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All Journal ComEngApp : Computer Engineering and Applications Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Buana Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Jurnal Teknik Elektro Dinamika Informatika Jurnal Ilmiah Kursor Journal of Engineering and Technological Sciences Jurnal Edukasi dan Penelitian Informatika (JEPIN) jurnal Teknologi Informasi Magister Proceeding of the Electrical Engineering Computer Science and Informatics Edu Komputika Journal Communications in Science and Technology Conference SENATIK STT Adisutjipto Yogyakarta Jurnal Otomasi Kontrol dan Instrumentasi IJID (International Journal on Informatics for Development) Jurnal Informatika IJITEE (International Journal of Information Technology and Electrical Engineering) Indonesian Journal of Electrical Engineering and Computer Science Jurnal Nasional Teknik Elektro dan Teknologi Informasi Prosiding Seminar Nasional Rekayasa Teknologi Industri dan Informasi ReTII Teknomatika: Jurnal Informatika dan Komputer
Teguh Bharata Adji
Departemen Teknik Elektro Dan Teknologi Informasi Fakultas Teknik, Universitas Gadjah Mada
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Physics Transportation

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KONVERSI DATA KE FORMAT DNA DAN PERBANDINGAN HASIL KOMPRESINYA MENGGUNAKAN GENCOMPRESS TERHADAP WINRAR Heilbert Armando Mapaly; Teguh Bharata Adji; Noor Akhmad Setiawan
Jurnal Teknomatika Vol 6 No 1 (2013): TEKNOMATIKA
Publisher : Fakultas Teknik dan Teknologi Informasi, Universitas Jenderal Achmad Yani Yogyakarta

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Abstract

Teknik kompresi data saat ini merupakan hal yang penting dalam menyimpan media dalam bentuk digital. Secara umum, teknik kompresi terbagi menjadi lossy compression dan loseless compression. Penelitian ini mencoba untuk menerapkan konsep kompresi DNA yang bersifat loseless. Karena DNA hanya terdiri dari huruf a, c, g, dan t, maka kompresi ini hanya dapat diaplikasikan pada media teks, padahal, media yang ada saat ini tidak hanya berupa teks saja. Oleh sebab itu penelitian ini bertujuan untuk menemukan cara agar kompresi tersebut dapat diaplikasikan pada media lain serta membandingkan hasil kompresi tersebut dengan aplikasi WinRAR. Untuk dapat dikompresi dengan teknik kompresi DNA, semua file tersebut harus dikonversikan ke dalam bentuk DNA. Proses konversi tersebut dilakukan dengan mengubah kode binary yang ada pada file menjadi untaian rantai DNA. 00 diubah menjadi ‘a’, 01 diubah menjadi ‘c’, 10 diubah menjadi ‘g’ dan 11 diubah menjadi ‘t’. Walaupun pada konversi data ke format DNA ukuran file DNA menjadi lebih besar dari file awal, namun ukuran file hasil kompresi menjadi lebih kecil dari ukuran file awal karena memanfaatkan kelebihan teknik kompresi DNA. Ukuran file hasil kompresi dengan GenCompress menunjukkan hasil yang lebih baik dari WinRAR ketika terdapat pengulangan dengan jumlah yang sama pada isi file.
Point of Interest (POI) Recommendation System using Implicit Feedback Based on K-Means+ Clustering and User-Based Collaborative Filtering Sulis Setiowati; Teguh Bharata Adji; Igi Ardiyanto
Computer Engineering and Applications Journal Vol 13 No 1 (2024)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v13i1.388

Abstract

Recommendation system always involves huge volumes of data, therefore it causes the scalability issues that do not only increase the processing time but also reduce the accuracy. In addition, the type of data used also greatly affects the result of the recommendations. In the recommendation system, there are two common types of data namely implicit (binary) rating and explicit (scalar) rating. Binary rating produces lower accuracy when it is not handled with the properly. Thus, optimized K-Means+ clustering and user-based collaborative filtering are proposed in this research. The K-Means clustering is optimized by selecting the K value using the Davies-Bouldin Index (DBI) method. The experimental result shows that the optimization of the K values produces better clustering than Elbow Method. The K-Means+ and User-Based Collaborative Filtering (UBCF) produce precision of 8.6% and f-measure of 7.2%, respectively. The proposed method was compared to DBSCAN algorithm with UBCF, and had better accuracy of 1% increase in precision value. This result proves that K-Means+ with UBCF can handle implicit feedback datasets and improve precision.
Comparison of Distributed K-Means and Distributed Fuzzy C-Means Algorithms for Text Clustering Agastya, I Made Artha; Adji, Teguh Bharata; Setiawan, Noor Akhmad
Communications in Science and Technology Vol 2 No 1 (2017)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.2.1.2017.46

