation to obtain more representative sentiment classification results on song royalty issues in Indonesian social media. The initial dataset was collected through Twitter scraping using keywords related to song royalties and music copyright. The data were then processed through preprocessing stages, including case folding, cleaning, tokenization, stopword removal, and stemming. Sentiment labeling was conducted using a semi-manual approach, involving lexicon-based pre-labeling followed by manual verification into three sentiment categories: positive, negative, and neutral. Model evaluation was performed using stratified 5-fold cross-validation with accuracy, precision, recall, and F1-score metrics. The results indicate that the SVM algorithm outperformed Multinomial Naive Bayes, achieving an accuracy of 93.21%, while Multinomial Naive Bayes obtained an accuracy of 82.53%. These findings demonstrate that SVM is more effective in handling high-dimensional textual data represented using TF-IDF for Indonesian sentiment analysis. This study is expected to provide insights into public perceptions regarding song royalty issues and serve as a reference for sentiment analysis applications on Indonesian social media data.
Support vector machine based discrete wavelet transform for magnetic resonance imaging brain tumor classification Ajib Susanto; Christy Atika Sari; Hidayah Rahmalan; Mohamed A. S. Doheir
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 21, No 3: June 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v21i3.24928

Abstract

Here, a brain tumor classification method using the support vector machine (SVM) algorithm by utilizing discrete wavelet transform (DWT) transformation and feature extraction of gray-level co-occurrence matrix (GLCM) and local binary pattern (LBP) has been implemented using the magnetic resonance imaging (MRI) image belong to the low-grade glioma (LGG) or high-grade glioma (HGG) group. SVM algorithm used as a classification method has been widely used in research that raises the topic of classification. Through the formation of a hyperplane between 2 data classes, the SVM algorithm can be said to be a reliable method but does not require complicated computations. The DWT transformation is intended to provide clearer feature details from the MRI image, so that when the feature extraction algorithm is applied, it is expected that the extracted features will differ between benign tumor MRI images and malignant tumor MRI images. In 1 level DWT using high-low (HL) sub-band yield the highest specificity, sensitivity, and accuracy than using 3 levels using HL or low-high (LH) sub-band in LGG MRI image.Compared with another research, our proposed method is slightly better in terms of accuracy to classify the brain tumor image with achieved the accuracy of 98.6486%.
Schizophrenia Classification using Fuzzy K-Nearest Neighbour on Patient Data from RSJD Dr. Amino Gondohutomo Ozagastra Caluella Prambudi; Ajib Susanto; Christy Atika Sari
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/t2mfvf14