Abstract

Text clustering has been developed in distributed system due to increasing data. The popular algorithms like K-Means (KM) and Fuzzy C-Means (FCM) are combined with MapReduce algorithm in Hadoop Environment to be distributable and parallelizable. The problem is performance comparison between Distributed KM (DKM) and Distributed FCM (DFCM) that use Tanimoto Distance Measure (TDM) has not been studied yet. It is important because TDM’s characteristics are scale invariant while allowing discrimination collinear vectors. This work compared the combination of TDM with DKM (DKM-T) and TDM with DFCM (DFCM-T) to acquire performance of both algorithms. The result shows that DFCM-T has better intra-cluster and inter-cluster densities than those of DKM-T. Moreover, DFCM-T has lower processing time than that of DKM-T when total nodes used are 4 and 8. DFCM-T and DKM-T could perform clustering of 1,400,000 text files in 16.18 and 9.74 minutes but the preprocessing times take hours.
Improving multi-class EEG-motor imagery classification using two-stage detection on one-versus-one approach Wijaya, Adi; Adji, Teguh Bharata; Setiawan, Noor Akhmad
Communications in Science and Technology Vol 5 No 2 (2020)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.5.2.2020.216

Abstract

The multi-class motor imagery based on Electroencephalogram (EEG) signals in Brain-Computer Interface (BCI) systems still face challenges, such as inconsistent accuracy and low classification performance due to inter-subject dependent. Therefore, this study aims to improve multi-class EEG-motor imagery using two-stage detection and voting scheme on one-versus-one approach. The EEG signal used to carry out this research was extracted through a statistical measure of narrow window sliding. Furthermore, inter and cross-subject schemes were investigated on BCI competition IV-Dataset 2a to evaluate the effectiveness of the proposed method. The experimental results showed that the proposed method produced enhanced inter and cross-subject kappa coefficient values of 0.78 and 0.68, respectively, with a low standard deviation of 0.1 for both schemes. These results further indicated that the proposed method has an ability to address inter-subject dependent for promising and reliable BCI systems.
Point of Interest (POI) Recommendation System using Implicit Feedback Based on K-Means+ Clustering and User-Based Collaborative Filtering Setiowati, Sulis; Adji, Teguh Bharata; Ardiyanto, Igi
Computer Engineering and Applications Journal (ComEngApp) Vol. 11 No. 2 (2022)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Recommendation system always involves huge volumes of data, therefore it causes the scalability issues that do not only increase the processing time but also reduce the accuracy. In addition, the type of data used also greatly affects the result of the recommendations. In the recommendation system, there are two common types of data namely implicit (binary) rating and explicit (scalar) rating. Binary rating produces lower accuracy when it is not handled with the properly. Thus, optimized K-Means+ clustering and user-based collaborative filtering are proposed in this research. The K-Means clustering is optimized by selecting the K value using the Davies-Bouldin Index (DBI) method. The experimental result shows that the optimization of the K values produces better clustering than Elbow Method. The KMeans+ and User-Based Collaborative Filtering (UBCF) produce precision of 8.6% and f-measure of 7.2%, respectively. The proposed method was compared to DBSCAN algorithm with UBCF, and had better accuracy of 1% increase in precision value. This result proves that K-Means+ with UBCF can handle implicit feedback datasets and improve precision.
The Social Engagement to Agricultural Issues using Social Network Analysis Tanty Yanuar Widiyanti; Teguh Bharata Adji; Indriana Hidayah
IJID (International Journal on Informatics for Development) Vol. 10 No. 1 (2021): IJID June
Publisher : Faculty of Science and Technology, Universitas Islam Negeri (UIN) Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2021.2185

Abstract

Twitter is one of the micro-blogging social media which emphasizes the speed of communication. In the 4.0 era, the government also promotes the distribution of information through social media to reach the community from various lines.  In previous research, Social Network Analysis was used to see the relationship between actors in a work environment, or as a basis for identifying the application of technology adoption in decision making, whereas no one has used SNA to see trends in people's response to agricultural information. This study aims to see the extent to which information about agriculture reaches the community, as well as to see the community's response to take part in agricultural development.  This article also shows the actors who took part in disseminating information. Data was taken on November 13 to 20, 2020 from the Drone Emprit Academic, and was taken limited to 3000 nodes. Then, the measurements of the SNA are represented on the values of Degree Centrality, Betweenness Centrality, Closeness Centrality, and Eigenvector Centrality. @AdrianiLaksmi has the highest value in Eigenvector Centrality and Degree Centrality, he has the greatest role in disseminating information and has many followers among other accounts that spread the same information. While the @RamliRizal account ranks the highest in Betweenness Centrality, who has the most frequently referred information, and the highest Closeness Centrality is owned by the @baigmac account because of the fastest to re-tweet the first information.
Similarity measurement on digital mammogram classification Erna Alimudin; Hanung Adi Nugroho; Teguh Bharata Adji
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 4: August 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i4.10698

Abstract

Breast cancer is one of the dominant causes of death in the world. Mammography is the standard for early detection of breast cancer. In examining mammograms, the overall parenchyma pattern of the left and right breast was placed side by side for symmetry assessed of left and right breast tissue by radiologist. Thus, in building computer-aided diagnosis (CAD) system for screening mammography, it is necessary to adapt the working procedure of the radiologist. In this study, 30 training images and 30 testing images from Kotabaru Oncology Clinic in Yogyakarta were used. The first step was to enhance the image quality with median filter and contrast limited adaptive histogram equalization (CLAHE). Then, feature extraction was processed by histogram-based and by gray level co-occurrence matrix (GLCM) based. Furthermore, the similarity measurement process was used to measure the difference value between selected features, i.e. angular second moment (ASM), inverse difference moment (IDM), contrast, entropy based GLCM, and energy, on the left and right mammograms. This process was intended to assess the symmetry of left and right mammograms as radiologists do in mammography screening. The obtained results of the classification between normal and abnormal images with backpropagation algorithm were accuracy of 0.933, sensitivity of 0.833, and specificity of 1.000.
Active-Reflective Learning Style Detection Using EEG and Abrupt Change Detection Rifkie Primartha; Teguh Bharata Adji; Noor Akhmad Setiawan
Communications in Science and Technology Vol 10 No 2 (2025)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.10.2.2025.1737

Abstract

Recognizing the varying learning styles of students is vital to creating customized educational approaches and maximizing academic success. While commonly used, conventional evaluation methods such as self-report surveys are frequently characterized by subjective biases and inconsistent accuracy. To address this limitation, this present study proposes an EEG-driven approach for learning style classification, specifically targeting the Active and Reflective dimensions of the Felder-Silverman Learning Style Model (FSLSM). Data was acquired from 14 participants using an 8-channel OpenBCI headset, with cognitive engagement stimulated through Raven’s Advanced Progressive Matrices (RAPM). Initially, the raw EEG data underwent bandpass filtering process purposely to remove noise. Subsequently, the data was divided into consecutive 1-second segments. For feature extraction, the CUSUM algorithm was employed, with an aim to effectively capture significant signal variations. These features were then fed into an LDA classifier for style discrimination. The performance evaluation revealed impressive results—98.26% accuracy in standard Train-Test validation, and an even higher 99.29% under LOOCV testing. Notably, our approach consistently outperformed existing techniques including 1-DCNN and TSMG across all metrics. Notably, computational efficiency and reliability were improved, with the "Odd-only" subset yielding peak accuracy (99.24%). These findings demonstrate that integrating EEG signals with conventional machine learning enables real-time, high-precision learning style detection. Additionally, this work addresses the computational constraints and dataset limitations observed in recent studies, providing a robust foundation for adaptive learning systems. It is recommended that future research explore larger, more diverse datasets and additional FSLSM dimensions to enhance generalizability and practical implementation of the research.
Co-Authors Abdul Kadir Achmad Noerkhaerin Putra Adhistya Erna Permanasari Adi Wijaya Adi Wijaya Agastya, I Made Artha Aris Budianto, Aris Bagus Kurniawan Buana, Ratna Lestari Budiani Dwi Retno Puspita Sari Dyah Mustika, Dyah Elfrida Ratnawati Erna Alimudin Faisal Najamuddin Fathan, Gess Fauziazzuhry Rahadian Gess Fathan Hairani Hairani Hamka, Cakra Aminuddin Hamka, Cakra Aminuddin Hanung Adi Nugroho Heilbert Armando Mapaly Igi Ardiyanto Ignatia Dhian Estu Karisma Ratri Indriana Hidayah KZ Widhia Oktoeberza Maghfirah Maghfirah Maghfirah Maghfirah Maghfirah Moch. Ari Nasichuddin Muhammad Esa Permana Putra Naufal Azmi Verdikha Nisrina Akbar Rizky Putri Noor Akhmad Setiawan Nugroho, Arseto Satriyo Nur Annisa Tresnasari Nurirwan Saputra, Nurirwan Rahadian, Fauziazzuhry Ratna Lestari Budiani Buana Ridi Ferdiana Rifkie Primartha Risanuri Hidayat Rudy Hartanto Sampurna Dadi Riskiono Selo Sulistyo, Selo Setialana, Pradana SRI RAHAYU Sri Suning Kusumawardani Stefani, Brillian Stefani, Brillian Sudaryanto . Sudaryanto Sudaryanto Sulis Setiowati Sulis Setiowati Syahroni Hidayat SYAIFULLOH AMIEN PANDEGA PERDANA Tanty Yanuar Widiyanti Tari Mardiana Titik Hinayah Widhi Hartanto Widhia K.Z Oktoeberza Widiyanti, Tanty Yanuar Widyawan Wijaya, Adi Yusufiyah, Hesti Khuzaimah Nurul