Abstract

Schizophrenia is a complex mental disorder with overlapping symptoms, making subtype diagnosis uncertain. This study aims to develop an automated classification method for schizophrenia subtypes using the Fuzzy K-Nearest Neighbour (FKNN) algorithm, which effectively handles uncertainty in medical data. The dataset includes 300 patients from RSJD Dr. Amino Gondohutomo, Central Java, aged 18–60 years, with balanced gender distribution. Four subtypes—paranoid, catatonic, hebephrenic, and undifferentiated—were classified. Symptom and demographic data were encoded and normalised using min-max scaling. The model was trained using k = 5 and evaluated via 10-fold cross-validation. The results achieved 94% accuracy with high precision and recall across all classes. However, limitations include a relatively small and single-source dataset and the lack of ROC/AUC analysis. These findings suggest that FKNN has strong potential as a data-driven decision support system for schizophrenia diagnosis, suitable for integration into psychiatric hospital information systems. Future research should explore oversampling techniques such as SMOTE and threshold tuning to improve model sensitivity.
Co-Authors - Wijanarto - Wijanarto -, Wijanarto -, Wijanarto Abdussalam Abdussalam Abdussalam Abdussalam Abdussalam Abiyyi, Ryandhika Bintang Aceng Sambas Adrian Angga Pramono Afrizal Aziz Maulana Agus Winarno Agus Winarno, Agus Akhmad Rizaldy Ali Muqoddas Ali, Rabei Raad Alif Fadhil Wibowo Alviana Dina Putri Anak Agung Gede Sugianthara Anggia Rizkika Hanin Anggraeny, Tiara Antonio Ciputra Antonius Erick Handoyo Antonius Wibowo Atmojo, Cahyo Tri Bayu Wicaksono Briliantino Abhista Prabandanu Bustami, Sri Heri Cahyani, Anis Putma Carmelita, Bastiaans Jessica Christy Atika Sari Ciputra, Antonio D.R.I.M. Setiadi Daniel Nomolas Wicaksono De Rosal Ignatius Moses Setiadi Desi Purwanti Desi Purwanti Kusumaningrum Dian Kristiawan Nugroho Didik Hermanto Dimas Irawan Ihya’ Ulumuddin Doheir, Mohamed Dwi Puji Prabowo Dwi Puji Prabowo, Dwi Puji Eko Hari Rachmawanto Elkaf Rahmawan Pramudya Ericsson Dhimas Niagara Erwin Erwin Etika Kartikadarma Fakhriyan Nur Rofiq Farrel Athaillah Putra Febrian, Muhamad Rizky Fajar Fikri Budiman Fikri Budiman Galih Setyo Wibowo Gan, Hong-Seng Gilang Raharjito Haqikal, Hafidz Hayu Wikan Kinasih Hidayah Rahmalan Hilmi, Muhammad Abror Auliya Hussain Md Mehedul Islam Ibnu Gemaputra Ramadhan Ibnu Utomo Ibnu Utomo W.M. Ibnu Utomo Wahyu Mulyono Ibnu Utomo Wahyu Mulyono Ibnu Utomo Wahyu Mulyono Ibnu Utomo Wahyu Mulyono Ibnu Utomo Wahyu Mulyono Ibnu Utomo Wahyu Mulyono Ibnu Utomo Wahyu Mulyono, Ibnu Utomo Ibnu Utomo WM Ihya Ulumuddin, Dimas Irawan Imam Kurniawan Imam Prayogo Pujiono Imanuel Harkespan Indra Kusuma Islam, Hussain Md Mehedul Istiqomah, Annisa Ayu Karis Widyatmoko Khafiizh Hastuti Krismawan, Andi Danang Kusuma, Tiara Widya Kusumawati, Yupie L. Budi Handoko Laksono, Enggar Adji Lalang Erawan Latifah Diah Kumalasari Lutfi Madiono Marjuni, Aris Md Kamruzzaman Sarker Md Kamruzzaman Sarker Mega Bintang Hatmi Moch Arief Soeleman Mochammad Lukman Mohamad Afendee Mohamed Mohamed A. S. Doheir Mohammad Arif Muttaqin Mohd Yaacob, Noorayisahbe Muhammad Atho’il Maula Muhammad Nur Haztinanto Mulyanto, Ibnu Utomo Mulyono, Ibnu Utomo Wahyu Mulyono, Ibnu Utomo Wahyu Mulyono, Ibnu Utomo Wahyu Musfiqur Rahman Sazal Muslih Muslih Muslih Muslih Muslih Nabiha Riandika, Muhammad Afiq Ningrum, Novita Kurnia Nova Rijati Novita Kurnia Ningrum Novita Kurnia Ningrum Nugroho, Widhi Bagus Ojugo, Arnold Adimabua Ozagastra Caluella Prambudi Ozagastra Caluella Prambudi Panjaitan, Yonathan Gani Panjaitan, Yonathan Gani Putri, Clara Edrea Evelyna Sony Raga Nufusula Rahadian Kristiyanto Rachman Raihan Yusuf Ramadhan, Aditya Wahyu Respatria, Nabila Maharani Rico Rian Alvian Rosyida, Ghaitsa Ardelia Sabilillah, Ferris Tita Saputra, The Manuel Eric Saraswati, Galuh Wilujeng Sarker, Md Kamruzzaman Sembiring, Rinawati Setiarso, Ichwan Sinaga, Daurat Sinaga, Daurat Sinar Setyawan Stefanus Santosa Sudaryanto Sudaryanto Sudaryanto Sudaryanto Sudaryanto Sudaryanto SUDARYANTO SUDARYANTO Suprayogi Suprayogi Syamsiar, Syamsiar Teresa Enades Hari Setia Tiara Anggraeny Tiara Widya Kusuma Tri Wulandari Utomo W.M, Ibnu Utomo W.M, Ibnu Wellia Shinta Sari Widyatmoko Karis Wijanarto Wijanarto Wijanarto Wijanarto Wijanarto Wijanarto Yaacob, Noorayisahbe Mohd. Yupie Kusumawati Zahrotul Umami, Zahrotul Zainal Arifin Hasibuan Zuama, Leygian Reyhan